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+    "key": "PSPH8U7P",
+    "title": "Progress Toward Hepatitis B Control and Elimination of Mother-to-Child Transmission of Hepatitis B Virus - World Health Organization African Region, 2016-2021.",
+    "abstract": "Chronic hepatitis B virus (HBV) infection is one of the leading causes of cirrhosis and liver cancer. In 2019, approximately 1.5 million persons newly acquired chronic HBV infection; among these, 990,000 (66%) were in the World Health Organization (WHO) African Region (AFR). Most chronic HBV infections are acquired through mother-to-child transmission (MTCT) or during early childhood, and approximately two thirds of these infections occur in AFR. In 2016, the World Health Assembly endorsed the goal of elimination of mother-to-child transmission (EMTCT) of HBV, documented by \u226590% coverage with both a timely hepatitis B vaccine (HepB) birth dose (HepB-BD) and 3 infant doses of HepB (HepB3), and \u22640.1% hepatitis B surface antigen (HBsAg) seroprevalence among children aged \u22645 years. In 2016, the WHO African Regional Committee endorsed targets for a 30% reduction in incidence (\u22642% HBsAg seroprevalence in children aged \u22645 years) and \u226590% HepB3 coverage by 2020. By 2021, all 47 countries in the region provided HepB3 to infants beginning at age 6 weeks, and 14 countries (30%) provided HepB-BD. By December 2021, 16 (34%) countries achieved \u226590% HepB3 coverage, and only two (4%) achieved \u226590% timely HepB-BD coverage. Eight countries (17%) conducted nationwide serosurveys among children born after the introduction of HepB to assess HBsAg seroprevalence: six countries had achieved \u22642% seroprevalence, but none had achieved \u22640.1% seroprevalence among children. The development of immunization recovery plans following the COVID-19 pandemic provides an opportunity to accelerate progress toward hepatitis B control and EMTCT, including introducing HepB-BD and increasing coverage with timely HepB-BD and HepB3 vaccination. Representative HBsAg serosurveys among children and a regional verification body for EMTCT of HBV will be needed to monitor progress.",
+    "full_text": "Morbidity and Mortality Weekly Report\n\nProgress Toward Hepatitis B Control and Elimination of Mother-to-Child Transmission of Hepatitis B Virus \u2014 World Health Organization African Region,\n2016\u20132021\nHyacinte J. Kabore, DDS1; Xi Li, MD2; Mary M. Alleman, PhD2; Casimir M. Manzengo, MD3; Mutale Mumba, MD4; Joseph Biey, MD5; Gilson Paluku, MD6; Ado M. Bwaka, MD1,5; Benido Impouma, MD, PhD7; Rania A. Tohme, MD2\n\nAbstract\nChronic hepatitis B virus (HBV) infection is one of the leading causes of cirrhosis and liver cancer. In 2019, approximately 1.5 million persons newly acquired chronic HBV infection; among these, 990,000 (66%) were in the World Health Organization (WHO) African Region (AFR). Most chronic HBV infections are acquired through mother-to-child transmission (MTCT) or during early childhood, and approximately two thirds of these infections occur in AFR. In 2016, the World Health Assembly endorsed the goal of elimination of mother-to-child transmission (EMTCT) of HBV, documented by \u226590% coverage with both a timely hepatitis B vaccine (HepB) birth dose (HepB-BD) and 3 infant doses of HepB (HepB3), and \u22640.1% hepatitis B surface antigen (HBsAg) seroprevalence among children aged \u22645 years. In 2016, the WHO African Regional Committee endorsed targets for a 30% reduction in incidence (\u22642% HBsAg seroprevalence in children aged \u22645 years) and \u226590% HepB3 coverage by 2020. By 2021, all 47 countries in the region provided HepB3 to infants beginning at age 6 weeks, and 14 countries (30%) provided HepB-BD. By December 2021, 16 (34%) countries achieved \u226590% HepB3 coverage, and only two (4%) achieved \u226590% timely HepB-BD coverage. Eight countries (17%) conducted nationwide serosurveys among children born after the introduction of HepB to assess HBsAg seroprevalence: six countries had achieved \u22642% seroprevalence, but none had achieved \u22640.1% seroprevalence among children. The development of immunization recovery plans following the COVID-19 pandemic provides an opportunity to accelerate progress toward hepatitis B control and EMTCT, including introducing HepB-BD and increasing coverage with timely HepB-BD and HepB3 vaccination. Representative HBsAg serosurveys among children and a regional verification body for EMTCT of HBV will be needed to monitor progress.\nIntroduction\nIn 2019, approximately 1.5 million persons newly acquired chronic hepatitis B virus (HBV) infection; among these, 990,000 (66%) were in the World Health Organization (WHO) African Region (AFR)* (1). Because most chronic HBV infections are acquired through mother-to-child\n\ntransmission (MTCT) or during early childhood (2), WHO recommends that all newborns receive a dose of hepatitis B vaccine (HepB) within 24 hours of birth (hepatitis B vaccine birth dose [HepB-BD]) followed by 2 or 3 doses\u2020 of HepB during the first year of life (2). In 2016, the World Health Assembly endorsed the goal of eliminating viral hepatitis as a public health threat by 2030, including the elimination of mother-to-child transmission (EMTCT) of HBV, documented by demonstration of \u226590% coverage with both a timely\u00a7 HepB-BD and 3 doses of HepB (HepB3), and \u22640.1% hepatitis B surface antigen (HBsAg)\u00b6 seroprevalence among children aged \u22645 years (3). In 2016, the WHO African Regional Committee endorsed two targets for hepatitis B control: 1) 30% reduction in incidence (equating to HBsAg prevalence of \u22642% in children aged \u22645 years), and 2) \u226590% HepB3 coverage by 2020. In 2021, AFR countries endorsed a call to develop strategies for elimination of MTCT of HBV, including increasing HepB-BD and HepB3 coverage and improving access to antenatal care and quality delivery services (4,5). This report describes progress made during 2016\u20132021 to achieve hepatitis B control and elimination of MTCT of HBV in AFR.\nMethods\nInformation on country immunization activities was obtained by review of administrative** or official\u2020\u2020 HepB coverage data reported to WHO and UNICEF that generate annual country vaccination coverage estimates. To identify HBsAg seroprevalence surveys conducted in AFR, a MEDLINE literature review was conducted using the following search criteria (Afro country names), and (\u201chepatitis B\u201d OR \u201cHBV\u201d) AND (2016/10/01:3000/12/31[Date - Publication]) AND (survey OR serosurvey OR serosurveillance OR seroepidemiology\n* The African Region, one of the six WHO regions, with a population of approximately 1.2 billion persons, includes 47 countries. https://www.afro. who.int/countries\n\u2020 Depending on the country\u2019s immunization schedule. \u00a7 Administration of a dose within 24 hours of birth. \u00b6 HBsAg seropositivity is an indicator of chronic HBV infection. ** Administrative vaccination coverage data are derived from the country\u2019s\nimmunization registry system. The coverage is calculated by dividing the total number of doses administered by the estimated target population for vaccination. \u2020\u2020 Official vaccination coverage estimates are reported by national authorities based on administrative data, immunization coverage surveys, and reports.\n\n782 US Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\nMorbidity and Mortality Weekly Report\n\nOR prevalence OR seroprevalence). Population-based surveys including the Population based HIV Impact Assessment (PHIA) surveys and Demographic Health Survey (DHS) were also used. This activity was reviewed by CDC and was conducted with applicable federal laws and CDC policy.\u00a7\u00a7\nResults\nImmunization Activities\nBy 2014, all 47 countries in AFR had introduced HepB3 infant vaccination (Table 1). By December 2021, 14 (30%) countries provided HepB-BD, eight (57%) of which were in the West subregion.\u00b6\u00b6 Although 10 countries had introduced HepB-BD before 2016, only four (Benin, C\u00f4te d\u2019Ivoire, Equatorial Guinea, and Senegal) introduced HepB-BD during 2016\u20132021. During this period, regional HepB3 coverage ranged from 75% in 2019 to 71% in 2021. Eighteen (38%) countries reached \u226590% HepB3 coverage in 2016; this number peaked at 20 (43%) in 2018; by 2021, the number of countries with \u226590% HepB3 coverage had declined to 16 (34%); nine of these countries were in the East and South subregions. Regional HepB-BD coverage increased from 10% in 2016 to 17% in 2021. During 2016\u20132021, Algeria and Cabo Verde reached HepB-BD coverage of \u226590%, and Namibia and Senegal achieved \u226550% coverage.\nHBsAg Seroprevalence Surveys\nBecause most chronic HBV infections (particularly those among young children) are asymptomatic, the impact of hepatitis B vaccination is usually measured by HBsAg seroprevalence among children born after the introduction of HepB, usually those aged \u22645 years*** (3,6). During 2016\u20132021, HBsAg seroprevalence surveys among children were conducted at national or regional levels in eight (17%) countries. Among children of various age ranges surveyed in Ethiopia, Mauritania, Rwanda, Sierra Leone, Uganda, and Zambia, HBsAg seroprevalence was \u22642%. Prevalence among\n\u00a7\u00a7 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.\n\u00b6\u00b6 AFR is organized into three functional subregions: Central subregion (Angola, Burundi, Cameroon, Central African Republic, Chad, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Republic of the Congo, and Sao Tome and Principe); East and South subregion (Botswana, Comoros, Eritrea, Eswatini, Ethiopia, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Rwanda, Seychelles, South Africa, South Sudan, Uganda, United Republic of Tanzania, Zambia, and Zimbabwe) and West subregion (Algeria, Benin, Burkina Faso, Cabo Verde, C\u00f4te d\u2019Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo).\n*** HBsAg seroprevalence can be measured among children aged 1 year, 5 years, or 1\u20135 years, according to existing country surveillance and data collection practices. For regions and countries with a long history of high hepatitis B vaccination coverage and those that already conduct school-based serosurveys, serosurveys might be conducted in children aged >5 years. https://www.who. int/publications/i/item/9789240039360\n\nchildren aged \u22645 years measured in the Democratic Republic of the Congo, Ethiopia, Mauritania, Nigeria, and Sierra Leone ranged from 0.7% (Mauritania) to 4.5% (Nigeria) (Table 2). No country achieved \u22640.1% HBsAg seroprevalence among children. Modeling studies estimated a HBsAg seroprevalence of 2.5% (95% CI = 1.7\u20134.0) among children aged \u22645 years in AFR, accounting for more than two thirds (4.3 million, approximately 69%) of all infected children worldwide (1).\nHBsAg seroprevalence among women of reproductive age or pregnant women provides an estimate of the risk for MTCT of HBV. Data from population-based HBsAg surveys among women of reproductive age or from screening of pregnant women available from 11 countries showed HBsAg seroprevalences ranging from 1.2% (Rwanda) to 9.8% (Sierra Leone) (Table 2).\nElimination of Mother-to-Child Transmission of HBV\nBy December 2021, although 21 (45%) AFR countries had developed a plan for EMTCT of HIV, syphilis, and HBV, only six countries\u2020\u2020\u2020 reported having implemented the EMTCT guidelines for routine HBsAg testing of pregnant women, provision of antiviral medications to eligible (HBsAg-seropositive) women,\u00a7\u00a7\u00a7 and administration of HepB-BD to newborns. As of December 2021, \u226590% of pregnant women in 29 (62%) AFR countries had at least one antenatal care visit (Table 3). Data from the most recent nationwide surveys showed that in 37 (79%) countries, approximately one half of women gave birth in health care facilities, and in 23 (49%) countries, \u226580% of women delivered in a health facility (Table 1). To acknowledge progress toward EMTCT of HBV in countries with high endemicity, WHO developed a certification mechanism for the path to elimination of MTCT of HBV, using three tiers (bronze, silver, and gold) indicating increasing levels of progress\u00b6\u00b6\u00b6 (6). Based on HepB immunization interventions in 2021, Botswana might be eligible for the bronze tier, three countries (Namibia, Sao Tome and Principe, and Senegal) might be eligible for the silver tier, and two countries (Algeria and Cabo Verde) might be eligible for the gold tier certification (Table 1) (Table 3).\n\u2020\u2020\u2020 Angola, Cabo Verde, Equatorial Guinea, Mozambique, Namibia, and Sao Tome and Principe.\n\u00a7\u00a7\u00a7 Pregnant women who received positive HBsAg test results and had an HBV DNA \u22655.3 log10 IU/mL (\u2265200,000 IU/mL) or received a positive HBsAg antigen test result are recommended by WHO to receive antiviral prophylaxis to prevent MTCT of HBV. https://apps.who.int/iris/bitstream/hand le/10665/333391/9789240002708-eng.pdf\n\u00b6\u00b6\u00b6 Bronze tier: 1) \u226590% HepB3 infant vaccination coverage, and 2) implementation of universal timely HepB-BD policy for \u22652 years. Silver tier: 1) \u226590% HepB3 infant vaccination coverage, 2) \u226550% universal timely HepB-BD coverage, and 3) availability of antenatal HBsAg testing in the public sector for \u22652 years. Gold tier: 1) \u226590% HepB3 infant vaccination coverage, 2) \u226590% universal timely HepB-BD coverage, and 3) >30% antenatal HBsAg testing coverage for \u22652 years. https://www.who.int/ publications/i/item/9789240039360\n\n783\n\nUS Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\nMorbidity and Mortality Weekly Report\n\nTABLE 1. Year of hepatitis B vaccine introduction, hepatitis B vaccination schedule and estimated coverage* with the third vaccine dose, a timely administered hepatitis B vaccine birth dose,\u2020 and rates of institutional delivery, by country \u2014 World Health Organization African Region,\n2016\u20132021\n\nRegion, country\n\nYear of introduction\n\nHepB3 coverage, %\n\nTimely HepB-BD coverage, %\n\nRates of institutional delivery, % (most recent\n\nHepB HepBD HepB Schedule 2016 2017 2018 2019 2020 2021 2016 2017 2018 2019 2020 2021 source and year)\n\nCentral subregion Angola Burundi Cameroon Central African Republic Chad Congo Democratic Republic of\nthe Congo Equatorial Guinea\nGabon Sao Tome and Principe\nEast and South subregion Botswana Comoros Eritrea Eswatini Ethiopia Kenya Lesotho Madagascar Malawi Mauritius\nMozambique Namibia Rwanda Seychelles South Africa\nSouth Sudan Uganda Tanzania Zambia Zimbabwe\nWest subregion Algeria Benin Burkina Faso Cabo Verde C\u00f4te d\u2019Ivoire The Gambia Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo\nAfrican Region\n\n2006 2004 2005 2003 2003 2003 2003\n2003\n2003 2003\n1994 2003 2002 1996 2007 2001 2003 2002 2002 1996\n2001 2009 2002 1996 1995\n2014 2002 2002 2005 1994\n2001 2002 2006 2002 2003 1995 2002 2006 2008 2008 2002 2005 2008 2004 2004 2007 2008\n\n2015 \u2014 \u2014 \u2014 \u2014 \u2014 \u2014\n\nB, 2, 4, 6 mos 6, 10, 14 wks 6, 10, 14 wks 6, 10, 14 wks 6, 10, 14 wks 8, 12, 16 wks 6, 10, 14 wks\n\n55 52 59 53 47 41 NR NR NR NR NR NR 45.6 (DHS 2015\u20132016)\n\n94 91 90 93 93 94 NA NA NA NA NA NA 83.9 (DHS 2016\u20132017)\n\n75 74 67 67 69 69 NA NA NA NA NA NA\n\n67.0 (DHS 2018)\n\n42 42 42 42 42 42 NA NA NA NA NA NA 58.3 (MICS 2018\u20132019)\n\n41 41 46 50 52 58 NA NA NA NA NA NA\n\n27.2 (MICS 2019)\n\n71 69 75 79 73 77 NA NA NA NA NA NA 91.5 (MICS 2014\u20132015)\n\n70 71 71 73 70 65 NA NA NA NA NA NA 81.5 (MICS 2017\u20132018)\n\n2018 B, 6, 10, 14 wks, 53 53 53 53 53 53 NA NA NA NR NR NR\n\n18 mos\n\n\u2014 6, 10, 14 wks\n\n75 75 70 70 63 75 NA NA NA NA NA NA\n\n2010\u00a7 B, 6, 10, 14 wks 96 95 95 95 96 97 NA NA NA 95 82 69\n\n67.3 (DHS 2011)\n90.2 (DHS 2012) 95.4 (MICS 2019)\n\n1998 B, 2, 3, 4 mos\n\n95 95 95 95 95 95 NR NR NR NR NR NR 99.7 (Other NS 2015)\n\n\u2014 6, 10, 14 wks\n\n91 91 91 91 87 85 NA NA NA NA NA NA 76.1 (DHS\u2013MICS 2012)\n\n\u2014 6, 10, 14 wks\n\n95 95 95 95 95 95 NA NA NA NA NA NA 33.7 (Other NS 2010)\n\n\u2014 6, 10, 14 wks\n\n90 90 90 90 83 77 NA NA NA NA NA NA\n\n87.7 (MICS 2014)\n\n\u2014 6, 10, 14 wks\n\n66 68 68 68 71 65 NA NA NA NA NA NA 47.5 (DHS (Mini) 2019)\n\n\u2014 6, 10, 14 wks\n\n89 82 92 91 91 91 NA NA NA NA NA NA\n\n61.2 (DHS 2014)\n\n\u2014 6, 10, 14 wks\n\n87 87 87 87 87 87 NA NA NA NA NA NA\n\n89.4 (MICS 2018)\n\n\u2014 6, 10, 14 wks\n\n68 65 65 68 66 55 NA NA NA NA NA NA\n\n38.7 (MICS 2018)\n\n\u2014 6, 10, 14 wks\n\n84 88 92 95 90 93 NA NA NA NA NA NA 96.7 (MICS 2019\u20132020)\n\n1996\u00a7 R,\u00b6 6, 10, 14 wks, 72 96 97 97 93 92 NA NA NA NA NA NA\n\n98.4 (MoH 2003)\n\n18 mos\n\n\u2014 6, 10, 14 wks\n\n88 88 88 88 79 61 NA NA NA NA NA NA\n\n54.8 (DHS 2011)\n\n2014 B, 6, 10, 14 wks 85 88 89 87 93 93 85 81 76 81 86 86\n\n87.4 (DHS 2013)\n\n\u2014 6, 10, 14 wks\n\n98 98 97 98 91 88 NA NA NA NA NA NA 93.1 (DHS 2019\u20132020)\n\n\u2014 3, 4, 5 mos\n\n96 97 99 99 97 94 NA NA NA NA NA NA\n\nNR\n\n\u2014 6, 10, 14 wks, 85 84 82 85 84 86 NA NA NA NA NA NA\n\n95.9 (DHS 2016)\n\n18 mos\n\n\u2014 6, 10, 14 wks\n\n45 47 49 49 49 49 NA NA NA NA NA NA\n\n11.5 (SHHS 2010)\n\n\u2014 6, 10, 14 wks\n\n93 94 93 93 89 91 NA NA NA NA NA NA\n\n73.4 (DHS 2016)\n\n\u2014 6, 10, 14 wks\n\n92 90 89 89 86 81 NA NA NA NA NA NA 62.6 (DHS 2015\u20132016)\n\n6, 10, 14 wks\n\n95 94 90 88 84 91 NA NA NA NA NA NA 83.8 (DHS 2018\u20132019)\n\n\u2014 6, 10, 14 wks\n\n90 89 89 90 86 86 NA NA NA NA NA NA\n\n85.5 (MICS 2019)\n\n2001 B, 2, 4, 12 mos 91 91 91 91 91 91 99 99 99 99 99 99 98.6 (MICS 2018\u20132019)\n\n2020 B, 6, 10, 14 wks 76 76 76 76 72 76 NA NA NA NA 21 71 83.9 (DHS 2017\u20132018)\n\n\u2014 8, 12, 16 wks\n\n91 91 91 91 91 91 NA NA NA NA NA NA 82.2 (Other NS 2015)\n\n2002 B, 2, 4, 6, 18 mos 96 97 99 97 94 94 96 96 97 96 96 96\n\n97.0 (IDSR 2018)**\n\n2019 B, 6, 10, 14 wks 87 83 84 81 75 76 NA NA NA 9 62 66\n\n69.8 (MICS 2016)\n\n1999 B, 2, 3, 4 mos\n\n95 92 93 88 86 82 NR NR NR NR NR 25 83.7 (DHS 2019\u20132020)\n\n\u2014 6, 10, 14 wks\n\n93 99 97 97 94 98 NA NA NA NA NA NA 77.9 (MICS 2017\u20132018)\n\n\u2014 6, 10, 14 wks\n\n47 45 47 47 47 47 NA NA NA NA NA NA\n\n52.6 (DHS 2018)\n\n\u2014 6, 10, 14 wks\n\n85 79 82 78 74 67 NA NA NA NA NA NA 50.4 (MICS 2018\u20132019)\n\n\u2014 6, 10, 14 wks\n\n73 80 80 70 65 66 NA NA NA NA NA NA 79.8 (DHS 2019\u20132020)\n\n\u2014 6, 10, 14 wks\n\n76 77 77 77 70 77 NA NA NA NA NA NA\n\n66.8 (DHS 2018)\n\n2013 B, 6, 10, 14 wks 74 76 77 80 72 68 NR NR NR NR NR NR\n\n69.3 (MICS 2015)\n\n\u2014 6, 10, 14 wks\n\n80 85 79 81 81 82 NA NA NA NA NA NA 44.3 (ENAFEME 2021)**\n\n2004 B, 6, 10, 14 wks 53 55 55 56 56 56 30 30 41 52 52 52\n\n39.4 (DHS 2018)\n\n2016 B, 6, 10, 14 wks 93 93 92 96 92 86 62 76 81 85 86 78\n\n80.3 (DHS 2019)\n\n\u2014 6, 10, 14 wks\n\n84 90 93 95 91 92 NA NA NA NA NA NA\n\n83.4 (DHS 2019)\n\n\u2014 6, 10, 14 wks\n\n82 83 81 84 82 83 NA NA NA NA NA NA\n\n80.0 (MICS 2017)\n\n\u2014\u2014\n\n73 74 74 75 73 71 10 10 12 15 16 17\n\n\u2014\n\nSee table footnotes on the next page.\n\n784 US Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\nMorbidity and Mortality Weekly Report\n\nTABLE 1. (Continued) Year of hepatitis B vaccine introduction, hepatitis B vaccination schedule and estimated coverage* with the third vaccine dose, a timely administered hepatitis B vaccine birth dose,\u2020 and rates of institutional delivery, by country \u2014 World Health Organization African Region, 2016\u20132021\nAbbreviations: B = birth; DHS = demographic health survey; ENAFEME = Enqu\u00eate Nationale sur la F\u00e9condit\u00e9 et la Mortalit\u00e9 des Enfants de Moins de 5 Ans; HepB = hepatitis B vaccine; HepB-BD = birth dose of monovalent hepatitis B vaccine; HepB3 = third dose of hepatitis B-containing vaccine; IDSR = integrated disease surveillance and response; MICS = multiple indicator cluster survey; MoH = Ministry of Health; NR = not reported; NS = national survey; R = restricted HepB-BD; SHHS = South Sudan Household Health Survey. * WHO-UNICEF Estimates of National Immunization Coverage. https://immunizationdata.who.int/pages/coverage/HEPB.html \u2020 Timely receipt of HepB-BD is defined as administration of a dose of HepB within 24 hours of birth. \u00a7 During 2010 to 2018: HepB-BD was selectively given to newborns of mothers who have received a positive for hepatitis B surface antigen test result; in 2019, the\ncountry switched to universal HepB-BD vaccination of all newborns. \u00b6 Restricted HepB-BD given only to children born to mothers with hepatitis B. ** Preliminary data.\n\nTABLE 2. Hepatitis B virus surface antigen seroprevalence based on population-based serosurveys among children and women of reproductive age or pregnant women during antenatal screening in selected countries \u2014 World Health Organization African Region, 2016\u20132021\n\nSurvey group, Country\n\nYear of most recent data (source)\n\nGeographic area\n\nHBsAg prevalence, %\n\nAge group No. of persons tested\n\n(95% CI)\n\nChildren born after HepB introduction\nDemocratic Republic of the Congo* Ethiopia\u2020\n\n2013\u20132014 (DHS)\n\nNationwide\n\n2017\u20132018 (PHIA) Nationwide (Urban)\n\nMauritania\u00b6\n\n2019\u20132021 (DHS)\n\nNationwide\n\nNigeria**\n\n2018 (NAIIS)\n\nRwanda\u2020\u2020 Sierra Leone\u00a7\u00a7\n\n2018\u20132019 (PHIA) 2018 (Household-based survey)\n\nUganda\u00b6\u00b6 Zambia***\n\n2016\u20132017 (PHIA) 2016 (PHIA)\n\nWomen of reproductive age\n\nBurkina Faso\u00a7\u00a7\u00a7 Cameroon\u00b6\u00b6\u00b6\n\n2010\u20132011 (DHS) 2017\u20132018 (PHIA)\n\nDemocratic Republic of the Congo*\n\n2013\u20132014 (DHS)\n\nKenya**** Mauritania\u00b6\n\n2018\u20132019 (PHIA) 2019\u20132021 (DHS)\n\nNigeria** Rwanda\u2020\u2020\n\n2018 (NAIIS) 2018\u20132019 (PHIA)\n\nSierra Leone\u00a7\u00a7 Tanzania\u2020\u2020\u2020\u2020\n\n2018 (Household based survey) 2016\u20132017 (PHIA)\n\nUganda\u00b6\u00b6\n\n2016\u20132017 (PHIA)\n\nZambia***\n\n2016 (PHIA)\n\nAntenatal screening of pregnant women\n\nNigeria\u00a7\u00a7\u00a7\u00a7\n\n2019\n\n(ANC screening in HIV facilities)\n\nNationwide\nNationwide 3 of 5 provinces\nNationwide Nationwide\nNationwide Nationwide Nationwide Nationwide Nationwide Nationwide Nationwide 3 of 5 provinces Nationwide Nationwide Nationwide\nNationwide (34 of 36 states)\n\n0\u22125 yrs 0\u221214 yrs\u00a7\n5\u22129 yrs 10\u221214 yrs\n1\u22124 yrs 5\u22129 yrs 10\u221214 yrs 2\u22124 yrs 5\u22129 yrs 2\u22129 yrs 10\u221214 yrs 4\u221230 mos 5\u22129 yrs 0\u221214 yrs 0\u221214 yrs\u2020\u2020\u2020\n15\u221249 yrs 15\u221249 yrs 15\u221259 yrs 15\u221249 yrs 15\u221249 yrs 15\u221249 yrs 15\u221249 yrs 15\u221249 yrs 15\u221249 yrs 15\u221249 yrs 15\u221259 yrs\nNA\n\n277 4,729\n539 655 2,642 3,447 2,939 2,968 3,620 6,588 869 1,889 2,025 10,345 8,015\n8,056 1,058\n368 1,652 4,420 8,682 1,813 1,776\n615 14,716 10,973\n200,473\n\n2.20 (0.3\u22124.1) 1.48 (NR) 3.34 (NR) 3.05 (NR) 0.70 (NR) 0.40 (NR) 2.40 (NR)\n4.50 (3.6\u22125.6) 6.60 (5.5\u22127.9) 5.80 (5.0\u22126.6)\n0.00 (NR) 1.30 (0.8\u22122.0) 1.60 (1.1\u22122.3)\n0.60 (NR) 1.30 (NR)\n7.80 (7.1\u22128.6) 6.00 (NR) 3.80 (NR) 2.70 (NR) 6.40 (NR)\n6.10 (5.1\u22127.0) 1.20 (NR)\n9.80 (8.1\u221211.7) 3.70 (NR) 3.10 (NR) 4.10 (NR)\n3.94 (NR)\n\nAbbreviations: ANC = antenatal care; DHS = demographic and health survey; HBsAg = hepatitis B virus surface antigen; HepB = hepatitis B vaccine; HIV = human immunodeficiency\nvirus; NA = not applicable; NAIIS = Nigeria HIV/AIDS Indicator and Impact Survey; NR = not reported; PHIA = population-based HIV impact assessment survey.\n* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609197/pdf/tpmd180883.pdf \u2020 https://onlinelibrary.wiley.com/doi/full/10.1111/hiv.13457 \u00a7 Includes children aged 11\u201313 years born before HepB introduction. \u00b6 https://dhsprogram.com/pubs/pdf/FR373/FR373.pdf\n** https://global-hepatitis.com/wp-content/uploads/2023/04/GHS2023-Abstract-Book-ONLINE_4.pdf?utm_source=mobile+app&utm_medium=link&utm_\ncampaign=abstract-book (abstract no. 047) \u2020\u2020 https://phia.icap.columbia.edu/wp-content/uploads/2020/11/RPHIA-Final-Report_Web.pdf \u00a7\u00a7 https://www.sciencedirect.com/science/article/pii/S0264410X22003607 \u00b6\u00b6 https://phia.icap.columbia.edu/wp-content/uploads/2020/02/UPHIA_Final_Report_Revise_07.11.2019_Final_for-web.pdf\n*** https://phia.icap.columbia.edu/wp-content/uploads/2019/03/ZAMPHIA-Final-Report__2.26.19.pdf \u2020\u2020\u2020 Includes children aged 11\u201314 years born before the introduction of HepB vaccine. \u00a7\u00a7\u00a7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6239015/ \u00b6\u00b6\u00b6 https://phia.icap.columbia.edu/wp-content/uploads/2021/09/53059-CAMPHIA-Report_EN_WEB_August1.pdf\n**** https://phia.icap.columbia.edu/kenya-final-report-2018/ \u2020\u2020\u2020\u2020 https://phia.icap.columbia.edu/wp-content/uploads/2020/02/FINAL_THIS-2016-2017_Final-Report__06.21.19_for-web_TS.pdf \u00a7\u00a7\u00a7\u00a7 https://pubmed.ncbi.nlm.nih.gov/34387113/\n\n785 US Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\nMorbidity and Mortality Weekly Report\n\nTABLE 3. Policies and interventions to prevent mother-to-child transmission of hepatitis B and tier eligibility* for the path to elimination of mother-to-child transmission of hepatitis B virus \u2014 World Health Organization African Region, 2021\n\nNo. (%) of countries with policy or intervention present or not present\n\nPolicies and interventions\n\nPresent\n\nNot present\n\nNational strategic plan for viral hepatitis\u2020\nNational plan for triple elimination of HIV, syphilis, and hepatitis B\u00a7\nNational guidelines for antenatal\nHBsAg testing and maternal treatment\u2020,\u00b6 ANC1 coverage \u226590%**,\u2020\u2020 HepB-BD coverage \u226590%\u00a7\u00a7 HepB-BD coverage \u226550%\u00a7\u00a7 HepB3 coverage \u226590%\u00a7\u00a7\nEligibility for bronze tier for path to elimination of MTCT of HBV *,\u00a7\u00a7\nEligibility for silver tier for path to elimination of MTCT of HBV *,\u00a7\u00a7\nEligibility for gold tier for path to elimination of MTCT of HBV *,\u00a7\u00a7\n\n21 (45)\n21 (45)\n17 (36)\n29 (62) 2 (4)\n6 (13) 16 (34)\n1 (2)\n3 (6)\n2 (4)\n\n26 (55)\n26 (55)\n30 (64)\n16 (34) 45 (96) 41 (87) 31 (66)\n\u2014\n\u2014\n\u2014\n\nAbbreviations: ANC1 = at least 1 antenatal care visit; HBsAg = hepatitis B surface antigen; HBV = hepatitis B virus; HepB-BD = birth dose of monovalent hepatitis B vaccine; HepB3 = three doses of a hepatitis B containing vaccine; MTCT = mother-to-child transmission; WHO = World Health Organization. * Eligibility for tier certification on the path to elimination of mother-to-child\ntransmission of hepatitis B is based on immunization interventions. Bronze tier: 1) \u226590% coverage of HepB3 infant vaccination, and 2) implementation of universal timely HepB-BD policy. Silver tier: \u226590% coverage of HepB3 infant vaccination, 2) \u226550% coverage of universal timely HepB-BD, and 3) Availability of antenatal HBsAg testing in the public sector. Gold tier: 1) \u226590% coverage of HepB3 infant vaccination, 2) \u226590% coverage of universal timely HepB-BD, and 3) >30% coverage of antenatal HBsAg testing. Indicators for each tier should be achieved for at least 2 years. https://www.who.int/publications/i/ item/9789240039360 \u2020 https://www.afro.who.int/publications/viral-hepatitis-scorecard2021-african-region \u00a7 All 21 priority countries reported by WHO regional office: Angola, Botswana, Burundi, Cameroun, Chad, C\u00f4te-d\u2019Ivoire, Democratic Republic of the Congo, Eswatini, Ethiopia, Ghana, Kenya, Lesotho, Malawi, Mozambique, Namibia, Nigeria, Uganda, South Africa, Tanzania, Zambia, Zimbabwe. \u00b6 Included in national testing and treatment guidelines. ** https://data.unicef.org/resources/dataset/maternal-newborn-health/ \u2020\u2020 Data are not available for two (4%) countries (Mauritius and Seychelles). \u00a7\u00a7 World Health Organization-UNICEF estimates. https://immunizationdata. who.int/pages/coverage/HEPB.html\n\nDiscussion\nAll 47 AFR countries have had HepB in their infant immunization schedule since 2014, and 16 (34%) have achieved \u226590% HepB3 coverage for \u22652 years, including four countries that documented <2% HBsAg seroprevalence in children, consistent with hepatitis B control. The COVID-19 pandemic led to disruptions in immunization services,**** resulting in fewer AFR countries attaining \u226590% HepB3 coverage, declining from a peak of 20 (43%) in 2018 to 16 (34%) in 2021. Strategies to recover and strengthen immunization programs\n\n**** https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_ continuity-survey-2022.1\n\nSummary\nWhat is already known about this topic?\nIn 2019, the World Health Organization African Region (AFR) accounted for 66% of all new chronic hepatitis B virus (HBV) infections. Chronic HBV infection is the leading causes of cirrhosis and liver cancer.\nWhat is added by this report?\nBy 2021, all 47 AFR countries provided 3 doses of hepatitis B vaccine (HepB3) to infants, and 14 (30%) provided a birth dose (HepB-BD). By December 2021, 16 (34%) countries achieved \u226590% HepB3 coverage; two (4%) achieved \u226590% timely HepB-BD coverage. Four countries achieved hepatitis B control; none achieved elimination of mother-to-child transmission (EMTCT).\nWhat are the implications for public health practice?\nIntroduction of HepB-BD, improving HepB3 and HepB-BD coverage, and monitoring implementation of EMTCT interventions are essential to accelerating progress toward hepatitis B control and EMTCT in AFR.\nsuch as catch-up vaccination campaigns, could help ensure that all eligible children who missed HepB vaccination receive the recommended doses (7).\nFewer than one third (30%, 14) of countries had introduced HepB-BD by 2021, and just two countries achieved \u226590% HepB-BD coverage. Scaling up HepB-BD introduction and coverage is critical to eliminating MTCT of HBV and preventing subsequent liver disease and associated mortality. During 2016\u20132021, four countries in AFR introduced HepB-BD which, in addition to increasing HepB-BD coverage in two of these countries (Nigeria and Senegal), resulted in an increase in regional HepB-BD coverage from 10% to 17%. However, in 2021, almost 33 million newborns in AFR did not receive timely HepB-BD. (Table 1) Based on modeled estimates, maintaining current HepB3 coverage and increasing HepB-BD coverage to \u226590% in all countries in the region could avert 554,318 HBV-related deaths among 2020\u20132030 birth cohorts (8). Among the 33 countries that did not have HepB-BD as part of their routine immunization schedules in 2021, two (Burkina Faso and Uganda) introduced it in 2022. Among the remaining 31 countries,\u2020\u2020\u2020\u2020 13\u00a7\u00a7\u00a7\u00a7 plan to introduce HepB-BD by 2025.\u00b6\u00b6\u00b6\u00b6 However, achieving the regional target\n\u2020\u2020\u2020\u2020 Burundi, Cameroon, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Eritrea, Eswatini, Ethiopia, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Republic of the Congo, Rwanda, Seychelles, Sierra Leone, South Africa, South Sudan, Togo, United Republic of Tanzania, Zambia, and Zimbabwe.\n\u00a7\u00a7\u00a7\u00a7 Burundi, Cameroon, Comoros, Eritrea, Ghana, Lesotho, Madagascar, Niger, Seychelle, Sierra Leone, South Africa, Togo, and Zimbabwe.\n\u00b6\u00b6\u00b6\u00b6 Obtained from workshop reports on National Immunization Plan; meetings were held during September\u2013October 2022.\n\n786 US Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\nMorbidity and Mortality Weekly Report\n\nof 35 countries by 2025 (5) would require six to seven countries to introduce HepB-BD each year. Following introduction, delivery in health facilities by skilled workers was shown to be significantly correlated with timely HepB-BD administration (9). Promoting and enabling delivery in health facilities, training health care workers, and integrating HepB-BD vaccination into newborn care, are essential to increasing timely HepB-BD coverage in AFR.\nIn addition to providing timely HepB-BD and HepB3, the identification of pregnant women with HBV infection and provision of antiviral medications for those who are eligible for treatment would further advance EMTCT of HBV (9,10). However, as of 2021, only 17 (36%) AFR countries had national policies for antenatal HBsAg testing and treatment, and nationally representative serosurveys in AFR were uncommon. HBsAg seroprevalence surveys would help document progress and guide policy decisions regarding hepatitis B control and elimination in the region.\nLimitations\nThe findings in this report are subject to at least two limitations. First, HepB-BD coverage data were not consistently reported by five countries,***** which might have resulted in the underestimation of overall HepB-BD regional coverage. Second, assessment of hepatitis B control and EMTCT is challenging in countries that have introduced HepB-BD and achieved high coverage with HepB3, because nationally representative seroprevalence surveys to estimate the prevalence of HBV infection among children are lacking in those countries.\nImplications for Public Health Practice\nEstablishing a regional verification mechanism for hepatitis B control and EMTCT of HBV could elevate the profile of elimination initiatives in AFR. Scaling up the introduction of HepB-BD and strategies to increase timely HepB-BD and HepB3 coverage would accelerate the reduction of preventable hepatitis B\u2013associated morbidity and mortality and progress toward 2030 hepatitis B elimination goals.\n***** Angola, Botswana, Equatorial Guinea, The Gambia, and Mauritania.\nAcknowledgments\nReggis Katsande, Vaccine-Preventable Disease Unit, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo.\nCorresponding author: Hyacinte J. Kabore, hkabore@cdc.gov.\n\n1Vaccine-Preventable Disease Unit, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo; 2Global Immunization Division, Global Health Center, CDC; 3HIV, Tuberculosis, Hepatitis Unit, Inter-country Support Team, World Health Organization, Libreville, Gabon; 4Vaccine Preventable Diseases Unit, World Health Organization Inter-country Support Team - East and South, Harare, Zimbabwe; 5Vaccine Preventable Diseases Unit, World Health Organization Inter-country Support Team - West, Ouagadougou, Burkina Faso; 6Vaccine Preventable Diseases Unit, World Health Organization Inter-country Support Team - Central, Libreville, Gabon; 7Universal Health Coverage/Communicable & Non-Communicable Diseases, World Health Organization Regional Office for Africa, Brazzaville, Republic of the Congo.\nAll authors have completed and submitted the International\nCommittee of Medical Journal Editors form for disclosure of potential\nconflicts of interest. No potential conflicts of interest were disclosed.\nReferences\n1. World Health Organization. Global progress report on HIV, viral hepatitis, and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016\u20132021: actions for impact. Web annex 1: key data at a glance. Geneva, Switzerland: World Health Organization; 2021. http://apps.who.int/iris/bitstream/hand le/10665/342808/9789240030985-eng.pdf\n2. World Health Organization. Hepatitis B vaccines: WHO position paper \u2013 July 2017. Wkly Epidemiol Rec 2017;92:369\u201392. PMID:28685564\n3. World Health Organization. Global HIV, Hepatitis and STIs programmes: global health sector strategies 2022\u20132030. Geneva, Switzerland: World Health Organization; 2022. https://www.who. int/teams/global-hiv-hepatitis-and-stis-programmes/strategies/ global-health-sector-strategies\n4. Regional Office for Africa. Prevention, care and treatment of viral hepatitis in the African Region: framework for action, 2016\u20132020. Brazzaville, Republic of the Congo: World Health Organization, Regional Office for Africa; 2017. https://www.afro.who.int/publications/prevention-care-andtreatment-viral-hepatitis-african-region-framework-action-2016\n5. Regional Committee for Africa, 71. Framework for an integrated multisectoral response to TB, HIV, STIs and hepatitis in the WHO African Region 2021\u20132030: report of the secretariat. Brazzaville, Republic of the Congo: World Health Organization, Regional Office for Africa; 2021. https://apps.who.int/iris/handle/10665/345321\n6. World Health Organization. Global guidance on criteria and processes for validation: elimination of mother-to-child transmission of HIV, syphilis, and hepatitis B virus. Geneva, Switzerland: World Health Organization; 2021. https://www.who.int/initiatives/triple-elimination-initiativeof-mother-to-child-transmission-of-hiv-syphilis-and-hepatitis-b/ validation\n7. World Health Organization. Guiding principles for recovering, building resiliency, and strengthening of immunization in 2022 and beyond. Geneva, Switzerland: World Health Organization; 2002. https://apps. who.int/iris/bitstream/handle/10665/364944/9789240052772-eng.pdf\n8. de Villiers MJ, Nayagam S, Hallett TB. The impact of the timely birth dose vaccine on the global elimination of hepatitis B. Nat Commun 2021;12:6223. PMID:34711822 https://doi.org/10.1038/ s41467-021-26475-6\n9. Allison RD, Patel MK, Tohme RA. Hepatitis B vaccine birth dose coverage correlates worldwide with rates of institutional deliveries and skilled attendance at birth. Vaccine 2017;35:4094\u20138. PMID:28668571 https://doi.org/10.1016/j.vaccine.2017.06.051\n10. Spearman CW, Afihene M, Ally R, et al.; Gastroenterology and Hepatology Association of sub-Saharan Africa (GHASSA). Hepatitis B in sub-Saharan Africa: strategies to achieve the 2030 elimination targets. Lancet Gastroenterol Hepatol 2017;2:900\u20139. PMID:29132759 https:// doi.org/10.1016/S2468-1253(17)30295-9\n\n787 US Department of Health and Human Services | Centers for Disease Control and Prevention | MMWR\u2002 |\u2002 July 21, 2023\u2002 |\u2002 Vol. 72\u2002 |\u2002 No. 29\n\n\n",
+    "authors": [
+      "Hyacinte J Kabore",
+      "Xi Li",
+      "Mary M Alleman",
+      "Casimir M Manzengo",
+      "Mutale Mumba",
+      "Joseph Biey",
+      "Gilson Paluku",
+      "Ado M Bwaka",
+      "Benido Impouma",
+      "Rania A Tohme"
+    ],
+    "doi": "10.15585/mmwr.mm7229a2",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "K9MVGPQT",
+    "title": "Determinants of hesitancy to childhood immunizations in a peri -urban settlement; a case study of Nansana municipality, Uganda.",
+    "abstract": "Vaccine hesitancy to immunization against the childhood vaccine-preventable diseases is increasingly becoming a concern worldwide, which negatively impacts the parents' willingness to vaccinate their children. The objective of this study was to establish the current prevalence of vaccine hesitancy and the factors that determine parent's hesitancy to childhood immunizations in Nansana Municipality, Wakiso District, Uganda. This was a cross-sectional mixed methods study, utilizing both qualitative and quantitative approaches. Evaluation of the determinants of vaccine hesitancy was carried out on 344 parents of children under 24 months, using simple random sampling on pre-tested structured questionnaires. Data was analyzed using SPSS 20.0 software. Additionally, 2 focus group discussions with parents were also conducted. Vaccine hesitancy was found to be 27.6%. Education level (AOR=4.9, 95% CI, 2.6 -29.5, p=0.01), belief in vaccine effectiveness (AOR= 0.47, 95% CI, 0.17 - 0.97, p=0.01), health workers attitude (AOR=0.22, 95% CI, 0.06 - 0.86, p=0.03), timing of immunization service clinic (AOR = 3.4, 95% CI, 1.8 - 6.4, p=0.01) and adequate information provision (AOR = 0.64, 95% CI, 0.16 - 0.99, p=0.04), were the factors that were independently determining vaccine hesitancy. The vaccine hesitancy prevalence rate is comparatively similar to previous urban area studies. Despite parents overwhelmingly believing in vaccines protecting their children from vaccine-preventable disease, these same parents, express concerns regarding timing of the clinic and the side effects of vaccines. There is a need to improve on communication and information flow to address the many vaccine safety concerns, such as side effects.",
+    "full_text": "Texila International Journal of Public Health ISSN: 2520-3134\nDOI: 10.21522/TIJPH.2013.09.03.Art019\nDeterminants of Hesitancy to Childhood Immunizations in a Peri -Urban Settlement; A Case Study of Nansana Municipality, Uganda\nAmos Kijjambu1*, Edgar Mugema Mulogo2 1MPH, PhD Candidate, Public Health, Texila American University, Guyana 2Professor, Department of Community Health, Mbarara University of Science and\nTechnology, Mbarara, Uganda\n\nAbstract\n\nVaccine hesitancy to immunization against the childhood vaccine-preventable diseases is increasingly becoming a concern worldwide, which negatively impacts the parents\u2019 willingness to vaccinate their children. The objective of this study was to establish the current prevalence of vaccine hesitancy and the factors that determine parent\u2019s hesitancy to childhood immunizations in Nansana Municipality, Wakiso District, Uganda. This was a cross-sectional mixed methods study, utilizing both qualitative and quantitative approaches. Evaluation of the determinants of vaccine hesitancy was carried out on 344 parents of children under 24 months, using simple random sampling on pre-tested structured questionnaires. Data was analyzed using SPSS 20.0 software. Additionally, 2 focus group discussions with parents were also conducted. Vaccine hesitancy was found to be 27.6%. Education level (AOR=4.9, 95% CI, 2.6 -29.5, p=0.01), belief in vaccine effectiveness (AOR= 0.47, 95% CI, 0.17 \u2013 0.97, p=0.01), health workers attitude (AOR=0.22, 95% CI, 0.06 - 0.86, p=0.03), timing of immunization service clinic (AOR = 3.4, 95% CI, 1.8 \u2013 6.4, p=0.01) and adequate information provision (AOR = 0.64, 95% CI, 0.16 \u2013 0.99, p=0.04), were the factors that were independently determining vaccine hesitancy. The vaccine hesitancy prevalence rate is comparatively similar to previous urban area studies. Despite parents overwhelmingly believing in vaccines protecting their children from vaccine-preventable disease, these same parents, express concerns regarding timing of the clinic and the side effects of vaccines. There is a need to improve on communication and information flow to address the many vaccine safety concerns, such as side effects.\n\nKeywords: Childhood, Determinants, Hesitancy, Immunization, Urban.\n\nIntroduction\nWorldwide, the parental vaccine hesitancy to childhood immunizations is a growing problem with a significant public health impact [1], which reflects concerns about the decision to vaccinate their children against the childhood Vaccine-Preventable Diseases (VPDS) [2], yet childhood immunization is a key intervention towards attaining Sustainable Development Goal (SDG) number 3 that aims at reduction of under-five mortality to less than 25/1000 live births by 2030 [3], as immunizations are protective measures against childhood vaccine-\n\npreventable infectious diseases [4], and is one of the most cost-effective health investments, with proven strategies that make it accessible to even the most hard-to-reach and vulnerable populations [5], therefore making immunization a reliable child survival strategy, that prevents more than 2.5 million child deaths each year globally [6]. However, worldwide, the VaccinePreventable Diseases (VPDs) burden has remained high, and the immunization coverage rates have plateaued for the last decade, with an estimated 19.7 million children under 1 year not receiving the basic vaccines in 2019 [7]. Approximately 10 million under-five deaths\n\n*Corresponding Author: jbkamosb@gmail.com\n1\n\noccur in low-income countries annually, most of which are from VPDs [8].\nVaccine hesitancy can take different forms, including parents refusing all recommended vaccines or delaying in taking the vaccines due to different reasons [9, 10, 11]. The World Health Organization defines vaccine hesitancy as a \u201cdelay in acceptance or refusal of vaccines despite availability of vaccination services [1]. Indeed, the majority of parents agree to vaccinate their children amid concerns [2].\nFor instance, in a Canadian survey, 70% of parents were concerned about potential side effects of vaccines, and 38% believed that a vaccine could cause the disease that it was supposed to prevent [12], while in another study, parents whose children were not immunized cited the lack of perceived necessity of vaccines (28%), concerns regarding vaccine safety (17%), and the perceived number of side effects (12%) as top reasons for not immunizing [13]. In a prevalence study for vaccine hesitancy among parents in the urban area of Western Bengal, India, indicated vaccine hesitancy prevalence of 29% [14], a result which was comparatively similar in a national study performed by Gust in Nigeria, where 28% of parents reported vaccine hesitancy, of which approximately two-thirds delayed or refused only certain vaccines [11].\nAccording to the Strategic Advisory Group of Expert (SAGE) on immunization, the reasons for vaccine hesitancy fit into 3 categories: lack of confidence in effectiveness, safety, the system, or policy makers, complacency with perceived low risk of acquiring VPDs, and lack of convenience in the availability, accessibility, and appeal of immunization services, including time, place, language, and cultural contexts [1]. Consequently, challenges to maintaining adequate vaccine coverage include overcoming negative vaccine- and individual-specific attitudes and beliefs amidst a continual barrage of external factors such as vaccine controversies and evolving vaccination\n\nschedules that can also affect vaccination acceptance [15].\nHowever, some parents are concerned about the cumulative pain and discomfort experienced by children who receive multiple shots at once, yet others worry about the potential health risks of receiving multiple vaccinations during one clinic visit, wondering whether the body can handle so many different antigens at once. In addition to that, parents\u2019 question whether the immune system may become overloaded by receipt of all the recommended vaccines during early childhood [16]. In some recent qualitative studies, it has been shown that mothers of infants who are afraid of vaccine adverse effects either decline or delay subsequent immunizations [17], yet results from earlier Qualitative studies have also suggested that vaccine-hesitant parents are significantly concerned about the immediate side effects of vaccines, such as redness, swelling or pain at the injection site [18]. The discomfort associated with vaccinations remains a significant barrier to vaccination, even as children age [15]. Although vaccines are well tolerated, no vaccine is entirely without risk, but due to lack of awareness, when children experience mild side effects, their mothers may refuse further immunizations [19, 20]. This vaccine refusal has been associated with outbreaks of invasive Haemophilus influenzae type b disease, varicella, pneumococcal disease, measles, and pertussis, resulting in the unnecessary suffering of young children and waste of limited public health resources. Vaccine hesitancy is an extremely important issue that needs to be addressed because effective control of vaccine-preventable diseases generally requires indefinite maintenance of extremely high rates of timely vaccination [2, 15, 20]. This, therefore, calls for much-needed information about vaccines and safety. Although childhood immunizations are free in Uganda and many strategies like radio talk shows are done in a bid to change socio-cultural, religious beliefs and attitudes\n\n2\n\ntowards immunization and address vaccine hesitancy issues, and improve immunization coverage, there is little success [8, 21, 22], but only 55% of children aged 12\u201323 months are fully vaccinated [23], yet the immunization agenda sets out seven priority areas and four core principles in, a world where everyone, everywhere, at every age, fully benefits from vaccines for good health and well-being [24].\nNansana Municipality is located in the Wakiso District of Central Uganda, in close proximity to Kampala City. It has a population of 532,800 people, with urban and peri-urban settlements. The municipality has persistently performed poorly with low routine immunization coverage and continues to frequently report outbreaks of vaccinepreventable diseases, especially measles. In the financial year 2018/19, the Municipality reported 41% and 42% coverage of Penta 3 and Measles vaccinations, respectively [25], which were far below the district performance at 82% and 87%, respectively [26] and the national target of >90% and 95% respectively [23], which could be due to increasing vaccine hesitancy.\nIn response to this, the current study explored the determinants of hesitancy to childhood immunizations of vaccinepreventable diseases in an urban setting like Nansana Municipality. The results of this study add to existing knowledge and guide policy makers to address the vaccine hesitancy issues to improve immunization programs in Uganda urban areas and sub-Saharan Africa and provide\n\nuseful information for further research on these issues.\nMaterials and Methods\nThis was a cross-sectional mixed methods study using both quantitative and qualitative data collection methods, which was conducted between June and August 2021 in Nansana Municipality. The area is made up of four administrative divisions, namely, Nansana, Nabweru, Gombe, and Busukuma, and 29 parishes. It has 54 health facilities, with only 39% (21/54) of the health facilities having EPI services, while some of the remaining health facilities are used as outreach posts for EPI services [25].\nThe evaluation of the determinants of hesitancy to childhood immunization for vaccine-preventable diseases was carried out using researcher-administered questionnaires of 344 parents/guardians of children under 24 months. The sample size for quantitative data was estimated using the Kish Leslie formulae, 1965 of unknown population, [27] with vaccine hesitancy is estimated to be 29 % [14]. Additionally, 2 focus group discussions of parents/guardians of children under 24 months from 2 randomly selected villages were all also conducted. The study employed simple random sampling strategies to identify respondents for the interviews. However, probability proportionate to size (PPS) was used for allocating the household according to the population of the divisions of Nansana Municipality, as shown in Table 1.\n\nTable 1. Sample Size and Distribution among the Divisions of Nansana Municipality 2021\n\nDivision Nansana Nabweru Gombe Busukuma Municipality\n\nNumber of Households 52,725 38,775 28,667 14,444 133,200\n\nPercentage (%) of HH 39% 29% 21% 11% 100%\n\nSample Size 134 100 72 38 344\n\nA total of 2 focus group discussions from 2 randomly selected villages were conducted for\n\nparents/guardians of children <24 months. Each focus group had 8 participants (socially\n\n3\n\ndistanced, following the COVID-19 protocols to avoid it transmission), with each session taking 45 to 60 minutes.\nParticipant Consent and Ethical Approvals\nEthical approval was obtained from Mbarara University of Science and Technology, Research and Ethics Committee (MUST -REC, REF MUST-2021-68), and the Uganda National Council for Science and Technology (UNCST-REF HS1507ES). The clearance for conducting the research study in Nansana Municipality was sought and granted from Wakiso District Health Officer\u2019s office and the Nansana Municipal Council Town Clerk\u2019s office. All the participants were subjected to informed consent.\nData Collection, Management, and Analysis\nBefore data collection, the research instruments were pre-tested through the pilot study to determine suitability and appropriateness to ensure clarity and relevancy of data collection instruments. The structured questionnaire was adopted from the world health organization (WHO)-SAGE on vaccine hesitancy [28].\nThe content of the questionnaire included: socio-demographic variables, questions related to health services delivery in immunization context. Trained research assistants collected data under the guidance of the principal investigator.\nData was collected, cleaned, edited, and entered in SPSS version 20. Descriptive statistics were expressed as means/medians, frequencies, and percentages, whereas inferential statistics were analyzed using the Chi-square (\u03c72) technique, where bivariate analysis was conducted to examine the association between the socio-demographics of parents, the health care system factors, and vaccine hesitancy. Crude Odds Ratios (COR) and corresponding 95% Confidence Intervals\n\n(CI) were reported. Significant variables (with a p-value <0.05) from the bivariate analysis were included in the models, the multivariate logistic regression to determine variables independently associated with vaccine hesitancy. Adjusted Odds Ratios (AOR) with corresponding 95% CI were reported, and significance levels of pvalue < 0.05 were used for hypothesis testing.\nQualitative data from the focus group discussions were captured as stated from the focus groups and key informants, transcribed and uploaded into the qualitative analysis software MAXQDA version 12. Data was analyzed following the six steps of the thematic approach developed by Braun and Clarke [29]. The data from the quantitative and qualitative analysis was triangulated. Data and information collected during the course of the study have been safely stored.\nOperational Definitions\nThe following operational definitions were used:\nHesitancy\nIf the parent/guardian has ever been reluctant or hesitant to take the child for vaccination against the childhood vaccine-preventable diseases, he/she was considered having hesitancy.\nRefusal\nIf the parents/guardian ever refused to take her or his child for immunization against the childhood vaccine-preventable diseases, he/she was considered a refusal.\nResults\nFrom the total of 344 sampled parents/guardians, the majority (72%) had reached a secondary level of education or above, and more than 86% of the respondents were married or cohabiting. Most of the respondents (81%) believed that immunization was beneficial for their children in preventing the occurrence and spread of diseases, and equally as many as 80% had some knowledge\n\n4\n\nabout childhood immunized diseases. The majority of the parents, 71%, had a positive view of the health workers attitude, about 60% of them living within 2 kilometers from the health facility providing immunization services, and as many as 65% of the parents having had an experience with side effects from childhood immunization, though still a slight majority 58% appreciated that they had received adequate information about immunization for their children. The prevalence of vaccine hesitancy for childhood immunization among parents/guardians was found to be 27.6%, while\n\nabout 3% ever had a history of refusal to take their children for immunizations.\n\nSocio-Demographic\n\nCharacteristics\n\nDetermining Hesitancy to Childhood\n\nImmunizations\n\nOf the socio-demographic characteristics of the respondents, only the education level and the parent\u2019s belief in vaccine importance were found to be significantly associated with parental hesitancy to childhood immunizations, as shown in Table 2.\n\nTable 2. Association between Socio-Demographic Factors and Vaccine Hesitancy in Bivariate Analysis, 2021\n\nVariables\n\nHesitancy to vaccinations\n\nYes\n\nNo\n\nFreq (%) Freq (%)\n\nAge of parent/guardian\n\n<25 years\n\n38 (27.1) 108 (72.9)\n\n25 \u2013 45 years\n\n54 (28.1) 138 (71.9)\n\n>45 years\n\n3 (25.0) 9 (75.0)\n\nGender of the parent/guardian\n\nMale\n\n16 (33.3) 32 (66.7)\n\nFemale\n\n79 (26.7) 217 (73.3)\n\nEducation level\n\nNever been to school 6 (85.7) 1(14.3)\n\nPrimary (P1 \u2013 P7) 22 (24.4) 68 (75.6)\n\nSecondary (S1 \u2013 S6) 54 (28.4) 136 (71.6)\n\nPost-secondary\n\n15 (26.3) 42 (73.7)\n\nMarital status\n\nNever married\n\n4 (16.0) 21 (84.0)\n\nMarried/cohabiting 86 (29.1) 210 (70.1)\n\nDivorced/separated 3 (15.8) 16 (84.2)\n\nWidowed\n\n2 (50.0) 2 (50.0)\n\nReligious affiliation\n\nCatholic\n\n31 (28.2) 79 (71.8)\n\nProtestant (Anglican) 20 (24.1) 63 (75.9)\n\nMuslim\n\n28 (35.0) 52 (65.0)\n\nSeventh Day\n\n4 (16.0) 21 (84.0)\n\nAdventist (SDA)\n\nPentecostal\n\n11 (26.2) 31 (73.8)\n\nOther religions\n\n1 (25.0) 3 (75.0)\n\nMonthly Income\n\n<48 USD\n\n21 (32.3) 44 (67.7)\n\n\u03c72\n\ndf\n\n0.082 2\n\n0.912 1 15.616 3\n\n4.326 3\n\n4.458 5\n\n1.222 2\n\np-value 0.960 0.340 0.01 0.228 0.486\n0.543\n\nCOR; (95% CI) 3.73(1.24 \u2013 18.7)\n\n5\n\n49 \u2013 143 USD\n\n53 (25.6) 154 (74.4)\n\n>144 USD\n\n21 (29.2) 51 (70.8)\n\nBelief in vaccine importance\n\nYes\n\n54 (19.4) 225 (80.6)\n\nNo\n\n36 (55.3) 29 (44.7)\n\nKnowledge of diseases\n\nYes\n\n71 (25.8) 204 (74.2)\n\nNo\n\n24 (34.8) 45 (65.2)\n\nThe other socio-demographic variables, such\n\nas the age of the parent/guardian, gender of the\n\nparent/guardian, marital status, religion,\n\nmonthly income, and knowledge of childhood\n\nimmunized diseases, were found not to be\n\ndeterminants of hesitancy to childhood\n\nvaccinations against vaccine-preventable\n\ndiseases.\n\nParents who had never received any formal\n\neducation were about 4 times more likely to\n\nhesitate to take their children for immunization\n\n(COR=3.7, 95% confidence interval, CI: 1.2 \u2013\n\n18.6), compared to those who had any formal\n\neducation. On the other hand, parents who\n\nbelieved in vaccines protecting their children\n\nfrom the vaccine-preventable diseases were 3\n\ntimes less likely to hesitate to take their\n\nchildren for immunization (COR= 0.3, 95% CI:\n\n0.1 \u2013 0.7), compared to those who did not\n\nbelieve in vaccine importance, see Table 2.\n\nBoth factors retained their significance of\n\ndeterminants of hesitancy to childhood\n\n34.264 1 0.004\n2.217 1 0.136\nimmunization after adjusting all other characteristics, as shown in Table 4.\nHealth Care Delivery System Determinants of Parental Hesitancy to Childhood Immunizations\nThere was significant association (p-value <0.05) between some health service deliveryrelated factors to the parent\u2019s hesitancy to taking their children for immunization. These included health workers attitude (COR=0.16, 95% CI: 0.03 \u2013 0.39), availability of vaccines (COR= 0.12, 95% CI: 0.04 \u2013 0.42), accessibility to immunization services (COR= 0.22, 95% CI: 0.07 \u2013 0.70), timing of the immunization clinic (COR= 5.2, 95% CI: 3.2 \u2013 8.7), side effects to vaccinations (0.25, 95% CI: 0.08 -0.79), and information provision on immunization (COR=0.24, 95% CI: 0.08 \u2013 0.69), were all significantly associated with hesitancy to childhood immunization, see Table 3.\n\nTable 3. Association between Health Care System Factors and Vaccine Hesitancy in Bivariate Analysis, 2021\n\nVariables\n\nHesitant to vaccination \u03c72\n\ndf p-value COR (95% CI)\n\nYes\n\nNo\n\nFreq (%) Freq (%)\n\nAttitude of health workers\n\nPositive (Friendly)\n\n52 (21.4) 193 (78.6) 20.856 1 0.000 0.16(0.06 \u2013 0.39)\n\nNegative (Rude)\n\n34 (34.4) 65 (65.6)\n\nDistance from the health facility\n\n<2 kilometers\n\n47 (22.8) 159 (77.2) 5.922 2 0.152\n\n3 \u2013 5 kilometers\n\n38 (34.9) 71 (65.1)\n\n>5 kilometers\n\n10 (34.5) 19 (65.5)\n\nAvailability of vaccines\n\nAlways available\n\n63 (21.7) 224 (78.3) 26.740 2 0.012 0.12(0.04 \u2013 0.42)\n\nSometimes not all available 24 (47.9) 26 (52.1)\n\n6\n\nMost times not any available 3 (42.9)\n\nAccessibility to immunization services\n\nYes\n\n70 (23.1)\n\nNo\n\n21 (52.6)\n\nPoor timing of immunization clinic\n\nYes\n\n58 (53.7)\n\nNo\n\n44 (18.6)\n\nWaiting time\n\n< 3 hours\n\n28 (25.2)\n\n4 \u2013 5 hours\n\n52 (27.4)\n\n>6 hours\n\n15 (34.9)\n\nSide effects after vaccinations\n\nYes\n\n72 (32.3)\n\nNo\n\n28 (22.9)\n\nAdequate information provision\n\nYes\n\n43 (21.6)\n\nNo\n\n58 (40.5)\n\n4 (57.1)\n235 (76.9) 18 (47.4)\n50 (46.2) 192 (81.4)\n83 (74.8) 138 (72.6) 28 (65.1)\n151 (67.7) 93 (77.1)\n158 (78.4) 85 (59.5)\n\n20.521 1 44.998 1 1.459 2\n10.56 1 16.301 1\n\n0.013 0.000 0.482\n0.027 0.018\n\n0.22(0.07 \u2013 0.70) 5.23(3.18 \u2013 8.77)\n0.25(0.08 \u2013 0.79) 0.24(0.08 \u2013 0.69)\n\nHealth worker\u2019s attitude, the timing of the immunization clinic, and adequate information provision all retained their significance of determinants of hesitancy to childhood immunization after adjusting all other characteristics as shown in Table 4. However, Other factors like distance from the health facility and waiting time were not significantly associated with vaccine hesitancy.\nIndependent Determinants of Parental Vaccine Hesitancy to Childhood Immunizations\nIn the final logistic regression model, education level, belief in vaccine importance, health workers attitude, the timing of immunization clinic, availability of vaccines, and adequate information provision were found to be independent determinants of hesitancy to childhood immunizations of vaccinepreventable diseases. Parents/guardians who had no formal education were 5 times more likely to hesitate to take their children for immunization (adjusted OR=4.9, 95%CI: 2.6 \u2013 29.5, p=0.01), compared to those who had attained a post-secondary level of education. Similarly, parents who viewed the timing of\n\nimmunization services as poor were 3 times more likely to hesitate to take their children for immunization than those who did have a negative view of the immunization service\u2019s timing (AOR=3.4, 95% CI: 1.8 -6.4, p=0.01). On the other hand, parents/guardians who believed in vaccines protecting their children against the vaccine-preventable diseases were 2 times less likely to hesitate to take their children for vaccination (AOR=0.47, 95% CI: 0.17 \u2013 0.97, p=0.01) compared to those who did not believe in vaccine importance, as well as those parents/guardians who had a positive view of health workers attitude being 5 times less likely to hesitate to take their children for vaccination compared to those who had a negative view of health workers attitude (AOR=0.22, 95% CI: 0.06- 0.86, p=0.03). In the same vein, parents who thought they were provided with adequate information on immunization were about 2 times less likely to hesitate to take their children for vaccination (AOR=0.64, 95% CI: 0.06 \u2013 0.99, p=0.04) compared to those who believed were not provided with adequate information concerning immunization as shown in Table 4.\n\n7\n\nTable 4. Multivariate Logistic Regression showing the Association between Socio-Demographic and Health Care System Factors and Parental Vaccine Hesitancy, 2021\n\nVariable\nEducation level Belief in vaccine importance Attitude of health workers Availability of vaccine\u2019s Easy accessibility to services Poor timing of clinic Side effects from vaccinations Adequate information\n\nResponse\nInformal Yes Friendly Always Yes Yes No Yes\n\nHesitant to vaccination\n\nYes\n\nNo\n\nFreq (%) Freq (%)\n\n6 (85.7) 1(14.3)\n\n54 (19.4) 225 (80.6)\n\n52 (21.4) 193 (78.6)\n\n63 (21.7) 224 (78.3)\n\n70 (23.1) 235 (76.9)\n\n58 (53.7) 50 (46.2)\n\n28 (22.9) 93 (77.1)\n\n43 (21.6) 158 (78.4)\n\nAOR (CI; 95%)\n4.91(2.6 \u2013 29.5) 0.47(0.17 \u2013 0.97) 0.22(0.06 \u2013 0.86) 1.09(0.23 \u2013 5.22) 2.74(0.62 \u2013 12.1) 3.40(1.81 \u2013 6.35) 1.01(0.17 \u2013 6.06) 0.64(0.16 \u2013 0.99)\n\np=value\n0.012 0.014 0.031 0.912 0.184 0.010 0.99 0.043\n\nKey Findings from the Focus Groups\n\nAbout 94% of the focus group discussion\n\nparticipants were females of median age 29\n\nyears. The focus area for the group discussions\n\nincluded (but was not limited to) the following\n\nquestions: Do you believe that vaccines can\n\nprotect children from serious diseases? Do you\n\nthink that most parents like you have their\n\nchildren vaccinated with all the recommended\n\nvaccines?\n\nHave you ever been reluctant or hesitated to\n\nget a vaccination for your child? Have you ever\n\nrefused vaccination for your child? Why do you\n\nthink\n\nchildren\n\nare\n\ngiven\n\nvaccines/immunization; are there situations\n\nwhen you failed to bring your child for\n\nimmunization and what were the reasons? Has\n\ndistance, the timing of clinic, the time needed to\n\nget to a clinic or wait at the clinic and/or costs\n\nin getting to the clinic prevented you from\n\ngetting your child immunized? Are there other\n\npressures in your life that prevent you from\n\ngetting your child immunized on time?\n\nParents/guardians expressed their concerns\n\nabout the long waiting time, inadequate health\n\nworker staffing numbers, and sometimes\n\nmissing vaccines due to stock-outs as\n\nsignificant gaps affecting immunization service\n\ndelivery.\n\n\u201cGenerally, the health workers are good, and I have no problem with them, only that they are few yet we are many mothers\u201d \u201cI have no issues with the nurses. They try their best, but they get tired because we many on that day. We get tired when we go as we wait for a very long time\u201d FGD1.\n\u201cSometimes we go, and they tell us the \u201cdrugs\u201d are not available, that we should come back another time, the \u201cdrugs\u201d for babies. Sometimes the nurse immunizing is not there. \u201cEven me mine of 11months has just received it that of 9 months because when I went there the last time, I could not get. Then I had to first be sure before going back\u201d \u201cYou can walk or get a motorcycle, yet when you reach there, they send you back\u201d FGD2.\nRespondents were asked about the side effects to childhood vaccinations, how often this happens?\n\u201cThey usually have pain and fever. The injections are very painful. My child\u2019s thigh got swollen after the injection, I came to the nurse, and she told me that all will be well\u201d, (FGD1).\n\u201cWe really need more information about side effects of vaccines to convince our husbands, because, if the child gets fever and cries, they refuse us to come back for another \u201cdose\u201d (FGD1)\u201cWe need more information from the health workers and VHTs why they our children all those injections\u201d, (FGD2).\n\n8\n\nThe religious issues concerning vaccines were also noted;\n\u201cSome of the parents refuse to take their children for vaccination because of their religious beliefs. \u201cSome believe in no need of vaccines as God protects them\u201d.\nThe effect of Covid-19 have also been felt, leading to fear of mothers taking their children for immunization, compounded by travel restriction and increased transport cost as noted by some mothers;\n\u201cWe fear this Covid disease which is killing many people, we shall wait till it safe to take our children for immunization\u201d (FGD1). \u201cThe transport cost for the \u201cboda boda\u201d has doubled. I can\u2019t afford that now\u201d (FGD2).\nDiscussion\nThe findings show that the major determinants of parental vaccine hesitancy to childhood immunizations against vaccinepreventable diseases in this urban context were; education level, belief in vaccine importance in protective the child against the preventable killer diseases, health workers attitude, the timing of clinic for immunization services and adequate information provision.\nParent\u2019s level of education impacts on understanding of issues and greatly influences their decisions to vaccinate their children or not. Consistent with several studies conducted elsewhere, parents with less or no formal education have greater distrust in the medical community, express more concerns about vaccine safety, and have less belief in the necessity and efficacy of vaccines [30-33]. However, in contrast to finding in this study, Opel et al. found that parents with higher levels of education were nearly four times as likely to be concerned about the safety of vaccines than those from lower education levels [30], giving mixed results on the level of education and it impacts on parental vaccine acceptance and uptake. In a SAGE group systematic reviews, having attained higher education status in India and Nigeria promoted parental vaccine\n\nacceptance, while in the USA, China, and Lebanon, parents with a higher level of education persistently had more doubts on vaccines compared to those with lower education levels [34].\nParent\u2019s belief in vaccines\u2019 effectiveness and safety promotes trust, improves individual attitude, and reduces vaccine hesitancy. Consistent with studies conducted elsewhere, parents who have faith in vaccines protecting their children against the VPDs are more likely to have immunization acceptance and uptake of immunization [1, 13, 15]. A study done in Nigeria reported that parents who individually believe in vaccine effectiveness and safety take their children for vaccination more consistently and complete their immunization schedules [20]. In the current study, the variation in believing in different types of vaccines\u2019 effectiveness in protecting the children might have impacted the results. However, information on the belief in different vaccine antigen effectiveness was not collected and is an area for further investigation.\nWhen the health worker\u2019s attitude is deemed as friendly by the parents/guardians positively impacts on parent\u2019s acceptance of immunization programs and therefore reduces on vaccine hesitancy. This consequently promotes a healthy parent-to-health worker relationship, resulting in addressing issues like the importance of completing the immunization schedule and the safety of vaccines [1, 28, 35]. According to [36], the study showed that parents who reported that their vaccination decisions were positively influenced by healthcare providers were also more likely to believe that vaccines were safe. In fact, parents with lower levels of trust in their child\u2019s doctor also have lower confidence in the safety of vaccines and therefore hesitate to vaccinate their children [32].\nThe convenience of the immunization services clinic also impacts on parents\u2019 access to immunization services, which, when is good, greatly reduces on hesitancy of parents to take\n\n9\n\ntheir children vaccination which improves childhood immunization uptake and completion of the immunization schedule [15, 17]. On the other hand, lack of convenience by parents and poor accessibility to immunization services greatly increases parental hesitancy to their children immunization [1, 28]. The frustration from parents because of the long waiting time negatively impacts their decisions to take their children to the health facility for immunization [19]. They would rather stay home or attend to the immediate financial needs, rather than endure the long waiting time of 4-7 hours, including the travel to and from the health facility, yet they have their businesses and other competing needs to attend to Abdulraheem 2011 [20]. This would indirectly contribute to a delay in the schedule for the affected children because these parents/guardians may hesitate to return on the next rescheduled date. This calls for appropriate area-specific planning and local research within the division to find the ways to reach to these vulnerable children for protection against these vaccine preventable diseases in order to achieve the IA2030 [24].\nGood communication leads to understanding and sharing of information. Adequate information provision, therefore, promotes vaccine acceptance and greatly reduces on vaccine hesitancy. In a study by Smith et al., effective communication and vital information sharing to parents reduce parental concerns about vaccine safety and side effects [36] and negative rumors about the vaccines [37]. When the parents are not given adequate information are more likely to hesitate to take their children for vaccination, despite overwhelmingly believing in vaccines protecting their children against the diseases [9]. Well-informed parents are likely to understand the importance of honoring the return dates and completing the immunization schedule [21]. As indicated in the focus group discussions, the parents need more adequate information on how to address the side effects.\n\nThe prevalence of 27.6% for parental hesitancy towards childhood immunization found in this study, does explain in part why immunization uptake is low in Nansana Municipality [25] and frequent outbreaks of measles in the area. A comparable study done in India from an urban of West Bangal had hesitancy marginally higher at 29% Sikder2020 [14], while from another study from Nigeria it was 28% [11]. The refusal parents prevalence of 3% was slightly higher than a previous study result of 1-2% from the USA, [9, 15].\nIn contrast to the findings of a review of studies conducted elsewhere, parents age, gender, religion, income, marital status, knowledge of vaccines, distance to the immunization center, and waiting time were not found to be associated with vaccine hesitancy [34, 37, 38]. In other studies, Parents of lowerincome brackets have greater levels of concern about the safety and necessity of vaccines as compared with those of higher income [30-32, 39]. However, socioeconomic factors appear to have conflicting associations with parental immunization acceptance, which could reflect differences in underlying beliefs about vaccines that differ by socioeconomic strata [15].\nAvailability of vaccines and side effects of vaccines were not independently associated with parental vaccine hesitancy. The availability of vaccine issues could be attributed to easy accessibility to other immunization centers since over 90% of parents live within a 5-kilometer distance from the health facility offering EPI services. On the other hand, the issue of side effects not being independently associated with vaccine hesitancy could probably be because, although some parents/guardians may get side effects or reactions to vaccines, they might have been given information about expected reactions to vaccinations like fever and pain at the site and therefore cannot hinder them taking their children for vaccination, which makes it of less importance. In contrast to studies elsewhere, the side effects of vaccines frequently and\n\n10\n\nconsistently were the major independent determinants to parental vaccine hesitancy [20, 34, 37, 40]. Side effects from vaccinations negatively affect parents decisions to delay or stop the immunization of their children [16, 17, 20].\nAdmittedly, however, as reported by SAGE, very few studies on vaccine hesitancy have been conducted in African compared to the Americas and Europe, yet the burden of unimmunized children as high as over 80% are from Africa [34]. In fact, little is known about the nature and causes of vaccine hesitancy in Africa, with most research in this area conducted in high-income countries [41]. A few of the studies conducted in Kenya, Malawi, and Ethiopia [37], and Nigeria [34], mostly use models developed in Western countries, which may not be suitable for African populations.\nThe impact of misinformation about childhood immunization on social media platforms and how they can affect parental vaccine hesitancy in an urban settlement have not been explored by this study and therefore recommended for further research.\nStudy Limitations\nAlthough the study was confined to one municipality, this study area is typical of other urban settings in terms of health infrastructure and in Uganda. The study findings are therefore comparable across similar settings. We also note that this was a cross-sectional study and therefore, we cannot define the temporal relationship between the independent variables and outcome.\nThe direction of causality can therefore only be regarded as suggestive. The data collected on a number of independent variables were based on self-reports that are likely to be subject to social desirability bias. As a result, there is a limit to which such responses can be considered accurate by foreknowledge of what, in the view of the respondent, would be a suitable response. However, the current findings do carry implications for health service\n\nmanagers, decision-makers, and health care providers in their consideration of designing the measures to address parental hesitancy to childhood vaccinations against vaccinepreventable diseases.\n\nConclusion\n\nThe parental vaccine hesitancy prevalence rate is comparatively similar to previous urban area studies.\nAlthough parents overwhelmingly share the belief that vaccines are a good way to protect their children from vaccine-preventable disease, these same parents express concerns regarding the convenience of the immunization clinic services and side effects of vaccines. Even though the information is available to address the many vaccine safety concerns, such information is not reaching many parents in an effective manner.\n\nAppendices\n\nAbbreviations\n\nAOR COR COVID-19 CI FGD IA2030 SPSS WHO\n\n: Adjusted Odds Ratio : Crude Odds Ratio : Corana Virus Disease -2019 : confidence interval : Focus Group Discussion : Immunization Agenda 2030 : Statistical Package for Social Sciences : World Health Organization\n\nConsent for publication\n\nNot applicable.\n\nAvailability of Data and Materials\n\nAll data supporting our findings are contained in the paper. There are no restrictions to data sources.\n\nCompeting interests\n\nThe authors declare that there are no competing interests.\n\nFunding\n\nThe study was self-sponsored.\n\n11\n\nAcknowledgements\nAll participants in this study\n\n(parents/guardians, Nansana Municipal Council health team, and the Research assistants).\n\nReferences\n\n[1] MacDonald, N. 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[40] Giambi et al., 2018 \u201cParental vaccine hesitancy in Italy \u2013 Results from a national survey,\u201d Vaccine,\n\n36 (6), 779\u2013787,\n\nhttps://doi.org/10.1016/j.vaccine.2017.12.074.\n\n[41] Cooper, S., Betsch, C., Sambala, E Z., Mchiza,\n\nN and Wiysonge, C. S, 2018 \u201cVaccine hesitancy\u2013a\n\npotential threat to the achievements of vaccination\n\nprogrammes in Africa,\u201d Hum. Vaccine.\n\nImmunother,\n\n14\n\n(10),\n\n2355\u20132357,\n\nhttps://doi.org/10.1080/21645515.2018.1460987.w.\n\n14\n\n\n",
+    "authors": [
+      "Amos Kijjambu",
+      "Edgar Mugema Mulogo"
+    ],
+    "doi": "10.21522/TIJPH.2013.09.03.Art019",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "FKIKKT3H",
+    "title": "Factors associated with uptake of immunizations for vaccine-preventable childhood diseases in a peri-urban settlement; a case study of Nansana municipality, Uganda.",
+    "abstract": "Immunization uptake for childhood vaccine-preventable diseases remains low in urban areas of Uganda, leading to repeated outbreaks of diseases like measles, despite easy communication and accessibility to services. The objectives of this study were to establish immunization coverage and to identify the factors that affect the uptake of immunization among the children aged 10 - 23 months in Nansana Municipality, Wakiso District, Uganda. This was a cross-sectional mixed methods study, utilizing both qualitative and quantitative approaches. Assessment of immunization uptake was carried out on 344 parents/guardians of children aged 10-23 months, using simple random sampling on pre-tested structured questionnaires. Data was analyzed using SPSS 20.0 software. Additionally, 2 focus group discussions with parents and key informant interviews with immunization focal persons were also conducted. Immunization coverage was found to be 90.4% for BCG, 89.3% for Penta1, 80.7% for Penta2, 72.5% for Penta3 and 73.9% for measles1. Availability of vaccines (AOR= 33, 95% CI, 1.44 - 792, p=0.03), accessibility to immunization services (AOR = 32, 95% CI, 2.0 - 513, p=0.01) and communication between the parents and health workers about the return dates (AOR = 0.03, 95% CI, 0.01 - 0.83, p=0.03), were the factors that were independently associated with immunization uptake. The coverage rates were higher than the national average, with the health care service-related factors identified as being critical for improving immunization uptake. There is a need for improved vaccine supply and communication about immunization services, which should be designed considering the local context in collaboration with slum-dwelling communities.",
+    "full_text": "Texila International Journal of Public Health ISSN: 2520-3134\nDOI: 10.21522/TIJPH.2013.09.04.Art008\nFactors Associated with Uptake of Immunizations for Vaccine-Preventable Childhood Diseases in a Peri-Urban Settlement; A Case Study of Nansana\nMunicipality, Uganda\n\nAmos Kijjambu1*, Edgar Mugema Mulogo2 1PhD, Public Health, Texila American University, Guyana 2Professor, Department of Community Health, Mbarara University of Science and\nTechnology, Mbarara, Uganda\n\nAbstract\n\nImmunization uptake for childhood vaccine-preventable diseases remains low in urban areas of Uganda, leading to repeated outbreaks of diseases like measles, despite easy communication and accessibility to services. The objectives of this study were to establish immunization coverage and to identify the factors that affect the uptake of immunization among the children aged 10 \u2013 23 months in Nansana Municipality, Wakiso District, Uganda. This was a cross-sectional mixed methods study, utilizing both qualitative and quantitative approaches. Assessment of immunization uptake was carried out on 344 parents/guardians of children aged 10\u201323 months, using simple random sampling on pre-tested structured questionnaires. Data was analyzed using SPSS 20.0 software. Additionally, 2 focus group discussions with parents and key informant interviews with immunization focal persons were also conducted. Immunization coverage was found to be 90.4% for BCG, 89.3% for Penta1, 80.7% for Penta2, 72.5% for Penta3 and 73.9% for measles1. Availability of vaccines (AOR= 33, 95% CI, 1.44 \u2013 792, p=0.03), accessibility to immunization services (AOR = 32, 95% CI, 2.0 \u2013 513, p=0.01) and communication between the parents and health workers about the return dates (AOR = 0.03, 95% CI, 0.01 \u2013 0.83, p=0.03), were the factors that were independently associated with immunization uptake. The coverage rates were higher than the national average, with the health care service-related factors identified as being critical for improving immunization uptake. There is a need for improved vaccine supply and communication about immunization services, which should be designed considering the local context in collaboration with slum-dwelling communities.\n\nKeywords: Childhood, Immunization, Uptake, Urban.\n\nIntroduction\nWorldwide, the Vaccine-Preventable Diseases (VPDs) burden has remained high, and the immunization coverage rates have plateaued for the last decade, with an estimated 19.7 million children under 1 year not receiving the basic vaccines in 2019 [1]. Childhood immunizations are protective measures against infectious diseases [2], and is one of the most cost-effective health investments, with proven strategies that make it accessible to even the most hard-to-reach and vulnerable populations\n\n[3]. Consequently, the VPDs, continue to be an important public health problem in developing countries [4], making immunization a reliable child survival strategy that prevents more than 2.5 million child deaths each year globally. Approximately 10 million under-five deaths occur in low-income countries annually, most of which are from VPDs [5]. Therefore, childhood immunization is a key intervention towards attaining Sustainable Development Goal (SDG) number 3 that aims to reduce under-five mortality to less than 25/1000 live births by 2030 [6].\n\n*Corresponding Author: jbkamosb@gmail.com\n1\n\nDespite the registered global progress in ensuring the provision of childhood vaccinations, difficulties still exist, especially on how to reach the most vulnerable, poorest, disadvantaged childhood populations in remote and slum communities, especially within subSaharan Africa. In 2020 17.1 million infants did not receive an initial dose of DTP vaccine, pointing to a lack of access to immunization and other health services, and an additional 5.6 million are partially vaccinated [1]. The Global Vaccine Action Plan (GVAP) was the global immunization strategy of the \u201cDecade of vaccines\u201d (2011\u20132020). As such, this would boost and propel the reach of every child (REC)/ reach every district (RED) concept that was introduced to ensure that all children receive their vaccination at all levels [4].\nEarlier studies and systematic reviews conducted across the globe pointed out low education level of parents/caretakers, cultural/religious beliefs, age of caretakers, transport difficulties, long distance to health facilities, a difficult geographical terrain, accessibility to health facilities, refugee status, mobility of populations, negative messaging/anti-vaccine sentiments, social, economic status and attitudes of the parents/caretakers [7-10]. In addition to that, the resurgence of outbreaks of vaccinepreventable diseases (VPDs), including measles, has prompted renewed attention on how vaccine misinformation and hesitancy can lead to the spread of infections and negatively impact public health [10].\nAlthough childhood immunizations are free in Uganda and many strategies like radio talk shows are done in a bid to change sociocultural, religious beliefs and attitudes towards immunization, as well as mass campaigns, static and outreach programmes to improve immunization coverage, there is little success [5, 11, 12], yet only 55% of children aged 12\u2013 23 months are fully vaccinated with coverage being relatively higher in urban areas (61%) than rural areas (50%), [12]. The immunization\n\nagenda sets out seven priority areas and four core principles in a world where everyone, everywhere, at every age, fully benefits from vaccines for good health and well-being [13].\nNansana Municipality is located in the Wakiso District of Central Uganda, in close proximity to Kampala City. It has a population of 532,800 people, with urban and peri-urban settlements. The municipality has persistently performed poorly with low routine immunization coverage and continues to frequently report outbreaks of vaccinepreventable diseases, especially measles. In the financial year 2018/19, the Municipality reported 41% and 42% coverage of Penta 3 and Measles vaccinations, respectively [14], which were far below the district performance at 82% and 87%, respectively [14] and the national target of >90% and 95% respectively [12].\nMost of the studies on immunization uptake in Uganda have been conducted in rural areas [7, 15] while that of Babirye, [16] on factors affecting immunization behavior in Kampala employed only qualitative data. In response to this, this current study explored the factors influencing immunization uptake in an urban setting like Nansana Municipality. The results of this study add to existing knowledge, and guide policymakers to improve immunization programs in Uganda urban areas and subSaharan Africa, and also to provide useful information for further research on these issues.\nMaterials and Methods\nThis was a cross-sectional mixed methods study using both quantitative and qualitative data collection methods, which was conducted between June and August 2021 in Nansana Municipality. The area with the majority of small business is made up of four administrative divisions, namely, Nansana, Nabweru, Gombe, and Busukuma, and 29 parishes. It has 54 health facilities, with only 39% (21/54) of the health facilities have EPI services, while some of the remaining health facilities are used as outreach posts for EPI\n\n2\n\nservices [17]. The assessment of the factors associated with childhood immunization uptake was carried out using researcher-administered questionnaires of 344 parents/guardians of children aged 10 - 23 months, focus group discussions with parents/guardians, and key informants\u2019 interviews of EPI providers/ focal points. The sample size was estimated using the World Health Organization\u2019s (WHO) 30 cluster sampling technique for cluster survey design [18] for Expanded Program on Immunizations (EPI). Each parish was considered as a cluster, except for Nansana, where one very densely populated parish was divided into 2 parishes, to make a total of 5 parishes.\nThe study employed simple random sampling strategies to identify respondents for the interviews. However, probability proportionate to size (PPS) was used for allocating the household according to the population of the divisions of Nansana Municipality, ranging from an average of 5 participants per parish/cluster in Busukuma Division to 27 in Nansana Division, as shown in Table 1. A total of 2 focus group discussions from 2 randomly selected villages were\n\nconducted for parents/guardians of children <24 months, on addition to 7 key informant interviews including the District Health Officer, District EPI focal person, Municipal EPI focal person, and 4 other EPI focal persons from randomly selected health facilities offering EPI. Each Focus group had 8 participants (socially distanced, following the COVID-19 protocols to avoid it transmission), with each session taking 45 to 60 minutes.\nParticipant Consent and Ethical Approvals\nEthical approval was obtained from Mbarara University of Science and Technology, Research and Ethics Committee (MUST -REC, REF MUST-2021-68), and the Uganda National Council for Science and Technology (UNCST -REF HS1507ES). The clearance for conducting the research study in Nansana Municipality was sought and granted from Wakiso District Health Officer\u2019s office and the Nansana Municipal Council Town Clerk\u2019s office. All the participants were subjected to informed consent.\n\nTable 1. Sample size and distribution among divisions and parishes/clusters\n\nDivision\nNansana Nabweru Gombe Busukuma Municipality\n\nNumber of Households 52,725 38,775 28,667 14,444 133,200\n\nNumber of Parishes/Clusters 5 6 11 8 30\n\nAverage Number of Participants per Cluster 27 17 7 5 11\n\nSample Size\n134 100 72 38 344\n\nData Collection, Management, and Analysis\nBefore data collection, the research instruments were pre-tested through the pilot study to determine suitability and appropriateness to ensure clarity and relevancy of data collection instruments. Data was collected by trained research assistants under the guidance of the principal investigator. Data\n\nwas collected, cleaned, edited, and entered in SPSS version 20. Descriptive statistics were expressed as means/medians, frequencies, and percentages, whereas inferential statistics were analyzed using the Chi-square (\u03c72) technique, where bivariate analysis was conducted to examine the association between the sociodemographics of parents, the health care system factors, and immunization uptake. Crude Odds Ratios (COR) and corresponding 95%\n\n3\n\nConfidence Intervals (CI) were reported.\n\nSignificant variables (with a p-value <0.05)\n\nfrom the bivariate analysis were included in the\n\nmodels, the multivariate logistic regression to\n\ndetermine\n\nvariables\n\nindependently\n\nimmunization uptake. Adjusted Odds Ratios\n\n(AOR) with corresponding 95% CI were\n\nreported, and significance levels of p-value <\n\n0.05 were used for hypothesis testing.\n\nQualitative data from the focus group\n\ndiscussions and key informant interviews were\n\ncaptured as stated from the focus groups and\n\nkey informants, transcribed and uploaded into\n\nthe qualitative analysis software MAXQDA\n\nversion 12. Data was analyzed following the six\n\nsteps of the thematic approach developed by\n\nBraun and Clarke [19]. These were interpreted\n\nthematically, woven, and added in the\n\ndiscussion together with the quantitatively interpreted data for the overall conclusion of the study findings according to the objectives of the study. The data from the quantitative and qualitative analysis were triangulated. Data and information collected during the course of the study has been safely stored.\nResults\nThe study successfully enrolled and analyzed 344 parents/guardians with children 10-23 months. From the 344 children surveyed, majority 51.7% (178/344) were female, 60% (206/344) of birth order 2nd to 4th, with mean and medians age of 16 months and 15 months respectively, where more than half (52.0%) were below 18 months, as shown in Table 2.\n\nTable 2. Socio Demographic Characteristics of the Children\n\nVariable Age of the child category <18 months >18 -23 months Birth order of the child First (1) 2nd \u2013 4th order 5th or more Gender of the child Male Female\n\nFrequency (n = 344)\n179 165\n94 206 44\n166 178\n\nPercent\n52.0 48.0\n27.3 59.9 13.8\n48.3 51.7\n\nAs regards to the socio demographic (332/344); most of them Catholics or characteristics of parents/guardians enrolled, Protestants (32.0% vs. 24.1%); and married or the majority, 86% (296/344), were female, cohabiting 86% (296/344). Other biological parents of the children 95.6% parents/guardians\u2019 characteristics are as shown (329/344), aged at or below 45 years 96.5% in Table 3.\nTable 3. Socio Demographic Characteristics of Parents/Guardians\n\nVariable\n\nFrequency (n = 344)\n\nAge of parent/guardian category (Years)\n\n<25 years\n\n140\n\n26 \u2013 45 years\n\n192\n\n>45 years\n\n12\n\nRelationship with the child\n\nParent\n\n329\n\nPercent (%)\n40.7 55.8 3.5\n95.6\n\n4\n\nOther\n\n15\n\n4.4\n\nGender of the parent/guardian\n\nMale\n\n48\n\n14\n\nFemale\n\n296\n\n86\n\nEducation level\n\nNever been to school\n\n7\n\n2.0\n\nPrimary (P1 \u2013 p7)\n\n90\n\n26.2\n\nSecondary (S1 \u2013 S6)\n\n190\n\n55.2\n\nPost-secondary (certificates, diplomas) 57\n\n16.6\n\nMarital status\n\nNever married\n\n25\n\n7.3\n\nMarried/cohabiting\n\n296\n\n86\n\nDivorced/separated\n\n19\n\n5.5\n\nWidowed\n\n4\n\n1.2\n\nReligion\n\nCatholic\n\n110\n\n32.0\n\nProtestant (Anglican)\n\n83\n\n24.1\n\nMuslim\n\n80\n\n23.3\n\nSeventh Day Adventist (SDA)\n\n25\n\n7.3\n\nPentecostal\n\n42\n\n12.2\n\nOther\n\n4\n\n1.2\n\nOccupation\n\nCivil servant\n\n13\n\n3.9\n\nNGO/private\n\n19\n\n5.6\n\nBusinessman/woman\n\n136\n\n39.5\n\nCasual laborer\n\n55\n\n15.9\n\nHouse wife\n\n94\n\n27.3\n\nUnemployed\n\n27\n\n7.8\n\nMonthly Household Income\n\n< 58 USD\n\n65\n\n18.9\n\n59 USD \u2013 143 USD\n\n207\n\n60.2\n\n>144 USD\n\n72\n\n20.9\n\nBelief in vaccine protecting against diseases\n\nYes\n\n279\n\n81.1\n\nNo\n\n65\n\n18.9\n\nKnowledge of children immunized diseases\n\nYes\n\n275\n\n79.9\n\nNo\n\n69\n\n20.1\n\nAs shown in Table 4, from the 344 parents/guardians who participated in the study, the majority, 71.2% (245/344), viewed the health workers attitude positively, were within 5kilometer distance from the health facility offering EPI 91.6% (315/344), had awareness\n\nabout the availability of immunization services 90.7% (312/344), noted the availability of vaccines 83.5% (287/344) and could easily access immunization services 88.7% (305/344). Other health care system characteristics are described in Table 4.\n\n5\n\nTable 4. Health care system characteristics related to childhood immunizations\n\nVariable\n\nFrequency (n = 344) Percent (%)\n\nAttitude of health workers\n\nPositive (Friendly)\n\n245\n\n71.2\n\nNegative (Rude)\n\n99\n\n28.8\n\nDistance from the health facility\n\n<2 kilometers\n\n206\n\n59.9\n\n3 \u2013 5 kilometers\n\n109\n\n31.7\n\n>5 kilometers\n\n29\n\n8.4\n\nAwareness of availability of immunization services\n\nYes\n\n312\n\n90.7\n\nNo\n\n32\n\n9.3\n\nAvailability of all vaccine antigens\n\nAlways available\n\n287\n\n83.5\n\nSometimes not all available\n\n50\n\n14.5\n\nMost times not any available 7\n\n2.0\n\nEasy access to immunization services\n\nYes\n\n305\n\n88.7\n\nNo\n\n39\n\n11.3\n\nSkipping services due to poor timing of the clinic\n\nYes\n\n108\n\n31.4\n\nNo\n\n236\n\n68.6\n\nWaiting time\n\n< 3 hours\n\n111\n\n32.3\n\n4 \u2013 5 hours\n\n190\n\n55.2\n\n>6 hours\n\n43\n\n12.5\n\nReturn dates information emphasis\n\nAlways\n\n258\n\n75.0\n\nSometimes\n\n78\n\n22.7\n\nNever\n\n8\n\n2.3\n\nReminders for return dates\n\nYes\n\n101\n\n29.3\n\nNo\n\n243\n\n70.7\n\nSide effects experience\n\nYes\n\n223\n\n64.9\n\nNo\n\n121\n\n35.1\n\nProvision of adequate information about immunization\n\nYes\n\n201\n\n58.4\n\nNo\n\n143\n\n41.6\n\nThe study results revealed immunization coverage rates for the municipality to be 90.4% for BCG, 89.3% for Penta1, 80.7% for Penta2,\n\n72.5% for Penta3, 73.9% for measles 1 and 43.8% for measles 2.\n\n6\n\nSocio-Demographic Factors Associated with Childhood Immunization Uptake\nTable 5 shows the socio-demographic and health care delivery factors associated with immunization uptake. Of the sociodemographic characteristics of the respondents, only the parent\u2019s belief in vaccine importance was found to be associated with immunization uptake. The other socio-demographic variables such as the age of the parent/guardian/child, gender of the parent/guardian/child, birth order of the child, relationship to the child, nature of\n\nthe occupation, marital status, religion, monthly income, and knowledge of childhood immunized diseases, were not associated with of immunization uptake. Parents who believed in vaccines protecting their children from the vaccine-preventable diseases were about two (2) times more likely to take their children for immunization and consequently take all required vaccines (COR= 1.9, 95% confidence interval, CI: 1.2 \u2013 5.2), compared to those who did not believe in vaccine importance.\n\nTable 5. Association between the socio-demographic and health care system factors and uptake of childhood immunizations in a bivariate analysis\n\nFactor\n\nResponse\n\nSocio demographic factors Belief in vaccine importance Health Care System factors Attitude of health workers Distance from the health facility Availability of all vaccine/antigens Easy accessibility to services Timing of immunization clinic Return dates emphasis Reminders for return dates Side effects or reactions Adequate information provision\n\nYes\nFriendly < 2 km Always\nYes Poor Always Yes Yes Yes\n\nFully Vaccinated at 9 Months COR (95% CI)\n\nYes\n\nNo\n\nFreq (%) Freq (%)\n\n125 (80.1%) 31 (19.9%)\n\n1.9(1.2 \u2013 5.2)\n\n113 (79.0%) 109 (79.0%) 130 (77.8%)\n\n30 (21.0%) 29 (21.0%) 37 (22.2%)\n\n4.4(1.4 \u2013 13.9) 3.6(1.3 \u2013 10.4) 5.8(1.3\u2013 25.6)\n\n139 (76.4%) 31 (47.7%) 125 (93.3%) 96 (82.1%) 51 (59.3%) 132 (84.6%)\n\n43 (23.6%) 34 (52.3%) 9 (6.7%) 21 (17.9%) 35 (40.7%) 24 (15.4%)\n\n63(12.54\u2013 318.9) 0.1(0.01 \u2013 0.3) 19.5(4.4 \u2013 87.2) 5.7(1.4 \u2013 22.8) 4.9(1.3 \u2013 24.1) 8.2(2.2 \u2013 31.2)\n\np-value\n0.02*\n0.01* 0.02* 0.019*\n0.000** 0.000** 0.000** 0.02* 0.01* 0.01*\n\nHealth Care System Factors Associated with Childhood Immunization Uptake\nThere was a significant association (p-value <0.05) between some health service deliveryrelated factors to the parent\u2019s uptake of their children\u2019s immunization. Health workers attitude (COR=4.4, 95% CI: 1.4 - 13.9), distance from the health facility (COR = 3.6, 95% CI: 1.3 - 10.4), availability of all vaccines (COR= 5.8, 95% CI: 1.3 - 25.6), , accessibility to immunization services (COR= 63, 95% CI: 12.5 \u2013 318.9), timing of the immunization clinic\n\n(COR= 0.1, 95% CI: 0.01 \u2013 0.3), return dates emphasis (COR= 19.5, 95% CI: 4.4 \u2013 87.2), reminder for return dates (COR= 5.7, 95% CI: 2.4 \u2013 22.8), side effects to vaccinations (4.9, 95% CI: 1.3 -24.1), and information provision on immunization (COR=8.2, 95% CI: 2.2 \u2013 31.2), were all significantly associated with childhood immunization uptake, see Table 5. However, Other factors like awareness of immunization services and waiting time were not significantly associated with immunization uptake.\n\n7\n\nIndependent Factors Associated with Childhood Immunization Uptake\nIn the final logistic regression model, the availability of vaccines, accessibility to immunization services, and return dates\n\nemphasis were found to be independently significantly associated with the childhood immunization uptake, as results are presented in Table 6.\n\nTable 6. Multivariable logistic regression showing the association between socio demographic and health service-related factors, and immunization uptake\n\nFactor Belief in vaccine importance Attitude of health workers Distance from health facility Availability of all vaccines Easy accessibility to services Poor timing of immunization clinic Return dates emphasis Reminders for return dates Had Side effects or reactions Adequate information\n\nResponse Yes Friendly <2km Always Yes Yes Never Yes Yes Yes\n\nAOR (CI; 95%) 3.30(0.62 \u2013 17.64) 0.98(0.06 \u2013 16.57) 0.32(0.02 \u2013 6.14) 33.8(1.44 \u2013 792) * 32.4(2.0 \u2013 513) * 1.20(0.21 \u2013 6.78) 0.03(0.01 \u2013 0.83) * 0.40(0.01 \u2013 12.24) 0.06(0.01 \u2013 4.43) 2.66(0.15 \u2013 46.16)\n\np- value 0.16 0.99 0.45 0.03* 0.01* 0.83 0.03* 0.60 0.43 0.50\n\nParents who always got the scheduled vaccine antigens when they had taken their children for immunization were 34 times more likely to take up immunization for their children and therefore complete the immunization schedule compared to those who missed any scheduled vaccine antigen (adjusted OR=33.8, 95%CI: 1.4 - 792, p=0.03), while those who had easy accessibility to getting their children vaccinated were also equally 32 times more likely to fully immunize their children (aOR= 32.4, 95% CI: 2.0 -513, p=0.01). However, the parents who were never emphasized on the return dates and its importance were also 33 less likely to take the opportunity to get their children immunized and accept the immunization services (aOR= 0.03, 95% CI: 0.01 \u2013 0.83, p=0.03).\nKey findings from the focus groups and key informant interviews\nAbout 86% of the key informants were female, with a median working experience of 7.5 (4\u201315) years. Respondents were interviewed on the overall performance of immunization in Nansana Municipality. The number of children who are vaccinated on weekly average ranges\n\nfrom 30 to 150 depending on the location and level of the health facility. All the health centers conduct outreaches, 3 -5 of them per month. Most health facilities conduct a weekly static immunization session despite the guidelines of daily immunization services by the Uganda National Expanded Programme on Immunization.\n\u201cWe carry out immunizations on Wednesday for static and Thursday for outreach sessions. In a week, we immunize between 130\u2013150 babies before COVID-19, though now we receive 80- 100 babies. We do outreach to ease access to immunization services because we serve 5 parishes/wards there some places which are far from the facility, so we realized the mothers used to miss out on immunization because they can\u2019t move up to the facility because of the long-distance\u201d.\nEPI focal person, at a HC II. Stock-outs of vaccines had been minimized, though could still be experienced when the Flight in Time (FIT), a pilot project for vaccine supplies, was operational until last year. However, they now have time to time shortages of vaccines, especially at H/C IIs and HC IIIs, leading to some hindrances to immunization\n\n8\n\nservice delivery. The stock-outs were frequent occurrences, as argued by respondents from the facilities.\n\u201cVaccines are supplied from the district stores. The Vaccine supply has been adequate when the FIT was supplying, and now, we have experienced some stock-outs of different antigens. However, the stock-outs can be for a week or 2 and not more than a month\u201d.\nMunicipal EPI focal person, EPI focal persons.\nThe main side effects from vaccinations were fever, injection site swelling, skin rash, abscesses, convulsions, and cough.\n\u201cThey usually report pain and fever. The injections are very painful. We see children who get an abscess. We had also received around 2 cases who got swelling at the injection site when we gave pneumococcal vaccine\u201d HC III EPI focal person.\nDiscussion\nThe findings show that the major factors associated with childhood immunization in this urban context were availability of vaccines, access to immunization services, and the return dates emphasis by health workers to parents/guardians of the children. The findings were more of health care system/service delivery-related factors than sociodemographics of parents and children.\nAvailability of vaccines is very important for effective vaccine acceptance and utilization by parents. Low vaccination coverage in children is largely a result on the shortage of vaccines supplies by healthcare providers to parents when they take their children for immunization. Consistent studies conducted elsewhere agreed that vaccine availability at the health facility level greatly impacted on immunization uptake [20-22]. Studies done in Uganda showed that the shortage of vaccines and the challenges in transportation them negatively on immunization uptake [7, 15, 16]. Other studies done in Ethiopia and Nigeria indicated that vaccine shortages at the health facility level and\n\ndifficulties of transporting vaccines were commonly reported to significantly hinder immunization services [2, 23] Thus, improving vaccine availability to health facilities is critical in increasing vaccines acceptance, utilization, and coverage. In the current study, the variation in the availability of particular vaccine antigens might have impacted the results. However, information on which vaccines antigens were more in shortage was not collected and is an area for further investigation.\nEqually important to acceptance of immunization and uptake is the easy access to immunization services by parents. Poor arrangement and coordination of immunization sessions at the health center level were identified as a barrier to immunization uptake by parents [21, 24]. This would result in delays and a long waiting time leading to frustration of the parents and resulting in defaulting the immunization schedules and incomplete immunization. As noted in some earlier studies, parents\u2019 difficulty in accessing immunization services could be because of a shortage of staff, therefore hindering required optimum childhood immunization coverage [7, 26, 27].\nAlso, worthy to note is that good communication leads to understanding and building of trust between two parties. Friendly interaction between the health workers and parents when communicating return dates for scheduled immunization results in immunization acceptance and uptake. This leads to the completion of the vaccination schedule [7, 22]. On the other hand, the effects of poor communications have been linked to poor uptake of immunization services by parents for their children, despite the vaccines being protective [21, 26]. Well-informed parents are likely to accept immunization for their children, understand the importance of honoring the return dates and completing the immunization schedule.\nThe immunization coverage rates for different antigens reported here are high when compared to the municipal and district and\n\n9\n\nnational average rates reported in the health management information system (HMIS) at the time of the study [14]. The lower district figures could be due to the reporting system failing to capture the child immunization information in a number of instances. For example, parents tend to shift from one immunization center to another without notifying the original immunization center. This happens when vaccines become unavailable at a given center, parents move to another area looking for a job, or any other circumstances. The original immunization center then records these children as defaulters (partially vaccinated) and thus is reflected in the district figures. Therefore, availability of vaccines, accessing them to the parents, and maintaining close communication between the parents and health workers is a function of the health care system, which also encompasses the health workers as a provider, whose attitude should be friendly to the service receiver. Studies done in Uganda, Cameroon and Nigeria indicated that providers\u2019 hostility and rude attitudes to mothers is a major barrier with immunization uptake [20, 28, 29].\nThough some studies found associations between distance and immunization outcomes, distance from health facilities was also not independently associated with immunization acceptance and uptake in this study, just as health worker\u2019s attitudes, reminders for return dates, side effects of vaccines, and information provision. This is probably because at least 90% of the parents in this study were with a 5kilometer distance from the health providing immunization services. Yet, according to Tefera\u2019s study [25], families whose home was at least an hour from the vaccination site were less likely to be fully vaccinated than families whose home was between 30 and 59 min away. Reportedly, the longer the distance from the vaccination site, the lower the chances of vaccination by day 7 (of life) of a child [30]. In contrast, the densely populated area with slums in Nansana and Nabweru divisions, where\n\nparents move from job to job looking for survival, makes it difficult to effectively provide immunization services.\nSome parents hold reservations towards immunization acceptance and uptake due to the side effects of vaccines to their children. The associated side effects of vaccines [16, 23]. Others express a total distrust of immunization programs and vaccine [15]. Thus, health education programs targeting the parents are critical in increasing vaccines acceptance, utilization, and coverage, which in effect also improves communication as it was also cited during focus group discussions and key informant interviews. In a study conducted by Mekonnen [31], it was noted that parents sometimes forgot the appointment date for the next immunization visit of their children, which greatly impacts on immunization uptake, and that when reminders are sent on time to parents about routine immunization schedules positively impacted on immunization uptake, [32].\nParents not being knowledgeable of immunization, the most frequently and consistently reported factor associated with childhood immunization was not found to be associated with childhood immunization in this urban context, as was with age, sex, education, occupation, marital status, and monthly income [2, 7, 15, 32, 33], in contrast to the findings of a review of studies conducted in Uganda and elsewhere. However, as reported by Wiysonge [34], the low parental knowledge of immunization and/or lack of access to information about childhood immunization could be an important contributor to the high burden of unimmunized children in subSaharan Africa and that parents with low education and low socioeconomic status attainment tend to show more uncertainty towards immunization, [7, 25, 35].\nThe findings in this current study were more of health care system/service delivery-related factors than socio-demographics of parents, contrary to the systematic review findings of\n\n10\n\nBangura [36]. The effects of misinformation about childhood immunization on social, mass, and community communications media and how they affect immunization uptake and completion of the immunization schedule have not been explored by this study and therefore recommended for further research.\nStudy limitations\nConclusion\nThe immunization coverage rates were higher than the municipal, district, and national averages, with the health care service-related factors identified as being critical for improving immunization uptake. There is a need for improved vaccine supply and communication about immunization services, which should be designed considering the local context in collaboration with slum-dwelling communities.\nAlthough the study was confined to one municipality, this study area is typical of other urban settings in terms of health infrastructure and in Uganda. The study findings are therefore comparable across similar settings. We also note that this was a cross-sectional study, and therefore, we cannot define the temporal relationship between the independent variables and outcome. The direction of causality can therefore only be regarded as suggestive. The data collected on a number of independent variables were based on self-reports that are likely to be subject to social desirability bias. As a result, there is a limit to which such responses can be considered accurate by foreknowledge of what, in the view of the respondent, would be a suitable response.\n\nHowever, the current findings do carry implications for health service managers, decision-makers, and health care providers in their consideration of designing and implementing immunization services.\nAppendices\nAbbreviations\nAOR: Adjusted Odds Ratio; COR: Crude Odds Ratio; CI: confidence interval; FGD: Focus Group Discussion; IA2030: Immunization Agenda 2030; SPSS: Statistical Package for Social Sciences; UNICEF: United Nations International Children Emergency Fund and WHO: World Health Organization.\nAvailability of data and materials\nAll data supporting our findings are contained in the paper. There are no restrictions to data sources. However, details of the full data may be accessed through Amos Kijjambu.\nCompeting interests\nThe authors declare that there are no competing interests.\nFunding\nThe study was self-sponsored.\nAcknowledgements\nAll participants in this study (parents/guardians, focal persons, Nansana Municipal Council health team, DHO and EPI focal person from Wakiso District, and Research assistants).\n\n11\n\nReferences\n\n[1] World Health Organization, 2021 \u201cWorld Health\n\nStatistics,\u201d World Health Organization, Geneva,\n\nSwitzerland,\n\nhttps://creativecommons.org/licenses/by-nc-\n\nsa/3.0/igo.\n\n[2] Tadesse, H., Deribew, A and Woldie, M, 2009\n\n\u201cPredictors of defaulting from completion of child\n\nimmunization in south Ethiopia, A case-control\n\nstudy,\u201d BMC Public Health, 9, 4\u20139,\n\nhttps://doi:10.1186/1471-2458-9-150.3\n\nWorld\n\nHealth Organization, 2009 \u201cWorld Health\n\nStatistics,\u201d WHO Library Cataloguing-in-\n\nPublication Data Geneva, World Health\n\nOrganization, Geneva, Switzerland.\n\n[3] Wolfson et al., 2008 \u201cEstimating the costs of\n\nachieving the WHO \u2013 UNICEF Global\n\nImmunization Vision and Strategy, 2006 \u2013 2015,\u201d,\n\nhttps://doi:10.2471/BLT.07.045096.\n\n[4] Rutherford, M. 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Child Health, 47(5), 276\u2013281. https://doi.org/10.1111/j.1440-1754.2010.01956.x. [23] Tefera, Y. H., Getachew, K., Assefa, T., Ababu, Y., Simireta, T., Zewdie Birhanu, Z, 2017 \u201cFactors and misperceptions of routine childhood immunization service uptake in Ethiopia: findings from a nationwide qualitative study,\u201d 8688, 1\u20139, https://doi:10.11604/pamj.2017.28.290.14133. [24] Tefera, Y. A., Wagner, A. L., and Boulton, M. L, 2018 \u201cPredictors and Barriers to Full Vaccination among Children in Ethiopia,\u201d Vaccines, 6(22)1\u201311, https://doi.org/10.3390/vaccines6020022. [25] Mthiyane et al., 2019 \u201cFactors associated with missed and delayed DTP3 vaccination in children aged 12-59 months in two communities in South Africa, 2012-2013,\u201d South African Med. J., 109 (8), 562\u2013569, https://doi.org/10.7196/SAMJ.2019.v109i8.13244. [26] Obasohan, P. E., Mustapha, M. A., Makada, A., and Obasohan, D. N, 2018 \u201cEvaluating the reasons for partial and non-immunization of children in Wushishi local government area, niger state, Nigeria: methodological comparison,\u201d Afr. J. Reprod. Health, 22 (4), 113\u2013122, 22:113: https://doi.org/10.29063/ajrh2018/v22i4.12. [27] Ambe, J. P., Omotara, B. A., and Baba, M.M, 2001 \u201cPerceptions, beliefs and practices of mothers in sub-urban and rural areas towards measles and measles vaccination in Northern Nigeria,\u201d Trop. Doct., 31(2), 89\u201390,\n\nhttps://doi.org/10.1177%2F004947550103100211. [28] Braka, R. F. L., Asiimwe, D., Soud, F., Makumbi, I and Gust, D, 2012 \u201cA Qualitative Analysis of Vaccine Safety Perceptions and Concerns Among Caretakers in Uganda,\u201d Matern. Child Heal. J., 16, 1045\u20131052, https://doi.org/10.1007/s10995-011-0826-5. [29] Miyahara et al., 2016 \u201cBarriers to timely administration of birth dose vaccines in The Gambia, West Africa,\u201d Vaccine, 34 (29) 3335\u20133341, https://doi.org/10.1016/j.vaccine.2016.05.017. [30] Mekonnen, A. G., Bayleyegn, A. D., and Ayele, E. T, 2019 \u201cImmunization coverage of 12\u201323 months old children and its associated factors in Minjar-Shenkora district, Ethiopia: a communitybased study,\u201d BMC Pediatr., 19(1):198. https://doi.org/10.1186/s12887-019-1575-7. [31] Akwataghibe, N.N., Ogunsola, E. A., Broerse, J. E.W., Popoola, O. A., Agbo, A. I and Dieleman, M. A, 2019 \u201cExploring factors influencing immunization utilization in Nigeria\u2014a mixedmethods study,\u201d Front. Public Health., 7, 392, https://doi.org/10. 3389/fpubh.2019.00392. [32] Ntenda, P. A. M, 2019 \u201cFactors associated with non-and under-vaccination among children aged 12\u2013 23 months in Malawi. A multinomial analysis of the population-based sample,\u201d Pediatr. Neonatol., vol. 60, no. 6, pp. 623\u2013633 19;60(6):623 \u201333, https://doi.org/10.1016/j.pedneo.2019.03.005. [33] Wiysonge, C. S., Uthman, O. A., Ndumbe, P.M., and G. D. Hussey, G. D, 2012 \u201cIndividual and contextual factors associated with low childhood immunisation coverage in Sub-Saharan Africa: A multilevel analysis,\u201d PLoS One, vol. 7, 5 e37905. https://doi.org/10.1371/journal.pone.0037905. [34] Kiptoo, E., 2015 \u201cFactors Influencing Low Immunization Coverage Among Children Between 12 - 23 Months in East Pokot, Baringo Country, Kenya,\u201d Int. Journal of. Vaccines., vol. 1, no. 2 https://doi.org/10.15406/ijvv.2015.01.00012. [35] Bangura, J. B., Xiao, S., Qiu, Ouyang, D. F., and Chen, L, 2020 \u201cBarriers to childhood immunization in sub-Saharan Africa: A systematic review,\u201d BMC Public Health, 20:1108, doi: https://doi.org/10.1186/s12889-020-09169-4.\n\n13\n\n\n",
+    "authors": [
+      "Amos Kijjambu",
+      "Edgar Mugema Mulogo"
+    ],
+    "doi": "10.21522/TIJPH.2013.09.04.Art008",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "LYU29WID",
+    "title": "Healthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low- and middle-income countries: An interrupted time-series analysis with mathematical modeling of administrative data",
+    "abstract": "Background The Coronavirus Disease 2019 (COVID-19) pandemic has had wide-reaching direct and indirect impacts on population health. In low- and middle-income countries, these impacts can halt progress toward reducing maternal and child mortality. This study estimates changes in health services utilization during the pandemic and the associated consequences for maternal, neonatal, and child mortality. Methods and findings Data on service utilization from January 2018 to June 2021 were extracted from health management information systems of 18 low- and lower-middle-income countries (Afghanistan, Bangladesh, Cameroon, Democratic Republic of the Congo (DRC), Ethiopia, Ghana, Guinea, Haiti, Kenya, Liberia, Madagascar, Malawi, Mali, Nigeria, Senegal, Sierra Leone, Somalia, and Uganda). An interrupted time-series design was used to estimate the percent change in the volumes of outpatient consultations and maternal and child health services delivered during the pandemic compared to projected volumes based on prepandemic trends. The Lives Saved Tool mathematical model was used to project the impact of the service utilization disruptions on child and maternal mortality. In addition, the estimated monthly disruptions were also correlated to the monthly number of COVID-19 deaths officially reported, time since the start of the pandemic, and relative severity of mobility restrictions. Across the 18 countries, we estimate an average decline in OPD volume of 13.1% and average declines of 2.6% to 4.6% for maternal and child services. We projected that decreases in essential health service utilization between March 2020 and June 2021 were associated with 113,962 excess deaths (110,686 children under 5, and 3,276 mothers), representing 3.6% and 1.5% increases in child and maternal mortality, respectively. This excess mortality is associated with the decline in utilization of the essential health services included in the analysis, but the utilization shortfalls vary substantially between countries, health services, and over time. The largest disruptions, associated with 27.5% of the excess deaths, occurred during the second quarter of 2020, regardless of whether countries reported the highest rate of COVID-19-related mortality during the same months. There is a significant relationship between the magnitude of service disruptions and the stringency of mobility restrictions. The study is limited by the extent to which administrative data, which varies in quality across countries, can accurately capture the changes in service coverage in the population. Conclusions Declines in healthcare utilization during the COVID-19 pandemic amplified the pandemic's harmful impacts on health outcomes and threaten to reverse gains in reducing maternal and child mortality. As efforts and resource allocation toward prevention and treatment of COVID-19 continue, essential health services must be maintained, particularly in low- and middle-income countries.  \u00a9 2022 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
+    "full_text": "PLOS MEDICINE\n\nRESEARCH ARTICLE\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low- and middle-income countries: An interrupted time-series analysis with mathematical modeling of administrative data\n\na1111111111 a1111111111 a1111111111 a1111111111 a1111111111\n\nTashrik AhmedID1*, Timothy Roberton2, Petra Vergeer1, Peter M. Hansen1, Michael A. PetersID3, Anthony Adofo OfosuID4, Charles Mwansambo5, Charles Nzelu6, Chea Sanford Wesseh7, Francis Smart8, Jean Patrick Alfred9, Mamoutou Diabate10,\nMartina Baye11, Mohamed Lamine Yansane12, Naod WendradID13, Nur Ali Mohamud14, Paul Mbaka15, Sylvain Yuma16, Youssoupha Ndiaye17, Husnia Sadat1, Helal Uddin18,\nHelen KiarieID19, Raharison Tsihory20, George MwinnyaaID1, Jean de Dieu Rusatira1, Pablo Amor FernandezID3, Pierre MuhozaID3, Prativa BaralID1, Salome\u00b4 Drouard3, Tawab Hashemi1, Jed FriedmanID3, Gil ShapiraID3\n\nOPEN ACCESS\nCitation: Ahmed T, Roberton T, Vergeer P, Hansen PM, Peters MA, Ofosu AA, et al. (2022) Healthcare utilization and maternal and child mortality during the COVID-19 pandemic in 18 low- and middleincome countries: An interrupted time-series analysis with mathematical modeling of administrative data. PLoS Med 19(8): e1004070. https://doi.org/10.1371/journal.pmed.1004070\n\n1 The Global Financing Facility for Women, Children, and Adolescents, Washington, DC, United States of America, 2 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States of America, 3 Development Research, The World Bank, Washington, United States of America, 4 Ghana Health Service, Accra, Ghana, 5 Ministry of Health, Lilongwe, Malawi, 6 Federal Ministry of Health, Abuja, Nigeria, 7 Ministry of Health, Monrovia, Liberia, 8 Ministry of Health and Sanitation, Freetown, Sierra Leone, 9 Minist\u00e8re de la Sante publique et de la population, Port-au-Prince, Haiti, 10 Minist\u00e8re de la Sante\u00b4 et de l\u2019Hygi\u00e8ne Publique, Bamako, Mali, 11 Ministe\u00b4 re de la Sante Publique\u00b4 , Yaounde\u00b4 , Cameroon, 12 Minist\u00e8re de la Sante, Conakry, Guinea, 13 Ministry of Health, Addis-Ababa, Ethiopia, 14 Federal Ministry of Health & Human Services, Mogadishu, Somalia, 15 Ministry of Health, Kampala, Uganda, 16 Ministe\u00b4re de la Sante, Kinshasa, Republique Democratique du Congo, 17 Ministere de la sante\u00b4 et de l\u2019action sociale, Dakar, Senegal, 18 Ministry of Health and Family Welfare, Dhaka, Bangladesh, 19 Ministry of Health, Nairobi, Kenya, 20 Minist\u00e8re de la Sante publique, Antananarivo, Madagascar\n* tahmed13@worldbank.org\n\nAcademic Editor: Lars \u00c5ke Persson, London School of Hygiene and Tropical Medicine, UNITED KINGDOM\n\nAbstract\n\nReceived: January 13, 2022\n\nAccepted: July 6, 2022\nPublished: August 30, 2022\nCopyright: \u00a9 2022 Ahmed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.\n\nBackground\nThe Coronavirus Disease 2019 (COVID-19) pandemic has had wide-reaching direct and indirect impacts on population health. In low- and middle-income countries, these impacts can halt progress toward reducing maternal and child mortality. This study estimates changes in health services utilization during the pandemic and the associated consequences for maternal, neonatal, and child mortality.\n\nData Availability Statement: The data underlying this article were provided by and are property of the ministries of health of the eighteen countries participating in the analysis. The data will be shared on reasonable request with permission of the eighteen ministries from gffsecretariat@worldbank. org.\n\nMethods and findings\nData on service utilization from January 2018 to June 2021 were extracted from health management information systems of 18 low- and lower-middle-income countries (Afghanistan, Bangladesh, Cameroon, Democratic Republic of the Congo (DRC), Ethiopia, Ghana, Guinea, Haiti, Kenya, Liberia, Madagascar, Malawi, Mali, Nigeria, Senegal, Sierra Leone,\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n1 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nFunding: The author(s) received no specific funding for this work.\nCompeting interests: The authors have declared that no competing interests exist.\nAbbreviations: BCG, bacillus Calmette\u2013Gue\u00b4rin; COVID-19, Coronavirus Disease 2019; CSSE, Center for Systems Science and Engineering; DRC, Democratic Republic of the Congo; GFF, Global Financing Facility for Women, Children, and Adolescents; LiST, Lives Saved Tool; OPD, outpatient consultation; RMNCH, reproductive, maternal, and child; SDG, Sustainable Development Goal; UHC, Universal Health Coverage; WHO, World Health Organization.\n\nSomalia, and Uganda). An interrupted time-series design was used to estimate the percent change in the volumes of outpatient consultations and maternal and child health services delivered during the pandemic compared to projected volumes based on prepandemic trends. The Lives Saved Tool mathematical model was used to project the impact of the service utilization disruptions on child and maternal mortality. In addition, the estimated monthly disruptions were also correlated to the monthly number of COVID-19 deaths officially reported, time since the start of the pandemic, and relative severity of mobility restrictions. Across the 18 countries, we estimate an average decline in OPD volume of 13.1% and average declines of 2.6% to 4.6% for maternal and child services. We projected that decreases in essential health service utilization between March 2020 and June 2021 were associated with 113,962 excess deaths (110,686 children under 5, and 3,276 mothers), representing 3.6% and 1.5% increases in child and maternal mortality, respectively. This excess mortality is associated with the decline in utilization of the essential health services included in the analysis, but the utilization shortfalls vary substantially between countries, health services, and over time. The largest disruptions, associated with 27.5% of the excess deaths, occurred during the second quarter of 2020, regardless of whether countries reported the highest rate of COVID-19-related mortality during the same months. There is a significant relationship between the magnitude of service disruptions and the stringency of mobility restrictions. The study is limited by the extent to which administrative data, which varies in quality across countries, can accurately capture the changes in service coverage in the population.\n\nConclusions\nDeclines in healthcare utilization during the COVID-19 pandemic amplified the pandemic\u2019s harmful impacts on health outcomes and threaten to reverse gains in reducing maternal and child mortality. As efforts and resource allocation toward prevention and treatment of COVID-19 continue, essential health services must be maintained, particularly in low- and middle-income countries.\n\nAuthor summary\nWhy was this study done?\n\u2022 Disruptions to essential health services during the SARS-CoV-2 (COVID-19) pandemic amplify the pandemic\u2019s impact on morbidity and mortality and pose a profound threat to the ability of low- and middle-income countries to achieve Universal Health Coverage (UHC) and the Sustainable Development Goals (SDGs).\n\u2022 While early studies projected mortality based on hypothesized scenarios, this study presents indirect child, neonatal, and maternal mortality projections during the COVID-19 pandemic based on actual service utilization data.\n\u2022 This study adds to multicountry evidence on changes in the utilization of essential health services from low- and middle-income countries.\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n2 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nWhat did the authors do and find?\n\u2022 We used data on service utilization in 18 countries to estimate the percent change in health services delivered between March 2020 and June 2021, compared to expected levels given prepandemic trends.\n\u2022 The analysis indicates that all countries experienced service disruptions, the largest of which occurred at the pandemic\u2019s start and during months with strict mobility restrictions, regardless of the reported monthly COVID-19 mortality rates, demonstrating variation in the magnitude of disruptions across countries, services, and time.\n\u2022 Using a mathematical model, we project these disruptions are associated with an additional 113,962 deaths among women in children in the 18 countries, representing increases of 3.6% in child mortality and 1.5% in maternal mortality.\nWhat do these findings mean?\n\u2022 The projected excess mortality caused by disruptions to essential health services is smaller than many initial projections, though still comprises a substantial increase in mortality.\n\u2022 There is strong evidence to recommend that context-specific measures to safeguard essential services be integrated into pandemic preparedness and response activities.\n\nIntroduction\nBy June 2021, more than a year after the World Health Organization (WHO) declared the Coronavirus Disease 2019 (COVID-19) outbreak a pandemic, nearly 4,000,000 deaths had been reported [1]. However, analysis of excess mortality in various countries has estimated that total mortality is larger than the reported number of COVID-19-related deaths [2\u20135]. While the gap between excess mortality and officially reported COVID-19-related deaths is partly explained by underreporting, previous outbreaks have demonstrated that indirect health effects caused by reductions in the delivery of routine health services could be as important as the direct consequences [6]. The threat of this double crisis is particularly worrying low- and middle-income countries, which on average have higher mortality rates, more fragile health systems, and health outcomes that are more sensitive to income shocks, such as those unleashed by the COVID-19 pandemic [6,7]. These factors heighten the risk of short-term downturns in the utilization of preventive, promotive, and curative care to erode the hardfought progress toward reducing global maternal and child mortality and lead to a prolonged secondary health crisis.\nPandemics can affect health service utilization through numerous pathways. Health systems may have reduced capacity to supply services and implement rapid adaptations due to limitations in infrastructure, health workforce, supply chains, and financial space. Limited resources to respond to a pandemic might necessitate reallocation away from routine activities and may impact the provision of essential health services through reduced clinic hours, caps on patient intake, and changes in the types of services offered. Demand-side factors, such as mobility\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n3 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nrestrictions, shutdowns of public transportation, perceived changes in quality of services, or fear of contracting COVID-19 at health facilities, may impede service accessibility and careseeking [8]. The economic contraction caused by the pandemic may constrain the ability to pay for health services [9].\nAt the start of the COVID-19 pandemic, statistical models projected maternal and child mortality increases based on hypothesized service disruption scenarios [10\u201312]. Multiple sources have since confirmed increases in adverse maternal outcomes but lower than expected utilization of reproductive, maternal, and child (RMNCH) services during the pandemic [13\u2013 16]. Studies have quantified decreases in total health facility attendance, complementing the qualitative reports by health workers and stakeholders [13,17,18]. In Nepal, a large cohort study of women found decreases of more than half in institutional delivery rates, poorer quality of care, and increases in stillbirth and neonatal mortality rates [19]. Phone surveys of households in 39 low- and middle-income countries in April to August 2020 found that a substantial proportion of households reported forgone care [20]. While there is clear evidence that service disruptions have occurred, there is substantial variation across countries, levels of care, and service types. For example, studies from Burkina Faso, Kenya, and Mozambique found limited disruptions in contraceptive use and quick recovery to expected levels. In Bangladesh, 40% of mothers reported disruptions to family planning services [21\u201323]. The findings on disruptions to child vaccination programs are more consistent across countries, as many countries temporarily paused mass vaccination campaigns between March and May 2020 [24\u201326].\nThis study estimates the reductions in essential health service utilization across low- and middle-income countries and projects indirect mortality caused by the pandemic. Most studies have involved either fully hypothetical scenarios or empirical data from only a small set of health facilities for a short duration of time. We present broader evidence of the impact of the COVID-19 pandemic on health service delivery by analyzing comprehensive data from 18 countries on essential services between March 2020 and June 2021. Based on the estimated decline in service coverage, the underlying burden of disease, and the effectiveness of different interventions in preventing deaths, we aim to generate more accurate forecasts of indirect maternal and child mortality.\nMethods\nWe used an interrupted time-series design to estimate the percent change in the volumes of essential health services delivered during the pandemic. These estimates of lost services were translated into relative changes in coverage of interventions delivered during those periods to project the number of lives lost. The estimated monthly disruptions were also correlated to officially reported COVID-19 mortality rates, time since the start of the pandemic, and relative severity of mobility restrictions to determine which drivers are associated with changes in measured disruptions over time. The analysis was modeled on a previous study described elsewhere [27]. No changes to the analysis plan were made due to comments from reviewers or observations in the data. Data sharing agreements were established with all governments. Analysis of these secondary data did not constitute human subjects research and was considered public health practice. Thus, institutional research board approval was not required nor sought.\nData sources and preparation\nMonthly administrative data on the volume of key essential health services between January 2018 and June 2021 were collated from 18 countries participating in a monitoring activity supported by the Global Financing Facility for Women, Children, and Adolescents (GFF). Eleven\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n4 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\ncountries are classified as low-income by the World Bank: Afghanistan, the Democratic Republic of Congo (DRC), Ethiopia, Guinea, Liberia, Madagascar, Malawi, Mali, Sierra Leone, Somalia, and Uganda. The other 7 countries, Bangladesh, Cameroon, Ghana, Haiti, Kenya, Nigeria, and Senegal, are classified as lower-middle-income. Seven services were selected to represent the continuum of reproductive, maternal, and child health: family planning, antenatal care initiation (ANC1), antenatal care completion (ANC4), delivery, postnatal care initiation (PNC1), bacillus Calmette\u2013Gue\u00b4rin (BCG) vaccine administration, and completion of pentavalent schedule (Penta3). These services were selected because they are high completeness across countries and serve as proxies for other services and interventions delivered at the same point. In addition, outpatient consultations (OPDs) were used as a proxy for the general use of health services. Outcome measures were not included since rare outcomes (e.g., maternal death, stillbirths) are difficult to accurately capture in facility data, or the data completeness was too poor for this analysis. Seven countries\u2019 administrative data systems were missing 1 indicator, and Uganda was missing 2 indicators. Family planning volume was the most frequently missing indicator and was not reported in 5 countries (see Table A in S1 Text). The analysis used available-case analysis, where facilities with partial facility-month observations were included in the analysis. Differences in indicator definitions were observed across countries, particularly in OPD (total attendance versus total outpatient consultations), delivery (institutional deliveries versus institutional deliveries with a skilled birth attendant), and PNC1 (first postnatal visit versus time-bound PNC visits). In countries with both versions of indicators, a sensitivity check was conducted to demonstrate that both reporting methods yielded similar results (see Tables B, C, and D in S1 Text).\nHMIS data validity is often assessed in the context of measuring service coverage levels and can reflect challenges due to factors such as poor representativeness and the accuracy of population denominators [28]. Despite finding shortcomings in measuring service coverage, previous authors have called for the greater use of HMIS data, specifically the absolute number of services provided each month, in research and policy decisions. In this study, we do not attempt to estimate population service coverage but rather assume that the change in servicespecific utilization reported by facilities in the HMIS represents the percentage change in population service coverage. We believe this use of HMIS, not as an estimate of coverage but as an estimate of coverage change, is less subject to various potential biases. The ability to rigorously estimate changes in service volume despite limitations of facility data has been previously demonstrated [29]. With the exception of Bangladesh and Nigeria, there should be high representativeness of facilities that report to HMIS since the public sector delivers the majority of care. The primary concern is the possible differential change in utilization between reporting and nonreporting facilities. Findings from household surveys and interviews with key health system stakeholders during the pandemic confirm that private facilities and community programs did not compensate for the disruptions in the public sector, and there were substantial levels of foregone care in the population [17,20].\nHMIS data were downloaded on 22 August 2021, and were prepared for analysis by removing outlier values and restricting data for indicators with low completeness. These preparation steps are detailed in the Supporting information (see Text A in S1 Text), and the advantages and disadvantages of HMIS data are discussed in previous work [14]. To further assess the quality and reliability of the data, we present a range of sensitivity tests; we describe data reporting completeness (see Fig A in S1 Text) and include a sensitivity check showing that changes in reporting patterns did not drive the results. We also specify for each country and indicator the dates dropped due to poor completeness, or data availability that may reduce the prepandemic follow-up (see Table L in S1 Text). The final dataset included 137,192 health facilities ranging from 478 facilities in Guinea to 34,701 in Nigeria. The reports cover 42\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n5 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nmonths and 8 services, for 21,421,125 nonmissing facility-month-service observations in the 18 countries.\nWe obtained data from 2 additional sources to assess whether service disruptions correlate with officially reported COVID-related death rates or with mobility restrictions. Data on reported COVID-19 deaths were obtained from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, which compiles data from official government COVID-19 surveillance reports. Official accounts are likely to underreport actual mortality in settings with limited testing capacity, particularly at the beginning of the pandemic [30]. However, even if the official reports are inaccurate, there are many mechanisms through which these reports may affect service utilization, such as changing perceptions by health providers and the population on the state of the outbreak. Therefore, one way to interpret these data is as a proxy for the perceived risk of infection. Information on the policy measures affecting population mobility was obtained from the Oxford COVID-19 Government Policy Tracker, which systematically tracks implementation dates and scores the stringency of policy interventions. We selected a subset of policies that may affect population access to health facilities: public transport closures, stayat-home requirements, movement limitations, school closures, and workplace closures. The dataset includes ordinal severity scores for each policy to capture the stringency of restriction, ranging from no restrictions to recommendations to requirements with minimal exceptions. We constructed an index representing the daily severity of mobility restrictions using the first component of a principal component analysis of these selected indicators. There is a correlation of 0.92 between the index we construct and the Oxford response stringency index, composed of a wider set of interventions. The Oxford COVID19 Policy Tracker captures as-written strictness of policies but not levels of enforcement. As we are unaware of a reliable source on levels of enforcement of restrictions, differences in levels of enforcement between countries were not taken into account.\nAnalysis of service utilization disruptions\nWe used an interrupted time-series approach to predict the volume of services that would have been delivered had the pandemic not occurred. The interruption period starts with the WHO pandemic declaration in March 2020, coinciding with the start of community transmission and mobility restrictions in most countries. Service and countries were modeled separately using a linear regression equation with the following form:\nYtf \u00bc b0 \u00fe b1T \u00fe b2::12Month \u00fe b13::29PandemicMontht \u00fe af \u00fe \u03b5tf\nwhere Ytf is the service volume reported by facility f in month t. T represents the time in months since January 2018 to account for a linear secular trend (\u03b21), Month represents calendar months to account for seasonality (\u03b22..12), and \u03b1f represents the facility-level fixed effect accounting for time-invariant facility characteristics. Fixed effects were replaced with facility characteristic covariates (province and facility type) in Uganda, where an update to the administrative system did not allow for consistent identification of facilities over time. PandemicMonth denotes a series of dummy variables for each of the months between March 2020 and June 2021. That is, \u03b213..29 contain estimated disruption for each month since the pandemic.\nTo calculate the percentage change in service utilization during the pandemic months, we first used the estimation results to calculate the expected volume in the absence of the pandemic (counterfactual). Then, we divided the reported volumes by these expectations. The cumulative shortfall was estimated using the same model with a single pandemic period. A 2-year prepandemic time horizon was chosen to minimize confounding from changes in data\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n6 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\ncollection practices, policy changes, or other health shocks while still allowing separation of seasonality effects from secular trends.\nCorrelate drivers of utilization changes\nWe further examined whether the estimated monthly changes in service volumes showed statistical association with the time elapsed since the start of the pandemic, the monthly number of official reported COVID deaths, and the stringency of mobility restrictions. These relationships were assessed by running the following linear regression separately for each service:\nDtc \u00bc g0 \u00fe g1Q \u00fe g2CovidMortalitytc \u00fe g3RestrictionStringencytc \u00fe ac \u00fe \u03b5tc;\nwhere Dtc is the estimated monthly change in service volume for month t in country c. CovidMortalitytc represents the officially reported monthly number of COVID-19-related deaths per 100,000 people, and RestrictionStringencytc represents the average monthly stringency of the mobility restrictions. \u03b1c represents a country fixed effect, and \u03b5tc is a normally distributed error term.\nMortality estimates\nWe estimated the impact of the service utilization disruptions on the absolute number of child, neonatal, and maternal deaths using the Lives Saved Tool (LiST). LiST is a mathematical model that forecasts mortality estimates from the coverage of 70+ RMNCH+N health interventions, considering the specific demographic and epidemiological context of a country [31]. We assumed that the relative change in the coverage of the interventions included in the LiST model was the same as the estimated relative changes in service utilization. Each intervention was linked to the service during which the intervention is typically delivered or proxied by the service assumed to have a similar utilization pattern. For interventions without a reasonable proxy, such as child nutrition services, the conservative default assumed no change in the intervention coverage. This linking of service indicators to LiST interventions is described in Table E of S1 Text. As multiple RMNCH interventions were linked to a small set of indicators, small variations in the few service indicators significantly affect the overall mortality results. To address this, we ran a sensitivity analysis using different linking combinations to understand how these linking decisions alter the results and limit their potential effect.\nFor each LiST intervention and country, we obtained coverage values from the most recent household survey for the country (typically a DHS or MICS), which we took as the coverage value that we would have expected in the absence of the pandemic (i.e., as a \u201ccounterfactual\u201d). To estimate the coverage value during the pandemic, we multiplied the counterfactual coverage value by the estimated disruption of the service (proxy) indicator. This approach assumes that, during the pandemic, the change in population-level coverage was proportional to the change in reported facility-level utilization. In this way, we obtained an estimated coverage value for each intervention, country, and period. We used 3-month periods (quarters), aggregating the service disruption for the relevant proxy indicator for each quarter and calculating disrupted coverage values for each quarter of the pandemic for each country and intervention.\nWe ran 2 LiST analyses for each country and quarter: first, a \u201cwithout pandemic\u201d scenario, using only the counterfactual coverage values, to obtain the expected deaths in the absence of the pandemic; and second, a \u201cwith pandemic\u201d scenario, to obtain the expected deaths during the pandemic. LiST only takes yearly input values, so we entered quarterly values as yearly values (for 2020 or 2021, as appropriate), and divided the resulting expected deaths by 4, to obtain the expected deaths for the quarter. We took the difference in expected deaths between the \u201cwith pandemic\u201d and \u201cwithout pandemic\u201d scenarios to represent the additional deaths due to\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n7 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nthe pandemic. For each country and age group, we report the number of deaths that we would have expected in the absence of the pandemic for the period March 2020 to June 2021, the estimated number of additional maternal and child (including neonatal) deaths due to the change in service utilization during this same period, and the relative increase in mortality because of service utilization declines during the pandemic. The RECORD checklist is included in the Supporting information (S1 RECORD Checklist). Study limitations include the assumption that the changes in service-specific utilization reported in the HMIS represents the percentage of service coverage change in the population, and the inability to account for differential changes in health-seeking behaviors across severity of need.\nResults\nA total of 137,192 facilities were included in the analysis, as shown in Table 1, though the number of facilities reporting information for various health services varied by country. The following sections describe findings of service disruptions, regression analysis, and LiST modeling, respectively.\nService disruptions\nWe focus first on the number of outpatient consultations as a proxy for general service utilization. As shown in Table 2, the cumulative number of reported outpatient consultations between March 2020 and June 2021 is significantly lower than expected, given the prepandemic trends in all countries apart from Cameroon, Liberia, and Somalia. These 3 countries also experienced substantial monthly declines in outpatient consultations compared to expected values, but the reductions over the 16-month duration are not statistically significant (see Table A in S1 Text). On average, the countries in this analysis experienced a cumulative reduction of 13% in outpatient consultations compared to historical utilization trends. The largest decline of 40% is estimated for Bangladesh, followed by 25% in Haiti and Kenya. Large declines between 10% and 20% are estimated for Ethiopia, Ghana, Guinea, Madagascar, Nigeria, Senegal, Sierra Leone, and Uganda. As seen in Fig 1, unweighted moving average monthly outpatient service volumes are below expected for all months between March 2020 and June 2021.\nThe disruptions to RMNCH services are smaller on average than those observed in outpatient consultations. For child vaccination, 10 out of 18 countries experienced significant cumulative reductions in the number of children receiving the third dose of the pentavalent vaccine. Out of the 14 countries with HMIS data on administered BCG vaccine doses, 8 experienced significant cumulative reductions. In most countries, the monthly reductions in vaccination were largest at the start of the pandemic and returned to the prepandemic expectation by July 2020 (see Fig 1). While the return to the expected levels is encouraging, we do not see an increase representing facility-based catch-up for the vaccinations missed early during the pandemic. A different pattern is observed for antenatal care initiation (ANC1). An initial decrease is followed by an increase above the expected volume, indicating that some women may have delayed their visit without completely forgoing antenatal care. Reproductive and maternal health services disruptions were more context-specific than disruptions in outpatient care and child vaccination. Significant declines in the delivery indicators, for example, are estimated in 10 out of the 18 countries. At the same time, Cameroon, Ethiopia, Madagascar, and Senegal reported volumes significantly exceeding those expected based on prepandemic trends. Significant cumulative reductions in family planning services are estimated for 6 out of 12 countries with available data. Large reductions in family planning volume of at least 10% were experienced in Guinea, Mali, and Sierra Leone.\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n8 / 20\n\nPLOS MEDICINE\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\nTable 1. Sample size used in primary analysis (number of facilities).\n\nFacility Level\n\nANC1 Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nANC4 Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nBCG Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nDELIVERY Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nFP\n\nHospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nOPD Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nPENTA3 Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nPNC1 Hospital\n\nHealth Center\n\nLower level\n\nOther/ Unknown\n\nTotal\n\nAfghanistan Bangladesh Cameroon DRC Ethiopia Ghana Guinea Haiti Kenya Liberia Madagascar Malawi Mali Nigeria Senegal\n\nSierra Leone\n\n125\n\n109\n\n317\n\n371 435\n\n494 17\n\n62 649 31\n\n3\n\n79\n\n2,916 17\n\n18\n\n1,505\n\n649\n\n5,164\n\n11,429 4,285\n\n1,858 315\n\n310 1,369 55\n\n2,747\n\n513\n\n13,974 124\n\n258\n\n863\n\n131\n\n5,200 12,526 2,887 103\n\n356 4,829 510\n\n83\n\n12,585 1,546 1,097\n\n55\n\n325\n\n1,088\n\n73\n\n2\n\n69 237 7\n\n484\n\n8\n\n107\n\n119\n\n104\n\n1,489\n\n632\n\n846\n\n48\n\n314 4,933 124 315\n\n364 435 11,255 4,294 4,887 13,028 1,046\n\n491 16\n\n1,834 316\n\n2,770 103\n\n71\n\n2\n\n62 771 30 285 1,662 54 290 5,957 494 60 453 6\n\n3 2,769\n484\n\n79\n\n2\n\n2,673 17\n\n14\n\n513\n\n1,529 13,163 124\n\n258\n\n81\n\n247 11,256 1,546 1,096\n\n8\n\n9\n\n106\n\n132\n\n1,152\n\n601\n\n851\n\n56\n\n91 10,642 2,503 213\n\n329 15\n\n59\n\n1,323 315\n\n286\n\n5,286 102\n\n335\n\n80\n\n1\n\n61\n\n31\n\n3\n\n54\n\n2,756\n\n487\n\n7\n\n221\n\n74\n\n2,216 2\n\n18\n\n532\n\n1,504 13,859 113\n\n257\n\n92\n\n6\n\n13,619 1,472 1,091\n\n3\n\n48\n\n128\n\n104\n\n1,488\n\n656\n\n828\n\n18\n\n58\n\n313 5,045 131 311\n\n444 406 11,437 3,709 5,764 3 1,211\n\n491 14\n\n1,765 305\n\n1,973 99\n\n64\n\n2\n\n59 754 31 181 1,462 53 164 4,094 495 39 251 7\n\n1 2,692\n353\n\n77\n\n2\n\n2,750 5\n\n25\n\n470\n\n1,507 12,278 114\n\n249\n\n36\n\n228 9,092 1,487 1,087\n\n6\n\n8\n\n61\n\n128 1,501 862\n\n2 69 12,250\n\n280 4,472 92 245\n\n427 4,344 15,287\n\n293 41\n\n1,439 304\n\n5,012 99\n\n75\n\n1\n\n58 752 29 295 1,763 48 336 6,742 484 66 575 8\n\n2 2,767\n496\n\n64\n\n1\n\n2,226\n\n517\n\n1,492 13,050\n\n118\n\n193 11,706\n\n17\n\n8\n\n145 1,509 872\n\n125 499 13,699\n\n322 5,317 133 361\n\n471 439 11,707 4,393 6,626 15,591 1,317\n\n516 37\n\n2,163 17\n\n3,027\n\n96\n\n2\n\n70 822 34 323 1,891 58 385 7,299 556 76 725 10\n\n4 2,807\n876\n\n75\n\n1\n\n3,159 18\n\n44\n\n537\n\n1,537 14,671 128\n\n253\n\n100\n\n269 15,993 1,577 1,096\n\n4\n\n11\n\n152\n\n126\n\n1,176\n\n598\n\n857\n\n56\n\n281 4,399 88 255\n\n101 362 10,818 4,138 2,714 15,619 240\n\n336 15\n\n1,413 315\n\n6,311 102\n\n86\n\n1\n\n59 730 32 290 1,528 54 338 5,535 489 61 317 7\n\n3 2,759\n256\n\n74\n\n2,201 3\n\n18\n\n532\n\n1,501 13,884 114\n\n257\n\n93\n\n6\n\n13,674 1,484 1,092\n\n4\n\n54\n\n125 1,505 863\n\n105 630 12,728 58\n\n311 4,809 120 293\n\n436 435 11,396 4,334 5,653 14,467 1,185\n\n492 3 1,780 13 2,408 66\n\n55 742 30 246 1,645 52 284 5,932 469 49 439 5\n\n1 2,581\n228\n\n58\n\n2\n\n2,356 7\n\n21\n\n346\n\n1,518 11,943 121\n\n253\n\n27\n\n220 9,548 1,521 1,082\n\n1\n\n8\n\n71\n\n2,528\n\n14,847\n\n6,214\n\n20,150 20,739 9,323 478\n\n861 10,899 659\n\n3,705\n\n783\n\n1,847 34,701 1,920 1,406\n\nSomalia Uganda\ufffd\n\nGrand Total\n\n51\n\n176\n\n5,870\n\n531\n\n1,815 46,901\n\n61\n\n1,711 44,488\n\n17\n\n198\n\n2,670\n\n50\n\n169\n\n5,713\n\n523\n\n1,648 47,281\n\n58\n\n1,673 44,456\n\n16\n\n151\n\n2,775\n\n56\n\n174\n\n3,200\n\n486\n\n2,031 35,911\n\n56\n\n1,644 27,600\n\n17\n\n89\n\n740\n\n52\n\n171\n\n5,827\n\n495\n\n1,043 44,949\n\n35\n\n1,632 27,166\n\n14\n\n146\n\n2,531\n\n34\n\n83\n\n4,420\n\n121\n\n1,357 33,539\n\n6\n\n1,010 54,197\n\n2\n\n146\n\n1,639\n\n52\n\n189\n\n6,523\n\n534\n\n2,946 51,290\n\n64\n\n1,795 69,082\n\n17\n\n480\n\n4,127\n\n56\n\n177\n\n4,574\n\n490\n\n2,162 46,428\n\n59\n\n1,669 50,186\n\n18\n\n104\n\n1,403\n\n48\n\n5,227\n\n523\n\n43,695\n\n57\n\n55,379\n\n17\n\n2,420\n\n680\n\n5,452 137,192\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\n\ufffdUganda sample size is given for January 2021, as facilities do not have consistent ID codes (see Table L of S1 Text). DRC is Democratic Republic of the Congo. ANC1 refers to the first antenatal care visit. ANC4 refers to the fourth antenatal care visit. BCG refers to bacillus Calmette\u2013Gue\u00b4rin vaccination. FP refers to family planning consultations. OPD refers to outpatient visits. Penta3 refers to the third dose of pentavalent vaccine. PNC1 refers to the first postnatal care visit.\n\nhttps://doi.org/10.1371/journal.pmed.1004070.t001\n\n9 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nTable 2. Cumulative change in service volumes during the pandemic period (March 2020\u2013June 2021) compared to expected volume based on prepandemic trends.\n\nCountry Afghanistan\n\nOutpatient consultation\n(OPD) \u22129.0%\ufffd\ufffd\ufffd\n(\u221212.5%, \u22125.4%)\n\nBangladesh\n\n\u221240.0%\ufffd\ufffd\ufffd\n\n(\u221247%, \u221232.9%)\n\nCameroon\n\n\u22122.70% (\u22126%, 0.7%)\n\nDRC Ethiopia\n\n\u22124.4%\ufffd\ufffd\ufffd (\u22126.6%, \u22122.1%)\n\u221215.4%\ufffd\ufffd\ufffd (\u221218.6%, \u221212.3%)\n\nGhana\n\n\u221218.1%\ufffd\ufffd\ufffd\n\n(\u221220.8%, \u221215.3%)\n\nGuinea\n\n\u221214.5%\ufffd\ufffd\n\n(\u221224.8%, \u22124.2%)\n\nHaiti\n\n\u221225.0%\ufffd\ufffd\ufffd\n\n(\u221232.3%, \u221217.8%)\n\nKenya\n\n\u221224.9%\ufffd\ufffd\ufffd\n\n(\u221227.2%, \u221222.5%)\n\nLiberia\n\n\u22126.00%\n\n(\u221214.5%, 2.5%)\n\nMadagascar\n\n\u221210.0%\ufffd\ufffd\ufffd\n\n(\u221215.2%, \u22124.9%)\n\nMalawi\n\n\u22127.0%\ufffd\ufffd\n\n(\u221213.2%, \u22120.9%)\n\nMali\n\n\u22125.0%\ufffd\ufffd\n\n(\u22128.8%, \u22121.3%)\n\nNigeria\n\n\u221214.3%\ufffd\ufffd\ufffd\n\n(\u221216.8%, \u221211.7%)\n\nSenegal\nSierra Leone\n\n\u221213.3%\ufffd\ufffd\ufffd (\u221219.2%, \u22127.4%)\n\u221213.6%\ufffd\ufffd\ufffd\n(\u221217.3%, \u22129.9%)\n\nSomalia\n\n4.00% (\u22121.8%, 9.8%)\n\nFamily Planning\n(FP) \u22126.3%\ufffd (\u221213.6%, 1.1%)\n-\n-\n-\n1.10% (\u22120.8%, 3%)\n-\n\u221210.4%\ufffd\ufffd (\u221218.5%, \u22122.3%)\n0.30% (\u22127.7%, 8.2%) 0.20% (\u22122.1%, 2.5%) \u22122.80% (\u221212.8%, 7.1%) \u22122.30% (\u22128.4%, 3.8%) 4.40% (\u22123.3%, 12.2%) \u22129.8%\ufffd\ufffd (\u221219.1%, \u22120.5%) \u22124.6%\ufffd\ufffd\ufffd (\u22126.9%, \u22122.2%)\n-\n\u221219.2%\ufffd\ufffd\ufffd\n\nFirst Antenatal Fourth Antenatal\n\nCare Visit\n\nCare Visit\n\n(ANC1)\n\n(ANC4)\n\nDelivery\n\n\u22128.0%\ufffd\ufffd\ufffd\n\n\u22126.1%\ufffd\n\n\u22129.4%\ufffd\ufffd\n\n(\u221212.1%, \u22123.8%) (\u221212.9%, 0.7%)\n\n(\u221215.6%, \u22123.2%)\n\n\u221226.2%\ufffd\ufffd\ufffd\n\n\u221233.1%\ufffd\ufffd\ufffd\n\n\u221211.40%\n\n(\u221234.2%, \u221218.2%) (\u221246.7%, \u221219.5%) (\u221228.9%, 6%)\n\n1.50%\n\n\u22122.30%\n\n2.3%\ufffd\ufffd\n\n(\u22120.6%, 3.6%)\n\n(\u22126.7%, 2.1%)\n\n(0.1%, 4.5%)\n\n1.3%\ufffd\ufffd\n\n2.0%\ufffd\ufffd\n\n\u22121.0%\ufffd\n\n(0.4%, 2.2%)\n\n(0.7%, 3.2%) (\u22122%, 0.1%)\n\n\u22124.2%\ufffd\ufffd\ufffd\n\n\u22123.4%\ufffd\ufffd\ufffd\n\n2.0%\ufffd\ufffd\n\n(\u22125.5%, \u22122.8%) (\u22125.2%, \u22121.5%)\n\n(0.3%, 3.7%)\n\n4.7%\ufffd \ufffd \ufffd\n\n\u22120.20%\n\n\u22120.30%\n\n(3.5%, 5.8%)\n\n(\u22123.3%, 2.9%)\n\n(\u22121.9%, 1.3%)\n\n\u22120.20%\n\n\u22126.1%\ufffd\ufffd\n\n0.50%\n\n(\u22123.8%, 3.4%) (\u221210.7%, \u22121.5%) (\u22124%, 5.1%)\n\n\u22127.5%\ufffd\ufffd (\u221213.1%, \u22121.8%)\n\u22123.9%\ufffd\ufffd\ufffd (\u22125.6%, \u22122.1%)\n\u22124.0%\ufffd (\u22128.7%, 0.7%)\n0.40% (\u22121.9%, 2.8%)\n\u22122.00% (\u22126.7%, 2.6%)\n-\n\u22125.4%\ufffd\ufffd\ufffd (\u22127.5%, \u22123.3%)\n\u221212.3%\ufffd\ufffd\ufffd (\u221215.1%, \u22129.5%)\n\u22121.60%\n\n\u221214.4%\ufffd\ufffd (\u221222.7%, \u22126.1%)\n\u221213.5%\ufffd\ufffd\ufffd (\u221216.9%, \u221210.2%)\n\u22127.90% (\u221217.9%, 2%)\n3.80% (\u22120.8%, 8.3%)\n3.1%\ufffd (\u22120.5%, 6.7%)\n-\n\u221214.5%\ufffd\ufffd\ufffd (\u221218.9%, \u221210.2%)\n1.00% (\u22121.2%, 3.1%)\n5.3%\ufffd\ufffd\n\n\u221225.5%\ufffd\ufffd\ufffd\n(\u221234%, \u221217.1%) \u22124.2%\ufffd\ufffd\n(\u22127%, \u22121.3%) \u22123.8%\ufffd\ufffd\n(\u22126.9%, \u22120.7%) 5.5%\ufffd\ufffd\n(2.2%, 8.8%) \u22123.6%\ufffd\ufffd\n(\u22126.7%, \u22120.5%) \u22123.4%\ufffd\n(\u22127.3%, 0.5%) \u22127.9%\ufffd\ufffd\ufffd\n(\u221211.1%, \u22124.7%) 4.2%\ufffd\ufffd\n(1%, 7.3%) \u22124.7%\ufffd\ufffd\n\nFirst Postnatal Care Visit (PNC1) \u22128.0%\ufffd\ufffd\n(\u221213.3%, \u22122.8%)\n\u221219.6%\ufffd\ufffd\ufffd (\u221227.4%, \u221211.7%) 4.0%\ufffd\ufffd (0.7%, 7.3%)\n\u22121.2%\ufffd\ufffd (\u22122.3%, \u22120.2%)\n1.10% (\u22120.8%, 2.9%)\n1.40% (\u22120.8%, 3.7%)\n-\n\u221219.0%\ufffd\ufffd\ufffd (\u221226%, \u221211.9%)\n\u22124.5%\ufffd\ufffd (\u22127.8%, \u22121.1%)\n\u22127.5%\ufffd\ufffd\ufffd (\u221211.6%, \u22123.4%)\n8.7%\ufffd \ufffd \ufffd (3.9%, 13.6%)\n\u22122.80% (\u22128.4%, 2.8%)\n\u22120.50% (\u22123.2%, 2.1%)\n\u22121.80% (\u22127.5%, 3.9%)\n3.70% (\u22120.9%, 8.2%)\n\u22124.4%\ufffd\ufffd\n\nBCG Vaccination\n3.00% (\u22121.7%, 7.8%)\n\u22123.5%\ufffd (\u22127.1%, 0.1%)\n-\n-\n-\n\u22123.7%\ufffd\ufffd\ufffd (\u22125.5%, \u22121.9%) \u22124.2%\ufffd\ufffd (\u22127.5%, \u22120.9%) \u221212.0%\ufffd\ufffd\ufffd (\u221217.2%, \u22126.7%)\n-\n2.60% (\u22121.3%, 6.5%)\n0.50% (\u22123.3%, 4.3%)\n\u22121.30% (\u22124.9%, 2.2%)\n\u221210.0%\ufffd\ufffd\ufffd (\u221213.5%, \u22126.4%)\n1.00% (\u22120.6%, 2.7%)\n4.3%\ufffd\ufffd (1.1%, 7.4%)\n\u22126.8%\ufffd\ufffd\n\n(\u221224.2%, \u221214.2%)\n-\n\n(\u22124.8%, 1.7%)\n1.10% (\u22123.6%, 5.8%)\n\n(0.4%, 10.2%)\n11.4%\ufffd\ufffd (3.5%, 19.2%)\n\n(\u22127.6%, \u22121.9%) 1.10%\n(\u22123.9%, 6%)\n\n(\u22127.5%, \u22121.3%)\n8.6%\ufffd\ufffd (8.6%, 1.1%)\n\n(\u221210.6%, \u22123%) \u22123.70%\n(\u22129.1%, 1.6%)\n\nThird Dose of the Pentavalent\nVaccination (Penta3) \u22123.30%\n(\u221210.6%, 4%)\n\u221212.9%\ufffd\ufffd\ufffd (\u221216.8%, \u22129.1%)\n\u22120.40% (\u22123.3%, 2.4%)\n\u22120.10% (\u22121.1%, 0.9%)\n\u22122.6%\ufffd\ufffd\ufffd (\u22124%, \u22121.2%)\n\u22120.70% (\u22123.3%, 1.8%)\n\u22125.8%\ufffd\ufffd (\u221210.2%, \u22121.5%)\n3.80% (\u22126.6%, 14.2%)\n\u22120.30% (\u22121.8%, 1.2%)\n\u22122.60% (\u22127.9%, 2.7%)\n\u22122.6%\ufffd (\u22125.6%, 0.4%)\n\u22123.5%\ufffd\ufffd (\u22126%, \u22121%)\n\u221212.5%\ufffd\ufffd\ufffd (\u221216.6%, \u22128.4%)\n\u22122.3%\ufffd\ufffd (\u22123.9%, \u22120.8%)\n\u22128.8%\ufffd\ufffd\ufffd (\u221211.2%, \u22126.3%)\n\u22129.5%\ufffd\ufffd\n(\u221214.7%, \u22124.3%)\n\u22123.60% (\u22129.3%, 2%)\n\n(Continued )\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n10 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nTable 2. (Continued)\n\nCountry Uganda\n\nOutpatient consultation\n(OPD) \u221217.2%\ufffd\ufffd\ufffd\n(\u221220%, \u221214.4%)\n\nMean\n\n\u221213.1%\n\nFamily Planning\n(FP) -\n\u22124.4%\n\nFirst Antenatal Care Visit (ANC1) \u22122.8%\ufffd\ufffd (\u22125.6%, 0%)\n\u22124.1%\n\nFourth Antenatal Care Visit (ANC4) \u22124.2%\ufffd\ufffd\n(\u22127.4%, \u22121%)\n\u22124.6%\n\nDelivery\n\u22126.3%\ufffd\ufffd\ufffd (\u22128.9%, \u22123.7%) \u22123.7%\n\nFirst Postnatal Care Visit (PNC1) -\n\u22122.6%\n\nBCG Vaccination\n\u22127.7%\ufffd\ufffd\ufffd (\u221210.6%, \u22124.8%)\n\u22123.0%\n\nThird Dose of the Pentavalent\nVaccination (Penta3) \u22126.3%\ufffd\ufffd\ufffd\n(\u22128.9%, \u22123.6%)\n\u22124.1%\n\nThe table presents regression coefficients and the 95% confidence intervals in parentheses. Changes in service volumes are estimated using an interrupted time-series approach. The reported mean is the average value across countries and is not population weighted. Details on indicator reporting can be found in Table L in S1 Text. Sample sizes by country and indicator are reported in Table 1. P values are calculated using t tests, with the magnitude indicated by asterisks as follows \ufffdp < 0.1 \ufffd\ufffd p < 0.05 \ufffd\ufffd\ufffd p < 0.001.\n\nhttps://doi.org/10.1371/journal.pmed.1004070.t002\n\nCorrelates\nIn addition to the cross-country variation, the magnitude of service volume disruption varied during the pandemic. Fig 2 presents the example of outpatient consultations and portrays a country-specific relationship between the magnitude of the estimated disruptions, the time\n\nFig 1. Percent change in volume from expected levels based on prepandemic trends by selected health services across 18 countries, March 2020\u2013February 2021. Note: The horizontal line at 0% represents the expected volume of services based on prepandemic trends. The gray lines plot country-specific changes in service utilization. The monthly country-specific results are presented in Table A in S1 Text. The red line is a multicountry unweighted moving average of the change in utilization plotted by a locally estimated scatterplot smoothing (LOESS) regression. Details on indicator reporting for each country can be found in Table L in S1 Text. ANC1 refers to First Antenatal Care Visit. ANC4 refers to the Fourth Antenatal Care Visit. BCG refers to bacillus Calmette\u2013Gue\u00b4rin vaccination. OPD refers to Outpatient visits. Penta3 refers to the Third dose of Pentavalent vaccine. PNC1 refers to First Postnatal Care Visit.\nhttps://doi.org/10.1371/journal.pmed.1004070.g001\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n11 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nFig 2. Estimated and observed volume of outpatient consultations with officially reported COVID-19 deaths per 100,000 and mobility restrictions by country, January 2018\u2013June 2021. Note: Outpatient consultations are used as a proxy for the utilization of general health services. Data on officially reported COVID-19 deaths are compiled from Johns Hopkins University Coronavirus dashboard [1]. Population denominators for all countries are based on 2019 estimates from the World Bank Development Indicators database. Utilization volume and mortality data are normalized across countries by dividing by the highest observed monthly value within each country. Data on mobility restrictions is summarized by an index of public transport closures, stay-at-home requirements, movement limitations, school closures, and workplace closures stringency scores provided by the Oxford COVID-19 Government Response Tracker. The scores from this index are normalized, and the categorized into quintiles. Gaps in the service volume data are due to months removed because of low completeness. Details on indicator reporting can be found in Table L in S1 Text and data completeness can be found in Fig A in S1 Text. DRC is Democratic Republic of the Congo. Results for ANC1, delivery, BCG, and Penta3 are visualized in Fig B in S1 Text.\nhttps://doi.org/10.1371/journal.pmed.1004070.g002\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n12 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nTable 3. Correlates of monthly disruption magnitude in service volume.\n\nDependent variable: percentage monthly decline in outpatient consultations volume\n\nMarch\u2013May 2020\n\nOPD \u22126.10\ufffd \ufffd \ufffd\n\nFP 4.61\ufffd\n\nANC1 \u22129.27\ufffd \ufffd \ufffd\n\nJune\u2013Aug 2020\n\n(\u22129.77, \u20132.43) (\u22120.38, \u20139.61) (\u221212.87, \u20135.66)\n\n\u22122.58\n\n8.34\ufffd \ufffd \ufffd\n\n\u22122.55\n\nSep\u2013Nov 2020\n\n(\u22126.11, 0.96) \u22120.91\n\n(3.41, 13.28) 4.42\ufffd\n\n(\u22126.05, 0.94) \u22122.22\n\n(\u22124.26, 2.43) (\u22120.13, 8.97) (\u22125.49, 1.04)\n\nDec 2020\u2013Feb 2021\n\n0.21\n\n\u22120.55\n\n\u22121.51\n\nOfficially reported deaths per 100k peoplea\n\n(\u22123.03, 3.45) 0.06\n\n(\u22125.1, 4.01) 0.03\n\n(\u22124.68, 1.67) \u22120.03\n\nIndex of mobility Restrictions stringency b\n\n(\u22120.16, 0.28) \u22123.87\ufffd \ufffd \ufffd\n(\u22125.67, \u20132.07)\n\n(\u22120.23, 0.29) \u22122.31\ufffd\n(\u22124.70, 0.08)\n\n(\u22120.23, 0.17) \u22120.73\n(\u22122.46, 1.00)\n\nNumber of observations\n\n279\n\n163\n\n257\n\nANC4 \u22127.23\ufffd \ufffd \ufffd (\u221210.53, \u20133.93) \u22123.968\ufffd \ufffd (\u22127.16, \u20130.78)\n\u22122.13 (\u22125.13, 0.87)\n\u22121.38 (\u22124.28, 1.52)\n\u22120.10 (\u22120.29, 0.09)\n\u22121.98\ufffd \ufffd (\u22123.57, \u20130.39)\n263\n\nDelivery \u22121.86\ufffd (\u22123.90, 0.19) \u22121.10 (\u22123.07, 0.87)\n0.18 (\u22121.68, 2.04)\n\u22120.86 (\u22122.66, 0.95)\n\u22120.04 (\u22120.16, 0.08)\n\u22120.49 (\u22121.50, 0.51)\n279\n\nPNC1 \u22124.25\ufffd \ufffd \ufffd (\u22127.32, \u20131.18) \u22122.73\ufffd (\u22125.63, 0.18)\n\u22120.55 (\u22123.31, 2.21)\n\u22121.30 (\u22123.99, 1.38)\n\u22120.08 (\u22120.25, 0.09)\n\u22120.56 (\u22122.04, 0.92)\n241\n\nBCG \u22126.91\ufffd \ufffd \ufffd (\u221211.36, \u20132.45)\n1.05 (\u22123.21, 5.30)\n\u22120.17 (\u22124.28, 3.95)\n\u22125.13\ufffd \ufffd (\u22129.14, \u20131.12)\n0.02 (\u22120.23, 0.27)\n\u22122.27\ufffd \ufffd (\u22124.49, \u20130.06)\n217\n\nPenta3 \u22126.72\ufffd \ufffd \ufffd (\u221210.46, \u20132.98)\n2.71 (\u22120.89, 6.31)\n4.82\ufffd \ufffd \ufffd (1.41, 8.23)\n\u22121.63 (\u22124.93, 1.68)\n0.10 (\u22120.12, 0.32)\n\u22122.67\ufffd \ufffd \ufffd (\u22124.50, \u20130.83)\n279\n\nThe table presents regression coefficients and the 95% confidence intervals in parentheses. The dependent variable is the estimated percentage monthly change in volume of outpatient consultations for a given service, presented in Fig 1 and in Table A in S1 Text. All regressions include country fixed effects. Details on indicator reporting can be found in Table L in S1 Text. DRC is Democratic Republic of the Congo. ANC1 refers to the first antenatal care visit. ANC4 refers to the fourth antenatal care visit. BCG refers to bacillus Calmette\u2013Gue\u00b4rin vaccination. FP refers to family planning consultations. OPD refers to outpatient visits. Penta3 refers to the third dose of pentavalent vaccine. PNC1 refers to the first postnatal care visit. aReported COVID-19 deaths were obtained from the CSSE at Johns Hopkins University. The monthly number of COVID-19-attributable deaths is population standardized per 100,000 people using the 2019 World Development Report estimated population. bAn index of mobility restrictions stringency is constructed with principal component analysis using data from the Oxford COVID-19 Government Policy Tracker on daily restriction. An average over the daily stringency is taken to compute the monthly index. P values are calculated using t tests, with the magnitude indicated by asterisks as follows: \ufffdp < 0.1; \ufffd\ufffdp < 0.05; \ufffd\ufffd\ufffdp < 0.001.\nhttps://doi.org/10.1371/journal.pmed.1004070.t003\n\nelapsed since the beginning of the pandemic, and the monthly number of reported COVIDrelated deaths. To assess the correlations of the disruptions with these factors, we present crosscountry regression results in Table 3. The largest utilization reductions were experienced in April and May 2020, and this decline is unrelated to pandemic severity as proxied by reported COVID19-related mortality. Family planning is the only service with large drops in the second quarter of 2021. Moreover, there is no significant relationship between the number of monthly officially reported COVID-19-related deaths and the magnitude of change in any service. There is, however, a significant relationship between imposed restrictions and the magnitude of the estimated disruptions in outpatient consultations, child vaccinations, and the fourth antenatal care visit. For example, a standard deviation in the mobility restrictions stringency is associated with a 3.9% reduction in outpatient consultation volume (Column 1 of Table 3).\n\nMortality estimates\nTable 4 shows estimates of the impact of service disruptions on child, neonatal, and maternal mortality. The country with the greatest estimated increase in mortality was Bangladesh, with a 14.9% increase in child mortality, 9.7% increase in neonatal mortality, and 3.9% increase in maternal mortality. Haiti, Kenya, Nigeria, Sierra Leone, and Uganda were also estimated to have child mortality increases of 5% or greater. Cameroon, Liberia, and Somalia were estimated to have small reductions in child mortality, and 6 countries were estimated to have minor reductions in maternal mortality. We estimate that 27.6% of the additional child deaths and 24.3% of the additional maternal deaths occurred due to utilization declines in Quarter 2 of 2020, reflecting the above results (see Table G in S1 Text). In sum, the absolute number of\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n13 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n\nTable 4. Expected and additional child, neonatal, and maternal deaths, March 2020 to June 2021.\n\nCountry\nAfghanistan Bangladesh Cameroon\nDRC Ethiopia\nGhana Guinea\nHaiti Kenya Liberia Madagascar Malawi\nMali Nigeria Senegal\nSierra Leone Somalia Uganda Total\n\nChild deaths (0\u201359 months)\n\nExpected deaths\n\nAdditional Relative increase\n\ndeaths\n\nin mortality\n\n100,472\n\n3,091\n\n3.1%\n\n118,156\n\n17,578\n\n14.9%\n\n89,319\n\n\u2212609\n\n\u22120.7%\n\n414,800\n\n4,928\n\n1.2%\n\n264,992\n\n3,267\n\n1.2%\n\n56,105\n\n1,607\n\n2.9%\n\n61,910\n\n1,728\n\n2.8%\n\n23,296\n\n1,396\n\n6.0%\n\n79,842\n\n4,328\n\n5.4%\n\n15,145\n\n\u2212120\n\n\u22120.8%\n\n62,158\n\n601\n\n1.0%\n\n40,202\n\n1,747\n\n4.3%\n\n103,991\n\n1,194\n\n1.1%\n\n1,194,118\n\n60,307\n\n5.1%\n\n31,762\n\n1,285\n\n4.0%\n\n36,034\n\n2,296\n\n6.4%\n\n106,149 101,096 2,899,546\n\n\u2212613 6,675 110,686\n\n\u22120.6% 6.6% 3.6%\n\nNeonatal deaths (<1 month)\n\nExpected deaths\n\nAdditional Relative increase\n\ndeaths\n\nin mortality\n\n67,835\n\n2,238\n\n3.3%\n\n75,257\n\n7,317\n\n9.7%\n\n36,249\n\n\u2212602\n\n\u22121.7%\n\n153,158\n\n2,352\n\n1.5%\n\n153,851\n\n664\n\n0.4%\n\n31,996\n\n695\n\n2.2%\n\n22,115\n\n332\n\n1.5%\n\n10,636\n\n703\n\n6.6%\n\n44,367\n\n1,578\n\n3.6%\n\n5,970\n\n36\n\n0.6%\n\n27,100\n\n\u221275\n\n\u22120.3%\n\n21,394\n\n1,119\n\n5.2%\n\n40,264\n\n396\n\n1.0%\n\n409,318\n\n11,827\n\n2.9%\n\n17,097\n\n1,051\n\n6.1%\n\n12,886\n\n561\n\n4.4%\n\n37,779 49,924 1,217,196\n\n\u2212289 2,158 32,061\n\n\u22120.8% 4.3% 2.8%\n\nExpected deaths 11,667 7,605 7,219 25,615 21,858 4,111 4,085 1,962 7,758 1,610 4,399 3,335 6,914 103,980 2,606 4,385\n8,328 9,411 236,851\n\nMaternal deaths\n\nAdditional Relative increase\n\ndeaths\n\nin mortality\n\n435\n\n3.7%\n\n300\n\n3.9%\n\n\u2212122\n\n\u22121.7%\n\n0\n\n0.0%\n\n\u221276\n\n\u22120.3%\n\n\u221251\n\n\u22121.2%\n\n2\n\n0.1%\n\n139\n\n7.1%\n\n57\n\n0.7%\n\n20\n\n1.2%\n\n\u221241\n\n\u22120.9%\n\n136\n\n4.1%\n\n0\n\n0.0%\n\n2,261\n\n2.2%\n\n168\n\n6.5%\n\n32\n\n0.7%\n\n\u221213 29 3,276\n\n\u22120.2% 0.3% 1.5%\n\nExpected deaths come from estimates from the UN Inter-Agency Group for Child Mortality Estimation (child and neonatal mortality) and WHO (maternal mortality). Additional deaths are projected from the LiST mathematical model, which estimates changes in mortality from changes in intervention coverage (more information and projection methods can be found at https://www.livessavedtool.org/). Child deaths are inclusive of neonatal deaths. Additional information on these results are available in the Supporting information: Table E in S1 Text summarizes the mapping between service indicators and interventions included in LiST. Table G in S1 Text provides the detailed breakdown of this table by calendar quarter. Table H in S1 Text reports these results using the 95% confidence interval generated from the service disruptions analysis in Table 2 to bound the mortality estimates. Table I in S1 Text provides sensitivity analyses on the mapping assumptions.\n\nhttps://doi.org/10.1371/journal.pmed.1004070.t004\n\nadditional deaths across the 18 countries from March 2020 to June 2021 is estimated to be 110,686 child deaths (0 to 59 months), 32,061 neonatal deaths (<1 month), and 3,276 maternal deaths. Many factors, including population size and baseline mortality rate, drive the absolute number of additional deaths. In general, estimated increases in maternal mortality across all countries were smaller than increases in child or neonatal mortality due to smaller facility delivery reductions than those in outpatient and vaccination services.\nWe conducted 2 sensitivity analyses to understand the potential error in the mortality results. First, we used the upper and lower 95% confidence intervals of the service disruption estimates and found that the additional deaths could be 43.8% higher or 42.9% lower than the estimates in Table 4. Given the perfect correlation in the error of the disruption estimates that this approach assumes, these bounds are overly conservative. Second, we varied the linkage of service indicators to LiST interventions by setting all interventions to each service in turn and randomizing the link between interventions and services. We found that the mortality estimates could be up to 31.8% higher or 55.1% lower than Table 4. However, given the extreme assumptions that we tested related to the linkage between the HMIS indicators and the LiST interventions, these bounds are also conservative. For more detail on these sensitivity analyses, see Tables G and H in S1 Text.\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n14 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nDiscussion\nCompared to the expected volumes based on prepandemic trends, we estimate statistically significant reductions in service utilization in most countries. The magnitudes of the cumulative shortfalls vary substantially by country, type of service, and time. The largest disruptions, on average, are estimated for outpatient consultations\u2014a proxy for general healthcare utilization. Smaller cumulative shortfalls in the number of children receiving the third dose of the pentavalent vaccine are detected in most countries. The identified disruptions between March 2020 and June 2021 were associated with 113,962 additional deaths among women and children across 18 low- and middle-income countries.\nThis study was not designed to estimate the share of all-cause excess mortality caused by service disruptions. The number of indirect deaths we project is higher than the COVID-19 mortality officially reported by the 18 countries over the same time. Still, the officially reported number of deaths due directly to COVID-19 is grossly underreported in many countries [32]. Estimates of all-cause excess mortality published in May 2022 by WHO indicate a much greater level of mortality, with 597,422 excess deaths estimated to have occurred in the 18 countries by June 2021. No data were provided on the relative share of direct and indirect deaths within the total estimated number of excess deaths by country or globally. Regardless of the exact numbers, our findings illustrate that indirect deaths due to reductions in service coverage threaten to reverse gains in maternal and child mortality reduction achieved over a multi-year period before the COVID-19 outbreak. Yet, there is a clear need to strengthen country systems to track levels and trends in mortality by cause.\nService disruptions were largest during the first quarter of the pandemic, regardless of the timing of high reported COVID-19-related mortality or the stringency of policies imposed to contain the virus\u2019 spread. This pattern may suggest a process of adaptation and learning. Individuals, health systems, and governments initially responded to the pandemic with uncertainty due to limited knowledge of the virus, transmission dynamics, risk, and safety measures. As more information became available, perceptions and behaviors might have changed. Alternatively, fatigue from mobility restrictions and social distancing could have influenced behavior patterns as the months elapsed. The duration of the pandemic may also have allowed time for health systems to adapt service provision, including combining multiple services in a single visit and transitioning care to the community level.\nWe also show a relationship between mobility restrictions and the magnitude of disruptions, highlighting the trade-offs inherent to the difficult policy decisions governments worldwide have had to make since the beginning of the pandemic. Imposing mobility and social gathering restrictions to contain the spread of the virus and protect those at high risk of COVID-19 mortality can come at the cost of reduced utilization of life-saving essential health services. In Nigeria, for example, a third of women surveyed during exit interviews after receiving RMNCH services reported not being able to access such services at some point since the start of the pandemic, with the most cited reasons being an inability to leave their household due to the lockdown, or because of the shutdowns and increased costs of public transportation [33]. These same issues were cited in other settings [33,34]. Even when the mobility restrictions do not specifically restrict health facility attendance, their introduction might affect individuals\u2019 perceptions of whether services are available and the infection risk associated with visiting the health facilities. When such restrictions are imposed, the population\u2019s ability to access essential health services must be maintained.\nWe estimate that the service disruptions were associated with increases in U5 and maternal mortality on the order of 2% to 5% for most countries in our analysis. The magnitude of the excess mortality is well below many scenarios presented at the onset of the pandemic,\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n15 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nincluding that proposed by Roberton and colleagues, which hypothesized larger reductions in service utilization and predicted a relative increase in mortality of 10% [10]. However, the impact is proportional to the extent of the service disruptions in each country: Some countries experienced indirect mortality in the range of 5% to 15% of the expected mortality in the absence of the pandemic. The type of services that were disrupted is also important. Countries that saw larger declines in the proportion of women delivering at a facility were more likely to see larger increases in maternal mortality, linked to reductions in the parenteral administration of uterotonics, antibiotics, and anticonvulsant interventions. Countries with larger disruptions to outpatient utilization saw larger increases in child mortality, driven by estimated reductions in curative child health services such as antibiotics for pneumonia and neonatal sepsis, oral rehydration solution for dehydration due to diarrhea, and artemisinin-combination therapy for malaria. Although the primary driver of mortality is the magnitude and duration of service disruptions and the consequent reduction in coverage of interventions, other factors such as a country\u2019s baseline coverage levels, baseline mortality rates, and cause-ofdeath structure were also important for country-specific mortality estimates.\nOur study has several important limitations. Data derived from country health management information systems used in this study predominantly reflect the utilization patterns in the public sector, and the type of public facilities reporting data can vary between indicators and countries (detailed in Table 1). Theoretically, there could be a shift between public and private providers, which our analysis would not account for as changes in utilization. Additionally, data gaps can affect the completeness and quality of the HMIS data. We conduct a robustness check confirming that these changes in reporting patterns do not drive our findings.\nThe mortality estimates generated by LiST are limited by the accuracy of the input data, the set of health interventions considered in the analysis, and the assumptions made in linking the disruption in specific services to the overall coverage of interventions. For some countries, the baseline coverage inputs may be inaccurate due to the most recent DHS or MICS survey being conducted several years prior to 2020. However, given that we generated our pandemic coverage estimates relative to the baseline coverage estimates and given that our LiST results are predominantly driven by this relative change (and not the absolute value of the pandemic estimate or the counterfactual), this issue is likely to have had little effect on our results. The analysis does not account for differential changes in health-seeking behaviors by the risk group of patients. Relative reductions in healthcare-seeking behavior among low-risk patients may cause overestimations in the predicted mortality. In contrast, relative reductions in the ability to access healthcare by high-risk patients may result in underestimated mortality.\nThe indirect effects of the COVID-19 pandemic may have changed mortality through other pathways not considered by the LiST analysis. For example, reduced quality of care may lower the effectiveness of interventions in saving lives, social distancing is likely to have changed patterns in the transmission of other communicable diseases beyond COVID-19, and people\u2019s behaviors more broadly may have changed disease incidence in ways we do not yet understand. We also do not consider effects such as malnutrition due to economic setbacks and disruptions to food markets. Other analyses have suggested that food insecurity could increase mortality by up to 10% in some countries [35]. Likewise, disruptions to family planning could affect birth outcomes and mortality rates for several years [12]. The selected services further represent an important but narrow set of services, and disruptions to chronic disease management, testing capacity, surgical services, and other life-saving health interventions are not considered. These broader effects are likely to be substantive and will result in cascading effects into the future.\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n16 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nOur findings have both short-term and long-term policy implications. Given the delays in COVID-19 vaccination access in low- and middle-income countries, the direct and indirect impacts of the COVID-19 crisis will likely persist in time. The prevalence of continued service disruptions, although to a lesser extent relative to the beginning of the pandemic, implies that children and mothers remain at higher risk of mortality during the protracted outbreak period. Continuity of essential health services during COVID-19 response must be monitored and maintained to minimize these preventable deaths. This study\u2019s findings also highlight the need to invest in health system resiliency. Future studies should investigate the reasons why certain countries experienced more (or less) severe service disruptions by studying specific causes of disruptions and adaptations made by health facilities to address them. Together, these studies can inform future efforts to strengthen health systems to better prepare for and minimize loss of life during future health emergencies.\nAdditionally, this work helps understand how COVID-19 poses a profound threat to countries\u2019 ability to progress toward UHC and SDGs (SDG 3.8.1). All 4 of the UHC service coverage index indicators for reproductive, maternal, newborn, and child health are represented in our analysis (i.e., family planning, ANC4, DTP3, and care-seeking for pneumonia). Given that most countries have experienced significant cumulative decreases in service volume in at least one of these 4 services since the start of the pandemic, our study provides evidence that COVID-19 is reversing longstanding progress toward achieving UHC by reducing coverage of essential health services.\nConclusions\nService volume reported from health facilities across 18 low- and middle-income countries were disrupted for outpatient care and key reproductive, maternal, and child vaccination services during the pandemic. This use of facility data highlights the potential, with additional investment and validation, for these systems to play an important role in monitoring the resilience of health systems during times of shock. Substantial variation in the magnitude of disruption was identified during the pandemic and across services and countries. Overall, though the average disruption to maternal and child services was lower than many hypothesized scenarios as the pandemic\u2019s onset, the decrease in intervention coverages is projected to be associated a substantial loss of life among women and children. These findings emphasize that safeguarding continuity of essential health services delivery must be maintained as part of the response to the COVID-19 pandemic and future crises particularly in low- and middle-income countries.\nSupporting information\nS1 RECORD Checklist. RECORD Checklist. (DOCX)\nS1 Text. Supporting Information. Table A. HMIS indicator definition and mapping. Table B. Sensitivity of disruption estimates between alternative definitions for deliveries. Table C. Sensitivity of disruption estimates between alternative definitions for outpatient consultations. Table D. Sensitivity of disruption estimates between alternative definitions for family planning. Table E. Linkage between service indicators to LiST interventions. Table F. Difference between expected and observed service coverage by month and country. Table G. Projections of mortality from LiST Model by Quarter. Table H. Bounding the mortality estimates using service disruption confidence intervals. Table I. Sensitivity analysis of linking decisions. Text A. Data notes. Fig A. Level of completeness by country and indicator. Table J. Cumulative change in\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n17 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nservice volume during the pandemic period (March 2020\u2013June 2021) in a balanced panel of facilities. Table K. Sample size for balanced panel analysis (number of facilities). Table L. Data considerations. Table M. Percentage of reporting outliers by country in the prepandemic (January 2018\u2013February 2020) and the pandemic (March 2020\u2013June 2021) periods. Table N. Population totals and mortality rates references for analyzed countries. Fig B. Estimated and observed volume of additional indicators with officially reported COVID-19 deaths per 100,000 and mobility restrictions by country, January 2018\u2013June 2021. (DOCX)\nAuthor Contributions\nConceptualization: Tashrik Ahmed, Petra Vergeer, Pablo Amor Fernandez, Salome\u00b4 Drouard, Tawab Hashemi, Jed Friedman, Gil Shapira.\nData curation: Anthony Adofo Ofosu, Charles Mwansambo, Charles Nzelu, Chea Sanford Wesseh, Francis Smart, Jean Patrick Alfred, Mamoutou Diabate, Martina Baye, Mohamed Lamine Yansane, Naod Wendrad, Nur Ali Mohamud, Paul Mbaka, Sylvain Yuma, Youssoupha Ndiaye, Husnia Sadat, Helal Uddin, Helen Kiarie, Raharison Tsihory, Jean de Dieu Rusatira.\nFormal analysis: Tashrik Ahmed, Timothy Roberton, George Mwinnyaa, Pablo Amor Fernandez, Pierre Muhoza, Prativa Baral, Salome\u00b4 Drouard, Gil Shapira.\nFunding acquisition: Petra Vergeer. Investigation: Tashrik Ahmed, Michael A. Peters, Pablo Amor Fernandez, Pierre Muhoza,\nPrativa Baral, Salome\u00b4 Drouard, Gil Shapira. Methodology: Tashrik Ahmed, Timothy Roberton, Salome\u00b4 Drouard, Jed Friedman,\nGil Shapira. Project administration: Tashrik Ahmed, Tawab Hashemi, Gil Shapira. Resources: Tawab Hashemi. Supervision: Tashrik Ahmed, Petra Vergeer, Peter M. Hansen, Tawab Hashemi, Gil Shapira. Validation: Tashrik Ahmed, Anthony Adofo Ofosu, Charles Mwansambo, Charles Nzelu,\nChea Sanford Wesseh, Francis Smart, Jean Patrick Alfred, Mamoutou Diabate, Martina Baye, Mohamed Lamine Yansane, Naod Wendrad, Nur Ali Mohamud, Paul Mbaka, Sylvain Yuma, Youssoupha Ndiaye, Husnia Sadat, Helal Uddin, Helen Kiarie, Raharison Tsihory, Jean de Dieu Rusatira, Gil Shapira. Visualization: Tashrik Ahmed, George Mwinnyaa, Salome\u00b4 Drouard. Writing \u2013 original draft: Tashrik Ahmed, Timothy Roberton, Michael A. Peters, Gil Shapira. Writing \u2013 review & editing: Tashrik Ahmed, Timothy Roberton, Petra Vergeer, Peter M. Hansen, Michael A. Peters, Anthony Adofo Ofosu, Charles Mwansambo, Charles Nzelu, Chea Sanford Wesseh, Francis Smart, Jean Patrick Alfred, Mamoutou Diabate, Martina Baye, Mohamed Lamine Yansane, Naod Wendrad, Nur Ali Mohamud, Paul Mbaka, Sylvain Yuma, Youssoupha Ndiaye, Husnia Sadat, Helal Uddin, Helen Kiarie, Raharison Tsihory, George Mwinnyaa, Jean de Dieu Rusatira, Pablo Amor Fernandez, Pierre Muhoza, Prativa Baral, Salome\u00b4 Drouard, Tawab Hashemi, Jed Friedman, Gil Shapira.\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n18 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\nReferences\n1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020; 20:533\u2013534. https://doi.org/10.1016/S1473-3099(20)30120-1 PMID: 32087114\n2. Weinberger DM, Chen J, Cohen T, Crawford FW, Mostashari F, Olson D, et al. 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Lancet Glob Health. 2020; 8:e1132\u2013e1141. https://doi.org/10.1016/S2214-109X(20) 30288-6 PMID: 32673577\n12. Riley T, Sully E, Ahmed Z, Biddlecom A. Estimates of the potential impact of the covid-19 pandemic on sexual and reproductive health in low-and middle-income countries. Int Perspect Sex Reprod Health. 2020. https://doi.org/10.1363/46e9020 PMID: 32343244\n13. Chmielewska B, Barratt I, Townsend R, Kalafat E, van der Meulen J, Gurol-Urganci I, et al. Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis. Lancet Glob Health. 2021; 9:e759\u2013e772. https://doi.org/10.1016/S2214-109X(21)00079-6 PMID: 33811827\n14. Shapira G, Ahmed T, Drouard SHP, Amor Fernandez P, Kandpal E, Nzelu C, et al. Disruptions in maternal and child health service utilization during COVID-19: analysis from eight sub-Saharan African countries. Health Policy Plan. 2021. https://doi.org/10.1093/heapol/czab064 PMID: 34146394\n15. Arsenault C, Gage A, Kim MK, Kapoor NR, Akweongo P, Amponsah F, et al. COVID-19 and resilience of healthcare systems in ten countries. Nat Med. 2022. https://doi.org/10.1038/s41591-022-01750-1 PMID: 35288697\n16. Amouzou A, Ma\u00efga A, Faye CM, Chakwera S, Melesse DY, Mutua MK, et al. Health service utilisation during the COVID-19 pandemic in sub-Saharan Africa in 2020: a multicountry empirical assessment with a focus on maternal, newborn and child health services. BMJ Glob Health. 2022: 7. https://doi.org/ 10.1136/bmjgh-2021-008069 PMID: 35501068\n17. World Health Organization. Pulse survey on continuity of essential health services during the COVID-19 pandemic. 2020. Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_ continuity-survey-2020.1\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n19 / 20\n\nPLOS MEDICINE\n\nHealthcare utilization and maternal and child mortality during the COVID-19 pandemic\n18. Rao SPN, Minckas N, Medvedev MM, Gathara D, et al. Small and sick newborn care during the COVID-19 pandemic: Global survey and thematic analysis of healthcare providers\u2019 voices and experiences. BMJ. Glob Health. 2021; 6. https://doi.org/10.1136/bmjgh-2020-004347 PMID: 33716220\n19. KC A, Gurung R, Kinney MV, Sunny AK, Moinuddin M, Basnet O, et al. Effect of the COVID-19 pandemic response on intrapartum care, stillbirth, and neonatal mortality outcomes in Nepal: a prospective observational study. Lancet Glob Health. 2020; 8: e1273\u2013e1281. https://doi.org/10.1016/S2214-109X (20)30345-4 PMID: 32791117\n20. Kakietek JJ, Dayton Eberwein J, Stacey N, Newhouse D, Yoshida N. Foregone Health Care During the COVID-19 Pandemic: Early Survey Estimates from 39 Low- and Middle-Income Countries. Health Policy Plan. 2022. https://doi.org/10.1093/heapol/czac024 PMID: 35274688\n21. Leight J, Hensly C, Chissano M, Ali L. Short-term effects of the COVID-19 state of emergency on contraceptive access and utilization in Mozambique. PLoS ONE. 2021; 16. https://doi.org/10.1371/journal. pone.0249195 PMID: 33765080\n22. Karp C, Wood SN, Guiella G, Gichangi P, Bell SO, Anglewicz P, et al. Contraceptive dynamics during COVID-19 in sub-Saharan Africa: longitudinal evidence from Burkina Faso and Kenya. BMJ Sexual & Reproductive. Health. 2021:bmjsrh-2020-200944. https://doi.org/10.1136/bmjsrh-2020-200944 PMID: 33579717\n23. Ehsan SMA, Jahan F. Analysing the impact of COVID-19 on the mothers of Bangladesh: hearing the unheard. J Public Health (Germany). 2021. https://doi.org/10.1007/s10389-021-01501-5 PMID: 33728259\n24. World Health Organization. Guiding principles for immunization activities during the COVID-19 pandemic: interim guidance, 26 March 2020. Geneva: World Health Organization; 2020. Available from: https://apps.who.int/iris/handle/10665/331590\n25. Shet A, Dhaliwal B, Banerjee P, Carr K, DeLuca A, Britto C, et al. COVID-19-related disruptions to routine vaccination services in India: perspectives from pediatricians. medRxiv. 2021; 2021.01.25.21250040. Available: https://doi.org/10.1101/2021.01.25.21250040\n26. Buonsenso D, Cinicola B, Kallon MN, Iodice F. Child Healthcare and Immunizations in Sub-Saharan Africa During the COVID-19 Pandemic. Front Pediatr. 2020; 8:6\u20139. https://doi.org/10.3389/fped.2020. 00517\n27. Shapira G, Ahmed T, Drouard SHP, Fernandez PA, Kandpal E, Nzelu C, et al. Disruptions in Essential Health Services During the First Five Months of COVID-19: Analysis of Facility-Reported Service Volumes in Eight Sub-Saharan African Countries. Health Policy Plan. 2021; 1:1\u201321. https://doi.org/10. 2139/ssrn.3757414\n28. Arsenault C, Yakob B, Kassa M, Dinsa G, Verguet S. Using health management information system data: case study and verification of institutional deliveries in Ethiopia. BMJ Glob Health. 2021; 6:6216. https://doi.org/10.1136/bmjgh-2021-006216 PMID: 34426404\n29. Wagenaar BH, Sherr K, Fernandes Q, Wagenaar AC. Using routine health information systems for well-designed health evaluations in low- and middle-income countries. Health Policy Plan. 2016; 31. https://doi.org/10.1093/heapol/czv029 PMID: 25887561\n30. Helleringer S, Queiroz BL. Commentary: Measuring excess mortality due to the COVID-19 pandemic: Progress and persistent challenges. Int J Epidemiol. 2022. https://doi.org/10.1093/ije/dyab260 PMID: 34904168\n31. Walker N, Tam Y, Friberg IK. Overview of the Lives Saved Tool (LiST). BMC Public Health. 2013; 13:1\u2013 6. https://doi.org/10.1186/1471-2458-13-S3-S1\n32. Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P, et al. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020\u201321. Lancet. 2022; 399. https://doi.org/10.1016/s0140-6736(21)02796-3 PMID: 35279232\n33. Balogun MI, Banke-Thomas A, Sekoni A, Boateng IDGO, Yesufu V, Wright O, et al. Challenges in access and satisfaction with reproductive, maternal, newborn and child health services in Nigeria during the COVID-19 pandemic: A cross-sectional survey. PLoS ONE. 2021. https://doi.org/10.1371/journal. pone.0251382 PMID: 33961682\n34. Singh DR, Sunuwar DR, Shah SK, Karki K, Sah LK, Adhikari B, et al. Impact of COVID-19 on health services utilization in Province-2 of Nepal: a qualitative study among community members and stakeholders. BMC Health Serv Res. 2021; 21. https://doi.org/10.1186/s12913-021-06176-y PMID: 33627115\n35. Osendarp S, Akuoku JK, Black RE, Headey D, Ruel M, Scott N, et al. The COVID-19 crisis will exacerbate maternal and child undernutrition and child mortality in low- and middle-income countries. Nat Food. 2021; 2:476\u2013484. https://doi.org/10.1038/S43016-021-00319-4\n\nPLOS Medicine | https://doi.org/10.1371/journal.pmed.1004070 August 30, 2022\n\n20 / 20\n\n\n",
+    "authors": [
+      "T. Ahmed",
+      "T. Roberton",
+      "P. Vergeer",
+      "P.M. Hansen",
+      "M.A. Peters",
+      "A.A. Ofosu",
+      "C. Mwansambo",
+      "C. Nzelu",
+      "C.S. Wesseh",
+      "F. Smart",
+      "J.P. Alfred",
+      "M. Diabate",
+      "M. Baye",
+      "M.L. Yansane",
+      "N. Wendrad",
+      "N.A. Mohamud",
+      "P. Mbaka",
+      "S. Yuma",
+      "Y. Ndiaye",
+      "H. Sadat",
+      "H. Uddin",
+      "H. Kiarie",
+      "R. Tsihory",
+      "G. Mwinnyaa",
+      "J. de Dieu Rusatira",
+      "P.A. Fernandez",
+      "P. Muhoza",
+      "P. Baral",
+      "S. Drouard",
+      "T. Hashemi",
+      "J. Friedman",
+      "G. Shapira"
+    ],
+    "doi": "10.1371/journal.pmed.1004070",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": "https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137126764&doi=10.1371%2fjournal.pmed.1004070&partnerID=40&md5=32f1cd887f5eb46121eb1e48abfcdaaf"
+  },
+  {
+    "key": "ZVNYT3V6",
+    "title": "Towards universal health coverage: Data for determinants of immunization coverage of Pneumococcal and Rota virus vaccines among under five children in Busolwe Town Council, Butaleja District, Eastern Uganda",
+    "abstract": "The data described stipulates the factors influencing the immunization coverage of Pneumococcal and Rota Virus Vaccines among under five children (U5C) in Butaleja district in Eastern Uganda. The data was obtained in three major sections of demographic characteristics, knowledge, and attitude and perceptions of care takers of U5C on immunization. Both qualitative and quantitative types of data obtained from Primary and Secondary data sources are presented. The Primary sources included administration of questionnaires to the caretakers of U5C in communities surrounding different health centers in Butaleja district. The secondary source of data was majorly the Health Management Information Systems (HMIS) records of Busolwe District Hospital. The data includes raw data from individual participants in form of Google forms portable document format, the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on immunization coverage in form of Microsoft excel format. The data provides a general outlook on the state of Butaleja district in terms immunization coverage of Pneumococcal and Rota Virus Vaccines. The data can be useful in taking action to decrease the burden of vaccine preventable diseases in Butaleja and elsewhere in similar settings. The data described is freely available in the Mendeley Data repository at the following site: https://doi.org/10.17632/zr2w886dg2.1 (Nabwana et al., 2019). (c) 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).",
+    "full_text": "Data in brief 25 (2019) 104269\nContents lists available at ScienceDirect\nData in brief\njournal homepage: www.elsevier.com/locate/dib\nData Article\nTowards universal health coverage: Data for determinants of immunization coverage of Pneumococcal and Rota virus vaccines among under \ufb01ve children in Busolwe Town Council, Butaleja District, Eastern Uganda\nBrenda Wafana Nabwana a, Sylvia Sidney Namayanja a, Collette Kemigisha a, Erina Kisakye a, Amos Kuddiza Kusetula a, Silvester Wakabi a, Ivan Wambi b, Innocent Musiime b, Rebecca Nekaka a, Yahaya Gavamukulya c, *\na Department of Community and Public Health, Faculty of Health Sciences, Busitema University, P.O. Box, 1460 Mbale, Uganda b Busolwe General Hospital, Butaleja District Local Government, Butaleja District, Uganda c Department of Biochemistry and Molecular Biology, Faculty of Health Sciences, Busitema University, P.O. Box, 1460 Mbale, Uganda\n\narticle info\nArticle history: Received 12 June 2019 Received in revised form 4 July 2019 Accepted 9 July 2019 Available online 15 July 2019\nKeywords: Immunization coverage PCV Rotavirus vaccine Under \ufb01ve children (U5C) Butaleja Eastern Uganda\n\nabstract\nThe data described stipulates the factors in\ufb02uencing the immunization coverage of Pneumococcal and Rota Virus Vaccines among under \ufb01ve children (U5C) in Butaleja district in Eastern Uganda. The data was obtained in three major sections of demographic characteristics, knowledge, and attitude and perceptions of care takers of U5C on immunization. Both qualitative and quantitative types of data obtained from Primary and Secondary data sources are presented. The Primary sources included administration of questionnaires to the caretakers of U5C in communities surrounding different health centers in Butaleja district. The secondary source of data was majorly the Health Management Information Systems (HMIS) records of Busolwe District Hospital. The data includes raw data from individual participants in form of Google forms portable document format, the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on\n\n* Corresponding author. E-mail address: gavayahya@yahoo.com (Y. Gavamukulya).\nhttps://doi.org/10.1016/j.dib.2019.104269 2352-3409/\u00a9 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).\n\n2\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\nimmunization coverage in form of Microsoft excel format. The data provides a general outlook on the state of Butaleja district in terms immunization coverage of Pneumococcal and Rota Virus Vaccines. The data can be useful in taking action to decrease the burden of vaccine preventable diseases in Butaleja and elsewhere in similar settings. The data described is freely available in the Mendeley Data repository at the following site: https://doi.org/10.17632/zr2w886dg2.1 (Nabwana et al., 2019).\n\u00a9 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.\norg/licenses/by/4.0/).\n\nSpeci\ufb01cations table\n\nSubject area\n\nHealth Sciences, Medical Sciences\n\nMore speci\ufb01c subject area Immunization, Public Health\n\nType of data\n\nMicrosoft Excel csv., Microsoft Excel Macros enabled \ufb01les, PDF \ufb01les\n\nHow data was acquired Online Researcher Administered Survey accessible at (https://forms.gle/PCi5rbK1mt5tgzhA8)\n\nData format\n\nRaw and \ufb01ltered\n\nExperimental factors\n\nA cross sectional study design was employed. Demographic and socio-economic characteristics\n\nwere some of the key concept variables and purposive sampling was preferred. Ethical approval\n\nand Permission to conduct the study were obtained from the relevant University and District\n\nauthorities. Care takers of U5C in Butaleja District who gave an informed consent were included\n\nin the study.\n\nExperimental features\n\nInformed consent was sought from the participants prior to participation in the data collection\n\nprocess. An online survey was administered through Google forms to recruited participants.\n\nData source location\n\nBusolwe Town Council, Butaleja District - Eastern Uganda; Busitema University Faculty of Health\n\nSciences, Mbale e Eastern Uganda\n\nData accessibility\n\nAll the reported datasets can be accessed via the Mendeley Data Repository at (https://doi.org/\n\n10.17632/zr2w886dg2.1) [1]\n\nValue of the data   The data is useful to governments in assessment of the immunization coverage and utilization of the vaccines by citizens.   The community members bene\ufb01t from this data in that feasible solutions can be accorded with respect to evidence-based\ninformation on declining trend of immunization.   The data can be used as a reference for comparative studies in similar settings.   The data can help to guide resource allocation and direction of work plan especially pertaining immunization of children.   The data can be useful in evaluation of health indicators such as utilization of immunization services by the line ministries.   The data collection tools can be used in conducting studies in other locations or on other diseases   This data can be used to determine possible trend of immunization coverage for other vaccine preventable diseases like\nHepatitis B or vaccinations that require multiple dosages.\n1. Data\nThe data described includes raw data from individual participants in form of Google forms portable document format (PDF), the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on immunization coverage in form of Microsoft excel format.\na. Individual responses for the determinants of Immunization Coverage in Busolwe 2019 - Google Forms.pdf [1].\nb. Consolidated data for determinants of Immunization Coverage in Busolwe 2019.csv [1]. c. Raw datasets for the secondary HMIS data on immunization.xlsm [1].\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\n3\n\n2. Experimental design, materials, and methods\n\n2.1. Area of study\nThe study fromwhich the data was obtained was carried out in Butaleja District in Eastern Uganda which is bordered by Budaka and Kibuku districts in the North, Mbale in the East, Tororo district in the South East and Namutumba in the West. Butaleja district has a total population of 244153 people of which 119466 (48.9%) are males and 124687 (51.1%) females according to the national population census 2014 [2]. The Busolwe General Hospital has a catchment population of 42298 people, with women in childbearing age being 8544, with number of pregnancies being 2114, number of live births 2051; number under \ufb01ve years is 8544.\n\n2.2. Target population\nThe study from which the data was obtained targeted the caregivers (primary care givers or parents) of U5C in homes in villages in the hospital's catchment area. Parent(s) and/or caretaker to the U5C who refused to give informed consent were excluded.\n\n2.3. Study design\nThe study from which the data was obtained followed a cross sectional study design to study representative samples of a population. Mixed qualitative-quantitative methods were employed using the questionnaires to the caretakers and more information was obtained from the HMIS records.\n\n2.4. Sample size determination\nThe minimum sample size was determined using the Cochran's formula N \u00bc (1.96)2pq/d2, with a con\ufb01dence level of approximately 95% (1.96).\nWhere, N \u00bc required sample size, P \u00bc proportion of population having the characteristics considering recent studies, q \u00bc (1-p) and d\u00bc ( \u00b15%) degree of precision. Therefore, considering \ufb01ndings from a current study on Knowledge and Perception of Caregivers about Risk Factors and Manifestations of Pneumonia among Under Five Children in Butaleja District, Eastern Uganda [3],p \u00bc 53.7, q \u00bc 1e0.537, d \u00bc 5/100 \u00bc 0.05.Thus, N \u00bc [(1.96)2 \u00c2 0.537 \u00c2 0.463]/(0.05)2 \u00bc 0.9551/0.0025 \u00bc 382 participants. In order to reduce errors, the sample population was enlarged from 382 participants to 434 participants.\n\n2.5. Data collection\nAn interviewer administered questionnaire was used to assess the perceptions and attitudes of the different correspondents towards the immunizable diseases as well as the factors associated with the immunization coverage in Butaleja district. A Google form (Determinants of Immunization Coverage in Busolwe 2019 - Google Forms Questionnaire.pdf [1]) was created and used to administer the questionnaire with datasets directly \ufb01lled to Excel worksheets. The questionnaire was pretested and validated among 2nd year Medical and Nursing students at BUFHS. Secondary data was obtained from the Busolwe district HMIS records to determine the number of people who immunized fully.\n\n2.6. Data storage\nThe raw data collected on questionnaires (Google forms) was automatically uploaded and securely stored online, and access to it was limited to only 3 administrators.\n\n2.7. Data analysis\nThe data can be analyzed by use of the compiled datasets to assess the concept variables, correlations, tendencies, among others. Excel and STATA programs can be majorly used in the data analysis.\n\n4\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\nThe analyzed data/information can be presented in form of statistical tables, charts, and generalized \ufb01gures, with interpretive descriptions of the information.\nConsent\n\nWritten informed consent from caretakers of the U5C was obtained before they participated in the study. Participants were informed that their privacy and con\ufb01dentiality would be respected and that there was no potential harm associated with participating in the study. It was made clear to the participants that participation in the study was voluntary and that they were free to opt out of the study at any time without any negative consequences.\n\nEthical approval\n\nThe study and all the protocols from which the data was obtained were approved and cleared by the Busitema University Faculty of Health Sciences Higher Degrees and Research Committee (BUFHSHDRC) as part of the Community Based Education, Research and Services (COBERS) Program for the 2018/2019 Academic year under the Course of Community Diagnosis and Communication Projects. Permission to conduct the study was sought from the District Health Of\ufb01cer Butaleja and the Medical Superintendent of Busolwe Hospital. The Chief Administrative Of\ufb01cer (CAO), community leaders and the members of the community consented to the research activities for the data collection; and in this all the participants signed a consent form which clearly stated their rights and the boundaries of the research. All the personal data was kept con\ufb01dential and participant items under lock and key.\n\nAcknowledgments\n\nThe research from which this data was obtained was funded by the Busitema University Faculty of Health Sciences COBERS Committee, the Regional Health Integration To Enhance Services in Eastern Uganda (RHITES-E) Team, as well as the Fogarty International Center of the National Institutes of Health, U.S. Department of State's Of\ufb01ce of the U.S. Global AIDS Coordinator and Health Diplomacy (S/ GAC), and President's Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1R25TW011213. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the of\ufb01cial views of the funders.\nWe thank Busitema University Faculty of Health Sciences Higher Degrees and Research Committee (BUFHS-HDRC) which gave in time to review and approve the proposal and accompanying protocols through The Busitema University Community Based Education, Research and Services (COBERS) Committee. Furthermore, the Authors would also like to express their explicit thanks to all the Doctors, Clinical Of\ufb01cers, Senior nursing of\ufb01cers, Laboratory technicians, HMIS personnel, Village Health Teams (VHTs) and all other staff who were of great help in our facility-based and community activities. Final thanks go to all the Volunteers who participated in the study. We also further extend our gratitude to the DHO, CAO, DHE and LCV of Butaleja District for having granted endorsement and acceptance for our interactions in the community, the VHTs and LCI chairpersons who guided us during our community activities especially home visits and the RHITES-E team especially Mr. Anoku Patrick for according the members transport and other support when called upon.\n\nCon\ufb02ict of interest\n\nThe authors declare that they have no known competing \ufb01nancial interests or personal relationships that could have appeared to in\ufb02uence the work reported in this paper.\n\nReferences\n[1] W.B. Nabwana, S.S. Namayanja, C. Kemigisha, E. Kisakye, A.K. Kusetula, S. Wakabi, I. Wambi, I. Musiime, R. Nekaka, Y. Gavamukulya, Data for Determinants of Immunization Coverage of PCV and Rota Virus Among under Five Children in Busolwe Town Council, Mendeley Data, Butaleja District, Eastern Uganda, 2019, https://doi.org/10.17632/zr2w886dg2.1 v1.\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\n5\n\n[2] Uganda Bureau of Statistics, The National Population and Housing Census 2014, Area Speci\ufb01c Pro\ufb01le Series -Butaleja District, Kampala, Uganda, 2017.\n[3] B. Aguti, G. Kalema, D.M. Lutwama, M.L. Mawejje, E. Mupeyi, D. Okanya, R. Nekaka, Y. Gavamukulya, Knowledge and perception of caregivers about Risk factors and Manifestations of Pneumonia among under \ufb01ve children in Butaleja district , Eastern Uganda, Microbiol. Res. J. Int. 25 (2018) 1e11, https://doi.org/10.9734/MRJI/2018/44179.\n\n\n",
+    "authors": [
+      "Brenda Wafana Nabwana",
+      "Sylvia Sidney Namayanja",
+      "Collette Kemigisha",
+      "Erina Kisakye",
+      "Amos Kuddiza Kusetula",
+      "Silvester Wakabi",
+      "Ivan Wambi",
+      "Innocent Musiime",
+      "Rebecca Nekaka",
+      "Yahaya Gavamukulya"
+    ],
+    "doi": "10.1016/j.dib.2019.104269",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "MDYFGMN4",
+    "title": "Indirect effects of COVID-19 on maternal, neonatal, child, sexual and reproductive health services in Kampala, Uganda.",
+    "abstract": "Background: COVID-19 impacted global maternal, neonatal and child health outcomes. We hypothesised that the early, strict lockdown that restricted individuals' movements in Uganda limited access to services.; Methods: An observational study, using routinely collected data from Electronic Medical Records, was carried out, in Kawempe district, Kampala. An interrupted time series analysis assessed the impact on maternal, neonatal, child, sexual and reproductive health services from July 2019 to December 2020. Descriptive statistics summarised the main outcomes before (July 2019-March 2020), during (April 2020-June 2020) and after the national lockdown (July 2020-December 2020).; Results: Between 1 July 2019 and 31 December 2020, there were 14 401 antenatal clinic, 33 499 deliveries, 111 658 childhood service and 57 174 sexual health attendances. All antenatal and vaccination services ceased in lockdown for 4 weeks.During the 3-month lockdown, the number of antenatal attendances significantly decreased and remain below pre-COVID levels (370 fewer/month). Attendances for prevention of mother-to-child transmission of HIV dropped then stabilised. Increases during lockdown and immediately postlockdown included the number of women treated for high blood pressure, eclampsia and pre-eclampsia (218 more/month), adverse pregnancy outcomes (stillbirths, low-birth-weight and premature infant births), the rate of neonatal unit admissions, neonatal deaths and abortions. Maternal mortality remained stable. Immunisation clinic attendance declined while neonatal death rate rose (from 39 to 49/1000 livebirths). The number of children treated for pneumonia, diarrhoea and malaria decreased during lockdown.; Conclusion: The Ugandan response to COVID-19 negatively impacted maternal, child and neonatal health, with an increase seen in pregnancy complications and fetal and infant outcomes, likely due to delayed care-seeking behaviour. Decreased vaccination clinic attendance leaves a cohort of infants unprotected, affecting all vaccine-preventable diseases. Future pandemic responses must consider impacts of movement restrictions and access to preventative services to protect maternal and child health.; Competing Interests: Competing interests: None declared. (\u00a9 Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)",
+    "full_text": "BMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nOriginal research\nIndirect effects of COVID-\u00ad19 on maternal, neonatal, child, sexual and reproductive health services in Kampala, Uganda\nJessica Florence Burt,1,2 Joseph Ouma,2 Lawrence Lubyayi,3 Alexander Amone,2 Lorna Aol,2 Musa Sekikubo,4 Annettee Nakimuli,4 Eve Nakabembe,4 Robert Mboizi,2 Philippa Musoke,2 Mary Kyohere,2 Emily Namara Lugolobi,2 Asma Khalil,5 Kirsty Le Doare3,5\n\nTo cite: Burt JF, Ouma J, Lubyayi L, et al. Indirect effects of COVID-\u00ad19 on maternal, neonatal, child, sexual and reproductive health services in Kampala, Uganda. BMJ Global Health 2021;6:e006102. doi:10.1136/ bmjgh-2021-006102\nHandling editor Sanni Yaya \u25ba Additional supplemental material is published online only. To view, please visit the journal online (http://\u200bdx.d\u200b oi.\u200borg/\u200b10.\u200b 1136/b\u200b mjgh-\u200b2021-0\u200b 06102).\nReceived 23 April 2021 Accepted 8 August 2021\n\u00a9 Author(s) (or their employer(s)) 2021. Re-\u00aduse permitted under CC BY-\u00adNC. No commercial re-\u00aduse. See rights and permissions. Published by BMJ. For numbered affiliations see end of article. Correspondence to Professor Kirsty Le Doare; \u200bkiledoar@\u200bsgul.\u200bac.\u200buk\n\nABSTRACT Background\u2002 COVID-\u00ad19 impacted global maternal, neonatal and child health outcomes. We hypothesised that the early, strict lockdown that restricted individuals\u2019 movements in Uganda limited access to services. Methods\u2002 An observational study, using routinely collected data from Electronic Medical Records, was carried out, in Kawempe district, Kampala. An interrupted time series analysis assessed the impact on maternal, neonatal, child, sexual and reproductive health services from July 2019 to December 2020. Descriptive statistics summarised the main outcomes before (July 2019\u2013March 2020), during (April 2020\u2013June 2020) and after the national lockdown (July 2020\u2013December 2020). Results\u2002 Between 1 July 2019 and 31 December 2020, there were 14 401 antenatal clinic, 33 499 deliveries, 111 658 childhood service and 57 174 sexual health attendances. All antenatal and vaccination services ceased in lockdown for 4 weeks. During the 3-\u00admonth lockdown, the number of antenatal attendances significantly decreased and remain below pre-\u00adCOVID levels (370 fewer/month). Attendances for prevention of mother-\u00adto-\u00adchild transmission of HIV dropped then stabilised. Increases during lockdown and immediately postlockdown included the number of women treated for high blood pressure, eclampsia and pre-\u00adeclampsia (218 more/month), adverse pregnancy outcomes (stillbirths, low-\u00adbirth-\u00adweight and premature infant births), the rate of neonatal unit admissions, neonatal deaths and abortions. Maternal mortality remained stable. Immunisation clinic attendance declined while neonatal death rate rose (from 39 to 49/1000 livebirths). The number of children treated for pneumonia, diarrhoea and malaria decreased during lockdown. Conclusion\u2002 The Ugandan response to COVID-\u00ad19 negatively impacted maternal, child and neonatal health, with an increase seen in pregnancy complications and fetal and infant outcomes, likely due to delayed care-\u00adseeking behaviour. Decreased vaccination clinic attendance leaves a cohort of infants unprotected, affecting all vaccine-\u00ad preventable diseases. Future pandemic responses must consider impacts of movement restrictions and access to preventative services to protect maternal and child health.\n\nWHAT IS ALREADY KNOWN?\n\u21d2 The response to COVID-\u00ad19 has been shown to have indirectly impacted on maternal, child, neonatal, sexual and reproductive health.\n\u21d2 This is largely related to access to services and fear of contracting COVID-1\u00ad 9 in outpatient departments.\n\u21d2 There has been very little data published on the health impacts of the COVID-1\u00ad 9 response in Uganda.\nWHAT ARE THE NEW FINDINGS?\n\u21d2 Antenatal attendances decreased dramatically in April, followed by increased numbers of low-\u00adbirth-\u00ad weight infants and neonatal deaths.\n\u21d2 Newborn immunisations against polio, tetanus, diphtheria, hepatitis B, Haemophilus influenzae, rotavirus and pneumococcus decreased significantly.\n\u21d2 Sexual and reproductive health services were reduced in number.\nWHAT DO THE NEW FINDINGS IMPLY?\n\u21d2 Although Uganda has been less affected directly by COVID-1\u00ad9 infections in the first wave, the indirect impacts are far-r\u00adeaching and will have future influences on population health.\n\u21d2 There is a degree of resilience within the healthcare service, but many services were adversely affected by the lockdown leading to poorer pregnancy and neonatal outcomes.\n\u21d2 Antenatal and vaccination services are of particular importance in ensuring the safety of mother and child and must be prioritised in the responses to future pandemics.\nINTRODUCTION Uganda, as with many nations in the WHO Africa region, has largely avoided the considerable infection rate and death toll from COVID-1\u00ad 9 that other nations saw during the first wave,1 2 with 84 116 confirmed cases and 1966 deaths reported as of 6 July 2021.3 While this is likely under-r\u00adepresentative of the true\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n \n\n1\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nBMJ Global Health\n\nmorbidity and mortality,4 Uganda has successfully minimised the spread and direct impact of COVID-1\u00ad 9 within its borders through its early, rapid and severe response. However, maternal and child health services were severely impacted by these measures during the height of the lockdown, which may have indirectly affected morbidity and mortality in this group.\nEffects of COVID-19\nSymptomatic COVID-\u00ad19 infection in pregnancy is linked to worse maternal and neonatal outcomes than for pregnancies without COVID-1\u00ad 9.5\u20137 Studies across the UK and USA have also shown increased preterm birth, stillbirth, small for gestational age babies and neonatal mortality over the COVID-\u00ad19 period and in relation to infection in pregnancy.8\nHowever, global estimates of the indirect impacts of COVID-\u00ad19 could amount to up to a 38.6% increase in maternal mortality, and 44.7% increase in child mortality per month across 118 low and middle-\u00adincome countries.9 The main factors proposed are disruptions to childbirth services and antenatal care (ANC) such as the management of pre-\u00adeclampsia and supplementation advice, wasting and curative child services,9 which the WHO has documented as being affected in many locations.10 Additionally, disruptions to family planning services including access to contraception and safe abortions will result in an additional rise in maternal deaths, abortion-\u00adrelated complications and a large unmet need for contraceptives.11 Further impacts on maternal and child outcomes may be seen through issues surrounding the provision of prevention and management of HIV,9 12 13 reduced lactation support14 and conflicting guidance on whether to avoid breast feeding if infected.15 These impacts have been reported in some low-r\u00ad esource settings globally,10 16 17 particularly with reduced antenatal attendances, linked to transportation restrictions, fear of transmission and lack of antenatal education.17\n\nHospital (KNRH) were prohibited for a short time (from 23 March 2020 to 21 April 2020), which included the closure of ANC and childhood immunisation clinics. The Ugandan Ministry of Health (MoH) implemented PCR-\u00ad based screening for symptomatic patients and any patient who was PCR positive was admitted to a dedicated ward.\nThis paper aims to quantify the indirect impact of COVID-1\u00ad9 on maternal, neonatal and childhood outcomes at KNRH in Kampala.\nMETHODS This was a single-\u00adsite observational study, which utilised retrospectively collected data, based on KNRH. This is a large, urban hospital with over 21 000 deliveries per annum, 200 antenatal clinic visits and 100 child admissions to hospital per day.24 The hospital provides preventative and curative care during pregnancy and intrapartum, newborn and postnatal care, a paediatric ward and vaccination services at a standard indicative of care in urban Uganda. After the initial lockdown period (4 weeks without outpatient services), measures to reduce the number of women attending ANC included reducing the number of appointments per day from 150 to 90 for ANC and all women <26 weeks gestation being sent away to return after 30 weeks. For infants, the vaccination clinic remained operating routinely. During the initial phases of lockdown (April and May 2020), 35/60 doctors were reassigned to acute care at COVID-\u00ad19 centres in anticipation of a large number of COVID-1\u00ad 9 cases, but 53 nurses were recruited at the same time with result-b\u00ad ased financing support raising the number of nurse/midwifes on site from 184 to 237 after April 2020.\nPatient and public involvement Patients were not involved directly in the formation of this study. We have involved women in a separate, dedicated qualitative study about their experiences of ANC during the pandemic.25\n\nCOVID-19 in Uganda\nPreparation and readiness measures against COVID-1\u00ad9\nin Uganda began between January and March 2020,\nfocusing on health systems strengthening and capacity building, aided by early allocation of WHO funding.18 19\nFrom 2 March, the public were informed of the threat\nof COVID-1\u00ad 9, with education and training subsequently disseminated.18 Testing focused on contacts of identified\ncases and those returning from travel, with population-\u00ad\nwide lockdown measures imposed quickly after the first case in Uganda was reported on 21 March 2020.20 21 This\nincluded border closures, port-o\u00adf-\u00adentry screenings and quarantines for travellers.18 By 25 March, this escalated\nto a ban on group gatherings and non-\u00adessential internal\ntravel, recommendation to work from home and close schools.18 22 The travel restrictions included the cessation\nof all public transport and a ban on the use of private vehicles without explicit permission to travel.23 At a local\nlevel, non-e\u00ad ssential visits to Kawempe National Referral\n\nData collection\nData were retrospectively collected in January 2021, by hospital staff with access to the Electronic Medical Records (EMR) system. This system is part of the Uganda Ministry of Health (MoH) eHealth Policy, Strategy and Implementation Plan and utilises the District Health Information Software 2 (DHIS2).26 The DHIS2 indicators for which data were collected are detailed in the available data set27 and were taken from health management information system data, which is reported to the MoH, covering pregnancy preventative services, pregnancy curative services, childbirth, care of the newborn, postnatal care, preventative childcare, curative childcare, preventative services for women of reproductive age, curative services for women of reproductive age and unavailability of medicines and commodities. Monthly totals were gathered for the period from July 2019 to December 2020. In accordance with the Sex and Gender Equity in Research guidelines, pregnancy, childbirth and\n\n2\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nsexual health-\u00adrelated indicators are reported for those of the female sex, and no segregation is made between male and female sex or gender for childcare indicators as this was not part of the reporting data.28\nNeonatal mortality was calculated as the sum of immediate neonatal deaths and deaths from neonatal sepsis 0\u20137 days, neonatal sepsis 8\u201328 days, neonatal pneumonia, neonatal meningitis, neonatal jaundice, premature baby (as condition that requires management) and other neonatal conditions.\nStatistical analysis Data were input into Microsoft Excel and exported to R V.4.0.4 (R Foundation for Statistical Computing, Vienna, Austria) for further analysis purposes. We calculated the number of attendances per month for each indicator and then analysed the aggregated data at the month level as a proportion of antenatal, labour and delivery, child health or sexual and reproductive health services attendance. For each indicator, the data were divided into pre-C\u00ad OVID (July 2019\u2014March 2020), lockdown (April\u2014June 2020) and post-\u00adCOVID lockdown (July\u2014December 2020). We used descriptive statistics to summarise demographic and clinical data and present summaries of outcomes before, during and after lockdown (the intervention) as medians and IQRs (online supplemental table 1). To identify suitable regression models for estimating the effects of lockdown, we first graphically plotted the number of events per month over time and assessed for stationarity and autocorrelation using the Durbin-\u00adWatson test, graphs of residuals from ordinary least square regression and graphs of auto and partial autocorrelation functions. We then conducted interrupted time series analyses using the generalised least square approach, which allows for inclusion of autoregressive or moving average autocorrelation processes. These models were used to estimate the effects of lockdown on preventative, curative, labour and delivery and child health services at KNRH. The model included a time variable (month), a dummy lockdown variable indicating prelockdown, lockdown and postlockdown and trend variables for the lockdown and postlockdown periods. This approach allows for estimation of the change in levels and trends of the outcomes following the multiple interruptions (start and lifting of lockdown). We use a 5% significance level and 95% CIs.\nRESULTS Between 1 July 2019 and 31 December 2020, there were 14 401 ANC attendances, 33 499 deliveries, 111 658 attendances for childhood services and 57 174 sexual and reproductive health (SRH) service attendances at KNRH. There was complete closure of all antenatal, sexual health and vaccination services for the first 4 weeks of the lockdown (from 23 March 2020 to 21 April 2020). An overview of the results of the interrupted time series analysis is displayed in online supplemental table 2.\n\nBMJ Global Health\nPregnancy Preventative services In the 9 months prior to lockdown at the end of March 2020, the median number of attendances for antenatal services was 894 per month (IQR 808\u20131035). During the 3 months of lockdown, there were 539 fewer visits a month compared with prelockdown (95% CI 195.0 to 516.3; p=0.001). After the lifting of restrictions, the overall trend was for 370 fewer attendances compared with previous periods (95% CI 202.7 to 536.3; p=0.001).\nThe proportion of women receiving iron supplementation, tetanus vaccination and blood pressure monitoring remained unchanged after the initial closure of services at lockdown, despite fewer women attending ANC (figure 1A\u2013F). However, due to stockouts of intermittent antimalarial prophylaxis and folic acid supplementation prior to lockdown, there was an increase in the proportion of women receiving medication during lockdown.\nThe proportion of women receiving HIV testing in ANC declined by a rate of 4% (95% CI 1.5% to 6.5% decline; p=0.01) during lockdown but increased back to baseline at the end of restrictions (figure 1G). The median number of women attending prevention of mother-\u00adto-\u00ad child transmission (PMTCT) of HIV services before lockdown was 113 (IQR 56\u2013146). Following the first month of lockdown, during which time the clinic closed, the number of attendances increased slowly with a jump of 85 more visits/month (95% CI 31.6 to 138.4; p=0.009) in the month that lockdown was lifted (figure 1H).\nCurative services The median number of women being treated for high blood pressure, pre-\u00adeclampsia and eclampsia prior to lockdown was 87/month (IQR 8\u2013180). However, during the 3 months of lockdown, there was a significantly increasing trend of 218 more women/month (95% CI 108 to 327; p=0.002) receiving treatment. There has been a declining trend of 259 women/month (95% CI 153 to 365; p<0.001) receiving treatment in the months since lockdown was lifted. There was no change in the trend of women being treated for bacterial infections throughout the study, although this is incompletely captured in EMR.\nLabour and delivery The median number of monthly deliveries was 1869 (IQR 1791\u20131924) before March 2020 (pre-C\u00ad OVID). At the time of lockdown, there were 320 more deliveries/ month (95% CI 199 to 441; p=0.0002). During lockdown, there was a trend of 109 (95% CI 55 to 163; p=0.002) fewer deliveries per month, although delivery trends have increased by a median of 117 (95% CI 54 to 180; p=0.003) deliveries per month since lockdown was lifted. During lockdown, there was an increase in the rate of low-b\u00adirth weight infants (1.7% increase, 95% CI 0.6% to 2.7%; p=0.011), and in the immediate postlockdown month, an increase in stillbirths (1% increase, 95% CI \u22122% to 4%; p=0.58) and preterm births (6% increase, 95% CI \u22123% to 15%; p=0.22), both not significant. There\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n3\n\nBMJ Global Health\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nFigure 1\u2003 Antenatal indicators for Kawempe National Referral Hospital (KNRH), July 2019 to December 2020. Interrupted time-\u00ad series analyses with generalised least square regression for antenatal clinic services. (A) Antenatal clinic (ANC) attendance total attendances and rates of interventions as a proportion of total ANC attendance for: (B) ANC iron administration, (C) ANC folic acid administration, (D) ANC intermittent antimalarial prophylaxis (IPTP) administration, (E) ANC blood pressure measurement, (F) ANC tetanus toxoid administration, (G) ANC HIV testing. (H) Number of women attending prevention of mother to child transmission of HIV (PMTCT) services as part of ANC, before, during and after the COVID-1\u00ad 9 lockdown in KNRH. Red dots represent numbers/rates per month. Blue lines connect the red dots to display the observed monthly trend. Red solid lines represent the fitted regression models. Red dashed lines represent the counterfactual scenario.\n\n4\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nwas no increase in the rates of maternal death over the course of the study (figure 2A\u2013G).\nCare of the newborn Prior to lockdown, there was a median of 700 admissions per month to the neonatal unit (NICU) (IQR 652\u2013706) and a neonatal mortality rate of 39.6/1000 livebirths (IQR 34.6\u201350.7). During lockdown, there was an increasing rate of NICU admissions of 5.6% (0%\u201311%; p=0.06). At the end of lockdown, the neonatal mortality rate increased by 10 neonatal deaths per 1000 livebirths/ month (IQR 2\u201310; p<0.001) (figure 3A,B).\nPostnatal care The median number of women receiving immediate routine postnatal care services (within 24 to 48 hours of delivery) pre-\u00adCOVID was 1873 (IQR 1823\u20131993). There were no cases of COVID-\u00ad19 during this study.\nChild health services Immunisations were offered on all nine scheduled immunisation days in every month in 2019 and 2020. The median number of immunisation clinic attendances in the pre-C\u00ad OVID period was 5871 (95% CI 5643 to 6094). Since the lifting of lockdown, there have been 960 fewer monthly attendances (771 to 2248; p=0.04) (figure 3C). There was no change in the rate of children receiving Bacille Calmette-G\u00ad uerin (BCG) at birth, oral polio, pneumococcal or rotavirus vaccines since the end of lockdown, although fewer children now attend the immunisation clinic. The increase in the rate of measles vaccine receipt is due to a catch-u\u00ad p campaign after a long stockout (online supplemental figure 1).\nThere was a decline in the number of children being treated for pneumonia, malaria and diarrhoea (figure 3D\u2013F). There was an increase in the number of children treated for malnutrition after lockdown (online supplemental table 1). We do not have data on infant or child mortality during the period.\nSexual and reproductive health There was a decrease in the number of women receiving the contraceptive pill (13.6 more women; 95% CI 8.8 to 18.5; p<0.001) after restrictions were lifted. There were no changes to the number of women receiving intrauterine devices, although numbers recorded are small (figure 4A,B). The number of sterilisation procedures and number of women treated for sexually transmitted diseases remained low both pre and postlockdown. The number of abortions and sterilisations related to abortions increased by 338 (95% CI 58 to 619 more procedures; p=0.04) at the start of lockdown.\nAvailability of medicines Several shortages were noted in medication and vaccination availability both prelockdown and postlockdown, which may have affected the ability to deliver effective services (figure 5).\n\nBMJ Global Health\nDISCUSSION\nDespite calls for the prioritisation of antenatal services and the consideration of the indirect impacts of lockdown restrictions on maternal health,9 12 29 30 the data from our study highlight that maternity, sexual and reproductive health, newborn and child health services were severely affected by COVID-\u00ad19 restrictions.\nSimilar to our findings, facilities in rural Uganda saw a drop in antenatal attendances,31 as have hospitals in Kenya, Ethiopia, Zimbabwe and Rwanda in the first months of the pandemic.32\u201339 The aim of preventative services is to reduce maternal and newborn morbidity and mortality and any reduction in their availability can give an indication of the potential longer term impacts\u2014 including increased rates of maternal anaemia, puerperal sepsis, stillbirth, low birth weight, preterm birth, malaria infection, pre-\u00adeclampsia/eclampsia, mother to child transmission of HIV and neonatal tetanus.40 Our study did not see an increase in maternal mortality despite fewer ANC attendances. This could be due to more women delivering in the community, as has been reported elsewhere;41 however, our delivery rates remained constant throughout the period of study, suggesting that there may be alternative reasons for these findings, including increased maternal and neonatal morbidity, rather than mortality.\nThere are many proposed reasons why the ANC service and immunisation clinic attendances decreased so drastically during the lockdown. The national guidance at the start of the pandemic resulted in the closure of public transport, which a large proportion of patients rely on to access healthcare facilities, hence impacting their physical ability to access care, as has been reported in Uganda31 42 and in other countries.32 36 38 41 Other themes which have been reported to have affected attendances are the lack of healthcare staff, fear of infection, disruption of services due to COVID-\u00ad19, lockdown orders restricting movement and the increased price of transport.35 43 These themes have been highlighted in other studies in the region,31 38 42 indicating the need to consider the implications of lockdown measures on public confidence in healthcare in future emergencies. The impacts of such public health measures must also be considered as to how they impact within different socioeconomic contexts, as changes to service use during the pandemic have not been universal, impacting more on those in lower socioeconomic circumstances.43\nWhile the number of ANC visits decreased, our delivery rate did not decline by the same amount. Kawempe hospital caters for a population of 2 million people, yet the number of women attending four ANC visits remains below 90%, although the majority of women in Kampala still deliver in hospital (94%).44 This data suggest that ANC and hospital delivery are not seen as a continuum of care in our setting and could account for the phenomenon of increased deliveries despite fewer ANC visits. Alternatively, fear of contracting COVID-\u00ad19 in\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n5\n\nBMJ Global Health\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nFigure 2\u2003 Labour and delivery indicators for Kawempe National Referral Hospital (KNRH), July 2019 to December 2020. Interrupted time-\u00adseries analyses with generalised least square regression for (A) total deliveries, (B) caesarean sections, (C) haemorrhage related to labour and delivery, (D) maternal mortality, expressed as rate per 100 000 maternities, (E) stillbirth rate as a percentage of deliveries, (F) preterm birth rate as a percentage of total livebirths, (G) low birth weight as a percentage of total livebirths, before, during and after the COVID-\u00ad19 lockdown in KNRH. Red dots represent numbers/rates per month. Blue lines connect the red dots to display the observed monthly trend. Red solid lines represent the fitted regression models. Red dashed lines represent the counterfactual scenario.\n\n6\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\nBMJ Global Health\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nFigure 3\u2003 Neonatal and child health indicators for Kawempe National Referral Hospital (KNRH), July 2019 to December 2020. Interrupted time-\u00adseries analyses with generalised least squaresregression for (A) neonatal intensive care unit admissions, (B) neonatal mortality rate expressed a rate per 1000 livebirths, (C) immunisation clinic attendance, (D) inpatient pneumonia treatment, (E) inpatient diarrhoea treatment and (F) inpatient malaria treatment, before, during and after the COVID-1\u00ad 9 lockdown in KNRH. Red dots represent numbers/rates per month. Blue lines connect the red dots to display the observed monthly trend. Red solid lines represent the fitted regression models. Red dashed lines represent the counterfactual scenario. NICU, neonatal intensive care unit; SCBU, special care baby unit.\n\nthe community may have influenced the decision to give\nbirth in a hospital environment.\nThe COVID-\u00ad19 pandemic has impacted on childbirth and deliveries across the region,5 45 with some reports of decreased hospital deliveries.32\u201334 Facilities in Kenya have\nreported an increase in the number and rate of C sections and fresh stillbirths,36 increased PPH33 and an increase\nin maternal deaths, disproportionately affecting adolescents.36 While an increase in stillbirth and preterm birth\n\nwas seen in our population at the end of lockdown, this was not significant. The age of the pregnant woman was not included in our data set and may shed a valuable light on any population-l\u00adevel disparities in outcomes. Similar to other studies from Africa,32 34 38 41 43 we advocate for prioritising safe and effective antenatal, intrapartum and postnatal in for future health emergencies.\nThe rise in neonatal deaths, low-b\u00adirth-\u00adweight babies and NICU admissions are likely a result of the lack of\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n7\n\nBMJ Global Health\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nFigure 4\u2003 Sexual and reproductive health service indicators for Kawempe National Referral Hospital (KNRH), July 2019 to December 2020. Interrupted time-\u00adseries analyses with generalised least square regression for (A) oral contraceptive recipients, (B) Intrauterine Devices (IUD) insertions, (C) abortions and (D) incomplete abortions, before, during and after the COVID-1\u00ad 9 lockdown in KNRH. Red dots represent numbers/rates per month. Blue lines connect the red dots to display the observed monthly trend. Red solid lines represent the fitted regression models. Red dashed lines represent the counterfactual scenario.\n\nANC from March to May.40 Sudden sharp changes in\nneonatal outcomes have been reported in South Africa,\nwhere an increase in neonatal mortality was linked to the\ndisruption of services and diversion of resources due to COVID-\u00ad19 necessities.46 As seen with our data, a hospital\nin Malawi found an increase in babies born earlier and\nat lower birth weights, however, the same study did not find this in a Zimbabwean hospital,47 suggesting there are\ndifferences between countries that remain unexplained.\nA decrease in children attending hospital, as seen in our findings, was also seen in South Africa46 and Ethiopia.39 This is likely also associated with fear of attending\n\nhealthcare settings, inaccessibility, and a reduction in self-\u00adreferrals, as seen with ANC.39 Conversely, there was\nan increase in malnutrition attendances, likely due to the\nsocietal impacts of COVID-1\u00ad 9 restrictions on child health and nutrition.48 The lack of therapeutic foods available\nmay have been affected by border closures and trade\nrestrictions, in a similar manner to medication availability in Nigeria.49\nThe reduction in immunisation clinic attendances\nin our cohort puts an estimated 20 000 children at risk\nof mortality from vaccine-p\u00ad reventable diseases such as tetanus and polio.50 Uganda was declared polio-\u00adfree in\n\nFigure 5\u2003 Medicine and vaccine unavailability in the prelockdown and postlockdown periods. Overview of medicine stockouts by month. Coloured blocks indicate no stocks available, black lines indicate start and end of lockdown. ORS, oral rehydration salts\n\n8\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\n2010.51 However, the situation remains precarious due to the possibility of imported virus from surrounding countries where polio is not yet irradicated.52 The likelihood of a potential infectious disease outbreak can be predicted based on the proportion of coverage lost by a period of reduction in immunisations, and The WHO estimates that at least 80 million children will be at risk of diseases like tetanus, polio, diphtheria and measles due to disruption of vaccination programmes during the pandemic.53 A reduction in the uptake of childhood immunisations was also seen in England,54 Singapore55 and Brazil56 at the start of the pandemic, highlighting the universal impact of COVID-\u00ad19 on child health. The follow-u\u00ad p response to vaccine catch-\u00adup in this pandemic is of key importance in mitigating future outbreaks and further impacts on child health.57 58\nWhile our data show some resiliency in sexual health and contraceptive services, the reduction in HIV clinic attendances is worrying and requires further outreach work to ensure the provision of care. PMTCT services bounced back quickly, in part due to international funding support mechanisms in place, such as that from United States Agency for International Development (USAID).59 Sexual and reproductive health services have also been impacted across other East African nations. Facilities in Kenya and Ethiopia reported that contraceptive services were limited and decreases were seen in family planning attendances due to the closure of services,34 39 although national data from Kenya show no change overall in the usage of services.36\nClinical and research implications While the effects of COVID-\u00ad19 are wide ranging, the continuation of changes to antenatal services, maternal and neonatal outcomes and reduced number of children being treated for pneumonia and malaria in hospital through to December 2020 may also be influenced by other social factors. Many staff and patients were affected by restrictions to movement in October and November 2020 due to political campaigning and riots relating to the presidential elections. This highlights the susceptibility of health and healthcare services to wider events, reinforcing the need for resilience and planning going forward.\nStrengths and limitations Although the limitations of this study lie in the use of data from a single site, collected retrospectively, this has allowed the inclusion of over 33 000 births, 14 401 antenatal attendances and 111 658 childhood immunisations, highlighting the massive impact on this population. Furthermore, the use of data from EMR rather than direct patient records mean these data are likely an under-r\u00adepresentation of the true values of each indicator. Statistical comparison using data from the full year of 2019 would have enabled a better understanding of how 2020 compared with the time before COVID-1\u00ad9. Even with these documented limitations, our findings\n\nBMJ Global Health\nreinforce the importance of considering maternal and child health in future pandemic responses.\nCONCLUSIONS Maternal, neonatal, child and sexual and reproductive health services were all impacted by the restrictions imposed by the Ugandan government in response to COVID-1\u00ad 9. Our results demonstrate the urgent need for pandemic responses that take into account the local context, where a stringent lockdown may be detrimental to the overall health of the population. Such responses must include the prioritisation of preventative care, including maintaining antenatal clinic visits, child health and vaccination services to prevent delayed impacts on maternal, neonatal and child health. Furthermore, any disruptions to immunisation schedules must be mitigated as rapidly as possible, to prevent further infectious disease outbreaks and future pandemics.\nAuthor affiliations 1School of Medicine, University of Leeds, Leeds, UK 2Makerere University Johns Hopkins University, Kampala, Uganda 3MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Wakiso, Uganda 4Obstetrics and Gynecology, Makerere University and Mulago National Referral Hospital, Kampala, Uganda 5Infection and Immunity, St. George's, University of London, London, UK\nTwitter Jessica Florence Burt @Jessica42187146\nAcknowledgements\u2002 We would like to thank the electronic medical records team at KNRH for access to the data for this study.\nContributors\u2002 JFB, JO and KLD developed the original research idea and drafted the manuscript, LL developed the statistics for this study, LA and AA collected and cleaned data, MS, AN, EN, PM, RM and AK all had input into the final manuscript.\nFunding\u2002 This publication was produced by periCOVID Africa which is part of the EDCTP2 programme supported by the European Union (grant number RIA2020EF2926 periCOVID Africa). The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP.\nCompeting interests\u2002 None declared.\nPatient consent for publication\u2002 Not required.\nEthics approval\u2002 This study received ethical approval from the School of Medicine Research Ethics (SOMREC 2020-\u00ad148), Committee Uganda Council for Science and Technology (HS913ES).\nProvenance and peer review\u2002 Not commissioned; externally peer reviewed.\nData availability statement\u2002 Data are available in a public, open access repository. https://doi.org/10.24376/rd.sgul.14501541.v1.\nSupplemental material\u2002 This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-\u00adreviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.\nOpen access\u2002 This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-\u00adNC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-c\u00ad ommercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-\u00adcommercial. See: http://creativecommons.org/licenses/by-nc/4.0/.\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n9\n\nBMJ Glob Health: first published as 10.1136/bmjgh-2021-006102 on 27 August 2021. Downloaded from http://gh.bmj.com/ on August 19, 2024 by guest. Protected by copyright.\n\nBMJ Global Health\nREFERENCES\n1 COVID-\u00ad19 response in the world Health organization African region 2020.\n2 World Health Organization. The coronavirus disease 2019 (COVID-\u00ad19) strategic preparedness and response plan for the WHO Africa region: 1 February 2021 - 31 January 2022. World Health Organization, 2021.\n3 Uganda: Johns Hopkins University & Medicine, 2021. Available: https://coronavirus.jhu.edu/region/uganda [Accessed 06 Jul 2021].\n4 Colombo S, Scuccato R, Fadda A, et al. COVID-\u00ad19 in Africa: the little we know and the lot we ignore. Epidemiol Prev 2020;44:408\u201322.\n5 Khalil A, Kalafat E, Benlioglu C, et al. 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Available: https://medicalxpress.com/\u200b news/2020-10-south-sudan-outbreak-vaccine-derived-polio.html [Accessed 12 Apr 2021]. 53 Abad-\u00adVergara D, Sidhu S, Fulker J. At least 80 million children under one at risk of diseases such as diphtheria, measles and polio as COVID-\u00ad19 disrupts routine vaccination efforts, warn Gavi. WHO and UNICEF, 2020. https://www.who.int/news/item/22-05-2020-\u200b at-least-80-million-children-under-one-at-risk-of-diseases-such-\u200b as-diphtheria-measles-and-polio-as-covid-19-disrupts-routine-\u200b vaccination-efforts-warn-gavi-who-and-unicef 54 McDonald HI, Tessier E, White JM, et al. Early impact of the coronavirus disease (COVID-\u00ad19) pandemic and physical distancing\n\nBMJ Global Health\nmeasures on routine childhood vaccinations in England, January to April 2020. Euro Surveill 2020;25. 55 Zhong Y, Clapham HE, Aishworiya R, et al. Childhood vaccinations: hidden impact of COVID-\u00ad19 on children in Singapore. Vaccine 2021;39:780\u20135. 56 Silveira MF, Tonial CT, Goretti K Maranh\u00e3o A, et al. Missed childhood immunizations during the COVID-\u00ad19 pandemic in Brazil: analyses of routine statistics and of a national household survey. Vaccine 2021;39:3404\u20139. 57 Masresha BG, Luce R, Shibeshi ME, et al. The performance of routine immunization in selected African countries during the first six months of the COVID-1\u00ad 9 pandemic. Pan Afr Med J 2020;37:12. 58 Mburu CN, Ojal J, Chebet R, et al. The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-1\u00ad 9 pandemic in Kenya. BMC Med 2021;19:35. 59 Jiang H, Zhou Y, Tang W. Maintaining HIV care during the COVID-1\u00ad 9 pandemic. Lancet HIV 2020;7:e308\u20139.\n\nBurt JF, et al. BMJ Global Health 2021;6:e006102. doi:10.1136/bmjgh-2021-006102\n\n11\n\n\n",
+    "authors": [
+      "Jessica Florence Burt",
+      "Joseph Ouma",
+      "Lawrence Lubyayi",
+      "Alexander Amone",
+      "Lorna Aol",
+      "Musa Sekikubo",
+      "Annettee Nakimuli",
+      "Eve Nakabembe",
+      "Robert Mboizi",
+      "Philippa Musoke",
+      "Mary Kyohere",
+      "Emily Namara Lugolobi",
+      "Asma Khalil",
+      "Kirsty Le Doare"
+    ],
+    "doi": "10.1136/bmjgh-2021-006102",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": "https://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=34452941&site=ehost-live&scope=site"
+  },
+  {
+    "key": "IAGNKDJF",
+    "title": "Socio-Cultural Factors Associated with Incomplete Routine Immunization of Children _ Amach Sub-County, Uganda.",
+    "abstract": "Immunization is one of the worlds's most cost-effective health interventions that help prevent childhood diseases. However, many infants are not usually fully vaccinated especially in developing countries. This contributes to the mortality of Vaccine-Preventable Diseases (VPD) in children. The study examined the socio-cultural factors that are associated with incomplete routine immunization of children aged 0\u20131 year old in Amach Sub-County, Lira District _ Uganda. An analytical cross-sectional-mixed study among a sample of 326 parents and three health workers were made. Simple random sampling and purposive techniques were used to select the respondents. In-depth interviews, focus group discussion and Interviewer administered questionnaires were used to collect data. A modified Poisson regression model was used to compute the prevalence ratios. Variables were analyzed at bivariate and multivariate levels for their association with incomplete immunization. Incomplete immunization was at 27.3%. Marital status (p = 0.05), wealth level (p = 0.001), and side effects of vaccines was found to be associated with incomplete routine immunization. Age, occupation, education, religion, utilization of other health services, family structure, and support, gender, accessibility, and health education were not found to be associated with incomplete routine immunization. Incomplete immunization rate is quite high in Amach Sub-county. [ABSTRACT FROM AUTHOR]Copyright of Cogent Medicine is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)",
+    "full_text": "",
+    "authors": [
+      "Omike Jillian",
+      "Omona Kizito",
+      "Tsai-Ching Hsu"
+    ],
+    "doi": "",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": "https://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=148721705&site=ehost-live&scope=site"
+  },
+  {
+    "key": "HY2VD427",
+    "title": "Gaps in measles vaccination coverage in Kasese district, Western Uganda: results of a qualitative evaluation",
+    "abstract": "BACKGROUND: Despite the availability of a highly effective vaccine, measles remains a substantial public health problem in many countries including Uganda. In this study, conducted between June-August 2020 following a local outbreak, we sought to explore the factors that could affect measles vaccination coverage in rural western Uganda. METHODS: We conducted a descriptive study using qualitative data collection approaches in the Kasese district. The research team utilized purposive sampling to identify and select participants from the public health sector and district government. We conducted key informant interviews (KII) and one focus group discussion (FGD). Responses were recorded using portable electronic devices with the FGD and KII guide installed. Interviews were conducted at the health centre and district headquarters. Data was coded and analysed using ATLAS.ti version 8 software through deductive thematic analysis to identify key themes. RESULTS: Barriers to measles vaccination identified in this study were premised around six themes including: (i) availability of supplies and stock management, (ii) health worker attitudes and workload, (iii) financing of vaccination outreach activities, (iv) effectiveness of duty rosters (i.e., health workers' working schedules), (v) community beliefs, and (vi) accessibility of healthcare facilities. Respondents reported frequent vaccine supply disruptions, lack of resources to facilitate transportation of health workers to communities for outreach events, and health centre staffing that did not adequately support supplemental vaccination activities. Furthermore, community dependence on traditional medicine as a substitute for vaccines and long distances traveled by caregivers to reach a health facility were mentioned as barriers to vaccination uptake. CONCLUSIONS: Health system barriers limiting vaccination uptake were primarily logistical in nature and reflect inadequate resourcing of immunization efforts. At the same time, local beliefs favouring traditional medicine remain a persistent cultural barrier. These findings suggest an urgent need for more efficient supply management practices and resourcing of immunization outreaches in order to achieve the Uganda Ministry of Health's targets for childhood immunization and the prevention of disease outbreaks.",
+    "full_text": "Walekhwa et al. BMC Infectious Diseases (2022) 22:589 https://doi.org/10.1186/s12879-022-07579-w\n\nRESEARCH\n\nOpen Access\n\nGaps in measles vaccination coverage in Kasese district, Western Uganda: results of a qualitative evaluation\nAbel Wilson Walekhwa1,2*, David Musoke2, Aisha Nalugya2, Claire Biribawa2,7, Godfrey Nsereko7, Solomon Tsebeni Wafula2, Brenda Nakazibwe2, Mary Nantongo3, Doreen Awino Odera4, Achangwa Chiara5, Ross Mathew Boyce1,6 and Edgar Mugema Mulogo1\u200a\n\nAbstract\u2003\nBackground:\u2002 Despite the availability of a highly effective vaccine, measles remains a substantial public health problem in many countries including Uganda. In this study, conducted between June\u2013August 2020 following a local outbreak, we sought to explore the factors that could affect measles vaccination coverage in rural western Uganda.\nMethods:\u2002 We conducted a descriptive study using qualitative data collection approaches in the Kasese district. The research team utilized purposive sampling to identify and select participants from the public health sector and district government. We conducted key informant interviews (KII) and one focus group discussion (FGD). Responses were recorded using portable electronic devices with the FGD and KII guide installed. Interviews were conducted at the health centre and district headquarters. Data was coded and analysed using ATLAS.ti version 8 software through deductive thematic analysis to identify key themes.\nResults:\u2002 Barriers to measles vaccination identified in this study were premised around six themes including: (i) availability of supplies and stock management, (ii) health worker attitudes and workload, (iii) financing of vaccination outreach activities, (iv) effectiveness of duty rosters (i.e., health workers\u2019 working schedules), (v) community beliefs, and (vi) accessibility of healthcare facilities. Respondents reported frequent vaccine supply disruptions, lack of resources to facilitate transportation of health workers to communities for outreach events, and health centre staffing that did not adequately support supplemental vaccination activities. Furthermore, community dependence on traditional medicine as a substitute for vaccines and long distances traveled by caregivers to reach a health facility were mentioned as barriers to vaccination uptake.\nConclusions:\u2002 Health system barriers limiting vaccination uptake were primarily logistical in nature and reflect inadequate resourcing of immunization efforts. At the same time, local beliefs favouring traditional medicine remain a persistent cultural barrier. These findings suggest an urgent need for more efficient supply management practices and resourcing of immunization outreaches in order to achieve the Uganda Ministry of Health\u2019s targets for childhood immunization and the prevention of disease outbreaks.\nKeywords:\u2002 Measles, Vaccination, Barriers, Rural district, Uganda\n\n*Correspondence: wabelwilson@gmail.com\n1 Department of Community Health, Faculty of Medicine, Mbarara University of Science & Technology, P.O. BOX 1410, Mbarara, Uganda Full list of author information is available at the end of the article\n\nBackground Global estimates suggest that approximately 14 million children under 5 years of age live areas highly endemic for measles and 8\u201312 million children remain unvaccinated\n\n\u00a9 The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://\u200bcreat\u200biveco\u200bmmons.\u200borg/\u200blicen\u200bses/\u200bby/4.\u200b0/. The Creative Commons Public Domain Dedication waiver (http://\u200bcreat\u200biveco\u200b mmons.\u200borg/\u200bpubli\u200bcdoma\u200bin/\u200bzero/1.\u200b0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 2 of 9\n\n[1]. In 2018 alone, models estimate that measles affected nearly 10 million children; resulting in 140,000 deaths globally [2]. The African region remains among the most impacted continents with 1,759,000 total cases and 52,600 deaths reported in 2019 [3], although recent trends continue to show increases in all regions [4]. The increase was particularly pronounced in the WHO Africa Region, however, which recorded a 700% increase in cases over the first 4 months of 2019, highlighting that measles outbreaks remain a substantial and growing public health challenge [5, 6].\nIn Uganda, measles remains a public health threat. For example, in 2018, 46 of the 146 districts reported outbreaks of the disease [7, 8]. Economically, measles costs over $135,627 per year in societal costs which translates to approximately $44 lost by each affected household per year [9]. The disease has repeatedly ranked among Uganda\u2019s top ten epidemic diseases risks registered by the Ministry of Health (MoH) as reported by the Emergency Outbreak Center (EOC) [10, 11].\nThe current Uganda National Expanded Program on Immunization (UNEPI) schedule recommends a single measles-rubella vaccination (MRV) administered to infants at 9 months of age. This policy is in contrast to WHO guidelines, which recommend children receive two doses of measles vaccine in the 1st year [12]. Despite the WHO policy Uganda, like many low-income countries, faces logistic and financial barriers to implementation of the two-dose vaccination schedule.\nThe vaccination program in Uganda is delivered in two ways: (i) a health facility-based model in which caregivers bring their children to the health facility and (ii) outreach programmes in which health workers move to central locations like schools, churches, or market areas to administer vaccines. Community leaders including local council leaders and community health extension workers usually communicate the scheduling of such events to mobilize residents [13]. Participating health facilities may be public or private, but the vaccines are supplied by the government of Uganda which is responsible for the procurement, transport and cost of administration. Uganda has a decentralized healthcare system run by different local governments across the country and there are various levels of these health care facilities ranging from community health worker to health care II, III, IV, general hospital, national referral hospitals, all of which offer vaccination services (Mafigiri, 2021).\nFrom December 2018 through October of 2019, Bugoye Health Centre III (BHC) in Bugoye sub-county, located in the Kasese district of Western Uganda, reported a large number of suspected measles cases [14]. Measles vaccination coverage among children at least 12 months of age for Kasese district and Bugoye HC III in 2018 was 72%\n\nand 69% respectively (HMIS, 2018), which is well below the national Ministry of Health targets of 95%. Therefore, our goal was to investigate the barriers to measles vaccination in Bugoye subcounty. To achieve this goal, we purposively interviewed healthcare providers and district leaders who have a direct role in planning and supervising immunization activities. We used a qualitative approach so as to understand the explanations and detailed narratives\u2014in terms of health workers\u2019 and those of district leaders\u2019 perspectives\u2014associated with the observed trends in our prior work, which indicated that female, and older children as well as those not vaccinated were more likely to get measles [14]. Understanding the perspectives of these stakeholders on the barriers to measles immunization among children would inform the planning authorities on strategies to mitigate future measles outbreaks through increased vaccine uptake. Furthermore, this information may be relevant to efforts to increase the uptake of other vaccines.\nMethods\nStudy design and setting A descriptive study utilizing qualitative data collection methods was conducted in Kasese district from June 15th to August 7th 2020. Participants included health workers at BHC and officials at Kasese district headquarters. Kasese borders the Democratic Republic of Congo (DRC) as represented in own map drawn (Fig. 1). The subcounty was selected following reports of measles cases presenting to local health centres [14]. Administratively, Bugoye sub-county is comprised of five parishes and 35 villages with a population of approximately 50,000, one-quarter of whom are children under 5 years of age [15]. The sub-county has a total of eight health facilities including seven level II facilities and two-level III health centres, one of which is a private-not-for-profit (PNFP) facility. BHC is the only public level III health facility in the sub-county. Level III health centres are staffed by a senior clinical officer, a clinical officer, laboratory technicians, health assistants, nurses, and midwives. Available services include antenatal care, an immunization clinic, a general outpatient clinic, an antiretroviral clinic for people living with HIV, and small inpatient and maternity wards. The facilities offer immunization services through two models: (i) facility based during week days (e.g., Monday to Friday) and (ii) outreaches (i.e., communitybased) which are organized once a month. MRV is available at all vaccination sessions for eligible children (i.e., \u2265\u20099 months of age).\nStudy participants, sampling, data collection and tools A purposive sampling approach was used to identify, recruit, and enroll study participants. We targeted\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 3 of 9\n\nFig.\u202f1\u2002 Map of Bugoye subcounty, Kasese district\n\nindividuals that play a central role in planning, execution and evaluation of immunization activities at the sub-county and district level. Interviews and focusgroup discussions were conducted using KII/FGD guide (Additional file 1), and collected data was analysed through deductive thematic analysis A total of seven key informants, including Resident District Commissioner, District Health Officer, District Expanded Programme on Immunization (EPI) focal person, District Surveillance Officer, In-charge and EPI of BHC and sub-county village health team (VHT) coordinator. Furthermore, a total of seven health workers at BHC were selected purposively to participate in FGD. These included the health workers in maternity and outpatient department (OPD). We elicited responses on the frequency of immunization activities in the area, vaccine supply, interest and competency of the health workers and transport means for immunization supplies. The KII were conducted at the participants\u2019 offices. The FGD were held at BHC after working hours. The participants had been informed about this FGD\n\nand they participated in scheduling it on the day they preferred.\nData management and analysis Interviews were conducted in English and were recorded on Android-equipped smart phones and tablet device with the KI and FGD guides installed. Each interview recording was transcribed verbatim by the trained research assistant at BHC. For convenience to the participant, the data collection was conducted at their place of work at a time that fitted their schedules. A total of 73 codes for KIs and 17 codes for FGD were initially generated. Additionally, patterns and themes that resulted from the codes that converged and diverged were recorded. The categorized data was used to identify the main themes of the results.\nCodes were developed from objective of the study and transcribed data, and then entered into the ATLAS. ti version 8 software for analysis. The software developed codes which were reviewed by the research team and enabled categorization of the study findings. Using\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 4 of 9\n\ndeductive thematic analysis, the categorized data was used to develop main themes which made the final results of our study. A total of six key themes emerged from this data analysis. These included; (1) availability of supplies and management, (2) healthcare worker attitude and workload, (3) financial remuneration for vaccination activities, (4) effectiveness of duty rosters, (5) community beliefs and (6) distances to healthcare facilities. These themes were then documented as key outcomes of our study and consequent quotes attached to back up our study findings.\nQuality control and assurance Research assistants were trained prior to data collection to ensure that they were well conversant with the study protocol, ethics of human subjects research, and data collection methods. The data collection tools were pretested with volunteers in a neighboring sub county in order to ensure that they were understood and elicited appropriate responses. The transcripts were then proofread by the corresponding author to ensure fidelity. All nine audio recordings were validated by a member of the research team who had expertise in conducting qualitative studies.\nResults\nSocio\u2011demographic characteristics of study participants Participants were mostly male (9/14), between 40\u201349 years of age (6/14), and had attained at least an ordinary diploma (6/14). The majority of these participants had worked for over 20 years in the Uganda civil service (7/14), and identified with the Anglican religion (Table 1).\nA total of KI-73 codes and 17-FGD codes were generated. These generated 13 convergent sub-themes and four divergent sub-themes (Additional file 2). Upon thorough review by the research team, a total of 6 key themes emerged (Table 2).\nThe results are presented under six key themes including; availability of supplies and management, health workers\u2019 attitude, lack of financial remuneration for vaccination activities, ineffective duty rosters, community beliefs and long distances to healthcare facilities.\nAvailability of measles vaccines and supplies management Whereas some participants reported that vaccines are always available at the healthcare facilities and district store, a large number mentioned that stock-outs were common. It was also noted that some refrigerators in health facilities were faulty, which adversely affected the storage of the vaccines. In addition, some participants reported that storage space is a major challenge that\n\nTable\u202f1\u2002 Socio-demographic characteristics of study participants\n\nVariable\n\nAttribute\n\nFrequency (n\u2009=\u200914)\n\nSex\n\nMale\n\n9\n\nFemale\n\n5\n\nAge category (years) 30\u201339\n\n3\n\n40\u201349\n\n6\n\n50 and above\n\n5\n\nEducation level\n\nOrdinary secondary school level 2\n\nCertificate level\n\n2\n\nOrdinary diploma\n\n6\n\nUndergraduate degree\n\n3\n\nMaster\u2019s degree\n\n1\n\nYears in service (years) 0\u20139\n\n3\n\n10\u201319\n\n4\n\n20 and above\n\n7\n\nReligious preference Anglican\n\n10\n\nSeventh Day Adventist\n\n2\n\nCatholic\n\n2\n\nlimited the ability to maintain sufficient supplies of vaccines at health facilities or district stores.\n\u201cI remember vaccines were present but of course on some one or two days, we would have stock outs which was mostly due to limited space for storing the vaccines, such as fridges. In some instances, the fridges are faulty which also contributes to the stockouts.\u201d (Female Healthcare worker, BHC) \u201c\u2026We have vaccine stock outs and power shortages on some days of the months, especially during the rainy season. As a result, a health worker cannot conduct vaccination outreaches in this place. Solar power is not enough to run the EPI fridge since it\u2019s big. We end up not conducting outreaches.\u201d (Healthcare worker, BHC (KI 111).\nHealth workers\u2019 attitude The study revealed that the health workers generally had a negative attitude toward engaging in vaccination activities. Respondents noted that due to the low staffing and high volume of work at the healthcare facility, vaccination activities were lower priority.\n\u201cOur services were okay and people still like them. Bugoye Health Centre is on top of our map as a district. However, the attitude of health workers is at 50%. Health workers are demoralized due to the overwhelming work. Sometimes staff are few at the facility and vaccination is done by those few.\u201d (District Health Team member (KI VI) \u201cI will strongly say the poor attitude of health work-\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 5 of 9\n\nTable\u202f2\u2002 Summary of themes and subthemes Codes generated for both key informants and FGDs\nKII-73 codes FGDs-17 codes\n\nSub themes that emerged\n13 convergent themes 04 divergent themes\n\nTop six themes\n(1) Availability of supplies and management, (2) Healthcare worker attitude and workload, (3) Financial remuneration for vaccination activities, (4) Effectiveness of duty rosters, (5) Community beliefs and (6) Distances to healthcare facilities\n\ners and heavy workload hinders vaccination in health centres\u201d (Healthcare worker, BHC (KI 111)\nLack of financial remuneration for vaccination activities It was noted that the lack of transportation hindered community outreach activities. Respondents mentioned that outreaches were often cancelled due to lack of transportation. Furthermore, it was suggested that inadequate funds were allocated to support other elements of community outreaches.\n\u201cThe communities were used to us picking the vaccines and finding them in the designated outreach areas. However, some outreaches did not happen during that time because we lacked transport to go and visit those places.\u201d (District Health Team member (KI 1) \u201cWhat I know is that we had vaccines in stock but the challenge was that the motorcycle responsible for transporting staff was not available, so no outreaches were conducted.\u201d (District Health team member) \u201cPoor health worker financial remuneration\u2026\u2026 Immunization activities only rely on Primary Health Care (PHC) funds which mostly cover administrative work. A few outreaches get funds, and even when they do, they are inadequately facilitated\u201d (Healthcare worker, BHC (FGD 4))\nIneffective duty rosters at the healthcare facility Respondents in this study reported that the duty rosters (i.e., schedule of work for the health workers) in place did not specify the team in charge of immunization or allocate health workers to vaccination duty. Duty rosters enable health workers work in a systematic manner ensuring equitable distribution of available human resource to have all services provided in a health facility. This could have negatively impacted immunization activities. Effective duty rosters are important for productivity, healthcare worker attendance, employee motivation and performance tracking.\n\u201c\u2026static immunization is done from Monday to Friday here. However, there is no designated team on\n\nImmunization and the duty roster doesn\u2019t specify who is to be in EPI for a particular day. This is a challenge.\u201d (Healthcare worker, BHC)\nCommunity beliefs Respondents noted that some communities used locally available herbs as a substitute for vaccines and only resorted to healthcare facilities when the herbs failed to work. This presented a substantial barrier to uptake because these individuals did not interact with health facilities or outreach events.\n\u201cSome community members use herbs as a substitution for vaccines. They still believe in the effectiveness of herbs and only run to the healthcare facility for assistance when these herbs fail to work. This is why they don\u2019t come for community outreaches\u201d (Male health worker, FGD2 at BHC)\nLong distances to the healthcare facility The study also revealed that long distances from the healthcare facility was a barrier to vaccination. Respondents mentioned that some mothers reside in parishes far away from the healthcare facility which discouraged them from traveling for vaccination.\n\u201cI will strongly say that the long distances moved by mothers especially those that stay in Katooke, Muhamba parishes and others that are far away, is a challenge.\u201d (Male, healthcare worker BHC (KI 1))\nThe study participants also suggested solutions to the observed challenges including: (1) increased budget allocation for the health workers, (2) increased supervision for EPI activities by the district health office, (3) conducting immunization activities in the planned outreaches, (4) organizing refresher trainings for health workers for immunization tracking, planning and documentation, and (5) intensifying community sensitization and mobilization for vaccination programmes in the sub-county.\n\u201cThere is need to intensity the mobilization and sensitization of the community members and care givers about how they can identify these diseases and\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 6 of 9\n\noutbreaks such that they can report them early.\u201d, FGD3 \u201cThere is need to intensify the mobilization and sensitization of the community members and care givers about how they can identify these diseases and outbreaks such that they can report them early\u201d, FGD3 \u201cIncrease financial remuneration for Immunization activities\u201d KI4 \u201cI would strongly recommend for more supervision of immunization activities in the district\u201d.KI3 \u2026\u201cyes, the issue of fridges and storage system needs to be looked into\u201d KI3 \u201cWe need to improve supplies to ensure constantly there, ensure we maintain cold chain more so use of solar system in most of the facilities as it reliable\u201d KI2 \u201ccontinue with intensive mobilization for EPI activities using VHTs including outreaches and even static, maintain the tracking system for the mothers and children who could be missing on the antigens, continue using the LC1s strategy\u201d KI1 \u201cNeed for capacity building of health workers in data management, also emphasize follow up and tracing of the mothers who are due to Measles vaccines and other antigens since some children disappear, develop a system of following all children from the time of birth to a time of last doses including Vitamin A and deworming. Conduct mass measles campaigns in this sub county to bring services nearer to the people here\u201d. KI4\nDiscussion Our study highlights the health system barriers that affect measles vaccination coverage including inconsistent vaccine stocks, inadequate cold chain storage, missed opportunities for community outreach, lack of dedicated transportation and relatively low prioritization of immunization activates among staff. Overall, these findings suggest an urgent need to better organize and resource vaccination activities in order to achieve set vaccination targets in rural areas of Uganda.\nVaccine stock outs were reported as one of the primary barriers to effective delivery of immunization services in the study area. Stock outs occur when vaccine supplies are depleted either arising from an unexpectedly high number of recipients or the orders underestimate the eligible underlying population. Many times, the children affected by stock outs are those who are already underserved due to distance to health facility. Vaccine stock outs have an established impact on immunization outputs as the children who are expected to get such\n\nvaccines at such slated times often miss out waiting for the delivery and distribution of the vaccines [16]. Some of the challenge of vaccine stock outs could also be due to the small amount of storage capacity that some facilities have, including at the district level, which is responsible for distribution of the vaccine. Another factor contributing to the vaccine stock outs could be the delays by the national supply system through the National Medical Stores (NMS) to supply vaccines. This impediment could also be coupled with the inefficiant management at both the facility and the District Local Government where requisitions could take long to reach NMS culminating into delays. Regardless of the source, caregivers experience frustration after traveling long distances only to be told that the vaccine is not available. Such experiences may also contribute to lower enthusiasm for future care seeking due to lack of trust in the vaccine supply.\nIn addition, the UNEPI policy of unpreserved vaccine which must be discarded at the end of the session or 6 h after reconstitution, should be modified so as to optimize this vaccine\u2019s availability, such that vials can in practice be opened whenever an eligible child presents. This policy has direct contribution of vaccination practice at health facility level for example, the ordering of vaccine in 5-dose rather than 10-dose vials increases the health workers\u2019 hesitation to open a vial for fear of being blamed for wastage, thereby reducing missed opportunities and raising timely coverage. These health workers tend to request caretakers to either wait for others or to go and come back on a particular where they anticipate more numbers of children to be vaccinated such that the opened vials serve more children. This is a common phenomenon in low- and middle-income countries which have limited budget allocation for vaccines but also low technological access that limits the planning and prediction of such supplies [17]. This study findings are in line with other studies conducted at Mulago National Referral Hospital, Uganda that showed that majority of the new borns miss immunization due to vaccine stock outs [18, 19]. This calls for budget increase for EPI activities at the district health offices, and proper planning by stakeholders like national medical stores (NMS), ministry of health and district health department to minimise the occurrence of stock outs [17].\nThe above factors outlined issues of vaccine supply. However, barriers also exist among the health care workers themselves, ranging from description of burn out, increased workloads, and inconsistencies in pay. Low prioritization of vaccine services among health care workers was one of the reported barriers in our study. Multiple health care workers reported having lost their motivation to work, while those that were working were the ones that needed the money despite being burned out.\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 7 of 9\n\nWe note that this study was conducted during the first wave of the novel coronavirus pandemic, which may have exacerbated these issues. During this period health workers stretched their routine activities to support COVID19 response. For example, some staff were redeployed to other health facilities that needed additional support. With some staff leaving due to burn out, the staff who have stayed are more reliant on pay to stay engaged. The sign of exhaustion among health workers was realized as many participants kept pointing out their frustrations arising from non-payments due to delays in release of Primary Health Care (PHC) funds. Although increased funding would be helpful, other non-financial incentives and improvements should also be explored. Staff performance could be motivated by promotions, further studies, more capacity building sessions. Our findings are in line with other studies done in Uganda that showed that health workers\u2019 attitude is critical for better immunization activities [20, 21]. The district leadership and management of BHC should explore various non-financial motivation mechanisms for their staff such that there is continuity of service delivery even in absence of PHC funds. Furthermore, there is need to draw out a clear work schedule for the health facility staff spelling out when they will be working in the EPI Section.\nThe absence of transport during the time when the district reported measles outbreak [14] was reported as a hindrance to increasing the accessibility of immunization activities. Movement of health workers from their respective health facilities to different outreach sites was difficult. Participants reported negotiating credit with local private transport companies including commercial motorcycle drivers, while awaiting the release of PHC funds for use as a refund. According to Ministry of Health EPI guidelines, the transportation of vaccines from the district vaccine store to lower health facilities is the mandate of the district health office which can be achieved if transport means and fuel is availed through PHC funds [22]. Our study revealed that this transportation of vaccines was affected due to lack of available transport facilities. This could be possible due to technical delays in the release of PHC funds which affects the fuelling of available vehicles. Transportation challenges could also be associated with diversion of EPI vehicles\u2014commonly referred to as \u201cGAVI vehicles\u201d\u2014for other administrative duties in the district hence affecting the immunization activities. Uganda being a low-income country, resources such as vehicles are shared by the district leaders depending on the priority demand at a particular time.\nThe rugged terrain of the area greatly limits the accessibility of some villages. Many caregivers cannot climb to high terrain areas where the outreach sites are located which greatly contributes to children missing their\n\nscheduled vaccinations thus increases the numbers of unvaccinated thus increases the chances of measles outbreaks. This finding is in line with other studies conducted in Uganda where it was clearly noted that physical barriers like hilly/mountainous geographical locations and road terrain greatly limited the accessibility of services for children under 5 years [13, 20, 21, 23]. This calls for geographical context planning for immunization services to cater for such difficulties such that outreach sites could be located in such places where critical need is known.\nThe findings showed that less attention was given by health workers to tracking the missed opportunities for measles immunization even when children were taken to the health facilities for other services. Health workers are few and the idea of tracking the vaccination status of children during routine service provision is not taken as a priority. In the same vein, there are no specific reminders to different caretakers on their scheduled dates although the community health workers normally remind many caretakers in totality on the need to go for health services in various health facilities. In addition, caretakers and caregivers forget to bring their eligible children for the measles vaccination at 9 months. This could be attributed to inconsistencies in the payment of staff for immunization activities, which (as reported by various stakeholders) results in less prioritization of those immunization activities. This poor remuneration of these health workers could also explain why many of them were reported to have lost interest in EPI activities leaving this to the community health workers (locally referred to as Village Health Teams) who may not be technical enough to track the missed children. Furthermore, the health workers also asserted that their large workload reduces their ability to concentrations on tracking children who miss particular vaccines, including the measles vaccine. This study finding is in agreement with previous studies done in similar regions (e.g., Hoima district) that showed that staff overload affected the attention to immunization activities which contributed to measles outbreak [13]. There should also be deliberate efforts to identify the children who missed the vaccination through review of health facility records, follow up of caregivers of children who are due for MRV leveraging local leaders and public communication (i.e., radio) to remind the caregivers about the need to complete the vaccination for their children. Furthermore, reminding caregivers of the importance of maintaining immunization cards may prove useful, as these cards show the dates for subsequent visits.\nOur study has a number of strengths. We interviewed the staff who are directly involved in the provision of measles vaccination services which helped to understand real issues they face. We also targeted both\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 8 of 9\n\nthe stakeholders in management positions and those of the implementing staff which, which method gave a broad picture to the perceived barriers. The study also employed two methods (e.g., KIIs and FGD) that helped to deeply explore the subject matter. Lastly, we captured ideas and potential solutions to improve measles vaccination coverage that stakeholders can utilize. However, this study also had limitations including the relatively small number of interviews and FGDs, which was primarily due to the fact this was a graduate student research project with a modest budget. We also interviewed only one VHT who was already involved in other activities at BHC and his views many have not be representative of other VHTs in communities. We also did not interview caregivers so our results reflect only those of the service providers, rather than the end-users. Lastly, we did not interview the Ministry of Health and NMS staff who could expand on issues related to vaccination supplies and budget allocation for the district.\nConclusion Logistical issues and inadequate resources, including vaccine supply, clinic staffing, and transportation for outreach events, represent substantial barriers to effective delivery of vaccination services in rural western Uganda. In addition to the solutions proposed herein, we recommend further study of (i) the existing surveillance capacities in the district that could be leveraged to identify unvaccinated children, (ii) perceptions of caregivers and caretakers on vaccination programmes in the district, (iii) novel interventions to remind caregivers of vaccination schedules.\nAbbreviations BHC: Bugoye Health Centre III; DHO: District Health Officer; DSO: District Surveillance Officer; EPI: Expanded Programme on Immunization; MoH: Ministry of Health; PHC: Primary Health Care; UNEPI: Uganda National Expanded programme on Immunisation; UVRI: Uganda Virus Research Institute; VHT: Village Health Teams; WHO: World Health Organization.\nSupplementary Information\nThe online version contains supplementary material available at https://\u200bdoi.\u200b org/\u200b10.\u200b1186/\u200bs12879-\u200b022-\u200b07579-w.\nAdditional file 1. Key informant/FGD guide.\nAdditional file 2. Summary of subthemes.\nAcknowledgements We want to thank; Julius Mutoro for his efforts during data collection, David Ayebare for his guidance during data cleaning, coding and analysis, Kasese district leadership, staff of BHC for their support in availing and reviewing existing district cold chain data. We also thank the respondents from Kasese district who accepted to participate in our data collection exercise. To the staff of MUST-UNC we are grateful for the support offered during line listing\n\nof measles cases and data collection for this project. We also thank the MUSTUNC collaboration for the financial support during data collection.\nDual publication The map of the study was published in this journal in the quantitative section of this similar study. The results/data in this manuscript have not been published elsewhere, nor are they under consideration (from you or one of your Contributing Authors) by another publisher.\nAuthor contributions AWW, DM, EMM, RMB-conceptualized the idea, developed the protocol, processed and obtained ethical approvals, collected data and developed first draft of manuscript. AN, STW, BN, MN, AC, CB, GN, DAO-Data cleaning, validation, analysis, and manuscript writing. All authors read and approved the final manuscript.\nFunding The data collection and results dissemination activities of this project were supported with funding from MUST-UNC collaboration housed at Mbarara University of Science and Technology, Department of Community Health (https://\u200bwww.\u200bmust.\u200bac.\u200bug/\u200bcolla\u200bborat\u200bions/\u200bthe-\u200bmust-\u200bbugho\u200bye-\u200bcommu\u200bnity-\u200b health-\u200bcolla\u200bborat\u200bion/\u200bthe-\u200bmust-\u200bunc-\u200bresea\u200brch-\u200bcolla\u200bborat\u200bion/).\nAvailability of data and materials All data supporting our findings are contained in the paper. There are no restrictions to data sources, however, details of the full data may be accessed through the corresponding author; Mr. Abel Wilson Walekhwa, Email: wabelwilson@gmail.com.\nDeclarations\nEthics approval and consent to participate Written informed consent was obtained from all study participants. All experimental protocols were approved by Mbarara University of Science and Technology research ethics committee (MUST-REC) reference number MUREC 1/7 and Uganda National Council for Science and Technology (Reference number HS760ES). In addition, written permission to conduct the study was obtained from Kasese District Local Government through the office of the District Health Officer. The collected data was only accessed by the research team and it was always kept under lock and key. The research assistants signed confidentiality agreements in bid to protect the data from leaking to the public. All methods were carried out in accordance with relevant guidelines and regulations.\nConsent for publication Not applicable.\nCompeting interests I declare that Ross M. Boyce is an Associate Editor of BMC-infectious diseases journal. Other authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\nAuthor details 1\u200aDepartment of Community Health, Faculty of Medicine, Mbarara University of Science & Technology, P.O. BOX 1410, Mbarara, Uganda. 2\u200aSchool of Public Health, College of Health Sciences, Makerere University, P.O. BOX 7072, Kampala, Uganda. 3\u200aSchool of Biomedical Sciences, College of Health Sciences, Makerere University, P.O. BOX 7072, Kampala, Uganda. 4\u200aDepartment of Public Health, North Dakota State University, Fargo, ND 58102, USA. 5\u200aDepartment of Public Health and Hygiene, University of Buea, Cameroon, P.O. Box 63, Buea, Cameroon. 6\u200aDepartment of Medicine, Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. 7\u200aMinistry of Health, Uganda, P.O BOX 7272, Kampala, Uganda.\nReceived: 27 June 2021 Accepted: 29 June 2022\n\nWalekhwa et al. BMC Infectious Diseases (2022) 22:589\n\nPage 9 of 9\n\nReferences 1. Takahashi S, et al. The geography of measles vaccination in the African\nGreat Lakes region. Nat Commun. 2017;8:15585. 2. Nwankwo A, Iboi EA, Okuonghae D. Impact of vaccine failure on the\ntransmission dynamics of measles in Nigeria. medRxiv. 2021. 3. WHO. More than 140,000 die from measles as cases surge worldwide.\nGeneva: World Health Organisation; 2019. 4. Mahase E. Measles cases rise 300% globally in first few months of 2019. Br\nMed J. 2019;365:l1810. 5. Melenotte C, et al. Atypical measles syndrome in adults: still around. BMJ\nCase Rep. 2015;2015:bcr2015211054. 6. Abad C, Safdar N. The reemergence of measles. Curr Infect Dis Rep.\n2015;17(12):51. 7. Monitor D. 30 people hospitalised following measles outbreak. 2019. 8. Organization WH. Weekly bulletin on outbreak and other emergencies:\nweek 27: 30 June\u201306 July 2018. 2018. 9. De Broucker G, et al. The economic burden of measles in children under\nfive in Uganda. Vaccine: X. 2020;6:100077. 10. MoH. Weekly epidemiological bulletin. Ministry of Health; 2021 11. Biribawa C, et al. Measles outbreak amplified in a pediatric ward: Lyan-\ntonde District, Uganda, August 2017. BMC Infect Dis. 2020;20:1\u20138. 12. WHO. Measles vaccines: WHO position paper\u2014April 2017. Wkly Epide-\nmiol Rec. 2017;92(17):205\u20138. 13. Malande OO, et al. Barriers to effective uptake and provision of immuniza-\ntion in a rural district in Uganda. PLoS ONE. 2019;14(2):e0212270. 14. Walekhwa AW, et al. Measles outbreak in Western Uganda: a case\u2013control\nstudy. BMC Infect Dis. 2021;21(1):596. 15. UBOS. National population and housing census 2014: provisional results.\nKampala: UBOS; 2014. 16. Gooding E, Spiliotopoulou E, Yadav P. Impact of vaccine stockouts on\nimmunization coverage in Nigeria. Vaccine. 2019;37(35):5104\u201310. 17. Uyar K, et al. Forecasting measles cases in Ethiopia using neuro-fuzzy\nsystems. In: 2019 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT). IEEE. 2019. 18. Nakatudde I, et al. Vaccination timeliness and associated factors among preterm infants at a tertiary hospital in Uganda. PLoS ONE. 2019;14(9):e0221902. 19. Nabirye J, et al. Health system factors influencing uptake of Human Papilloma Virus (HPV) vaccine among adolescent girls 9\u201315 years in Mbale District, Uganda. BMC Public Health. 2020;20(1):171. 20. Faith MR, et al. Factors associated with the utilization of inactivated polio vaccine among children aged 12 to 23 months in Kalungu District, Uganda. Health Policy Plan. 2020;35(Supplement_1):i30\u20137. 21. Kwikiriza NM, Bajunirwe F, Bagenda F. Geographic location of health facility and immunization program performance in Hoima district, western Uganda, a facility level assessment. 2020. 22. Kamya C, Abewe C, Waiswa P, Asiimwe G, Namugaya F, Opio C, Ampeire I, Lagony S, Muheki C. Uganda\u2019s increasing dependence on development partner\u2019s support for immunization\u2013a five yearresource tracking study (2012\u20132016). BMC Public Health. 2021;21(1):1\u201311. 23. Allen EP, et al. Health facility management and access: a qualitative analysis of challenges to seeking healthcare for children under five in Uganda. Health Policy Plan. 2017;32(7):934\u201342.\nPublisher\u2019s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\nReady to submit your research ? Choose BMC and benefit from:\n\u2022 fast, convenient online submission \u2022 thorough peer review by experienced researchers in your \ufb01eld \u2022 rapid publication on acceptance \u2022 support for research data, including large and complex data types \u2022 gold Open Access which fosters wider collaboration and increased citations \u2022 maximum visibility for your research: over 100M website views per year\nAt BMC, research is always in progress.\nLearn more biomedcentral.com/submissions\n\n\n",
+    "authors": [
+      "A. W. Walekhwa",
+      "D. Musoke",
+      "A. Nalugya",
+      "C. Biribawa",
+      "G. Nsereko",
+      "S. T. Wafula",
+      "B. Nakazibwe",
+      "M. Nantongo",
+      "D. A. Odera",
+      "A. Chiara",
+      "R. M. Boyce",
+      "E. M. Mulogo"
+    ],
+    "doi": "10.1186/s12879-022-07579-w",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "JF48IWZU",
+    "title": "Comparing full immunisation status of children (0-23 months) between slums of Kampala City and the rural setting of Iganga District in Uganda: a cross-sectional study",
+    "abstract": "BACKGROUND: Immunisation remains the most cost-effective public health intervention in preventing morbidity and mortality due to Vaccine-Preventable Diseases (VPDs). The study aims to compare the differences in immunisation coverage amongst children aged 0 to 23 months living in slums of Kampala city and Iganga as rural districts in Uganda. METHODS: This study utilises data from a cross-sectional survey done in 2019 in the slums of Kampala City and the rural district of Iganga within the Health and Demographic Surveillance Site (HDSS). It included 1016 children aged 0-23 months and their parents. A logistic regression model was used to analyse the relationship between multiple independent variables and the binary dependent variables (fully immunised) using Stata statistical software. The measures of association were odds ratios reported with a corresponding 95% confidence interval. RESULTS: Out of the 1016 participants, 544 participants live in the rural area and 472 participants in the slums. Slums had 48.9% (n\u2009=\u2009231) of fully immunised children whilst rural areas had 43.20% (n\u2009=\u2009235). The multivariate analysis showed that children living in slums are more likely to be fully immunised as compared to their counterparts in rural areas (Odds ratio:1.456; p\u2009=\u20090.033; CI:1.030-2.058). Immunisation coverage for BCG (98.9%), Polio 0 (88.2%), Penta1 (92.7%), and Pneumo1 (89.8%) were high in both settlements. However, the dropout rate for subsequent vaccines was high 17%, 20% and 41% for Penta, pneumococcal and rota vaccines respectively. There was poor uptake of the new vaccines with slums having 73.4% and 47.9% coverage for pneumococcal and rota vaccines respectively and rural areas had 72.1% and 7.5% for pneumococcal and rota vaccines respectively. CONCLUSION: The low full immunisation status in this study was attributed to the child's residence and the occupation of the parents. Lack of education and poor access to messages on immunisation (inadequate access to mass media) are other contributing factors. Educational messages on the importance of immunisation targeting these underserved populations will improve full immunisation coverage.",
+    "full_text": "Jammeh et al. BMC Health Services Research (2023) 23:856 https://doi.org/10.1186/s12913-023-09875-w\n\nBMC Health Services Research\n\nRESEARCH\n\nOpen Access\n\nComparing full immunisation status of children (0\u201323 months) between slums of Kampala City and the rural setting of Iganga District in Uganda: a cross-sectional study\n\nAwa Jammeh1,2*, Michael Muhoozi3, Asli Kulane1 and Dan Kajungu3,4\n\nAbstract\nBackground\u2002 Immunisation remains the most cost-effective public health intervention in preventing morbidity and mortality due to Vaccine-Preventable Diseases (VPDs). The study aims to compare the differences in immunisation coverage amongst children aged 0 to 23 months living in slums of Kampala city and Iganga as rural districts in Uganda.\nMethods\u2002 This study utilises data from a cross-sectional survey done in 2019 in the slums of Kampala City and the rural district of Iganga within the Health and Demographic Surveillance Site (HDSS). It included 1016 children aged 0\u201323 months and their parents. A logistic regression model was used to analyse the relationship between multiple independent variables and the binary dependent variables (fully immunised) using Stata statistical software. The measures of association were odds ratios reported with a corresponding 95% confidence interval.\nResults\u2002 Out of the 1016 participants, 544 participants live in the rural area and 472 participants in the slums. Slums had 48.9% (n\u2009=\u2009231) of fully immunised children whilst rural areas had 43.20% (n\u2009=\u2009235). The multivariate analysis showed that children living in slums are more likely to be fully immunised as compared to their counterparts in rural areas (Odds ratio:1.456; p\u2009=\u20090.033; CI:1.030\u20132.058). Immunisation coverage for BCG (98.9%), Polio 0 (88.2%), Penta1 (92.7%), and Pneumo1 (89.8%) were high in both settlements. However, the dropout rate for subsequent vaccines was high 17%, 20% and 41% for Penta, pneumococcal and rota vaccines respectively. There was poor uptake of the new vaccines with slums having 73.4% and 47.9% coverage for pneumococcal and rota vaccines respectively and rural areas had 72.1% and 7.5% for pneumococcal and rota vaccines respectively.\nConclusion\u2002 The low full immunisation status in this study was attributed to the child\u2019s residence and the occupation of the parents. Lack of education and poor access to messages on immunisation (inadequate access to mass media)\n\n*Correspondence: Awa Jammeh awajammeh56@gmail.com\nFull list of author information is available at the end of the article\n\u00a9 The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 2 of 10\n\nare other contributing factors. Educational messages on the importance of immunisation targeting these underserved populations will improve full immunisation coverage.\nKeywords\u2002 Full immunisation status, Slums, Rural area, Individual dose vaccines\n\nBackground Immunisation remains the most cost-effective intervention in public health. Globally, vaccination currently prevents 2\u20133 million deaths annually as more than a billion children were vaccinated in the past decade [1]. There has been a significant reduction in childhood mortalities related to vaccine-preventable diseases (VPDs) from 5.1 million in 1990 to 1.8 million in 2017 [2]. The World Health Organisation (WHO) defines immunisation as the process whereby a person is made immune or resistant to infection, typically by the administration of a vaccine [3] through a process of giving antigenic material. Vaccination coverage on the other hand is defined by the WHO as \u201cthe proportion of a given population that has been vaccinated in a given period. It accounts for each vaccine and, for multi-dose vaccines, for each dose received\u201d [4].\nIn May 2012, the World Health Assembly developed the Global Vaccine Action Plan (GVAP) to help avert millions of childhood mortality attributed to VPDs through equal access to vaccines by 2020 and beyond to tackle inequalities associated with access to life-saving vaccines, especially in Low and Middle Income Countries (LMICs) [5, 6]. Despite the WHO efforts, the coverage is still below the global target [1, 2, 7]. Studies on immunisation coverage in Africa have shown improved but suboptimal coverage in most settings [8\u201310]. Therefore, most countries are not in line with reaching the SDG target 3.2 of reducing childhood mortality [11].\nUganda has also succeeded in reducing child mortality, however, the level is still high at 40.564 deaths per 1000 live births as compared to the global target as of 2022 [12]. Most of the under-5 deaths are due to VPDs making it a public health concern. In 1983, the Ugandan National Expanded Program on Immunisation (UNEPI) was established in partnership with Global Alliance for Vaccines and Immunisation (GAVI) to achieve vaccination goals; following the vaccination schedule on S1 Table 1 [13]. The immunisation services are offered by trained healthcare workers in health facilities and through selected community outreaches. Periodical supplementary immunisation is carried out during outbreaks as well. The immunisation services aim to completely immunise children and women of childbearing age against Diphtheria, Hepatitis B infection, Polio, Whooping cough, Tuberculosis, Tetanus, Measles, Haemophilus influenza and Pneumococcal Infections whilst 10-year-old females and women of reproductive age are immunised against Human Papilloma Virus that causes Cancer of the Cervix and Tetanus [14] (S1 Table 1).\n\nDespite immense efforts by the government of Uganda and its developmental partners to increase immunisation coverage, a decline in immunisation coverage has been noticed. A 90% vaccine coverage is the national acceptable target however, only BCG (96%) is at the national acceptable level whilst the other vaccines are below the accepted levels [15] (S2 Fig. 1).\nAccording to the WHO data on childhood immunisation coverage in Uganda, there has been a further decline in immunisation coverage in the past years and it has significantly dropped from 90% to below 80% as of 2022.\nThe full immunisation status of children cannot be assessed without discussing the factors that determine immunisation. As modernization grows with increasing urbanisation, more people tend to live in urban areas with many being marginalised in the slums and unable to access immunisation services. This is coupled with challenges in reaching remote rural populations with difficulty in technological development for the cold chain into the bargain [16].\nMost studies have shown that the educational level of the parents, awareness of the availability of immunisation services, health-seeking behaviour, wealth index, place of residence, parents\u2019 educational level and occupation and distance to the service delivery points are the main factors contributing to low coverage of full immunisation in children. In Sub-Saharan African settings, these individuals and contextual factors play an important role in improving the immunisation status of children.\nStudies in Sub-Saharan Africa comparing coverage of full immunisation status of children in urban cities, slums and rural settlements showed that slums have better coverage compared to rural settings [17, 18]. Other studies showed significantly poor coverage of full childhood immunisation status in both slums and rural areas [19\u201321]. Many works of literature, however, studied coverage of full immunisation status in urban settings comparing it to the rural area with urban areas having better coverage [10, 22\u201328]. Literature has also shown that children from educated parents [10, 23, 28\u201330], parents with access to information from mass media [9, 31], and parents who are employed coupled [32, 33] with high wealth index [34] had higher chances of being fully immunised as compared to their counterparts.\nTherefore, this study compares the differences in full immunisation status among children aged 0 to 23 months living in slums of Kampala city and Iganga District as rural districts in Uganda. It also highlighted some of the\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 3 of 10\n\nfactors that influence the completion of all immunisation doses in both settings.\nMethodology\nStudy design The study utilised a comparative cross-sectional study between the slums of Kampala city and the Iganga District as the rural setting in Uganda. Unlike a longitudinal study, the data was collected at one point in time. With this type of study, we cannot prove causality [35].\nStudy setting Kampala is the capital city of Uganda and it is the largest city in the country with a population of about 1.5 million in the 2014 census [36]. According to Uganda Demographic and Health Survey UDHS data 2016, Kampala has childhood vaccination coverage of 51% with a high under-5 mortality of 64 deaths per 1000 live births [37].\nSimilarly, Iganga is one of the districts with many rural settings and has a population of over 500,000 people as of the 2014 census [38]. About 38% of people aged 18 and above are illiterate with a higher number being females; and 20% of the people are 5 km away from the nearest public health facility [38]. Generally, under-5 mortality in rural Uganda is at 68 deaths per 1000 live births in 2016 which is higher than the urban under-5 mortality.\nThe slums in this study are in Kampala city. Rapid urbanisation has led to increased slums in many urban settlements. These slums are usually marginalised and lack access to proper health care services. This could be the cause of high childhood mortality in Kampala city [39]. Overall, childhood mortality in Uganda has decreased over the past years. It has declined to 40.564 deaths per 1000 live births in 2022. However, these are far apart from the global targets of meeting SDG 3.2 which aspires to reduce avoidable death in newborns and children under the age of five.\nData source This study relied on a cross-sectional survey done in 2019 in the slums of Kampala city and the rural settings of Iganga District within the Iganga Mayuge Health and Demographic Surveillance Site (HDSS) catchment area. Data collection lasted for about three [3] months. The survey employed a simple random sampling of villages and households with eligible children. Selected households were interviewed to obtain data on the child and their parents. Immunisation status data for a child was taken from the welfare card record and for children without the cards; verbal reports from the mother were used.\nThe Iganga Mayuge Health and Demographic Surveillance System (IMHDSS) operates within the Iganga and Mayuge District in Eastern Uganda covering seven subcounties and 65 villages. The IMHDSS collects bi-annual\n\ndata on basic demographic events (births, migration, marriage and deaths) of all individuals in the demographic surveillance area [40].\nDependent variable The child\u2019s immunisation status was the dependent variable, and the information was either taken from the child\u2019s welfare card or the mothers\u2019 recall if the card was not available. The dependent variable had a binary outcome as to whether the child is fully immunised or not. A child was regarded as fully immunised when he/ she received all the basic vaccines according to age as required by the national schedule. Any child that misses a dose of the vaccine according to age was regarded as not fully immunised. Children without vaccination cards and whose mothers could not remember their vaccination status were also regarded as not immunised.\nIndependent variables The area of residence (urban or rural) was the explanatory variable of interest and a stratifying variable.\nThe predisposing characteristics include the child\u2019s sex, mother\u2019s marital status, parent\u2019s highest level of education attained, mother\u2019s access to mass media and number of children in the family. In the analysis, the observations for the child\u2019s sex were male or female. Marital status was represented as 1\u2009=\u2009in union, 2\u2009=\u2009not in union. The mother\u2019s access to media information was also categorised and analysed as to whether the mother heard information about immunisation campaigns and whether she also heard information about newly introduced vaccines (pneumococcal and rota vaccine).\nParents\u2019 highest educational level attained includes both mother\u2019s and father\u2019s educational level. The observations include none (no education), primary, secondary, and tertiary. Finally, the enabling factors included the child\u2019s place of birth, the child having a health card and parents\u2019 occupation. The child\u2019s place of birth was either in the hospital or outside the hospital. The parents\u2019 occupation includes both the mother and father and was represented as 1\u2009=\u2009small home business, 2\u2009=\u2009market vendors, 3\u2009=\u2009professional, 4\u2009=\u2009other (farmer, unemployed etc.). For health card, it includes children with health card, children without but has mother\u2019s recall and children without health card and no maternal recall.\nA conceptual model first developed in the 1960s by Ronald M. Andersen was adopted in this study. This model aims at denoting the factors that lead to an individual\u2019s use of health services which is divided into three characteristics. The characteristics include predisposing factors, enabling factors and need factors [41]. This model has been widely used in assessing the determining factors of health service utilisation (immunisation coverage as well) [27, 42, 43]. Therefore, this study modified\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 4 of 10\n\nAnderson\u2019s model to characterise its variable. Area of residence (rural/urban) was the explanatory variable of interest in this study as it was comparing the immunisation status of two residential settings. However, covariates were classified and assessed as illustrated in the framework below (Fig. 1).\nData management and statistical analysis Data was electronically collected on the Open Data Kit (ODK) platform. Preliminary data management including cleaning, manipulations and edits were done in Microsoft Excel and statistical analysis was done using Stata version 16.1 software. Descriptive statistics were produced to summarise categorical variables that were presented as frequencies and percentages while continuous variables were presented as means and standard deviations. For coverage of individual vaccines, proportions with their 95% confidence intervals were used to report vaccination coverage in both settlements.\nA logistic regression model was fitted to analyse the relationship between multiple independent variables and the binary dependent variables (fully immunised). A child was classified as fully immunized if they received all the recommended vaccines at an appropriate age. A binomial distribution was adopted for the two possible outcomes either \u201cfully immunised\u201d (success) or \u201cnot fully immunised\u201d (failure). Univariate logistic regression was conducted to establish the relationship between full\n\nimmunisation status and each independent variable. Furthermore, multivariate logistic regression was also conducted to know the effect of the independent variables on the full immunisation coverage. The measures of association in this analysis were odds ratios reported with a corresponding 95% confidence interval and the p-value were used to determine the precision of the estimate and the statistical significance.\nResults\nDescriptive characteristics of the participants The total population of 1016 participants were studied and out of which, 544 participants live in the rural Iganga District and 472 participants are from the urban slums of Kampala city. The percentage of fully vaccinated children in slums and that in rural district presented 53% of the children that participated in the study as shown in S3 Fig. 2. Slums had 48.9% of fully immunised children whilst rural areas had 43.20% as shown in Table 1.\nAs shown in Table 1, mothers in both settlements attained a secondary level of education of 55.5% and 49.14% in rural and slums respectively. A small percentage of the rural dwellers were not educated (3.9%) as well as those living in the slums (6.7%). Similarly, most fathers from both settlements attained a secondary level of education whilst only small percentages were not educated. A greater majority of the parents that participated were doing non-professional jobs (market vendor, small home\n\nFig. 1\u2002 Modified Anderson\u2019s behavioural model for healthcare utilisation. The variables are classified as predisposing characteristics, external environment and enabling factors. It shows how these factors influence the utilisation of health services (i.e. to be fully immunised)\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 5 of 10\n\nTable 1\u2002 Description of study population\n\nCharacteristics\n\nRural, n (%) Urban Slum, n (%)\n\nChild received all vaccines recommended\n\nNo\n\n309 (56.80) 241\n\n(51.06)\n\nYes\n\n235 (43.20) 231\n\n(48.94)\n\nHealth card for the child available\n\nYes, seen\n\n388 (71.32) 289 (61.23)\n\nYes, not seen\n\n150 (27.57) 150 (31.78)\n\nNo\n\n6 (1.10) 33 (6.99)\n\nWhere child was born\n\nHospital\n\n371(68.20) 366(77.54)\n\nOutside hospital\n\n173(31.80) 106(22.46)\n\nMother\u2019s Occupation\n\nSmall home business\n\n135(25.19) 110(24.39)\n\nMarket vendor\n\n32(5.97) 25(5.54)\n\nProfessional\n\n49(9.14) 22(4.88)\n\nOther(specify)\n\n320(59.70) 294(65.19)\n\nFather\u2019s occupation\n\nSmall home business\n\n54(10.25) 78(17.85)\n\nMarket vendor\n\n54(10.25) 35(8.01)\n\nProfessional\n\n143(27.13) 72(16.48)\n\nOther\n\n276(52.37) 252(57.67)\n\nSex of the child\n\nFemale\n\n257(47.24) 243(51.48)\n\nMale\n\n287(52.76) 229(48.52)\n\nMother\u2019s highest level of education\n\nNone\n\n21(3.88) 31(6.68)\n\nPrimary\n\n157(29.02) 142(30.60)\n\nSecondary\n\n300(55.45) 228(49.14)\n\nTertiary\n\n63(11.65) 63(13.58)\n\nFather\u2019s highest level of education\n\nNone\n\n10(2.18) 21(5.48)\n\nPrimary\n\n45(9.83) 70(18.28)\n\nSecondary\n\n233(50.87) 171(44.65)\n\nTertiary\n\n170(37.12) 121(31.59)\n\nMarital status of child\u2019s mother\n\nNot in union\n\n67(13.29) 116(25.44)\n\nIn union\n\n437(86.88) 340(74.56)\n\nRespondent has heard messages about immunisation campaign messages\n\nYes\n\n152(29.63) 135(29.16)\n\nNo\n\n361(70.37) 328(70.84)\n\nRespondent has heard messages about new vaccines-pneumococcal and Rota\n\nYes\n\n202(39.45) 23(4.98)\n\nNo\n\n310(60.55) 439(95.02)\n\nAbbreviation: n-number\n\nTable 2\u2002 Coverage of individual vaccine doses comparing Urban\n\nslums and Rural area\n\nArea of Residence\n\nIndividual doses\n\nUrban Rural Slums covercoverage age (%) (%)\n\nOverall, % (95% Confidence Interval)\n\nBCG\n\n97.9\n\n99.5\n\n98.9 (97.6\u201399.5)\n\nPolio 0\n\n87.5\n\n90.1\n\n88.2 (85.2\u201390.6)\n\nPenta 1\n\n97.1\n\n91.4\n\n92.7 (90.3\u201394.6)\n\nPneumo 1\n\n93.9\n\n89.6\n\n89.8 (87.0\u201392.1)\n\nRota 1\n\n71.5\n\n70.8\n\n67.6 (63.5\u201371.4)\n\nPolio 3\n\n78.7\n\n70.9\n\n72.1 (68.2\u201375.7)\n\nPenta 3\n\n80.2\n\n74.3\n\n74.6 (70.8\u201378.1)\n\nPneumo 3\n\n73.4\n\n72.1\n\n70.1 (66.2\u201373.8)\n\nRota 3\n\n47.9\n\n7.5\n\n26.4 (22.9\u201330.3)\n\nMeasles\n\n53.8\n\n44.4\n\n48.7 (44.8\u201352.9)\n\nAbbreviations: BCG \u2013 Bacillus of Calmette and Guerin; Penta \u2013 Pentavalent vaccine (Diphtheria, Pertussis, Tetanus, Haemophilus Influenza B, Hepatitis B); Pneumo \u2013 Pneumococcal Conjugate Vaccine (PCV); Rota \u2013 Rotavirus\n\nbusiness, farming etc.) in both settlements. A considerable number of mothers (87% and 65% from both rural and slums respectively) were in a union and living with their spouses. However, rural settlers had more women in a union than women not in a union compared to the slum dwellers. A higher percentage of parents in both communities had not heard about messages on immunisation campaigns and messages about the newly introduced vaccines (poor access to mass media).\nA higher percentage of rural dwellers had health cards as compared to their slums counterparts of 71% and 61% respectively. However, hospital births were more in the slums than the rural settings.\nComparing vaccination coverage in slums and rural settlements The individual vaccine dose percentages and the vaccination dropout rate and the uptake of the new vaccines are shown in Table 2. Immunisation status for BCG, Polio0, Penta1, and Pneumo1 were high in both settlements. BCG had 97.9% coverage in slums and 99.5% coverage in rural areas. The slums had a higher coverage of 97.1% for Penta 1 and a lower coverage of 80.2% for Penta 3 indicating a dropout rate of 17%. Similarly, the rural area also had a dropout rate of 17% for the Pentavalent vaccine. In slums, pneumococcal vaccines had a coverage range of 93.3% and 73.4% for pneumo 1 and 3 respectively indicating a dropout rate of about 20%. The rural area had slightly lower percentages for pneumococcal vaccine. The percentages for Rota 1 were low and almost the same in both settlements. However, Rota 3 coverage was low in the slums (47.9%) and significantly lower in rural areas (7.5%). Generally, the Rota vaccine had a high dropout rate of 41%. The percentages for the measles vaccine\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 6 of 10\n\nwere 53.8% and 44.4% for the slums and rural areas respectively.\nThe logistic regression was fitted to assess the association between the dependent variable (Full immunisation according to age) with the selected independent variable. The results interpreted are only for the multivariate model with no interaction terms as shown in Table 3. Children living in slums were 45% more likely to be fully immunised compared to their counterparts in rural settings (aOR\u2009=\u20091.456; 95% CI:1.030\u20132.058, p-value\u2009=\u20090.033) and male children had a 21% higher chance of being fully immunised compared to females (aOR\u2009=\u20091.208; 95% CI: 0.889\u20131.641, p-value\u2009=\u20090.227) but this was not statistically significant. Although not significant, children whose mothers were more educated had lower chances of being fully immunised compared to those whose mothers had no education at all. A similar pattern is observed for fathers\u2019 education status. Mothers in any marital union were 31% less likely to have fully immunised Children compared to those not in a marital union (aOR\u2009=\u20090.692; 95% CI: 0.455\u20131.053, p-value\u2009=\u20090.086) but was not statistically significant.\nA caretaker hearing about immunisation campaigns did not increase the child\u2019s chances of being fully immunized while the Children whose caretaker had heard about new vaccines were more likely to be fully vaccinated. Children coming from larger families (more than seven children in a household) were less likely to be fully immunized compared to those whose families were smaller (less than 4). Children who did not have a health card but had mother\u2019s recall of immunisation during the interview had higher chances of being fully immunized compared to those who had cards. Children born outside the hospital had lower chances (10% less likely) of being fully immunized compared to those born in the hospital (aOR\u2009=\u20090.991, 95%CI: 0.702\u20131.398, p-value\u2009=\u20090.957).\nDiscussions/analysis This paper compared the state of immunisation coverage in the slums of Kampala the capital city of Uganda with coverage in the rural-based district of Iganga in Eastern Uganda for children aged of 0\u201323 months. Additionally, the study investigated how full immunisation status is influenced by a range of factors grouped as external environment, predisposing, and enabling in nature. Just over half of the children (53%) were fully immunised against vaccine-preventable diseases which was considerably low compared to the global target of 90% coverage [44] which could be responsible for high morbidity and mortality rates among infants and children.\n\nExternal environment (area of residence) influences a child\u2019s full immunisation status This study has shown that children living in slums had a higher chance of being fully immunised as compared to children in rural areas. This could be because slum dwellers had a higher percentage of hospital deliveries. Studies have shown that children delivered at a health facility usually have a full immunisation status [6]. A similar finding was made in Nigeria where it was established that slum dwellers had better coverage than rural settlers [17]. A recent study in India comparing urban and rural areas showed a significant difference with urban areas having a higher coverage. The study further compared the slums with the rural area and concurred that slum dwellers had better coverage despite their poor socioeconomic status [18].\nThe higher chance of full immunisation in slums compared to rural could also be due to the easier access to immunisation services in urban areas than the rural area as the rural settlements might be hard to reach. As discussed above, 20% of the rural settlers live more than 5 km from a health facility. It could also be because slum settlers had information on new vaccines. Therefore, they might have education on information on immunisation and its importance.\nRole of area of residence Furthermore, full immunisation status for individual vaccines was also influenced by the child\u2019s area of residence with children living in slums having a better coverage of the individual vaccines as compared to the rural dwellers. However, there was poor uptake of the 3rd doses of most vaccines in both settlements. This indicates a significant dropout rate of the vaccines. The study showed a huge gap (44% decline) in slum vaccine coverage between BCG birth dose vaccine (97,9%) and measles vaccine (53.8%) given at 9 months. A low uptake of measles vaccine at 9 months is a challenge in obtaining full immunisation coverage. The reason in this study could be attributed to inadequate access to mass media as most mothers have not heard about immunisation campaigns. Therefore, campaigns on vaccination need to be improved to improve parents\u2019 consciousness of vaccine uptake. Similar results were seen in research done in the slums of Nairobi [19].\nLikewise, a drop in coverage was noticed at 8.8%, 16.9%, 20.5% and 23.6% from the first to the 3rd dose of Polio vaccine, Penta, Pneumo and Rota vaccines respectively. The rural areas even had a higher dropout rate in the individual vaccines mentioned. A similar problem has been seen in rural Hoima District, Uganda showing a low coverage of measles as compared to the BCG birth dose vaccine and a dropout rate of 28.5% on DPT (between DPT 1 and 3) [21]. These high dropout rates\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 7 of 10\n\nTable 3\u2002 Univariate and multivariate logistic regression model for full immunisation with selected determinants\n\nFull Immunisation\n\nOR (95% CI)\n\np-value\n\naOR(95% CI)\n\nEXTERNAL FACTORS\n\nArea of residence\n\nRural\n\nReference\n\nReference\n\nUrban Slum\n\n1.260 (0.984\u20131.615)\n\n0.048\n\n1.456 (1.030\u20132.058)\n\nPREDISPOSING FACTORS\n\nSex\n\nFemale\n\nReference\n\nReference\n\nMale\n\n1.317 (1.028\u20131.687)\n\n0.029\n\n1.208 (0.889\u20131.641)\n\nMother\u2019s educational level\n\nNone\n\nReference\n\nReference\n\nPrimary\n\n0.796 (0.441\u20131.437)\n\n0.450\n\n1.179 (0.540\u20132.571)\n\nSecondary\n\n0.682 (0.385\u20131.208)\n\n0.190\n\n0.881 (0.409\u20131.911)\n\nTertiary\n\n0.602 (0.314\u20131.154)\n\n0.127\n\n0.747 (0.310\u20131.803)\n\nFather\u2019s educational level\n\nNone\n\nReference\n\nReference\n\nPrimary\n\n0.704 (0.317\u20131.562)\n\n0.388\n\n0.840 (0.332\u20132.128)\n\nSecondary\n\n0.710 (0.340\u20131.478)\n\n0.360\n\n0.971 (0.407\u20132.321)\n\nTertiary\n\n0.665 (0.316-1.400)\n\n0.283\n\n0.960 (0.387\u20132.381)\n\nMother\u2019s marital status\n\nNot in union\n\nReference\n\nReference\n\nIn union\n\n0.743 (0.538\u20131.026)\n\n0.071\n\n0.692 (0.455\u20131.053)\n\nHeard about immunisation Campaigns\n\nYes\n\nReference\n\nReference\n\nNo\n\n1.257 (0.952\u20131.660)\n\n0.107\n\n1.224 (0.846\u20131.772)\n\nHeard about new vaccines\n\nYes\n\nReference\n\nReference\n\nNo\n\n0.950 (0.705\u20131.281)\n\n0.737\n\n0.829 (0.548\u20131.254)\n\nTotal children\n\n1\u20133\n\nReference\n\nReference\n\n4\u20137\n\n1.223 (0.900-1.663)\n\n0.199\n\n1.388 (0.946\u20132.041)\n\n8\u201312\n\n0.772 (0.250\u20132.381)\n\n0.652\n\n0.553 (0.096\u20133.197)\n\nENABLING FACTORS\n\nHealth card\n\nYes, seen\n\nReference\n\nReference\n\nYes, not seen\n\n1.372 (1.045\u20131.803)\n\n0.023\n\n1.192 (0.851\u20131.672)\n\nNo\n\n1.538 (0.805\u20132.940)\n\n0.193\n\n1.376 (0.569\u20133.325)\n\nChild\u2019s place of birth\n\nHospital\n\nReference\n\nReference\n\nOutside hospital\n\n1.062 (0.806-1.400)\n\n0.669\n\n0.991 (0.702\u20131.398)\n\nMother\u2019s occupation\n\nSmall home business\n\nReference\n\nReference\n\nMarket vendor\n\n2.661 (1.466\u20134.830)\n\n0.001\n\n1.976 (0.966\u20134.044)\n\nProfessional\n\n1.348 (0.792\u20132.295)\n\n0.272\n\n1.592 (0.806\u20133.147)\n\nOther\n\n1.345 (0.994\u20131.818)\n\n0.054\n\n1.369 (0.942\u20131.988)\n\nFather\u2019s occupation\n\nSmall home business\n\nReference\n\nReference\n\nMarket vendor\n\n1.524 (0.887\u20132.621)\n\n0.127\n\n1.716 (0.829\u20133.552)\n\nProfessional\n\n1.296 (0.835\u20132.011)\n\n0.247\n\n1.858 (1.046\u20133.297)\n\nOther\n\n1.281 (0.869\u20131.887)\n\n0.211\n\n1.641 (0.997\u20132.702)\n\nAbbreviations: OR- odds ratio, aOR-adjusted odds ratio\n\np-value\n0.033\n0.227\n0.680 0.754 0.517\n0.713 0.948 0.929\n0.086\n0.283\n0.375\n0.096 0.508\n0.307 0.479\n0.957\n0.062 0.181 0.099\n0.146 0.034 0.051\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 8 of 10\n\nand gaps indicate a high prevalence of missed opportunities in childhood vaccination which is a global public health concern and an obstacle to attaining the Sustainable Development Goal of reducing child mortality. The knowledge of parents on the importance of immunisation is the key driver in achieving full immunisation. The uptake of new vaccines in both settings was also assessed and there was significantly low coverage of Pneumococcal vaccine and Rota vaccine with worse uptake in the rural area. This could be due to the poor dissemination of messages on the availabilities of immunisation services in the communities and the importance of having children take them. Both slums and rural areas need promotions and activities geared towards educating mothers on the importance of having their children receive all the vaccines on time. The lack of sensitization which is shown as parents not having adequate knowledge of childhood immunisation might be an important factor in both poor uptake of new vaccines and the high dropout rate of the other vaccine.\nOne might expect slums to have a better full childhood immunisation coverage than the rural areas as it is in the urban area and we can argue that it should be closer to healthcare resources. However, the evidence presented in this study shows the contrary. The slum dwellers are marginalised and have little to no access to services. Due to the rapid urbanisation of Kampala city, many people searching for better livelihood tend to live in slums as it is more affordable and accessible for the poor and unemployed [45]. There is no plan in place to organise the needs of the slum dwellers. The lack of services and individual attitudes become contributing factors to poor full-childhood immunisation in slums as the purpose of living shifts to daily survival instead of providing the merits of responsible and organised living, planning, compliance and proper service delivery [46]. This can be a leading factor in hindering progress in achieving good health. Therefore both slums and rural areas have a very low uptake of childhood vaccines.\nPredisposing factors to attaining full immunisation status Continuing with the predisposing factors, it showed that male children tend to be fully immunised compared to female children. The mother\u2019s marital status was not statistically significant in this study. However, other studies have shown the contrary that married women tend to have their children fully immunised as well. This is because married women tend to have better healthcareseeking behaviour [47, 48]. Moreover, the involvement of the partner could contribute to the financial support and the utilisation of healthcare.\n\nEnabling factors to attain full immunisation status Finally, the enabling factors were mostly associated with full immunisation. Children without health cards had a higher chance of full immunisation. Children reported not having a health card but received immunisation could be because of a recall bias of the parents. The findings further suggest that parents\u2019 educational attainment had no impact on full immunisation and that could be the reason why mothers that were market vendors had higher chances of immunising their children compared to those in a professional job. This could be because the market vendors are self-employed and can therefore have time to immunise their children.\nLimitations of this study A limited set of variables were used to study the determinants of immunisation coverage. Factors such as mother\u2019s antenatal visits could not be assessed. Maternal recall was used to determine whether a child was fully immunised or not and this increases the chances of recall bias in the study. Unlike a longitudinal study, the data was collected at one point in time. With this type of study, we cannot prove causality [35].\nConclusion and recommendation We have found out that the low full immunisation coverage in this study was mainly attributed to the residence of the child and the occupation of the parents. However, lack of education and poor access to messages on immunisation (inadequate access to mass media) are also contributing factors. Other factors that can be studied further could be related to poor supply chain, poor healthcare delivery system, and religious and cultural beliefs. Therefore, policies on improving the education of the population on the importance of immunisation need to target these populations to improve their immunisation coverage. Immunisation programs can be done through the improvement of primary health care facilities, training more staff and improving awareness by broadcasting on radios, television and announcing in markets/open places where people gather. These will improve access to information and thereby improve knowledge of the importance of immunisation which is an important factor in determining a child\u2019s immunisation status [49]. Immunisation processes should also include integrating maternal and child health services to improve hospital deliveries, having health cards and immunising children.\nAbbreviations aOR\t Adjusted Odds Ratio BCG\t Bacillus Calmette-Gu\u00e9rin GAVI\t Global Alliance for Vaccines and Immunisation GVAP\t Global Vaccine Action Plan HDSS\t Health and Demographic Surveillance Site IMHDSS\t Iganga Mayuge Health and Demographic Surveillance System LMICs\t Low and Middle Income Countries\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 9 of 10\n\nODK\t Open Data Kit PENTA\t Pentavalent Vaccine PNEUMO\t Pneumococcal Conjugated Vaccine ROTA\t Rotavirus Vaccine SDGs\t Sustainable Development Goals UDHS\t Uganda Demographic and Health Survey UNEPI\t Ugandan National Expanded Program on Immunisation VPDs\t Vaccine-Preventable Diseases WHO\t World Health Organisation\nSupplementary Information\nThe online version contains supplementary material available at https://doi. org/10.1186/s12913-023-09875-w.\nSupplementary Material 1\nAcknowledgements The author would like to acknowledge the IMHDSS and Makerere University for the opportunity to use their data in this manuscript.\nAuthors\u2019 contributions AJ did the literature review, research problem, data analysis and interpretation, and draft manuscript. DK revised and provided feedback throughout all processes as a supervisor. MM, DK and AK revised the manuscript and provided feedback. All authors approved the final copy for publication.\nFunding No funding was received for this manuscript.\nData Availability The data that support the findings of this study are available from Makerere University Center for Health and Population Research (MUCHAP) but restrictions apply to the availability of the data, which were used under the license for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of MUCHAP.\nDeclarations\nCompeting interests The authors declare no competing interests.\nEthics approval and consent to participate The IMHDSS received ethical approval from the Makerere University School of Public Health Research and Ethics Committee (IRB #: 042). Consent was sought from the head of households and the caretakers before any interview was conducted. All participants and/or their legal guardians gave their written informed consent for participation in the study following a detailed explanation of the research purpose. There is no personal identification disclosed in the data as individual participants were given codes to maintain anonymity. The data was stored in Microsoft Excel and it was password protected to achieve a high level of data protection to maintain the confidentiality of the study participants. All methods employed in this research are per relevant guidelines and regulations in the Declaration of Helsinki.\nConsent for publication Not applicable.\nAuthor details 1Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden 2Edward Francis Small Teaching Hospital, Banjul, The Gambia 3Center for Health and Population Research, Makerere University, Kampala, Uganda 4Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa\n\nReceived: 14 October 2022 / Accepted: 4 August 2023\nReferences 1. Immunization coverage [Internet]. [cited 2021 Feb 15]. Avail-\nable from: https://www.who.int/en/news-room/fact-sheets/detail/ immunization-coverage. 2. Vanderslott S, Dadonaite B, Roser M, Vaccination - Our World in Data. Our World in Data [Internet]. 2013 May 10 [cited 2021 Feb 15]; Available from: https://ourworldindata.org/vaccination. 3. World Health Organization. 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Kesarwani P, Singh N, Keshari SS, Dixit S. Cross sectional study of immunization coverage in urban slum areas of Lucknow region. Int J Community Med Public Health. 2017;4(9):3310.\n\nJammeh et al. BMC Health Services Research\n\n(2023) 23:856\n\nPage 10 of 10\n\n21. Malande OO, Munube D, Afaayo RN, Annet K, Bodo B, Bakainaga A. Barriers to effective uptake and provision of immunization in a rural district in Uganda. PLoS ONE. 2019;14(2):e0212270.\n22. Kagon\u00e9 M, Y\u00e9 M, N\u00e9bi\u00e9 E, Sie A, Schoeps A, Becher H. Vaccination coverage and factors associated with adherence to the vaccination schedule in young children of a rural area in Burkina Faso. Glob Health Action. 2017;10(1):1399749.\n23. Adedokun ST, Uthman OA, Adekanmbi VT, Wiysonge CS. Incomplete childhood immunization in Nigeria: a multilevel analysis of individual and contextual factors. BMC Public Health. 2017;17(1):236.\n24. Tesfaye TD, Temesgen WA, Kasa AS. 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Factors affecting complete and timely childhood immunization coverage in Sindh, Pakistan; a secondary analysis of cross-sectional survey data. PLoS ONE. 2018;13(10):e0206766.\n30. Anello P, Cestari L, Baldovin T, Simonato L, Frasca G, Caranci N. Socioeconomic factors influencing childhood vaccination in two northern italian regions. Vaccine. 2017;35(36):4673\u201380.\n31. Melovic B, Jaksic Stojanovic A, Vulic TB, Dudic B, Benova E. The impact of online media on parents\u2019 Attitudes toward Vaccination of Children-Social Marketing and Public Health.Int J Environ Res Public Health.2020;17(16).\n32. Oleribe O, Kumar V, Awosika-Olumo A, Taylor-Robinson SD. Individual and socioeconomic factors associated with childhood immunization coverage in Nigeria. Pan Afr Med J. 2017;26:220.\n33. Tamirat KS, Sisay MM. Full immunization coverage and its associated factors among children aged 12\u201323 months in Ethiopia: further analysis from the 2016 Ethiopia demographic and health survey. 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Coverage and Drivers to reaching the last child with vaccination in Urban Settings: a mixed-methods study in Kampala, Uganda.Glob Health Sci Pract.2022;10(4).\n40. Kajungu D, Hirose A, Rutebemberwa E, Pariyo GW, Peterson S, Guwatudde D. Cohort Profile: the Iganga-Mayuge Health and demographic surveillance site, Uganda (IMHDSS, Uganda). Int J Epidemiol. 2020;49(4):1082\u20131082 g.\n41. Health Care Utilization Theories and Models -Case Western\u2026Internet]. [cited 2021 Apr 20]. Available from: https://www.yumpu.com/en/document/ read/18269764/health-care-utilization-theories-and-models-case-western-.\n42. Tolera H, Gebre-Egziabher T, Kloos H. Using Andersen\u2019s behavioral model of health care utilization in a decentralized program to examine the use of antenatal care in rural western Ethiopia. PLoS ONE. 2020;15(1):e0228282.\n43. Herliana P, Douiri A. Determinants of immunisation coverage of children aged 12\u201359 months in Indonesia: a cross-sectional study. BMJ Open. 2017;7(12):e015790.\n44. Immunization Agenda 2030 - A Global Strategy To Leave No One Behind. Available from: https://www.unicef.org/supply/media/10121/file/14-IA2030Summary-Rajinder-Suri.pdf.\n45. Slums: A fierce challenge to Uganda\u2019s urbanisation drive - New Vision Official [Internet]. [cited 2023 Jul 21]. Available from:https://www.newvision.co.ug/ news/1304923/slums-fierce-challenge-uganda-urbanisation-drive.\n46. Kwiringira JN, Kabumbuli R, Zakumumpa H, Mugisha J, Akugizibwe M, Ariho P. Re-conceptualizing sustainable urban sanitation in Uganda: why the roots of \u201cSlumification\u201d must be dealt with. BMC Public Health. 2021;21(1):992.\n47. Wiysonge CS, Uthman OA, Ndumbe PM, Hussey GD. Individual and contextual factors associated with low childhood immunisation coverage in sub-saharan Africa: a multilevel analysis. PLoS ONE. 2012;7(5):e37905.\n48. Umoke PCI, Umoke M, Nwalieji CA, Igwe FO, Umoke UG, Onwe RN. Investigating factors Associated with Immunization Incompletion of Children under five in Ebonyi State, Southeast Nigeria: implication for policy dialogue. Glob Pediatr Health. 2021;8:2333794X21991008.\n49. Sarker AR, Akram R, Ali N, Sultana M. Coverage and factors associated with full immunisation among children aged 12\u201359 months in Bangladesh: insights from the nationwide cross-sectional demographic and health survey. BMJ Open. 2019;9(7):e028020.\nPublisher\u2019s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\n\n",
+    "authors": [
+      "A. Jammeh",
+      "M. Muhoozi",
+      "A. Kulane",
+      "D. Kajungu"
+    ],
+    "doi": "10.1186/s12913-023-09875-w",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "7HFGCHXU",
+    "title": "Does mothers'\u00a0and caregivers' access to information on their child's vaccination card impact the timing of their child's measles vaccination in Uganda?",
+    "abstract": "INTRODUCTION: On-time measles vaccination is essential for preventing measles\u00a0infection among children as early in life as possible, especially in areas where measles outbreaks occur frequently. Characterizing the timing of routine\u00a0measles vaccination\u00a0(MCV1) among children and identifying risk factors for delayed measles vaccination is important for addressing barriers to recommended childhood\u00a0vaccination and increasing on-time MCV1 coverage.\u00a0We aim to assess the timing of children's\u00a0MCV1 vaccination and to investigate the association between demographic and healthcare factors,\u00a0mothers'/caregivers' ability to identify information on their child's vaccination card, and achieving\u00a0on-time (vs. delayed) MCV1 vaccination. METHODS: We conducted a population-based, door-to-door survey in Kampala, Uganda, from June-August of 2019. We surveyed mothers/caregivers of children aged one to five years to determine how familiar they were with their child's vaccination card and to determine their child's MCV1 vaccination\u00a0status\u00a0and timing. We assessed the proportion of children vaccinated for MCV1 on-time and delayed, and we evaluated the association between mothers'/caregivers'\u00a0ability to identify key\u00a0pieces of information (child's birth date, sex, and MCV1 date) on their child's vaccination card and achieving on-time MCV1 vaccination. RESULTS: Of the 999 mothers/caregivers enrolled, the\u00a0median age was 27\u2009years (17-50), and median child age was 29\u2009months (12-72). Information on vaccination status was available for 66.0%\u00a0(n\u2009=\u2009659) of children. Of those who had documentation of MCV1\u00a0vaccination\u00a0(n\u2009=\u2009475), less than half (46.5%; n\u2009=\u2009221) achieved on-time MCV1 vaccination and 53.5% (n\u2009=\u2009254) were delayed. We found that only 47.9% (n\u2009=\u2009264) of the 551\u00a0mothers/caregivers who\u00a0were asked to identify key pieces of information on their child's vaccination card were able to identify the information, but ability to identify the key pieces of\u00a0information on the card was not independently associated with achieving on-time MCV1\u00a0vaccination. CONCLUSION: Mothers'/caregivers'\u00a0ability to identify key pieces of\u00a0information on their child's vaccination card was not associated with achieving on-time MCV1 vaccination. Further research can shed light on interventions that may prompt or remind mothers/caregivers of the time\u00a0and age when their child is due for measles vaccine\u00a0to increase the chance of the child receiving it at the recommended time.",
+    "full_text": "Griffith et al. BMC Public Health (2022) 22:834 https://doi.org/10.1186/s12889-022-13113-z\n\nRESEARCH\n\nOpen Access\n\nDoes mothers\u2019 and caregivers\u2019 access to information on their child\u2019s vaccination card impact the timing of their child\u2019s measles vaccination in Uganda?\nBridget C. Griffith1,2*, Sarah E. Cusick3, Kelly M. Searle2, Diana M. Negoescu4, Nicole E. Basta1 and Cecily Banura5\u200a\n\nAbstract\u2003\nIntroduction:\u2002 On-time measles vaccination is essential for preventing measles infection among children as early in life as possible, especially in areas where measles outbreaks occur frequently. Characterizing the timing of routine measles vaccination (MCV1) among children and identifying risk factors for delayed measles vaccination is important for addressing barriers to recommended childhood vaccination and increasing on-time MCV1 coverage. We aim to assess the timing of children\u2019s MCV1 vaccination and to investigate the association between demographic and healthcare factors, mothers\u2019/caregivers\u2019 ability to identify information on their child\u2019s vaccination card, and achieving on-time (vs. delayed) MCV1 vaccination.\nMethods:\u2002 We conducted a population-based, door-to-door survey in Kampala, Uganda, from June\u2013August of 2019. We surveyed mothers/caregivers of children aged one to five years to determine how familiar they were with their child\u2019s vaccination card and to determine their child\u2019s MCV1 vaccination status and timing. We assessed the proportion of children vaccinated for MCV1 on-time and delayed, and we evaluated the association between mothers\u2019/caregivers\u2019 ability to identify key pieces of information (child\u2019s birth date, sex, and MCV1 date) on their child\u2019s vaccination card and achieving on-time MCV1 vaccination.\nResults:\u2002 Of the 999 mothers/caregivers enrolled, the median age was 27\u2009years (17\u201350), and median child age was 29\u2009months (12\u201372). Information on vaccination status was available for 66.0% (n\u2009=\u2009659) of children. Of those who had documentation of MCV1 vaccination (n\u2009=\u2009475), less than half (46.5%; n\u2009=\u2009221) achieved on-time MCV1 vaccination and 53.5% (n\u2009=\u2009254) were delayed. We found that only 47.9% (n\u2009=\u2009264) of the 551 mothers/caregivers who were asked to identify key pieces of information on their child\u2019s vaccination card were able to identify the information, but ability to identify the key pieces of information on the card was not independently associated with achieving on-time MCV1 vaccination.\nConclusion:\u2002 Mothers\u2019/caregivers\u2019 ability to identify key pieces of information on their child\u2019s vaccination card was not associated with achieving on-time MCV1 vaccination. Further research can shed light on interventions that may\n\n*Correspondence: bridgetcgriffith@gmail.com 1 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University Faculty of Medicine and Health Sciences, 2001 McGill College, Suite 1200, QC H3A 1G1 Montreal, Canada Full list of author information is available at the end of the article\n\u00a9 The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://\u200bcreat\u200biveco\u200bmmons.\u200borg/\u200blicen\u200bses/\u200bby/4.\u200b0/. The Creative Commons Public Domain Dedication waiver (http://\u200bcreat\u200biveco\u200b mmons.\u200borg/\u200bpubli\u200bcdoma\u200bin/\u200bzero/1.\u200b0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 2 of 17\n\nprompt or remind mothers/caregivers of the time and age when their child is due for measles vaccine to increase the chance of the child receiving it at the recommended time.\nKeywords:\u2002 Child health, Immunisation, Public health, Measles, Cross-sectional survey\n\nIntroduction Measles is a highly contagious disease caused by Measles morbillivirus (MeV); it was responsible for millions of deaths worldwide annually before the introduction of measles vaccines [1]. Even with the availability of safe and effective vaccines, measles remains an important cause of death among young children globally, especially in low- and middle-income countries (LMICs), where measles has yet to be eliminated [2]. Although there has been marked reduction of measles-associated mortality worldwide over the past several decades, the World Health Organization (WHO) African Region (AFRO) continues to report the highest measles incidence of any region, with 118 cases per one million people, and the highest incidence of measles-related deaths of any region, with 52,600 deaths reported in 2018 [3].\nIn Uganda, at the time of this study, the recommended measles vaccination was one dose at nine months of age, referred to as measles-containing vaccine 1 (MCV1). Delayed immunization is a strong risk factor for disease, because it leads to children having little to no immune protection via measles-containing vaccine (MCV) against measles infection after the waning of maternally acquired antibodies [4, 5]. An analysis of the timing of measles vaccination in Uganda found that the median delay in the administration of MCV1 was 2.7\u2009weeks, but with an interquartile range (IQR) of 9.6\u2009weeks, indicating a wide distribution in the number of weeks MCV1 was delayed [6].\nDespite a steady improvement in Uganda\u2019s measles vaccination coverage from an estimated 70% (2008) to 87% (2019) of children 12\u201323\u2009months of age, outbreaks of measles remain common in both urban and rural settings [7\u20139]. The occurrence of these outbreaks, despite relatively high overall vaccination coverage, is attributed to a high proportion of susceptible children clustered within geographical areas, due to heterogeneity in vaccination coverage [10\u201312].\nThe degree to which delayed vaccination may contribute to epidemiologic trends in measles-endemic areas is not known. Estimating the prevalence of delayed measles vaccination, the amount of time vaccination is delayed, and elucidating factors associated with risk of delayed measles vaccination is one of the important steps toward addressing barriers to vaccination and improving ontime measles vaccination coverage.\n\nRoutine infant vaccination is available at government health facilities, private health facilities, and outreach posts within communities at specific times during the week throughout the year in Uganda. Mothers or other female caregivers are primarily responsible for ensuring that their children are vaccinated for measles at the recommended time [13\u201315]. Mothers/caregivers bring their child to the health facility, along with the child\u2019s Uganda Ministry of Health Child Health Card (UCHC) or other vaccination documentation, and wait for their child\u2019s turn to be vaccinated.\nBased on the Uganda National Expanded Program on Immunisation (UNEPI)-recommended infant vaccination schedule, children are recommended to receive pneumococcal conjugate vaccine (PCV), diphtheria/tetanus/ pertussis/Hemophilus influenzae/hepatitis B vaccine (DTwPHibHepB), and inactivated polio vaccine (IPV) at 14 weeks of age; then five and a half months later, they are recommended to receive MCV1 at nine months of age [14]. At the 14-week visit, healthcare workers overseeing childhood vaccinations are trained to verbally inform the mother/caregiver about the date to return for their child\u2019s MCV1. In this situation, the child\u2019s vaccination document is meant to serve as a guide to let mothers/caregivers know when their child is due for their next vaccine, and this is likely the only reminder that they receive about when their child is due [16\u201318]. In addition, the MCV1 vaccination at nine months does not coincide with other routine health visits, which may further reduce the chance that mothers/caregivers receive any other prompts besides the age and date on the vaccination card that would remind them of when their child is due for MCV1. In some contexts, children may receive MCV before nine months of age; this is common in settings where there is an ongoing measles outbreak. If children receive MCV before nine months of age, this is noted as measles-containing vaccine 0 (MCV0) in the child\u2019s UCHC, and mothers/caregivers are still advised to bring the child for MCV1 when they reach nine months.\nIn addition to routine vaccination, MCV is accessible via non-routine immunization campaigns during periods of high transmission. During these campaigns, teams of healthcare workers set up vaccination service delivery posts across the country to vaccinate children with MCV from six months to 15\u2009years of age. These campaigns are meant to supplement, but not replace, routine vaccination [19, 20].\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 3 of 17\n\nChildren\u2019s UCHCs are typically issued at birth, if the child was born in a health facility. If a child is born outside the health facility, the UCHC is issued the first time the child is brought for healthcare. In both cases, mothers/caregivers are instructed to retain the UCHCs until the child reaches six years of age. These cards are a record of a child\u2019s health status from birth, including deworming and Vitamin A supplementation, growth monitoring, and immunization. Despite the importance of these cards, they are sometimes not retained until the recommended age, or they are lost or damaged [21]. In previous studies in Uganda, the possession of a UCHC was associated with childhood vaccination completion [22].\nThese UCHCs are often the only reminders to mothers/ caregivers about upcoming childhood vaccines. It is not known whether vaccination cards are an effective method for conveying this information and whether mothers/caregivers use their child\u2019s UCHCs for this purpose. Parental knowledge of the contents of the UCHC has been assessed in similar settings, with one study finding that parental knowledge of the timing of MCV1 increased with possession of a vaccination card [23].\nThe relationship between ability to identify information on the UCHC and achieving on-time MCV1 vaccination for their child is unclear. Understanding if and how mothers/caregivers locate vaccination information on their child\u2019s UCHC is important for determining if the card serves as a reminder for when a child is due for vaccination, and if that results in a child being vaccinated on-time. In this study, our primary aims are to 1) assess the proportion of children who were\n\nvaccinated with MCV1 on-time and delayed and 2) investigate the association between demographic factors, ability to identify key pieces of information on the child\u2019s UCHC, and on-time MCV1 vaccination (vs. delayed). Our secondary aims are to 1) investigate the association between demographic and healthcare factors and mothers\u2019/caregivers\u2019 ability to identify key pieces of information on the UCHC (vs. not being able to) and 2) investigate the association between demographic and healthcare factors and retaining the UCHC (vs. not retaining). Estimating the proportion of delayed MCV1 vaccination and assessing factors potentially associated with delayed MCV1 vaccination is an important step toward addressing and eliminating barriers to on-time vaccination.\nMethods\nStudy design We conducted a population-based, cross-sectional, door-to-door survey in Rubaga Division\u2019s high-density, low-resource informal settlements, located in Kampala district of Uganda. Surveys were administered from June to August 2019.\nStudy area Rubaga Division is one of the five sub-counties of Kampala district. It comprises 14 informal settlements spread throughout its 13 parishes. Based on the 2014 Uganda National Population Census, we selected three Parishes containing large informal settlements: Nakulabye, Busega, and Ndeeba. Nakulabye (Fig. 1, Area A)\n\nFig.\u202f1\u2002 \u00a9 OpenStreetMap Contributors. OpenStreetMap 2022 [24]. The three parishes that were selected for sampling are: Nakulabye (Area A); Busega (Area B); and Ndeeba (Area C)\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 4 of 17\n\nhas an estimated 8,000 households, spread throughout its nine villages (also referred to as zones in urban settings); Busega (Fig. 1, Area B) has an estimated 6,000 households, spread throughout its nine villages; and Ndeeba (Fig. 1, Area C) has an estimated 8,000 households, spread throughout its 15 villages (Fig. 1).\nWe designated Local Council 1 areas (LC1s) as the study administrative unit (AU). LC1s are the smallest political-administrative unit in Uganda; in urban areas, they are comprised of multiple geographically adjacent villages. Prior to the survey administration, the study team approached community leaders to obtain necessary permissions and ask them to identify a local guide familiar with the boundaries of the selected AU. Each LC1 within an informal settlement has clearly demarcated boundaries.\nNext, the study team leaders, accompanied by a local guide, conducted a household census by AU. The purpose was to enumerate and mark all households with a serial number for easy identification within the AU. Using the household census enumeration list as a sampling frame, study team leaders established a sampling interval and then randomly selected 45 potential households, which were then visited by the study team for eligibility screening of mothers/caregivers. A household was defined as a group of individuals who live under the same roof and eat from the same cooking pot [25]. If there was no eligible mother/caregiver in the selected household, the study team members visited the next household. If the mother/ caregiver was away at the time of eligibility screening, the study team member returned to that household at least twice before visiting the next household. This process was repeated in each AU until the sample size was achieved.\nParticipant eligibility screening and selection Trained study staff approached each household and asked to speak with the mother/caregiver of the household. If more than one mother/caregiver was identified in the enumeration step, study staff screened all for eligibility. Potential participants were eligible if they were the mother/caregiver of a child aged one to five years of age (defined as the child had not yet reached their sixth birthday) at the time of the survey, a resident of Kampala district for more than six months during the past year, a current resident of a household in Rubaga Division, and able to understand spoken Luganda or English. If more than one mother/caregiver in a household was eligible, one was selected for inclusion via an anonymized random selection method.\nSample size As our primary aim was to determine the proportion of children who were vaccinated on-time among all\n\nvaccinated children, we calculated the minimum sample size necessary, assuming that 50% of vaccinated children would be vaccinated on-time and with the desire to estimate the value within plus or minus five percentage points. With an alpha of 0.05, we would need to sample 383 vaccinated children to achieve the desired power. Assuming 50% of participants would have their child\u2019s vaccination card, based on a study in a similar setting [16], and 80% of those children would be vaccinated, we increased to a target sample size of 1000.\nSurvey administration A study staff member informed eligible participants of the objectives of the study and study procedures and invited them to participate. Next, the study staff member asked the participant if their preferred language was English or Luganda and if they could read in that language. For those who confirmed that they could read in their preferred language, they were given the informed consent form to read. For those who indicated that they were unable to read or write in English or Luganda, the study staff member read them the informed consent form in the presence of a witness. The study staff member gave participants the opportunity to ask any questions, and then the participant signed two copies of the informed consent form, if they were able, or they provided a thumbprint and their witness signed two copies of the form. One copy was retained by the study staff member, and the participant kept the other copy.\nA study staff member immediately administered a 96-question survey orally to consenting participants. The interviewing study staff member recorded participant responses on a handheld tablet computer, using a series of customized REDCap questionnaire forms [26, 27]. Because the survey asked questions about the participant and their child, participants were instructed to answer all questions with respect to their child who most recently celebrated their first birthday and had not yet celebrated their sixth birthday (the index child), even if they had other children between their first and sixth birthdays. The survey took approximately 50\u2009min to complete, on average. Upon completion of the survey, participants were given a hygiene kit to thank them for their time.\nSurvey content The survey captured demographics of the mother/caregiver and index child, mother\u2019s/caregiver\u2019s past healthcare seeking behaviour, including who in their household made decisions about the index child\u2019s medical care, the number of antenatal care visits during their pregnancy with the index child, and the place of birth of the index child.\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 5 of 17\n\nThe survey included a section where the study staff requested permission to view and take a photograph of the vaccine-related information on the index child\u2019s UCHC. If a child\u2019s UCHC was not available, participants were asked to present any other documentation that included the child\u2019s dates of vaccination, and study staff applied the same procedures. All vaccination records are referred to as the child\u2019s vaccination card in the sections that follow.\nIdentification of information on the child\u2019s vaccination card Study staff asked participants who presented a UCHC or other official documentation of vaccination that contained the index child\u2019s basic information to identify information on their child\u2019s card by pointing to the line where the following information was located on the card: the child\u2019s date of birth (Fig. 2, Item A), child\u2019s sex (Fig. 2, Item B), and date of measles vaccination (Fig. 2, Item C). Study staff categorized participants\u2019 answers as either \u201ccorrect\u201d or \u201cincorrect\u201d, based on whether the\n\nmother/caregiver could locate and identify each piece of information.\nData management We designed and administered the surveys using the REDCap electronic data capture software Versions 9.1.2 and 9.2 [26, 27]. Study staff reviewed and entered the date of MCV from the photograph of the vaccination cards into a form created in REDCap [26, 27]. Vaccination data were double entered, compared, and any discrepancies resolved before being merged into the survey database via a unique participant identifier.\nAnalysis We used Stata 16 for data management and analysis of survey data, including calculating summary statistics and regression modelling [28]. We used R version 4.1.2 [29] and ggplot [30] to create OR plots of the model output. We considered p-values \u22640.05 to be\n\nFig.\u202f2\u2002 Two pages of the Uganda Ministry of Health Child Health Card (UCHC). These pages include key pieces of information the participants were asked to point to in the survey: Child\u2019s date of birth (Item A); Child\u2019s sex (Item B); and Information on child\u2019s MCV1 (Item C), including date given\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 6 of 17\n\nstatistically significant. Participants with nonmissing information were included in the final versions of each model.\nPrimary aim 1: determining the proportion of children who received MCV1 on\u2011time vs. delayed We first calculated descriptive statistics of demographic and healthcare characteristics of both mothers/caregivers and index children. To estimate the child\u2019s age at time of receiving MCV, we subtracted the index child\u2019s month and year of birth, reported by the participant, from the month and year of MCV vaccination, which we abstracted from the vaccination card. To calculate the child\u2019s age at the time of the survey, we subtracted the date of the survey from their date of birth. Index children who were missing information about their month and year of birth in the survey or the date of MCV vaccination were excluded from the primary aim 1 analysis. We considered index children to have received MCV1 on-time if they were nine months of age at the time of MCVvaccination,to have received MCV1 delayed if they were ten months of age or older at the time of MCV vaccination, or to have received MCV early (received MCV0) if they were younger than nine months at the time of MCV vaccination. Index children who were vaccinated early were not included in the analysis of on-time MCV1 vaccination vs. delayed MCV1 vaccination. We used a one-sample test of equality of proportions with a confidence level of 0.95 to determine if there was a significant difference in the proportion of children vaccinated on-time, compared to the hypothesized proportion of 50%. We conducted sensitivity analyses to compare demographic and other characteristics of card retention using chisquare tests.\nPrimary aim 2: evaluating the association between mothers\u2019/ caregivers\u2019 and index children\u2019s demographic factors, healthcare factors, ability to identify information on the child\u2019s vaccination card, and achieving on\u2011time MCV1 vaccination To determine the participants\u2019 ability to identify information (index child\u2019s date of birth, sex, and date of MCV1) on the index child\u2019s vaccination card, we created a new dichotomous variable from the three responses : the participant is able to identify all three key pieces of information on the document vs. they are able to identify fewer than three or none.\nUsing univariate logistic regression, we evaluated the association between mothers\u2019/caregivers\u2019 and index children\u2019s demographic factors, health care factors, ability to identify information on the child\u2019s vaccination\n\ncard as independent variables and achieving on-time MCV1 vaccination, compared to delayed MCV1 vaccination, as the dependent variable. We computed crude odds ratios (cORs) with corresponding 95% CIs and p-values. Factors from these univariate models with p\u2009<\u20090.2 (mother/caregiver age, employment status, education, index child\u2019s birth order, index child age, and index child sex) were included in an unconditional multivariable logistic regression model in which achieving on-time MCV1 vaccination (vs. delayed) was the dependent variable. We computed adjusted odds ratios (aORs) with corresponding 95% CIs and p-values.\nSecondary aim 1: factors associated with ability to identify information on the child\u2019s vaccination card Using univariate logistic regression, we evaluated the association between mother/caregiver and index children\u2019s demographic factors and health care factors as independent variables and ability to identify information on the child\u2019s vaccination card as the dependent variable (defined as being able to identify three pieces of information on the index child\u2019s vaccination card vs. not able to identify all three). We computed cORs with corresponding 95% CIs and p-values. Factors from these univariate models with p\u2009<\u20090.2 (who decides medical care for the child, mother/caregiver age, tribe, education, relationship to index child\u2019s father, index child\u2019s birth order, index child age, and index child sex) were included in an unconditional multivariable logistic regression model in which ability to identify information on the child\u2019s vaccination card is the dependent variable. We report graphically the aOR and 95% CI for each covariate included in the full model, and cORs and aORs in Supplementary Table 1.\nSecondary aim 2: factors associated with child\u2019s vaccination card retention Using univariate logistic regression, we evaluated the association between mothers\u2019/caregivers\u2019 and index children\u2019s demographic factors and health care factors as independent variables and retention of the index child\u2019s vaccination card, compared to not retaining the card, as the dependent variable. We computed cORs with corresponding 95% CIs and p-values. Factors from these univariate models with p\u2009<\u20090.2 (moved to Rubaga in the index child\u2019s lifetime, mother/caregiver age, tribe, employment, education, index child\u2019s birth order, index child age, index child sex, index child\u2019s place of birth, and who decided medical care for the index child) were included in an unconditional multivariable logistic regression model in which retention of the index child\u2019s vaccination card is the dependent variable. We report\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 7 of 17\n\nScreened for inclusion in the study (n =1073)\n\nDid not meet inclusion criteria or did not consent to participate (n =74)\nMet inclusion criteria and consented to participate (n =999)\nChild's vaccination card not present at time of survey (n =340)\nChild's vaccination card present at time of survey (n =659)\nNo record of receiving measlescontaining vaccine (MCV) on vaccination card (n =151)\nRecord of receiving MCV on vaccination card (n =508)\nIncomplete information to calculate the timing of child receiving MCV (n =1)\nComplete information to calculate the timing of child receiving MCV (n =507)\nChild received MCV0 (received MCV before nine months of age) (n =32)\n\nChild received MCV1 (n =475)\n\nChild received MCV1 at nine months of age (n =221)\n\nChild received MCV1 at ten months of age or older (n =254)\n\nFig.\u202f3\u2002 Study participants eligibility, availability of index children\u2019s vaccination cards, and the timing of index children receiving measles vaccination (MCV)\n\ngraphically the aOR and 95% CI for each covariate included in the full model, and cORs and aORs in Supplementary Table 2.\n\nEthical review This study was reviewed and approved by the Makerere University School of Medicine Research and Ethics\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 8 of 17\n\nTable\u202f1\u2002Characteristics of survey participants (mothers/caregivers) overall, and by achievement of on-time measles vaccination (MCV1) for the index child\n\nTotal (n\u2009=\u2009999)\n\nAmong children with a vaccination record who were vaccinated with MCV1 on-time or delayed (n\u2009=\u2009475)\n\nn\n\n%a\n\n95% CI\n\nDelayed (n\u2009=\u2009254)\n\nOn-time (n\u2009=\u2009221)\n\nn\n\n%a\n\nn\n\n%a\n\nAge (years) \u2003 Under 20 \u200320\u201324 \u200325\u201329 \u200330\u201334 \u200335+ Age (years) (Median [Range]) Number of living children \u20031 \u20032 \u20033 \u20034+ Number of living children (Median [Range]) Tribe \u2003Muganda \u2003Muyankole \u2003Other Missing Highest level of education completed \u2003 Did not attend/do not know \u2003Primary \u2003Secondary \u2003Post-secondary \u2003Missing Religion \u2003Catholic \u2003Anglican \u2003Muslim \u2003 Other religion \u2003Missing Relationship status with index child\u2019s father \u2003 Currently married or living together \u2003 Never married and never living together \u2003 Formerly married Missing Employed outside the home \u2003No \u2003Yes \u2003Missing\na Percentages may not equal 100 because of rounding Abbreviations: CI Confidence Interval\n\n37\n\n3.7\n\n267\n\n26.7\n\n345\n\n34.5\n\n185\n\n18.5\n\n165\n\n16.5\n\n27 [17,50]\n\n230\n\n23.0\n\n275\n\n27.5\n\n223\n\n22.3\n\n271\n\n27.1\n\n2 [1,13] 2.8\n\n532\n\n53.3\n\n138\n\n13.8\n\n328\n\n32.8\n\n1\n\n0.1\n\n34\n\n3.4\n\n382\n\n38.2\n\n495\n\n49.6\n\n85\n\n8.5\n\n3\n\n0.3\n\n352\n\n35.2\n\n232\n\n23.2\n\n217\n\n21.7\n\n189\n\n18.9\n\n9\n\n0.9\n\n771\n\n77.2\n\n88\n\n8.8\n\n138\n\n13.8\n\n2\n\n0.2\n\n556\n\n55.7\n\n442\n\n44.2\n\n1\n\n0.1\n\n2.7, 5.1 24.1, 29.6 31.6, 37.5 16.2, 21.1 14.3, 19.0\n20.5, 25.7 24.8, 30.4 19.8, 25.0 24.4, 30.0 [1,13]\n50.1, 56.3 11.8, 16.1 30.0, 35.8\n2.4, 4.7 35.3, 41.3 46.5, 52.7 6.9, 10.4\n32.3, 38.3 20.7, 25.9 19.3, 24.4 16.6, 21.5\n74.5, 79.7 7.2, 10.7 11.8, 16.1\n52.6, 58.7 41.2, 47.3\n\n8\n\n3.2\n\n59\n\n23.2\n\n92\n\n36.2\n\n48\n\n18.9\n\n47\n\n18.5\n\n28 [17,43]\n\n52\n\n20.5\n\n69\n\n27.2\n\n68\n\n26.8\n\n65\n\n25.6\n\n3 [1,9]\n\n132\n\n52.0\n\n36\n\n14.2\n\n86\n\n33.9\n\n9\n\n3.5\n\n96\n\n37.8\n\n126\n\n49.6\n\n23\n\n9.1\n\n0\n\n0.0\n\n91\n\n35.8\n\n62\n\n24.4\n\n43\n\n16.9\n\n55\n\n21.7\n\n3\n\n1.2\n\n203\n\n79.9\n\n21\n\n8.3\n\n30\n\n11.8\n\n130\n\n51.2\n\n124\n\n48.8\n\n3\n\n1.4\n\n78\n\n35.3\n\n78\n\n35.3\n\n31\n\n14.0\n\n31\n\n14.0\n\n26 [19,45]\n\n68\n\n30.8\n\n67\n\n30.3\n\n42\n\n19.0\n\n44\n\n19.9\n\n2 [1,8]\n\n122\n\n55.2\n\n30\n\n13.6\n\n69\n\n31.2\n\n2\n\n0.9\n\n69\n\n31.2\n\n125\n\n56.6\n\n24\n\n10.9\n\n1\n\n0.5\n\n85\n\n38.5\n\n45\n\n20.4\n\n49\n\n22.2\n\n39\n\n17.7\n\n3\n\n1.4\n\n183\n\n82.8\n\n16\n\n7.2\n\n22\n\n10.0\n\n139\n\n62.9\n\n82\n\n37.1\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 9 of 17\n\nTable\u202f2\u2002 Characteristics of the index children overall, and by achievement of on-time measles vaccination (MCV1)\n\nTotal (n\u2009=\u2009999)\n\nn\n\n%a\n\n95% CI\n\nAmong children with a vaccination record who were vaccinated with MCV1 on-time or delayed (n\u2009=\u2009475)\n\nDelayed (n\u2009=\u2009254)\n\nn\n\n%a\n\nOn-time (n\u2009=\u2009221)\n\nn\n\n%a\n\nAge (months) \u200312\u201323\n\n354\n\n35.4\n\n\u200324\u201335\n\n264\n\n26.4\n\n\u200336\u201347 \u200348\u201359 \u200360+\n\n174\n\n17.4\n\n119\n\n11.9\n\n71\n\n7.1\n\n\u2003Missing\n\n17\n\n1.7\n\nAge (months) (Median [Range])\n\n29 [12,72]\n\nSex\n\n\u2003Female \u2003Male\n\n470\n\n47.1\n\n527\n\n52.8\n\n\u2003Missing\n\n2\n\n0.2\n\nBirth order\n\n\u2003First\n\n304\n\n30.4\n\n\u2003Second\n\n253\n\n25.3\n\n\u2003 Third or higher\n\n442\n\n44.2\n\nPart of a multiple birth\n\n\u2003No \u2003Yes\n\n971\n\n97.2\n\n24\n\n2.4\n\n\u2003Missing\n\n4\n\n0.4\n\na Percent totals may not equal 100 due to rounding Abbreviations: CI Confidence Interval\n\n32.5, 38.5 23.8, 29.3 15.2, 19.9 10.0, 14.1 5.7, 8.9\n44.0, 50.2 49.6, 55.8\n27.7, 33.4 22.7, 28.1 41.2, 47.3\n96.0, 98.1 1.6, 3.6\n\n95\n\n37.4\n\n67\n\n26.4\n\n40\n\n15.8\n\n31\n\n12.2\n\n21\n\n8.3\n\n28 [12,71]\n\n105\n\n41.3\n\n149\n\n58.7\n\n73\n\n28.7\n\n61\n\n24.0\n\n120\n\n47.2\n\n245\n\n96.5\n\n9\n\n3.5\n\n91\n\n41.2\n\n54\n\n24.4\n\n44\n\n19.9\n\n19\n\n8.6\n\n10\n\n4.5\n\n3\n\n1.4\n\n2, [12,70]\n\n104\n\n47.1\n\n117\n\n52.9\n\n96\n\n43.4\n\n46\n\n20.8\n\n79\n\n35.8\n\n212\n\n95.9\n\n7\n\n3.2\n\n2\n\n0.9\n\nCommittee (SOMREC) (Study number: 2018\u2013117), the Uganda National Council for Science and Technology (UNCST), and the University of Minnesota Institutional Review Board (Study number: STUDY00004955).\nResults\nParticipant characteristics In total, 1073 eligible individuals were approached for study inclusion, and 999 (93.0%) completed the survey (Fig. 3). Participants ranged in age from 17 to 50\u2009years, with a median of 27\u2009years. The most commonly reported tribe was Baganda (singular: Muganda) (53.3%, n\u2009=\u2009532) and highest level of education completed was secondary school (49.6%, n\u2009=\u2009495). About one third of participants (35.2%, n\u2009=\u2009352) were Catholic and about half of participants (55.7%, n\u2009=\u2009556) reported not being employed outside the home. Approximately one quarter (23.0%, n\u2009=\u2009230) of participants reported having one living child, and a similar proportion (27.5%, n\u2009=\u2009275) reported having two living children (Table 1). The majority (77.2%, n\u2009=\u2009771) of participants reported being currently married or living together with the index child\u2019s father.\n\nThe age of index children ranged from 12 to 72\u2009months, with a median of 29\u2009months (2.4\u2009years). Slightly over half (52.8%, n\u2009=\u2009527) of the children were male, and about one third (30.3%, n\u2009=\u2009304) were the first-born child. Only 24 (2.4%) children were part of a multiple birth (Table 2).\nThe majority of participants (71.1%, n\u2009=\u2009710) reported giving birth to the index child in a public hospital/ clinic. Most participants reported having completed the Uganda Ministry of Health-recommended number of four antenatal care visits during their pregnancy, with 40.0% (n\u2009=\u2009400) reporting four visits and 34.9% (n\u2009=\u2009349) reporting more than four. When asked who makes decisions about medical care for the index child, most (66.7%, n\u2009=\u2009666) participants reported joint decision making with their spouses, while 18.5% (n\u2009=\u2009185) said that they make the decisions on their own (Table 3).\nAchievement of on\u2011time MCV1 vaccination Among all 999 index children, 50.9% (n\u2009=\u2009508) had documentation that they were vaccinated with MCV, 15.1% (n\u2009=\u2009151) had documentation that they were not vaccinated with MCV (presented a vaccination card with no\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 10 of 17\n\nTable\u202f3\u2002 Healthcare characteristics of participants and index children overall, and by achievement of on-time measles vaccination (MCV1) for the index child\n\nTotal (n\u2009=\u2009999)\n\nAmong children with a vaccination record who were vaccinated with MCV1 on-time or delayed (n\u2009=\u2009475)\n\nn\n\n%a\n\n95% CI\n\nDelayed (n\u2009=\u2009254)\n\nOn-time (n\u2009=\u2009221)\n\nn\n\n%a\n\nn\n\n%a\n\nLocation of birth of index child\n\n\u2003 Public hospital/clinic \u2003 Private hospital\n\n710\n\n71.1\n\n68.2, 73.8\n\n194\n\n76.4\n\n153\n\n69.2\n\n230\n\n23.0\n\n20.5, 25.7\n\n51\n\n20.1\n\n59\n\n26.7\n\n\u2003 At home\n\n56\n\n5.6\n\n4.3, 7.2\n\n9\n\n3.5\n\n9\n\n4.1\n\n\u2003Missing\n\n3\n\n0.3\n\nNumber of antenatal care visits\n\n\u2003 No visits\n\n12\n\n1.2\n\n0.7, 2.1\n\n2\n\n0.8\n\n1\n\n0.5\n\n\u2003 Less than four\n\n235\n\n23.5\n\n21.0, 26.3\n\n65\n\n25.6\n\n49\n\n22.2\n\n\u2003 Four visits\n\n400\n\n40.0\n\n37.0, 43.1\n\n85\n\n33.5\n\n96\n\n43.4\n\n\u2003 More than four visits \u2003Missing\n\n349\n\n34.9\n\n32.0, 37.9\n\n102\n\n40.2\n\n75\n\n33.9\n\n3\n\n0.3\n\nWho makes medical care decisions for the index child?\n\n\u2003 Mother/caregiver alone\n\n185\n\n18.5\n\n16.2, 21.1\n\n43\n\n16.9\n\n34\n\n15.4\n\n\u2003 Mother/caregiver and spouse\n\n666\n\n66.7\n\n63.7, 69.5\n\n181\n\n71.3\n\n159\n\n72.0\n\n\u2003 Spouse alone\n\n64\n\n6.4\n\n5.0, 8.1\n\n13\n\n5.1\n\n10\n\n4.5\n\n\u2003Other\n\n79\n\n7.9\n\n6.4, 9.8\n\n16\n\n6.3\n\n17\n\n7.7\n\n\u2003 Nobody does/blank/missing\n\n5\n\n0.5\n\n1\n\n0.4\n\n1\n\n0.5\n\nMoved to Rubaga in index child\u2019s lifetime\n\n\u2003No\n\n755\n\n77.0\n\n74.3, 79.5\n\n196\n\n77.2\n\n183\n\n82.8\n\n\u2003Yes\n\n225\n\n22.5\n\n20.0, 25.2\n\n58\n\n22.8\n\n34\n\n15.4\n\n\u2003Missing\n\n19\n\n0.5\n\n4\n\n1.8\n\na Percentage may not equal 100 due to rounding Abbreviations: CI Confidence Interval\n\nFig.\u202f4\u2002 Distribution of index child\u2019s age in months at the time of receiving MCV vaccination (n\u2009=\u2009507)\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 11 of 17\n\nTable\u202f4\u2002 Logistic regression models to evaluate the association between ability to identify all three pieces of information (index child\u2019s sex, date of birth, and MCV1 receipt information) on their vaccination card and achieving on-time MCV1 vaccination. cOR and aOR plus 95% CIs for the estimates obtained for univariate and multivariable logistics regression models are reported, respectively\n\nUnivariate models (n\u2009=\u2009469)\n\nMultivariable model (n\u2009=\u2009340)\n\ncOR\n\n95%CI\n\np-value\n\naOR\n\n95% CI\n\np-value\n\nMother/caregiver was able to identify key pieces of information on index child\u2019s vaccination card\n\n\u2003No\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u2003Yes\n\n0.9\n\n0.6, 1.4\n\n0.75\n\n0.7\n\nMother/caregiver age (years)\n\n\u2003 Under 20\n\n0.4\n\n0.1, 1.7\n\n0.24\n\n0.1\n\n\u200320\u201324\n\n1.6\n\n1.0, 2.5\n\n0.06\n\n1.2\n\n\u200325\u201329\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u200330\u201334\n\n0.8\n\n0.4, 1.3\n\n0.33\n\n0.8\n\n\u200335+\n\n0.8\n\n0.5, 1.3\n\n0.37\n\n1.2\n\nMother/caregiver employed outside the home\n\n\u2003No\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u2003Yes\n\n0.6\n\n0.4, 0.9\n\n0.01\n\n0.7\n\nMother/caregiver highest level of education completed\n\n\u2003 Did not attend/do not know\n\n0.3\n\n0.1, 1.5\n\n0.14\n\n0.4\n\n\u2003Primary\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u2003Secondary\n\n1.4\n\n0.9, 2.1\n\n0.11\n\n1.4\n\n\u2003Post-secondary\n\n1.5\n\n0.8, 2.8\n\n0.26\n\n1.7\n\nIndex child birth order\n\n\u2003First\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u2003Second\n\n0.6\n\n0.4, 0.9\n\n0.03\n\n0.6\n\n\u2003 Third or higher\n\n0.5\n\n0.3, 0.8\n\n0.01\n\n0.6\n\nIndex child age (months)\n\n\u200312\u201323\n\n1.0\n\n\u2013\n\n\u2013\n\n1.0\n\n\u200324\u201335\n\n0.8\n\n0.5, 1.3\n\n0.46\n\n0.9\n\n\u200336\u201347\n\n1.1\n\n0.7, 1.9\n\n0.60\n\n1.2\n\n\u200348\u201359\n\n0.6\n\n0.3, 1.2\n\n0.17\n\n0.8\n\n\u200360+\n\n0.5\n\n0.2, 1.1\n\n0.09\n\n0.5\n\nIndex child sex\n\n\u2003Female\n\n1.0\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2003Male\n\n0.8\n\n0.6, 1.1\n\n0.21\n\n0.8\n\nWho makes medical care decisions for the index child?\n\n\u2003 Mother/caregiver alone\n\n1.0\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2003 Mother/caregiver and spouse\n\n1.1\n\n0.7, 1.8\n\n0.68\n\n\u2013\n\n\u2003 Spouse alone\n\n1.0\n\n0.4, 2.5\n\n0.95\n\n\u2013\n\n\u2003Other\n\n1.3\n\n0.6, 3.0\n\n0.48\n\n\u2013\n\nRelationship to index child\u2019s father\n\n\u2003 Currently married or living together\n\n1.0\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2003 Never married and never living together\n\n0.8\n\n0.4, 1.7\n\n0.63\n\n\u2013\n\n\u2003 Formerly married\n\n0.8\n\n0.5, 1.5\n\n0.49\n\n\u2013\n\nAbbreviations: cOR Crude Odds ratio, aOR Adjusted Odds ratio, CI Confidence Interval\n\n\u2013\n\n\u2013\n\n0.5, 1.1\n\n0.18\n\n0.01, 1.0\n\n0.05\n\n0.7, 2.1\n\n0.52\n\n\u2013\n\n\u2013\n\n0.5, 1.5\n\n0.57\n\n0.6, 2.3\n\n0.54\n\n\u2013\n\n\u2013\n\n0.4, 1.0\n\n0.06\n\n0.08, 2.0\n\n0.26\n\n\u2013\n\n\u2013\n\n0.9, 2.3\n\n0.13\n\n0.8, 3.7\n\n0.18\n\n\u2013\n\n\u2013\n\n0.3, 1.0\n\n0.07\n\n0.3, 1.0\n\n0.05\n\n\u2013\n\n\u2013\n\n0.5, 1.5\n\n0.73\n\n0.7, 2.1\n\n0.51\n\n0.4, 1.6\n\n0.47\n\n0.2, 1.1\n\n0.08\n\n\u2013\n\n\u2013\n\n0.5, 1.2\n\n0.30\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\n\u2013\n\ndate of measles vaccination listed), and 34.0% (n\u2009=\u2009340) had no documentation at all and thus had unknown vaccination status.\nAmong the 508 index children who had documentation that they were vaccinated with MCV, 43.5%\n\n(n\u2009=\u2009221) had documentation that they were vaccinated with MCV1on-time, 50.0% (n\u2009=\u2009254) had documentation that their MCV1 vaccination was delayed, 6.3% (n\u2009=\u200932) had documentation that they were vaccinated early (vaccinated with MCV0), and one index child was\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 12 of 17\n\nFig.\u202f5\u2002 Multivariable logistic regression model to assess the factors associated with participant\u2019s ability to identify all three key pieces of information (index child\u2019s sex, date of birth, and MCV1 information), compared to identifying less than three or none (n\u2009=\u2009542)\n\nmissing information to calculate timing of MCV vaccination (0.2%) (Figs. 3, 4).\nOf the 475 index children with known MCV1 vaccination status who were vaccinated on time or delayed, less than half 46.5% (n\u2009=\u2009221) were vaccinated on-time and 53.5% (n\u2009=\u2009254) were delayed, which was not significantly different from the hypothesized proportion of 50% (one-sample test of equality of proportions p-value\u2009=\u20090.13).\nFactors associated with achievement of on\u2011time MCV1 vaccination There was no association between a participant being able to identify information on their child\u2019s vaccination card and achieving on-time MCV1vaccination in the univariate analysis, or after adjusting for healthcare and demographic factors in the multivariable analysis (Table 4).\n\nFactors associated with ability to identify information on the vaccination card Of the 659 participants who had their child\u2019s vaccination card present at the time of the survey, 551 answered all three questions about identifying information on the card. Of those, about half (47.9%, n\u2009=\u2009264) could identify or point to all three pieces of information on the document: child\u2019s date of birth, child\u2019s sex, and child\u2019s MCV1 information (Items A, B, and C of Fig. 2). We found that mothers/caregivers who were part of other tribes (compared to Muganda [aOR\u2009=\u20090.5; 95%CI:0.3, 0.8]) and children who were thirdborn or higher in the birth order (compared to the firstborn, [aOR\u2009=\u20090.5; 95%CI:0.3, 0.9]) had lower odds of being able to identify the information on the vaccination card. Compared to participants who reported completing primary education, those who reported completing secondary education [aOR\u2009=\u20094.2; 95%CI:2.7,6.5] or post-secondary education [aOR\u2009=\u200915.7;\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 13 of 17\n\nFig.\u202f6\u2002 Multivariable logistic regression model to assess the factors associated with retention of the index child\u2019s vaccination card at the time of the survey (vs. did not retain vaccination card) (n\u2009=\u2009973)\n\n95%CI:6.7,36.8]), and those who reported that medical decisions for the index child were made jointly with their spouse (compared to making medical decisions on her own [aOR\u2009=\u20092.4; 95%CI:1.2, 4.9]) had a higher odds of being able to identify the information on the vaccination card (Fig. 5).\nFactors independently associated with retention of child\u2019s vaccination card We found that participants who retained their index child\u2019s vaccination card were significantly different from those who did not on certain characteristics, including whether they had moved into the district during the index child\u2019s lifetime, child\u2019s age, and the child\u2019s birth order (Fig. 6). Participants who were under 20\u2009years of age, compared to those who were 25 to 29\u2009years of age, had lower odds of having their child\u2019s vaccination card [aOR\u2009=\u20090.4; 95%CI:0.2, 0.8]. Compared to firstborn\n\nchildren, children higher in the birth order had a lower odds of having their child\u2019s vaccination card (second born [aOR\u2009=\u20090.5; 95%CI:0.3, 0.8], third born or higher [aOR\u2009=\u20090.5; 95%CI:0.3, 0.8]), and those whose index child was 36\u201347\u2009months of age [aOR\u2009=\u20090.6; 95%CI: 0.4, 0.9] or 48\u201359\u2009months of age [aOR\u2009=\u20090.5; 95%CI:0.3, 0.9] (compared to children who were 12\u201323\u2009months of age) had a lower odds of having their vaccination card. Additionally, participants who reported moving to Rubaga Division within the child\u2019s lifetime, compared to participants who did not move during the child\u2019s lifetime [aOR\u2009=\u20090.6; 95%CI:0.4, 0.8] had a lower odds of retaining their vaccination card (Fig. 6).\nDiscussion The primary aims in this study were to assess the proportion of children who were vaccinated with MCV1 on-time and delayed, and investigate the association between demographic\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 14 of 17\n\nfactors, ability to identify information on the child\u2019s vaccination card, and achieving on-time MCV1 vaccination. Our secondary aims were to investigate the association between demographic and healthcare factors and being able to identify key pieces of information on the vaccination card (vs. not being able to) and to investigate the association between demographic and healthcare factors and retaining the vaccination card (vs. not retaining). We found that over half of all participants were able to present some type of documentation of their child\u2019s vaccination at the time of the survey.\nAlthough 61.2% of the study population who had their child\u2019s vaccination card at the time of the survey reported completing a secondary or post-secondary level of education, over half of participants with a vaccination card were not able to locate key pieces of information on the card that may help mothers or caregivers determine when their child was to be vaccinated for MCV1. Among participants who retained their child\u2019s vaccination card, their ability to identify key pieces of information on the card, including the child\u2019s measles vaccination information, is not independently associated with on-time vaccination, after accounting for multiple demographic and healthcare factors. Due to the structure of the study, we only explored these factors among a sample of vaccinated children. It is possible that factors such as retaining the vaccination card and being able to identify information on it may have a greater impact on children getting vaccinated, rather than achieving on-time vaccination. It is also possible that there are other factors beyond card retention and utilization that influence achiecement of on-time vaccination.\nAmong participants with information on the date of their child receiving MCV1, less than half achieved ontime vaccination, and over half were delayed. Our findings differ from a 2012 study conducted by Babirye et al. in a similarly aged, urban population in Uganda that found about two-thirds of children received vaccination within the recommended age. The difference may have arisen because that study defined on-time vaccination as occurring between 38 weeks to 12 months, whereas we defined on-time vaccination as receiving MCV1 in the ninth month of age. Despite the broader time frame for achieving on-time vaccination, the authors noted that on-time measles vaccination was lower than any of the other recommended childhood vaccinations [16].\nThe purpose of the Uganda Ministry of Health Child Health Card is to monitor multiple aspects of a child\u2019s health and growth, including to indicate the age at which a child should get a vaccination and the type of vaccine, to mothers/caregivers and healthcare workers. The vaccination card plays an important role in monitoring a child\u2019s health, and hence the importance of retaining these records. We found that over half of study participants retained the card and presented it at the\n\ntime of the survey. Not surprisingly, the majority of children with retained vaccination cards were born in either a public or private health facility. The proportion of participants who retained their child\u2019s vaccination card at the time of the survey is similar to the proportion of children aged 0 to 24\u2009months with vaccination cards in a study in a similar setting [21]. That study also found that children delivered at a health facility were four times as likely to have a vaccination card, compared to those that were delivered at home. Another study conducted in a similarly aged population (0 to 24\u2009months) in Nepal found that overall retention of the vaccination card was higher (82.2%) than in our study sample. In the Nepal study, vaccination card retention was 90.3% among 0\u201312\u2009months children age group and 74% among children aged 12 to 24\u2009months, indicating that child\u2019s age may have an influence on retention of the card [31].\nRelatedly, we found that the child\u2019s birth order was independently associated with retention of the child\u2019s vaccination card, with the odds of a participant retaining their child\u2019s vaccination card being lower for children secondborn or higher in the birth order, compared to firstborn. Furthermore, our univariate analyses revealed a strong association between birth order and achieving on-time vaccination, with children higher in the birth order having a lower odds of being vaccinated on time, compared to firstborn children. These findings are consistent with the findings of a study conducted in a similar population in Uganda, in which vaccinations that were not received during the recommended timeframe were associated with a higher number of children per woman (adjusted hazard ratio (AHR) 1.84, 95% CI 1.29, 2.64) [16, 32].\nWhile about one third of the children in the study sample were one to two years of age, the majority were older than two years. This gap of time, which ranged from three months up to four years and three months between use of the card for vaccination and this survey may have influenced both the probability of the participants retaining the child\u2019s vaccination card and the way participants responded to prompts about finding information on the card. Although this is the case, parents are instructed to retain their child\u2019s vaccination card for documentation of routine vitamin A supplementation, deworming activities, and growth monitoring through five years of age [33].\nWe found that mother/caregiver-reported completion of secondary or post-secondary education, compared to primary education, was independently associated with their ability to identify information on their child\u2019s vaccination card, including their child\u2019s measles vaccination information, which may have influenced the timing of their child\u2019s vaccinations.\nIn previous studies, mother\u2019s education level was found to be a predictor of vaccination timing in multiple\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 15 of 17\n\nsettings, with lower educational status associated with delayed vaccination in a study in Senegal, which used Demographic and Health Survey (DHS) data [34], and in other population-based surveys in Ghana [35] and Kenya [18]. Our analysis did not yield a similar finding, which may be due to the moderately high level of education in the study sample overall (49.7% of participants reported completing secondary education or higher).\nThe strengths of this study include a relatively large sample size within a population that is at an increased risk for measles infection. The clear aims of the study, along with the use of the child\u2019s vaccination card increase the accuracy of the measured outcome of vaccination timeliness. Assessing the timing of the measles vaccination, in addition to whether the vaccine was received, increases opportunities to identify gaps in care and to advance research to improve vaccination timing and therefore increase protection from measles infection. Furthermore, the assessment of the vaccination card as a reminder tool for timely vaccination generates important new questions about how these cards are utilized and how their value can be increased.\nThe findings should be interpreted in light of a few limitations. First, vaccination timing was based on documented information on the child\u2019s vaccination card, and only a subset of the surveyed population had a vaccination card. The significant difference between participants who retained and did not retain the card may have introduced selection bias into the sample, which limits the generalizability of the findings. Furthermore, for pragmatic reasons, the survey was only available in two languages: Luganda and English, which again introduced selection bias. This may not substantially limit the generalizability of the sample to other groups, because English is the national language of Uganda and Luganda is one of the most widely spoken languages. Third, this was a study of mothers/caregivers with surviving children who have not reached their sixth birthday and hence might have a survivor bias, as children who did not survive to this age might have been more likely to be under-vaccinated.\nFinally, this study collected the mothers\u2019/caregivers\u2019 selfreport of the month and year of the child\u2019s birth, thus it is not possible to assess the timing of the MCV1 dose to the day. Rather, this was estimated to the month. We expect some misclassification of age at measles vaccination by one month if the day of birth is greater than the day of survey administration. Therefore, there is also misclassification of on-time MCV1 vaccination status, based on when within the month the child was born. We consider this misclassification to be nondifferential, as the day of survey administration varied throughout the months that the data collection was taking place. Collecting date\n\nof birth information from the vaccination card itself may have improved the accuracy of child age calculations and reduced misclassification, although that information would be missing from the about one-third of children who did not have a vaccination card available at the time of the survey.\nNevertheless, these study findings are important for understanding the complex factors that are associated with achieving on-time measles vaccination, especially within a population at a high risk for measles infection.\nConclusion Being able to identify information on their child\u2019s vaccination card was not associated with achieving on-time measles vaccination. New strategies are needed to both ensure that mothers/caregivers understand and can access the information on their child\u2019s vaccination card, as this is the only documentation that indicates the age at which a child is due for MCV1 and is the only documentation that parents have of vaccine receipt. Further research can shed light on mechanisms by which measles vaccination is delayed and investigate factors that may prompt or remind mothers and other primary caregivers of the time when their child is due for a measles vaccine.\nAbbreviations AFRO: WHO African Region; AHR: Adjusted Hazard Ratio; aOR: Adjusted odds ratio; AU: Administrative unit; CI: Confidence interval; cOR: Crude odds ratio; DHS: Demographic and Health Survey; DTwPHibHepB: Diphtheria and tetanus and pertussis and Hemophilus influenzae and hepatitis B vaccine; IPV: Inactivated polio vaccine; LMICs: Low- and Middle- Income Countries; MCV/MCV0/ MCV1: Measles-containing vaccine/Measles-containing vaccine, dose 0/ Measles-containing vaccine, dose 1; MeV: Measles morbillivirus; PCV: Pneumococcal conjugate vaccine; SOMREC: Makerere University School of Medicine Research and Ethics Committee; UCHC: Uganda Ministry of Health Child Health Card; UNCST: Uganda National Council for Science and Technology; UNEPI: Uganda National Expanded Program on Immunisation; UNICEF: United Nations Children\u2019s Fund; WHO: World Health Organization.\nSupplementary Information\nThe online version contains supplementary material available at https://\u200bdoi.\u200b org/\u200b10.\u200b1186/\u200bs12889-\u200b022-\u200b13113-z.\nAdditional file 1.\nAcknowledgements We thank Derrick Bary Abila, Kabahweza Josephine, Mazinga Mark, Mbabazi Irene, Nasirumbi Bridget, Nakawunde Robinah, Nsubuga Kikoyo Joachim, Taremwa Seti, Ssekyanzi Henry, and Stewart Walukaga for their contributions to making this research possible.\nFinancial disclosure The authors have no financial relationships relevant to this article to disclose.\nAuthors\u2019 contributions NB, DN, CB, and BG designed the study and survey content. CB, BG, and NB coordinated data collection. BG cleaned and analysed the data. BG, NB, SC, KS, and CB drafted the manuscript. All authors read and approved the final manuscript.\n\nGriffith et al. BMC Public Health (2022) 22:834\n\nPage 16 of 17\n\nFunding This work was funded in part by the National Center for Advancing Translational Sciences of the National Institutes of Health Award [#UL1TR000114 (PI: Nicole E. Basta)], the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01 AI132496 (PI: Nicole E. Basta), and the Bill and Melinda Gates Foundation Grand Challenges [#OPP1182642 (PI: Diana M. Negoescu)]. The authors gratefully acknowledge support from the Minnesota Population Center (P2C HD041023) and the Interdisciplinary Population Health Science Training Program (T32HD095134). Both are funded by the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\nAvailability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\nDeclarations\nEthics approval and consent to participate This study was reviewed and granted approval by the Makerere University School of Medicine Research and Ethics Committee (SOMREC) (2018\u2013117), the Uganda National Council for Science and Technology (UNCST), and the University of Minnesota Institutional Review Board (STUDY00004955) as a study that involves no greater than minimal risk. All study methods and data collection procedures were conducted in accordance with the guidelines and recommendations of the approving IRBs and Uganda National Council for Science and Technology. All participants provided written informed consent prior to participation in this research. If a participant indicated that they were unable to read a written informed consent form, interested participants were asked to select an adult member of the household to witness the informed consent process. The interviewer read them the content of the informed consent form in the participants\u2019 preferred language and responded to any arising questions or clarifications to the potential participants\u2019 satisfaction. Finally, the potential participant signed the informed consent forms with a thumbprint and the witness wrote the participants name above the thumbprint and also signed the consent forms.\nConsent for publication Not applicable.\nCompeting interests The authors declare that they have not competing interests.\nAuthor details 1\u200aDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University Faculty of Medicine and Health Sciences, 2001 McGill College, Suite 1200, QC H3A 1G1 Montreal, Canada. 2\u200aDivision of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA. 3\u200aDepartment of Pediatrics, University of Minnesota Medical School Twin Cities, Minneapolis, MN, USA. 4\u200aDepartment of Industrial and Systems Engineering, University of Minnesota College of Science and Engineering, Minneapolis, MN, USA. 5\u200aChild Health and Development Centre, School of Medicine, Makerere University, Kampala, Uganda.\nReceived: 5 November 2021 Accepted: 15 March 2022\nReferences 1. Moss WJ, Griffin DE. Measles. Lancet. 2012;379(9811):153\u201364. 2. World Health Organization. Measles vaccines: WHO position paper - April\n2017. Releve Epidemiologique Hebdomadaire. 2017;92(17):205\u201327. 3. Patel MK, Dumolard L, Nedelec Y, Sodha SV, Steulet C, Gacic-Dobo M,\net al. Progress toward regional measles elimination - worldwide, 20002018. MMWR Morb Mortal Wkly Rep. 2019;68(48):1105\u201311.\n\n4. Fadnes LT, Nankabirwa V, Sommerfelt H, Tyllesk\u00e4r T, Tumwine JK, Engebretsen IMS. Is vaccination coverage a good indicator of ageappropriate vaccination? A prospective study from Uganda. Vaccine. 2011;29(19):3564\u201370.\n5. Heininger U, Zuberbuhler M. Immunization rates and timely administration in pre-school and school-aged children. Eur J Pediatr. 2006;165(2):124\u20139.\n6. Clark A, Sanderson C. Timing of children\u2019s vaccinations in 45 low-income and middle-income countries: an analysis of survey data. Lancet. 2009;373(9674):1543\u20139.\n7. Malande OO, Munube D, Afaayo RN, Annet K, Bodo B, Bakainaga A, et al. Barriers to effective uptake and provision of immunization in a rural district in Uganda. Plos One. 2019;14(2):e0212270.\n8. World Health Organization. WHO recommendations for routine immunization - summary tables 2018. Available from: https://\u200bwww.\u200bwho.\u200bint/\u200bimmun\u200b izati\u200bon/\u200bpolicy/\u200bimmun\u200bizati\u200bon_\u200btables/\u200ben/. Updated 21 December 2018\n9. UNICEF. Immunization coverage survey data. UNICEF, editor. Immunization Data 2020.\n10. Ferrari MJ, Grais RF, Bharti N, Conlan AJ, Bjornstad ON, Wolfson LJ, et al. The dynamics of measles in sub-Saharan Africa. Nature. 2008;451(7179):679\u201384.\n11. Takahashi S, Metcalf CJE, Ferrari MJ, Tatem AJ, Lessler J. The geography of measles vaccination in the African Great Lakes region. Nat Commun. 2017;8(1):15585.\n12. Uganda Ministry of Health. Measles-rubella & polio immunisation campaign fact sheet 2018.\n13. Independent reporter. Gov\u2019t moves to introduce second dose of measles vaccine The Independent [Internet]. 2020 10 December 2020 [cited 2021]. Available from: https://\u200bwww.\u200bindep\u200bendent.\u200bco.\u200bug/\u200bgovt-\u200bmoves-\u200btoint\u200b roduce-\u200bsecond-\u200bdose-\u200bof-\u200bmeasl\u200bes-\u200bvacci\u200bne/.\n14. Uganda Ministry of Health. UNEPI Immunisation Guidelines Kampala, Uganda. 2019.\n15. Babirye JN, Rutebemberwa E, Kiguli J, Wamani H, Nuwaha F, Engebretsen IMS. More support for mothers: a qualitative study on factors affecting immunisation behaviour in Kampala, Uganda. BMC Public Health. 2011;11(1):723.\n16. Babirye JN, Engebretsen IM, Makumbi F, Fadnes LT, Wamani H, Tylleskar T, et al. Timeliness of childhood vaccinations in Kampala Uganda: a community-based cross-sectional study. Plos One. 2012;7(4):e35432.\n17. Binyaruka P, Borghi J. Validity of parental recalls to estimate vaccination coverage: evidence from Tanzania. BMC Health Serv Res. 2018;18(1):440.\n18. Mutua MK, Kimani-Murage E, Ngomi N, Ravn H, Mwaniki P, Echoka E. Fully immunized child: coverage, timing and sequencing of routine immunization in an urban poor settlement in Nairobi, Kenya. Trop Med Health. 2016;44:13.\n19. Ainebyoona E. More than 18 million children in Uganda to be immunized against measles, rubella and polio in mass campaign. WHO Regional Office for Africa Newsroom [Internet]. 2019 15 October 2019 [cited 2021] Available from: https://\u200bwww.\u200bafro.\u200bwho.\u200bint/\u200bnews/\u200bmore-\u200b18-\u200bmilli\u200bonchi\u200bldren-\u200buganda-\u200bbe-\u200b immun\u200bized-\u200bagain\u200bst-\u200bmeasl\u200bes-\u200brubel\u200bla-\u200band-\u200bpolio-\u200bmass-\u200bcampa\u200bign.\n20. Ainebyoona E. Statement from Uganda\u2019s Minister of Health on the National Measles-Rubella and Polio Immunisation Campaign 2019. WHO Regional Office for Africa Newsroom [Internet]. 2019 15 November 2019. Available from: https://\u200bwww.\u200bafro.\u200bwho.\u200bint/\u200bnews/\u200bstate\u200bment-\u200bugand\u200bas-\u200bminis\u200b ter-\u200bhealth-\u200bnatio\u200bnal-\u200bmeasl\u200bes-\u200brubel\u200bla-\u200band-\u200bpolio\u200bimmun\u200bisati\u200bon-\u200bcampa\u200bign.\n21. Mukanga DO, Kiguli S. Factors affecting the retention and use of child health cards in a slum community in Kampala, Uganda, 2005. Matern Child Health J. 2006;10(6):545\u201352.\n22. Okello G, Izudi J, Ampeire I, Nghania F, Dochez C, Hens N. Two decades of regional trends in vaccination completion and coverage among children aged 12-23\u2009months: an analysis of the Uganda demographic health survey data from 1995 to 2016. BMC Health Serv Res. 2022;22(1):40.\n23. Sato R, Fintan B. Women\u2019s understanding of immunization card and its correlation with vaccination behaviors. Hum Vacc Immunother. 2020;16(10):2408\u201314.\n24. \u00a9 OpenStreetMap Contributors. OpenStreetMap 2022 [Available from: https://\u200bwww.\u200bopens\u200btreet\u200bmap.\u200borg/].\n25. Uganda Bureau of Statistics (UBOS), Uganda National Household Survey 2019/2020 Report. Kampala, Uganda: Uganda Bureau of Statistics (UBOS); 2021.\n\nGriffith et al. BMC Public Health (2022) 22:834\n26. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O\u2019Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.\n27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)\u2014a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377\u201381.\n28. StataCorp. Stata statistical software: release 14. 14th ed. College Station: StataCorp LP; 2015.\n29. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2021.\n30. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.\n31. Paudel KP, Bajracharya DC, Karki K, K CA. Factors determining availability, utilization and retention of child health card in Western Nepal. J Nepal Health Res Counc. 2016;14(33):99\u2013103.\n32. Babirye JN, Engebretsen IM, Rutebemberwa E, Kiguli J, Nuwaha F. Urban settings do not ensure access to services: findings from the immunisation programme in Kampala Uganda. BMC Health Serv Res. 2014;14(1):111.\n33. Uganda Ministry of Health. Uganda Clinical Guidelines 2016. Kampala: Uganda Ministry of Health; 2016.\n34. Mbengue MAS, Mboup A, Ly ID, Faye A, Camara FBN, Thiam M, et al. Vaccination coverage and immunization timeliness among children aged 12-23 months in Senegal: a Kaplan-Meier and cox regression analysis approach. Pan Afr Med J. 2017;27(Suppl 3):8.\n35. Gram L, Soremekun S, ten Asbroek A, Manu A, O\u2019Leary M, Hill Z, et al. Socio-economic determinants and inequities in coverage and timeliness of early childhood immunisation in rural Ghana. Trop Med Int Health. 2014;19(7):802\u201311.\nPublisher\u2019s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\nPage 17 of 17\n\nReady to submit your research ? Choose BMC and benefit from:\n\u2022 fast, convenient online submission \u2022 thorough peer review by experienced researchers in your \ufb01eld \u2022 rapid publication on acceptance \u2022 support for research data, including large and complex data types \u2022 gold Open Access which fosters wider collaboration and increased citations \u2022 maximum visibility for your research: over 100M website views per year\nAt BMC, research is always in progress.\nLearn more biomedcentral.com/submissions\n\n\n",
+    "authors": [
+      "B. C. Griffith",
+      "S. E. Cusick",
+      "K. M. Searle",
+      "D. M. Negoescu",
+      "N. E. Basta",
+      "C. Banura"
+    ],
+    "doi": "10.1186/s12889-022-13113-z",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "KJKDBW95",
+    "title": "Compulsory Vaccination Coverage in 12 Sub-Saharan African Countries Two Years Following the COVID-19 Pandemic",
+    "abstract": "The coronavirus disease 2019 (COVID-19) pandemic is a global threat, challenging health services' provision and utilization. This study aimed to assess compulsory vaccination coverage in 12 Sub-Saharan African countries two years following the COVID-19 pandemic using the Health Belief Model. A cross-sectional survey was conducted from November 1 to December 15, 2022. Multivariate logistic regression was conducted to identify the determinants of vaccination coverage. Among the 5032 respondents, 73.1% reported that their children received compulsory vaccination. The lowest coverage was observed in Ghana (36.5%), while the highest was in Burkina Faso and Congo (92.0%). Factors associated with non-vaccination included older mothers (adjusted odds ratio (AOR)\u2009=\u20091.04, 95%CI: 1.03-1.05), lower mothers' education, older children (AOR\u2009=\u20090.76, 95%CI: 0.60-0.96), children with chronic illnesses (AOR\u2009=\u20090.55, 95%CI: 0.45-0.66), and difficult accessibility to healthcare facilities (AOR\u2009=\u200911.27, 95%CI: 9.48-13.44). Low perceived risk, in which non-vaccinated children were believed to be at no higher risk for infectious diseases and the disease severity would not worsen among non-vaccinated children, increased the likelihood of non-vaccination (AOR\u2009=\u20092.29, 95%CI: 1.75-2.99 and AOR\u2009=\u20092.12, 95%CI: 1.64-2.73, respectively). Perceiving vaccines as unnecessary, and needless for breastfed babies increased the probability of non-vaccination (AOR\u2009=\u20091.38, 95%CI: 1.10-1.73 and AOR\u2009=\u20091.69, 95%CI: 1.31-2.19, respectively). Higher odds of non-vaccination were found when the provision of vaccine information did not motivate parents to vaccinate their children (AOR\u2009=\u20094.29, 95%CI: 3.15-5.85). Conversely, believing that vaccines were safe for children decreased the odds of non-vaccination (AOR\u2009=\u20090.72, 95%CI: 0.58-0.88). Parental perceptions and concerns should be considered in interventions aiming to increase compulsory vaccine acceptance and coverage.",
+    "full_text": "",
+    "authors": [
+      "R. M. Ghazy",
+      "A. Gebreal",
+      "M. R. A. Saleeb",
+      "M. Sallam",
+      "A. E. N. El-Deen",
+      "S. D. Sheriff",
+      "E. A. Tessema",
+      "S. Ahurwendeire",
+      "N. Tsoeu",
+      "P. C. Chamambala",
+      "P. B. Cibangu",
+      "D. U. Okeh",
+      "A. S. Traor\u00e9",
+      "G. Eshun",
+      "N. E. Kengo",
+      "A. E. Kubuka",
+      "L. B. Awuah",
+      "A. Salah",
+      "M. Aljohani",
+      "N. Fadl"
+    ],
+    "doi": "10.1007/s10900-023-01261-1",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "63VDNTKL",
+    "title": "Towards universal health coverage: Data for determinants of immunization coverage of Pneumococcal and Rota virus vaccines among under five children in Busolwe Town Council, Butaleja District, Eastern Uganda",
+    "abstract": "The data described stipulates the factors influencing the immunization coverage of Pneumococcal and Rota Virus Vaccines among under five children (U5C) in Butaleja district in Eastern Uganda. The data was obtained in three major sections of demographic characteristics, knowledge, and attitude and perceptions of care takers of U5C on immunization. Both qualitative and quantitative types of data obtained from Primary and Secondary data sources are presented. The Primary sources included administration of questionnaires to the caretakers of U5C in communities surrounding different health centers in Butaleja district. The secondary source of data was majorly the Health Management Information Systems (HMIS) records of Busolwe District Hospital. The data includes raw data from individual participants in form of Google forms portable document format, the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on immunization coverage in form of Microsoft excel format. The data provides a general outlook on the state of Butaleja district in terms immunization coverage of Pneumococcal and Rota Virus Vaccines. The data can be useful in taking action to decrease the burden of vaccine preventable diseases in Butaleja and elsewhere in similar settings. The data described is freely available in the Mendeley Data repository at the following site: https://doi.org/10.17632/zr2w886dg2.1 (Nabwana et\u00a0al., 2019).",
+    "full_text": "Data in brief 25 (2019) 104269\nContents lists available at ScienceDirect\nData in brief\njournal homepage: www.elsevier.com/locate/dib\nData Article\nTowards universal health coverage: Data for determinants of immunization coverage of Pneumococcal and Rota virus vaccines among under \ufb01ve children in Busolwe Town Council, Butaleja District, Eastern Uganda\nBrenda Wafana Nabwana a, Sylvia Sidney Namayanja a, Collette Kemigisha a, Erina Kisakye a, Amos Kuddiza Kusetula a, Silvester Wakabi a, Ivan Wambi b, Innocent Musiime b, Rebecca Nekaka a, Yahaya Gavamukulya c, *\na Department of Community and Public Health, Faculty of Health Sciences, Busitema University, P.O. Box, 1460 Mbale, Uganda b Busolwe General Hospital, Butaleja District Local Government, Butaleja District, Uganda c Department of Biochemistry and Molecular Biology, Faculty of Health Sciences, Busitema University, P.O. Box, 1460 Mbale, Uganda\n\narticle info\nArticle history: Received 12 June 2019 Received in revised form 4 July 2019 Accepted 9 July 2019 Available online 15 July 2019\nKeywords: Immunization coverage PCV Rotavirus vaccine Under \ufb01ve children (U5C) Butaleja Eastern Uganda\n\nabstract\nThe data described stipulates the factors in\ufb02uencing the immunization coverage of Pneumococcal and Rota Virus Vaccines among under \ufb01ve children (U5C) in Butaleja district in Eastern Uganda. The data was obtained in three major sections of demographic characteristics, knowledge, and attitude and perceptions of care takers of U5C on immunization. Both qualitative and quantitative types of data obtained from Primary and Secondary data sources are presented. The Primary sources included administration of questionnaires to the caretakers of U5C in communities surrounding different health centers in Butaleja district. The secondary source of data was majorly the Health Management Information Systems (HMIS) records of Busolwe District Hospital. The data includes raw data from individual participants in form of Google forms portable document format, the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on\n\n* Corresponding author. E-mail address: gavayahya@yahoo.com (Y. Gavamukulya).\nhttps://doi.org/10.1016/j.dib.2019.104269 2352-3409/\u00a9 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).\n\n2\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\nimmunization coverage in form of Microsoft excel format. The data provides a general outlook on the state of Butaleja district in terms immunization coverage of Pneumococcal and Rota Virus Vaccines. The data can be useful in taking action to decrease the burden of vaccine preventable diseases in Butaleja and elsewhere in similar settings. The data described is freely available in the Mendeley Data repository at the following site: https://doi.org/10.17632/zr2w886dg2.1 (Nabwana et al., 2019).\n\u00a9 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.\norg/licenses/by/4.0/).\n\nSpeci\ufb01cations table\n\nSubject area\n\nHealth Sciences, Medical Sciences\n\nMore speci\ufb01c subject area Immunization, Public Health\n\nType of data\n\nMicrosoft Excel csv., Microsoft Excel Macros enabled \ufb01les, PDF \ufb01les\n\nHow data was acquired Online Researcher Administered Survey accessible at (https://forms.gle/PCi5rbK1mt5tgzhA8)\n\nData format\n\nRaw and \ufb01ltered\n\nExperimental factors\n\nA cross sectional study design was employed. Demographic and socio-economic characteristics\n\nwere some of the key concept variables and purposive sampling was preferred. Ethical approval\n\nand Permission to conduct the study were obtained from the relevant University and District\n\nauthorities. Care takers of U5C in Butaleja District who gave an informed consent were included\n\nin the study.\n\nExperimental features\n\nInformed consent was sought from the participants prior to participation in the data collection\n\nprocess. An online survey was administered through Google forms to recruited participants.\n\nData source location\n\nBusolwe Town Council, Butaleja District - Eastern Uganda; Busitema University Faculty of Health\n\nSciences, Mbale e Eastern Uganda\n\nData accessibility\n\nAll the reported datasets can be accessed via the Mendeley Data Repository at (https://doi.org/\n\n10.17632/zr2w886dg2.1) [1]\n\nValue of the data   The data is useful to governments in assessment of the immunization coverage and utilization of the vaccines by citizens.   The community members bene\ufb01t from this data in that feasible solutions can be accorded with respect to evidence-based\ninformation on declining trend of immunization.   The data can be used as a reference for comparative studies in similar settings.   The data can help to guide resource allocation and direction of work plan especially pertaining immunization of children.   The data can be useful in evaluation of health indicators such as utilization of immunization services by the line ministries.   The data collection tools can be used in conducting studies in other locations or on other diseases   This data can be used to determine possible trend of immunization coverage for other vaccine preventable diseases like\nHepatitis B or vaccinations that require multiple dosages.\n1. Data\nThe data described includes raw data from individual participants in form of Google forms portable document format (PDF), the consolidated raw data from all the participants in Microsoft excel format, as well as raw data from secondary HMIS record on immunization coverage in form of Microsoft excel format.\na. Individual responses for the determinants of Immunization Coverage in Busolwe 2019 - Google Forms.pdf [1].\nb. Consolidated data for determinants of Immunization Coverage in Busolwe 2019.csv [1]. c. Raw datasets for the secondary HMIS data on immunization.xlsm [1].\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\n3\n\n2. Experimental design, materials, and methods\n\n2.1. Area of study\nThe study fromwhich the data was obtained was carried out in Butaleja District in Eastern Uganda which is bordered by Budaka and Kibuku districts in the North, Mbale in the East, Tororo district in the South East and Namutumba in the West. Butaleja district has a total population of 244153 people of which 119466 (48.9%) are males and 124687 (51.1%) females according to the national population census 2014 [2]. The Busolwe General Hospital has a catchment population of 42298 people, with women in childbearing age being 8544, with number of pregnancies being 2114, number of live births 2051; number under \ufb01ve years is 8544.\n\n2.2. Target population\nThe study from which the data was obtained targeted the caregivers (primary care givers or parents) of U5C in homes in villages in the hospital's catchment area. Parent(s) and/or caretaker to the U5C who refused to give informed consent were excluded.\n\n2.3. Study design\nThe study from which the data was obtained followed a cross sectional study design to study representative samples of a population. Mixed qualitative-quantitative methods were employed using the questionnaires to the caretakers and more information was obtained from the HMIS records.\n\n2.4. Sample size determination\nThe minimum sample size was determined using the Cochran's formula N \u00bc (1.96)2pq/d2, with a con\ufb01dence level of approximately 95% (1.96).\nWhere, N \u00bc required sample size, P \u00bc proportion of population having the characteristics considering recent studies, q \u00bc (1-p) and d\u00bc ( \u00b15%) degree of precision. Therefore, considering \ufb01ndings from a current study on Knowledge and Perception of Caregivers about Risk Factors and Manifestations of Pneumonia among Under Five Children in Butaleja District, Eastern Uganda [3],p \u00bc 53.7, q \u00bc 1e0.537, d \u00bc 5/100 \u00bc 0.05.Thus, N \u00bc [(1.96)2 \u00c2 0.537 \u00c2 0.463]/(0.05)2 \u00bc 0.9551/0.0025 \u00bc 382 participants. In order to reduce errors, the sample population was enlarged from 382 participants to 434 participants.\n\n2.5. Data collection\nAn interviewer administered questionnaire was used to assess the perceptions and attitudes of the different correspondents towards the immunizable diseases as well as the factors associated with the immunization coverage in Butaleja district. A Google form (Determinants of Immunization Coverage in Busolwe 2019 - Google Forms Questionnaire.pdf [1]) was created and used to administer the questionnaire with datasets directly \ufb01lled to Excel worksheets. The questionnaire was pretested and validated among 2nd year Medical and Nursing students at BUFHS. Secondary data was obtained from the Busolwe district HMIS records to determine the number of people who immunized fully.\n\n2.6. Data storage\nThe raw data collected on questionnaires (Google forms) was automatically uploaded and securely stored online, and access to it was limited to only 3 administrators.\n\n2.7. Data analysis\nThe data can be analyzed by use of the compiled datasets to assess the concept variables, correlations, tendencies, among others. Excel and STATA programs can be majorly used in the data analysis.\n\n4\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\nThe analyzed data/information can be presented in form of statistical tables, charts, and generalized \ufb01gures, with interpretive descriptions of the information.\nConsent\n\nWritten informed consent from caretakers of the U5C was obtained before they participated in the study. Participants were informed that their privacy and con\ufb01dentiality would be respected and that there was no potential harm associated with participating in the study. It was made clear to the participants that participation in the study was voluntary and that they were free to opt out of the study at any time without any negative consequences.\n\nEthical approval\n\nThe study and all the protocols from which the data was obtained were approved and cleared by the Busitema University Faculty of Health Sciences Higher Degrees and Research Committee (BUFHSHDRC) as part of the Community Based Education, Research and Services (COBERS) Program for the 2018/2019 Academic year under the Course of Community Diagnosis and Communication Projects. Permission to conduct the study was sought from the District Health Of\ufb01cer Butaleja and the Medical Superintendent of Busolwe Hospital. The Chief Administrative Of\ufb01cer (CAO), community leaders and the members of the community consented to the research activities for the data collection; and in this all the participants signed a consent form which clearly stated their rights and the boundaries of the research. All the personal data was kept con\ufb01dential and participant items under lock and key.\n\nAcknowledgments\n\nThe research from which this data was obtained was funded by the Busitema University Faculty of Health Sciences COBERS Committee, the Regional Health Integration To Enhance Services in Eastern Uganda (RHITES-E) Team, as well as the Fogarty International Center of the National Institutes of Health, U.S. Department of State's Of\ufb01ce of the U.S. Global AIDS Coordinator and Health Diplomacy (S/ GAC), and President's Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1R25TW011213. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the of\ufb01cial views of the funders.\nWe thank Busitema University Faculty of Health Sciences Higher Degrees and Research Committee (BUFHS-HDRC) which gave in time to review and approve the proposal and accompanying protocols through The Busitema University Community Based Education, Research and Services (COBERS) Committee. Furthermore, the Authors would also like to express their explicit thanks to all the Doctors, Clinical Of\ufb01cers, Senior nursing of\ufb01cers, Laboratory technicians, HMIS personnel, Village Health Teams (VHTs) and all other staff who were of great help in our facility-based and community activities. Final thanks go to all the Volunteers who participated in the study. We also further extend our gratitude to the DHO, CAO, DHE and LCV of Butaleja District for having granted endorsement and acceptance for our interactions in the community, the VHTs and LCI chairpersons who guided us during our community activities especially home visits and the RHITES-E team especially Mr. Anoku Patrick for according the members transport and other support when called upon.\n\nCon\ufb02ict of interest\n\nThe authors declare that they have no known competing \ufb01nancial interests or personal relationships that could have appeared to in\ufb02uence the work reported in this paper.\n\nReferences\n[1] W.B. Nabwana, S.S. Namayanja, C. Kemigisha, E. Kisakye, A.K. Kusetula, S. Wakabi, I. Wambi, I. Musiime, R. Nekaka, Y. Gavamukulya, Data for Determinants of Immunization Coverage of PCV and Rota Virus Among under Five Children in Busolwe Town Council, Mendeley Data, Butaleja District, Eastern Uganda, 2019, https://doi.org/10.17632/zr2w886dg2.1 v1.\n\nB.W. Nabwana et al. / Data in brief 25 (2019) 104269\n\n5\n\n[2] Uganda Bureau of Statistics, The National Population and Housing Census 2014, Area Speci\ufb01c Pro\ufb01le Series -Butaleja District, Kampala, Uganda, 2017.\n[3] B. Aguti, G. Kalema, D.M. Lutwama, M.L. Mawejje, E. Mupeyi, D. Okanya, R. Nekaka, Y. Gavamukulya, Knowledge and perception of caregivers about Risk factors and Manifestations of Pneumonia among under \ufb01ve children in Butaleja district , Eastern Uganda, Microbiol. Res. J. Int. 25 (2018) 1e11, https://doi.org/10.9734/MRJI/2018/44179.\n\n\n",
+    "authors": [
+      "B. W. Nabwana",
+      "S. S. Namayanja",
+      "C. Kemigisha",
+      "E. Kisakye",
+      "A. K. Kusetula",
+      "S. Wakabi",
+      "I. Wambi",
+      "I. Musiime",
+      "R. Nekaka",
+      "Y. Gavamukulya"
+    ],
+    "doi": "10.1016/j.dib.2019.104269",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "5VZ5D9GB",
+    "title": "Socio-demographic Determinants of Vaccine Coverage for Pneumococcus and Rotavirus among under Five Children in Busolwe Town Council, Butaleja District, Eastern Uganda: A Cross Sectional Study",
+    "abstract": "BACKGROUND AND AIMS: There is a high burden of vaccine-preventable diseases in the children under five years of age, particularly pneumonia diarrhea and which is greatly affected by low immunization coverage despite the existing efforts and policies. This study was carried out in Butaleja district and was aimed at establishing the socio-demographic determinants of vaccine coverage for pneumococcus and rotavirus among under five children (U5C) in the district. STUDY DESIGN: This was a mixed methods cross-sectional study. PLACE AND DURATION OF STUDY: Busolwe Town Council, Butaleja District, Eastern Uganda. METHODOLOGY: Structured researcher administered questionnaires were administered to 434 caregivers of U5C in different parts of Butaleja district. In-depth interviews with key informants and focused group discussions with Village Health Teams and community members were conducted. Review of Health Management Information Systems records was done. STATA 15 was used to analyze the data. RESULTS: The study found that there is a declining trend in completion of the doses of Pneumococcal vaccine (PCV) and Rotavirus vaccine. For example, in quarter 1 of 2019, out of the 312 children who started immunization, only 2 completed Rota virus immunization and only 117 completed PCV vaccinations a trend that has been observed since 2016. The factors that showed a significant association with the the fact that they gave their child at least one dose of the vaccine were having been sensitized on the current immunisation schedule(P-value = <0.001), misunderstanding that vaccine is harmful for child (P-value = 0.007), willingness to take children to vaccination (P-value = <0.001), and social factors such as family (P-value = <0.030). Gender also played a key determinant role where the children's fathers lacked knowledge on significance of immunization and thus discouraged the mothers from taking the children for immunization. Inadequate funding was also highlighted from the Focus Group Discussions. CONCLUSION: Vaccine coverage for pneumococcus and rotavirus is still low in Butaleja district mainly due to the attitudes and perceptions of caregivers as well as the knowledge gap. There is need for extensive sensitization of all community members to enable them understand the significance of immunization. It would further be important to increase the funding of the immunization programme to intensify and ensure effectual outreaches as well as the establishment and enforcement of a policy for immunization compliance.",
+    "full_text": "International Journal of TROPICAL DISEASE & Health\n39(3): 1-13, 2019; Article no.IJTDH.52839 ISSN: 2278\u20131005, NLM ID: 101632866\n\nSocio-demographic Determinants of Vaccine Coverage for Pneumococcus and Rotavirus among\nunder Five Children in Busolwe Town Council, Butaleja District, Eastern Uganda: A Cross Sectional\nStudy\nBrenda Wafana Nabwana1, Sylvia Sidney Namayanja1, Collette Kemigisha1, Erina Kisakye1, Amos Kuddiza Kusetula1, Silvester Wakabi1, Ivan Wambi2, Innocent Musiime2, Rebecca Nekaka1 and Yahaya Gavamukulya3*\n1Department of Community and Public Health, Faculty of Health Sciences, Busitema University, P.O.Box 1460, Mbale, Uganda.\n2Busolwe General Hospital, Butaleja District Local Government, Butaleja District, Uganda. 3Department of Biochemistry and Molecular Biology, Faculty of Health Sciences, Busitema University, P.O.Box 1460, Mbale, Uganda.\nAuthors\u2019 contributions\nThis work was carried out in collaboration among all authors. Authors BWN, SSN, CK, EK, AKK and SW conceived, designed the study, participated in data collection, analysis and manuscript writing. Authors IW and IM supervised the data collection and analysis. Author RN participated in the study conception, design, coordinated the entire COBERS program and reviewed the manuscript. Author YG was a research mentor and supervisor who participated in the study conception, design, preparation for approval and proof reading\nof the final results and manuscript. All authors read and approved the final version of the manuscript.\nArticle Information\nDOI: 10.9734/IJTDH/2019/v39i330209 Editor(s):\n(1) Dr. Thomas I. Nathaniel, Department of Biomedical Sciences, School of Medicine -Greenville, University of South Carolina, Greenville, USA. Reviewers:\n(1) Giuseppe Gregori, Italy. (2) Indianara Maria Grando, Brazil. Complete Peer review History: http://www.sdiarticle4.com/review-history/52839\n\nOriginal Research Article\n\nReceived 28 September 2019 Accepted 03 December 2019 Published 07 December 2019\n\nABSTRACT\nBackground and Aims: There is a high burden of vaccine-preventable diseases in the children under five years of age, particularly pneumonia diarrhea and which is greatly affected by low immunization coverage despite the existing efforts and policies. This study was carried out in\n_____________________________________________________________________________________________________\n*Corresponding author: Email: gavayahya@yahoo.com;\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nButaleja district and was aimed at establishing the socio-demographic determinants of vaccine coverage for pneumococcus and rotavirus among under five children (U5C) in the district. Study Design: This was a mixed methods cross-sectional study. Place and Duration of Study: Busolwe Town Council, Butaleja District, Eastern Uganda. Methodology: Structured researcher administered questionnaires were administered to 434 caregivers of U5C in different parts of Butaleja district. In-depth interviews with key informants and focused group discussions with Village Health Teams and community members were conducted. Review of Health Management Information Systems records was done. STATA 15 was used to analyze the data. Results: The study found that there is a declining trend in completion of the doses of Pneumococcal vaccine (PCV) and Rotavirus vaccine. For example, in quarter 1 of 2019, out of the 312 children who started immunization, only 2 completed Rota virus immunization and only 117 completed PCV vaccinations a trend that has been observed since 2016. The factors that showed a significant association with the the fact that they gave their child at least one dose of the vaccine were having been sensitized on the current immunisation schedule(P-value = <0.001), misunderstanding that vaccine is harmful for child (P-value = 0.007), willingness to take children to vaccination (P-value = <0.001), and social factors such as family (P-value = <0.030). Gender also played a key determinant role where the children\u2019s fathers lacked knowledge on significance of immunization and thus discouraged the mothers from taking the children for immunization. Inadequate funding was also highlighted from the Focus Group Discussions. Conclusion: Vaccine coverage for pneumococcus and rotavirus is still low in Butaleja district mainly due to the attitudes and perceptions of caregivers as well as the knowledge gap. There is need for extensive sensitization of all community members to enable them understand the significance of immunization. It would further be important to increase the funding of the immunization programme to intensify and ensure effectual outreaches as well as the establishment and enforcement of a policy for immunization compliance.\n\nKeywords: Vaccine coverage; Pneumococcus Vaccine (PCV); rotavirus vaccine; under five children (U5C); Butaleja; Eastern Uganda; COBERS; knowledge.\n\nABBREVIATIONS\n\nBUFHS-HDRC : BUFHS Busitema University\n\nFaculty of Health Sciences\n\nHigher Degrees and Research\n\nCommittee\n\nCOBERS\n\n: Community Based Education,\n\nResearch and Services\n\nHMIS\n\n: Health Management\n\nInformation Systems\n\nPCV\n\n: Pneumococcal Vaccine\n\nRHITES-E : Regional Health Integration to\n\nEnhance Services in Eastern\n\nUganda.\n\nU5C\n\n: Under Five Children\n\nVHTs\n\n: Village Health Teams\n\n1. INTRODUCTION\nImmunization is the process whereby a person is made immune or resistant to an infectious disease, typically by the administration of a vaccine [1]. The World Health Organization (WHO) launched the Expanded Program for Immunization (EPI) in 1974, and many developing countries adopted it. Despite this\n\neffort, over 24,000 children die of vaccinepreventable diseases every day around the world equivalent to 1 child dying every 3.6 seconds, 16-17 children dying every minute, and just about 9 million children dying every year. In 2008 there was a bigger proportion of deaths in subSaharan Africa (4.4 million) and South Asia (2.8 million) compared to Latin America, the Caribbean, and industrialized countries (0.1 million) [2].\nVaccination is key in prevention of some infectious diseases as indicated by the reduction in incidence rates of invasive pneumococcal disease were lower after vaccine introduction. It was noted that the incidence rates of pneumococcal invasive disease were 19.0 cases per 100,000 for whites, 54.9 for blacks, and 13.7 for other racial groups compared to 2002,where the incidence rates of pneumococcal invasive disease were 12.1 for whites, 26.5 for blacks, and 5.6 for other racial group as obtained from Analysis of data from the Active Bacterial Core Surveillance (ABCs)/Emerging Infections Program Network, an active, population-based surveillance system in 7 states. Patients were 15\n\n2\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\n923 persons with invasive pneumococcal disease occurring between January 1, 1998, and December 31, 2002.\nAdditionally, the incidence of Pneumonia is estimated at 0.29 episodes per child which equals 21% of deaths in under five children in developing countries [3]. Furthermore, the prevalence of diarrhea, according to Uganda Demographic Health Survey (UDHS 2011) done by Uganda Bureau of Statistics is estimated at 23% [4,5]. Busolwe District Hospital records indicate an increase in the prevalence of both diarrhea and pneumonia despite all efforts to do away with these diseases. Low vaccine coverage has been highly associated to this trend.\nDPT3-Hib3-Heb3 coverage in 2017/18 was at 95% and measles coverage was 88% in 2016/17 and still below the target of 95% in Uganda [5]. However, the DPT3 coverage showed a decline from 99.2% in 2016/17 [6]. Some districts showed a lower than 60 percent measles coverage for example Nakasongola 59%, Mayuge 58.4%, Apac 58.2%, Bukomansimbi 55.5%, Bulambuli 53.6% and Amudat 53.4% [6]. There seems to be lack of statistical information on immunization coverage for some districts and most the information is generalized.\nLow immunization coverage and vaccine hesitancy in Uganda and Butaleja district specifically, has been in existence but has not been solved yet it is set as one of the ten major health threats in 2019 by the World Health Organization. In a study done in Busolwe aimed at determining the knowledge and perception of caregivers about risk factors and manifestations of pneumonia among under five children in Butaleja district, for the 302 respondents it was found that among the caregivers\u2019 children only 39 percent were fully immunized, 56 percent partially immunized and 5 percent were not immunized [7].\nLow immunization coverage is further set to be a major cause of childhood mortality if not addressed since these childhood diseases are set to have a negative impact on children health in absence of complete immunization for example pneumonia accounted for 14 percent of mortality (third major cause) in children under 5 in 2017 and diarrheal diseases associated with Rota Virus accounting for 4500,000 deaths each year with 95 percent in poor communities [5]. There is likely to be an increase in the vaccine preventable disease outbreaks in the community\n\nshould this issue remain unaddressed as evidenced by the current measles outbreaks. Furthermore, there seems to be a gap in information and statistics on district specific immunization coverage data for some districts. To address this issue awareness is key but for this to be achieved, the root cause of this problem should be recognized and the missing link or gap can be closed up. It was also important to assess the standpoint of the community members to discover why the community members did not take their children for immunization even when the services were availed.\n\nThe aim of this study was therefore to determine the factors associated with vaccine coverage particularly for PCV and Rota Virus vaccine in order to provide evidence-based education and sensitization to the community and thus reduce the prevalence and risks associated with vaccine hesitancy and low immunization coverage in Butaleja district, Eastern Uganda.\n\n2. MATERIALS AND METHODS\n\n2.1 Study Area and Target Population\n\nThe study was carried out in Butaleja District in Eastern Uganda which is bordered by Budaka and Kibuku districts in the North, Mbale in the East, Tororo district in the South East and Namutumba in the West, as shown in Fig. 1 [4]. Butaleja district has a total population of 244153 people of which 119466 (48.9%) are males and 124687 (51.1%) females according to the national population census 2014. It also has a population of 50448 of children under five [4,5]. The Busolwe General Hospital has a catchment population of 42298 people, with women in childbearing age being 8544, with number of pregnancies being 2114, number of live births 2051; number under five years is 8544.\n\n2.2 Study Design\n\nThe study included: A Cross-Sectional Study\n\namong sample population which was done in two\n\nphases. The first phase was a pilot study which\n\naimed at ascertaining the community diagnosis\n\nof the Busolwe District Hospital Catchment Area\n\nbetween June to July, 2018. The second phase\n\nwhich included Data Collection of Vaccine\n\ncoverage for pneumococcus and rotavirus was\n\nth\n\nrd\n\ndone from 8 april,2019 to 3 may,2019. Primary\n\ndata collected using interviewer- administered\n\nquestionnaires to a total of 434 care takers of\n\n3\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nchildren under five years of age, whereby 402 were female and 32 were male in the households of Budumba, Bubalya, Kachonga sub-counties and Busolwe Town council in Butaleja district.\nSecondary data from Health Management Information Systems (HMIS) records of of Vaccine coverage for pneumococcus and rotavirus of 2016, 2018 and 2019(Jan to March) for Busolwe District Hospital.\n2 Focus Group Discussions (FGD) were held; the first one on the 10th April, 2019 in Dundo village, Busolwe Town Council, Butalejja district. A total of 15 interviewees participated in the session of which 2 were married males in the age group of 30-38, and the 13 participants were females; 3 0f whom were unmarried and the 10 females were married.\nThe second FGD was held on the 18th of April in Budumba village near Budumba health Centre III, in Butaleja district during one of immunisation community outreach programmes. A total of 11 interviewees participated, where by 3 of these were married males in the age groups 0f 40-45\n\nyears, and 8 females of which all of them were married. All participants in the FGD were caretakers of children under five who agreed to take part in the study, by giving informed consent.\n2.3 Sample Size Determination\nThe minimum sample size was determined using the Cochran\u2019s formula N = (1.96)2pq/d2, with a confidence level of approximately 95% (1.96). Where, N = required sample size, P = proportion of population having the characteristics considering recent studies, q = (1-p) and d= (+/5%) degree of precision. Therefore, considering findings from a current study on Knowledge and Perception of Caregivers about Risk Factors and Manifestations of Pneumonia among Under Five Children in Butaleja District, Eastern Uganda [7]. p = 53.7, q = 1-0.537, d = 5/100 = 0.05. Thus, N = [(1.96)2 x 0.537x 0.463] / (0.05)2= 0.9551 / 0.0025= 382 participants.In order to reduce errors, the sample population was enlarged from 382 participants to 434 participants.\n\nFig. 1. A map of Uganda showing the location of Butaleja district [4] 4\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\n2.4 Sampling Strategy\n\nHomogeneous purposive sampling method was used. Recruitment was by the VHT leaders and members introducing the students to the community, particularly to homes or households they knew to have at least one child under the age of five.\n\n2.5 Inclusion and Exclusion Criteria\n\nInclusion into the study required one to be a parent (mother or father) and/or caretaker of the under-five child (ren) in the community or facilityBusolwe District Hospital; who has given informed consent, whereby both the literate (381participants) and illiterate (53participants) we explained to the purpose of the study and thereafter asked to consent either by signing the consent forms or by using thumb print respectively. Exclusion from the study was to any though being parent(s) and/or caretaker to the under-five child (ren), if they refused to give informed consent.\n\n2.6 Data Collection\n\n2.6.1 Primary data sources\n\nWe developed an electronic data collection and\n\nentry (storage) tool in form of Google forms on\n\ntablets, smart phones and even laptop\n\ncomputers, from which the researcher\n\nadministered questionnaire was used to assess\n\nthe perceptions and attitudes of the different\n\ncorrespondents towards the immunizable\n\ndiseases as well as the factors associated with\n\nthe immunization coverage in Butaleja district\n\n[8,9]. The Google form was developed at\n\nGoogle Inc. and could be easily accessed at\n\nthe following universal resource locator\n\n(https://forms.gle/PCi5rbK1mt5tgzhA8).\n\nThe\n\nquestionnaire was pretested and validated among 2nd year Medical and Nursing students at\n\nBUFHS, who had taken part in the pilot study\n\nbefore the data collection process, and also\n\nbecause the these questionnaires were\n\ninterviewer-administered.\n\n2.6.2 Secondary data sources\n\nSome of the data was collected from the Busolwe district hospital HMIS records.\n\n2.7 Data Storage\n\nThe raw data collected on questionnaires (Google forms) was automatically and securely\n\nstored online, and access to it was limited to only three administrators.\n2.8 Data Analysis\nThe data was analyzed using STATA version 15 that is \u201cStataCorp.2017.Stata Statistical, Release 15. College Station, TX: Stata Corp LLC.\u201d Sociopsychological factors of care givers which could correlate with of the fact that they gave their child at least one dose of vaccine were evaluated by chi square test or Fischer's exact test. P value <0.05 were considered statistically significant. In case the expected frequency was less than 5, Fisher`s exact test was performed.\n3. RESULTS AND DISCUSSION\n3.1 Results\nVaccine Coverage Trend of Pneumococcus and Rota virus from 2016 to 2019. There is a decrease in the number of children who receive the last doses of both PCV and Rota virus immunization compared to those who actually start the doses, as showed by the BCG results, since this vaccine is given at birth. In 2016, 169 children started on immunization at birth with BCG versus the 91 children who completed the last dose of PCV. This trend follows through to 2019 (January to March) whereby 312 children started on BCG and only 2 and 117 completed the doses of Rota virus and PCV respectively as shown in Fig. 2. Thus, BCG is being used as a reference standard for the children who were started on immunization in that period.\nIn 2016, 109 children started immunization of PCV1, 102 received PCV2, and only 91 returned for PCV3. In 2018, 168 received PCV1, 140 PCV2, 108 PCV3 indicating 60 children didn\u2019t finish their immunization. In 2019, 153 children were started on PCV1, of these 120 received PCV2, and only 117 received PCV 3, showing that 36 children didn\u2019t finish immunization of PCV (Fig. 2).\nFor Rota virus immunization; in 2018, 168 children received Rota1, 133 Rota2, and only 14 received Rota3, indicating that 119 children did not complete immunization for Rota virus. In 2019, 102 children started immunization of Rota1, 80 received Rota2, and only 2 received Rota3, indicating 100 children who didn\u2019t complete immunization for Rota virus. In comparison with 2016, it is noted that there has been almost no change in the trend with regards to completion of vaccination.\n\n5\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\n350\n\n300\n\nNumber of the children Immunized\n\n250\n\n200\n\n150\n\n100\n\n50\n\n0 BCG\n\nPCV1\n\nPCV2\n\nPCV3 ROTA 1 ROTA 2 ROTA 3\n\nImmunization Vaccines\n\n2016 2018(oct to dec) 2019(jan to march)\n\nFig. 2. A graph showing the number of children immunized per quarter for subsequent doses of the immunization vaccines\n\nTable 1. A table representing the socio-demographic characteristics of participants in the study\n\nSex Marrital status Education level\nPlace of residence Religion\n\nDemographic characteristics Female Male Married Not Married Certificate course Diploma level Primary Secondary (A\u2019 level) Secondary (O'level) Uneducated University Town Trading Centre Village Anglican Born Again Christian Catholic Muslim SDA\n\nFreq. 402 32 413 21 7 3 269 4 97 53 1 68 98 268 115 29 50 232 8\n\nPercent 92.63 7.37 95.16 4.84 1.61 0.69 61.98 0.92 22.35 12.21 0.23 15.67 22.58 61.75 26.5 6.68 11.52 53.46 1.84\n\n6\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nSocio-Demographic Characteristics of Participants: A total of 434 caregivers participated in the study on immunization coverage of PCV and Rota Virus vaccine and its determinants out of which (402) 92.63% were female and (32) 7.37% were male. The majority of the respondents (413) 95.16% were married and the other (21) 4.84% were not married. In terms of education, majority were primary school dropouts; (269) 61.98%, (97) 22.35% at secondary O\u2019 level and (53) 12.21% were uneducated. Only 15 had pursued education beyond O\u2019 level that is diploma, certificate, A\u2019 level or University as shown in Table 1.The major religion in the community was Islam (232) 53.46%, Anglicans were (115) 26.5%, Catholics at (50) 11.52%, Born-Again Christians (29) 6.68 percent and SDAs the least being 1.84%.\n\nIn regards to pneumonia and diarrhea, for diarrhea only 24.65% believed it could be prevented by immunization, and the rest by washing hands before drinking and eating, 50.69%, 22.35% did not know. For pneumonia it was perceived that only 35.94% believed it could be prevented by immunization, putting on warm clothes 28.34% and 31.57 % did not know.\nThe factors that showed a significant association with the the fact that they gave their child at least one dose of the vaccine were knowledge (Pvalue = <0.001), beliefs and perceptions (P-value = 0.007), attitudes (P-value = <0.001), and social factors such as family (P-value = <0.030) as shown in Table 4.\n3.2 Results from Focus Group Discussion\n\nKnowledge and Perceptions of caregivers of children under five about immunization coverage of PCV and Rota virus vaccine: From Table 2 and Table 3, as large percentage of the respondents (99.54%) claimed to have heard about immunization and only 0.46 percent hadn\u2019t. The commonest source of information was health workers at 87.33%, VHTs (8.33%), mass media (TV and radio) at 3 percent and family members lastly at 1.33%.\n99.31% knew that immunization helps in prevention of diseases in comparison with the minority 0.69% and with 79.49% having mentioned a correct disease, 12.68% mentioned a wrong disease, for example malaria and 7.83% didn\u2019t know.\n96.77% knew about the availability of immunization services offered at Busolwe hospital, 2.53 didn\u2019t know while 0.69% claimed there were no immunization services. In terms of access to availability of advice on immunization services, 71.66 percent believed they had good access, 9.68% said they had poor access to the services while 18.66% did not know. 69.35% admitted to having been sensitized on the current immunization schedule while 30.65% claimed they were not.\nIn terms of knowledge, 26.5% of the respondents believed a child could fall sick from immunization, 61.29% were against this and 12.21% did not know. Despite this ideology,96.54% of the respondents said they would still take their children for immunization, 3% said they would not and 0.46% said they were not sure.\n\n3.2.1 Problems relating with caregivers\n1) Caregivers fear the health workers, because the health workers scold care caregiver when they lose immunization card, or forget appointment date. As a result, the care giver does not bring the child for the second dose of the vaccines or even the subsequent ones.\n2) Husband misunderstand that the vaccines are harmful for the child because the child cries a lot on the night of vaccination. Then, husband stops his wife from taking the child for another dose of the vaccine.\n3) Caregivers misconception that one dose of vaccine is enough. Accordingly, they do not come back for next dose of vaccine resulting in incomplete vaccine protocol.\n4) Caregivers do not know why children need vaccination i.e. they do not know that the vaccines prevent children from developing these diseases.\n5) When caregivers get divorced, they move to other districts, as a result, continuation of vaccine protocol becomes difficult.\n6) Negligence by the caregivers. Although the caregivers know necessity of vaccine, they abandon their responsibility complaining lack of time and physical tiredness.\n7) Most of caregivers have a lot of children, because they lack knowledge about proper family planning method. As a result, they forget vaccine schedule on second dose and after,\n\n7\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nbecause the vaccine schedules are too many to remember for each of children.\n3.2.2 Problems relating with health workers\n1) Health workers also lack knowledge of vaccination and vaccine protocol.\n3.2.3 Problem relating to funding\n1) Facilitation of the health workers such as lunch and transport is not availed on the scheduled vaccination days because of inadequate of funding by the government.\n2) There are no Permanent place (building) for vaccination constructed because of lack of fund of government. As a result, if it rains heavily, vaccination cannot be performed on the scheduled date.\n3.3 Discussion\nChild health and survival are reliant on several factors and these include high immunization coverage, however, based on the results of this study, there was a noted decline in the immunization coverage for PCV and Rota virus vaccines as shown in the results from the HMIS data collected from the region of study. This is related to a report by the Uganda Bureau of Statistics in 2017 where there was also a noted decline in coverage for subsequent doses with 79% of the children receiving the recommended doses of the DPT- HepB- Hib, 66% the three doses of polio and 64% the three doses of pneumococcal vaccine [10].\nAdditionally, as one of the national challenges, it was noted that no district has reached the full immunization coverage of 80% for children below one year which leaves the children exposed to\n\nthe risk of vaccine preventable diseases [11]. This is supported by a report by World Health Organization whereby growing level of vaccine hesitancy were an additional risk to the failure in attaining maximal immunization coverage [12] which is emphasized more by the data collected thus showing the study area as having greatly substandard vaccine coverage.\nNon-compliance to the immunization schedule makes the children\u2019s bodies unable to form the intended immune defenses against the childhood killer diseases, and this makes them susceptible and even easily succumb to these infections which are so widespread in these low-income communities of Butalejja district and eastern Uganda at large.\nThe demographic factors also do influence the immunization coverage. It was noted that majority of the care takers were school dropouts who stopped in primary school (61.98%), O\u2019 level (22.35%) and some uneducated (12.21%). Education of the care takers is important as it plays a role in modification of the perception, attitude and practices towards immunization as evidenced by data from the questionnaires whereby it was observed that even among those who took their children for immunization some still believed the children would get sick and this could be attributed to the low education level. Since some of these are basics taught in school. This is likened to a cohort study on how Maternal education is associated with vaccination status of infants less than 6 months in Eastern Uganda, where by Infants whose mothers had a secondary education were at least 50% less likely to miss scheduled vaccinations compared to those whose mothers only had primary education and there was improved primary health care service utilization [13].\n\nTable 2. Knowledge and perceptions of respondents towards immunization\n\nKnowledge and perceptions Have you ever heard about immunization? Can a child get sick from being immunized? Would you take your child for immunization? Do you have any immunization services in Busolwe district hospital? Have you been sensitized on the current immunization schedule? Does immunization help in the prevention of diseases Do you feel you have good access to the advice you need on immunization?\n\nI don\u2019t know (%)\n12.21 0.46 2.53\n\nNo (%) 0.46 61.29 3 0.69\n\nYes (%) 99.54 26.5 96.54 96.77\n\n30.65 69.35\n\n18.66\n\n0.69 99.31 9.68 71.66\n\n8\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nTable 3. Knowledge and perceptions of respondents towards immunization and available sources of information\n\nQuestion Source of Information on Immunization\nPrevention of diarrhea\nPrevention of pneumonia\n\nResponse Family members Health worker TV/Radio (Mass media) VHT Others Don't know Immunization Washing hands before eating and drinking Don't know Immunization Putting on warm clothes Take to hospital\n\nFrequency 4 262 9 25 10 97 107 220 137 156 123 18\n\npercent 1.33 87.33 3 8.33 2.3 22.35 24.65 50.69 31.57 35.94 28.34 4.15\n\nTable 4. Association of the different factors with the the fact that they gave their child at least one dose of the vaccine\n\nQuestion / indicator for the factors Have you taken your child for Total\n\nimmunization\n\nNo\n\nYes\n\nHave you been\n\nNo\n\n12(80.00)\n\n121(28.88) 133(30.65)\n\nsensitized on the Yes\n\n3(20.00)\n\n298(71.12) 301(69.35)\n\ncurrent schedule Total\n\n15(100.00)\n\n419(100.00) 434(100.00)\n\nCan a child get sick I don\u2019t know\n\n2(13.33)\n\n51(12.17) 53(12.21)\n\nfrom immunization No\n\n4(26.67)\n\n262(62.53) 266(61.29)\n\nYes\n\n9(60.00)\n\n106(25.30) 115(26.50)\n\nTotal\n\n15(100.00)\n\n419(100.00) 434(100.00)\n\nDo you have good I don\u2019t know\n\n2(13.33)\n\n79(18.85) 81(18.66)\n\naccess to the advise No\n\n5(33.33)\n\n37(8.83)\n\n42(9.68)\n\nyou need on\n\nYes\n\n8(53.33)\n\n303(72.32) 311(71.66)\n\nimmunization\n\nTotal\n\n15(100.00)\n\n419(100.00) 434(100.00)\n\nWould you take your I don\u2019t know\n\n0(0.00)\n\n2(0.48)\n\n2(0.46)\n\nchild for\n\nNo\n\n6(40.00)\n\n7(1.67)\n\n13(3.00)\n\nimmunization\n\nYes\n\n9(60.00)\n\n410(97.85) 419(96.94)\n\nTotal\n\n15(100.00)\n\n419(100.00) 434(100.00)\n\nFamily type\n\nExtended\n\n3(20.00)\n\n1105(25.06) 108(24.88)\n\nMonogamous 0(0.00)\n\n12(2.86)\n\n12(2.76)\n\nNuclear\n\n9(60.00)\n\n252(60.14) 261(60.14)\n\nPolygamous\n\n1(6.67)\n\n48(11.46) 49(11.29)\n\nSibling household 2(13.33)\n\n2(0.48)\n\n4(0.92)\n\nTotal\n\n15(100.00)\n\n419(100.00) 434(100.00)\n\nP-value (fisher\u2019s exact) <0.001\n0.007\n0.018\n<0.001\n0.030\n\nLow education level (maternal and paternal) was noted as one of the main factors associated with under vaccination of children [14]. In another study, immunization coverage was also associated with educational level of the father and the mother. Children whose mothers\u2019 education level was at least primary school were more likely to be fully immunized than those whose mothers had no education [15]. Related studies in Zimbabwe have also shown that\n\nmaternal education accounted for a high likelihood of child vaccination [16].\nAge of the care takers has an impact on participation in immunization thus influencing immunization coverage for example from this study\u2019s findings, the biggest number of participants were in the age bracket 20-30, and it was noted these started giving birth as early as 16 years old to an extent, impacts immunization\n\n9\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\ncoverage where by teenage mothers have a poor compliance to immunization since they are timid, ignorant about the immunization schedule and thus they cannot partake in what they don\u2019t know. This is related to a study by Mukungwa in 2015 on Factors Associated with full Immunization Coverage amongst children aged 12 \u2013 23 months in Zimbabwe whereby the likelihood of childhood immunization correlates with maternal age since more experience is accumulated over time on importances of immunization and problems associated with lack of immunization [16]. Similarly, maternal age was given as one of the factors which have a significant association with childhood immunization on Uganda [17].\nIn a related study to measure full immunization status and associated factors among children aged 12-23 months old in Hosanna Town, South Ethiopia showed that age of mothers had significant association with immunization status of the children [2]. Age of respondents was stated as a very important demographic factor in affecting immunization coverage in a study do describe immunization coverage for DPT, Polio and Measles among children of ages between 12 to 18 months in Kawempe Division and to investigate factors associated with Immunization coverage [15].\nFrom this study, marital status had a significant association with immunization coverage, where by 95.16 percent were married and 4.84 percent were unmarried. Of these, majority of the children belonging to unmarried couples were either partially or completely unimmunized due to the unsettled nature of the mothers as they move from family to family and abandon the children with their grandparents, while some lose the immunization cards (most people in this study lacked cards) and others fear to continue immunization in the new areas to which they have moved or migrated. This goes hand in hand with attendance of Antenatal services during pregnancy, whereby married women were more likely than the unmarried to attend these services. In this particular study, 98.39 percent of the participants believed antenatal services are important in ensuring immunization of the infant while 1.61 percent thought otherwise. This is supported by a Community-based crosssectional study done on Timeliness of Childhood Vaccinations in Kampala Uganda whereby Mothers who sought prenatal and postnatal care had a higher likelihood of their children being immunized which is attributed to sensitization in\n\nprenatal and postnatal lessons taken where the importance of timely immunization is emphasized [18]. In another study, one of the predisposing characteristics to inconsistencies in immunization status of children was marital status [16]. Another study indicates that marital status is significantly associated with non-completion of the immunization schedule by children less than five years [19]. Relatedly, marital status was identified to consistently influence immunization uptake and completion rates [20].\nDespite the existing efforts by the different stake holders to educate people about immunization, there is still a knowledge gap on the specifics of the immunization schedule among the care takers as a 59.91 and 73.04 percent of the participants did not know that pneumonia and diarrhea respectively, could be prevented via immunization. This still agrees with a study on Knowledge and Perception of Caregivers about Risk Factors and Manifestations of Pneumonia among Under Five Children in Butaleja District, Eastern Uganda, where many of the respondents were not knowledgeable about the causes of pneumonia with only 7.6% believing it to be preventable by immunization [7].\nSimilarly, a study in Kawempe-Uganda, on immunization coverage and factors associated with failure to complete childhood immunization showed that the knowledge on immunization activities enhances the use of immunization services [15]. Another study on assessment of child immunization coverage and its determinants showed that children whose mothers had good knowledge on vaccines were 2.5 times more likely to be fully vaccinated that children of mothers who had poor knowledge on vaccines [21]. Additionally, a similar study on Factors influencing childhood immunization points out lack of knowledge as a key factor [22].\nThe focus group discussions revealed key problems relating with care givers, health workers and to funding. These three categories of problems are very rich and impactful in the results of the vaccination coverage and corroborate with many articles that have been cited. Most importantly is that they demonstrate a wide knowledge gap that is clearly graded between the illiterate and the literate caregivers. Furthermore, it is not unsurprising to reveal that some of the health workers lack the necessary information in relation to vaccination which would be mostly due to the heavy workload and changing schedules a problem that underscores\n\n10\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nthe need for continuous professional development and increased funding.\n4. CONCLUSION\nImmunization coverage of PCV and Rota virus vaccines is still low in Butaleja district as evidenced by the decline in the trend of the immunization dosages of the above vaccines as seen from the data reviewed from the HMIS, yet low immunization coverage is set as one of the ten major health threats in 2019 by the World Health Organization.\n\nCONSENT\nWritten informed consent from caretakers of the U5C was obtained before they participated in the study. Participants were informed that their privacy and confidentiality would be respected and that there was no potential harm associated with participating in the study. It was made clear to the participants that participation in the study was voluntary and that they were free to opt out of the study at any time without any negative consequences.\n\nThis low immunization coverage is attributed to a number of factors such as the existing knowledge gap about the specifics of the immunization schedule among the caregivers of children under five which was majorly seen from data from the cross sectional study among the sample population, fear of being embarrassed by the health workers, inadequate funding to carry out the outreach programmes and lack of male involvement among others as seen in the problems relating caregivers, health workers and funding. However as seen from this study most of the gap exists among the caregivers and a link must be developed between the health workers and care givers. Emphasis should be put in improving the immunization coverage in Butaleja district because pneumonia and diarrhea are highly prevalent diseases in this area especially in the rainy season, as this is most likely to result into increased mortality rates among children, increased morbidity rates since the immune systems of the children wouldn\u2019t be strong enough and consequently, this poses a big financial burden to the country and undermines development.\nKey recommendations from the study can include: 1) Extensive sensitization of the community members on the importance of immunization, 2) Intensification of health education programmes especially on the immunization schedule, 3) Enforcement of the health policy on immunization to improve on compliance of the community members, 4) Increase funding to the immunization budget of the district and 5) Enhancing people\u2019s knowledge on underlying factors like family planning which in the long run affect immunization coverage.6) Improving male partner participation in matters with regards to immunization. 7) Study exploring the health professionals' knowledge, attitudes, and practices when they receive a child with a late vaccine.\n\nETHICAL APPROVAL\nThe study and all the protocols were approved and cleared by the Busitema University Faculty of Health Sciences Higher Degrees and Research Committee (BUFHS-HDRC) as part of the Community Based Education, Research and Services (COBERS) Program for the 2018/2019 Academic year under the Course of Community Diagnosis and Communication Projects. Permission to conduct the study was sought from the District Health Officer Butaleja and the Medical Superintendent of Busolwe Hospital.\nAVAILABILITY OF DATA AND MATERIALS\nAll data on which the results, discussions and conclusions of this manuscript are drawn are contained in the main manuscript. Additional data sets can be accessed via the Mendeley Data Repository(http://dx.doi.org/10.17632/zr2w886dg 2.1), where all the data used in the study has been deposited [8].\nFUNDING\nThis research was funded by Busitema University Faculty of Health Sciences COBERS Committee, the Regional Health Integration To Enhance Services in Eastern Uganda (RHITESE) Team, as well as the Fogarty International Center of the National Institutes of Health, U.S. Department of State\u2019s Office of the U.S. Global AIDS Coordinator and Health Diplomacy (S/GAC), and President\u2019s Emergency Plan for AIDS Relief (PEPFAR) under Award Number 1R25TW011213. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.\n\n11\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\nACKNOWLEDGEMENTS\n\n5. Uganda Bureau of Statistics. 2018\n\nStatistical Abstract. Kampala, Uganda;\n\nWe thank Busitema University Faculty of Health Sciences Higher Degrees and Research 6.\nCommittee (BUFHS-HDRC) which gave in time\nto review and approve the proposal and accompanying protocols through The Busitema 7.\nUniversity Community Based Education,\n\n2018. Uganda Ministry of Health. Annual Health Sector Perfomance Report. Kampala, Uganda; 2018. Aguti B, Kalema G, Lutwama DM, Mawejje ML, Mupeyi E, Okanya D, et al. Knowledge\n\nResearch and Services (COBERS) Committee.\n\nand perception of caregivers about risk\n\nThe COBERS Committee is further thanked for\n\nfactors and manifestations of pneumonia\n\nthe funding provided that enabled most of the\n\namong under five children in Butaleja\n\nstudy to be conducted. Furthermore, the Authors would also like to express their explicit thanks to all the Doctors, Clinical Officers, Senior nursing officers, Laboratory technicians, HMIS personnel, 8. Village Health Teams and all other staff who were of great help in our facility-based and community activities. Final thanks go to all the Volunteers who participated in the study. We also further extend our gratitude to the DHO,\n\nDistrict, Eastern Uganda. Microbiol Res J Int. 2018;25:1\u201311. DOI: 10.9734/MRJI/2018/44179. Nabwana WB, Namayanja SS, Kemigisha C, Kisakye E, Kusetula AK, Wakabi S, et al. Data for determinants of immunization coverage of PCV and rota virus among under five children in Busolwe Town Council, Butaleja District, Eastern Uganda.\n\nCAO, DHE and LCV of Butaleja District for having granted endorsement and acceptance for our interactions in the community, the VHTs and 9. LCI chairpersons who guided us during our community activities especially home visits and the RHITES-E team especially Mr. Anoku Patrick for according us transport and other support when called upon.\n\nMendeley Data. 2019;v1. DOI: 10.17632/zr2w886dg2.1 Nabwana WB, Namayanja SS, Kemigisha C, Kisakye E, Kusetula AK, Wakabi S, et al. Towards universal health coverage: Data for determinants of immunization coverage of Pneumococcal and Rota virus vaccines among under five children in Busolwe Town Council, Butaleja District,\n\nCOMPETING INTERESTS\n\nEastern Uganda. Data Br. 2019;25: 104269.\n\nDOI: 10.1016/j.dib.2019.104269 Authors have declared that no competing\n10. World Health Organization, UNICEF. interests exist.\nUganda National Expanded Programme\n\nREFERENCES\n\nOn Immunization Multi Year Plan 20122016. Kampala, Uganda: Uganda Ministry\n\nof Health; 2012.\n\n1. World Health Organization, Health Topics: 11. Uganda Ministry of Health, UNICEF. What\n\nImmunization; 2018.\n\nNational and District Leaders Need to do\n\nAvailable:https://www.who.int/topics/immu\n\nPromotion of Routine Immunisation In\n\nnization/en\n\nUganda. Kampala, Uganda; 2015.\n\n(Accessed April 26, 2019).\n\n12. World Health Organization. Global vaccine\n\n2. Bizuneh A. Factors affecting fully\n\naction plan Report by the Director -\n\nimmunization status of children aged 12-\n\nGeneral. Geneva, Switzerland; 2018.\n\n23 months. J Pregnancy Child Heal. 13. Fadnes LT, Nankabirwa V, Sommerfelt H,\n\n2015;2.\n\nTyllesk\u00e4r T, Tumwine JK, Engebretsen\n\nDOI:10.4172/2376-127X.1000185.\n\nIMS, et al. Is vaccination coverage a good\n\n3. Onyango D, Kikuvi GM, Amukoye E,\n\nindicator of age-appropriate vaccination? A\n\nOmolo J. Risk factors of severe\n\nprospective study from Uganda. Vaccine.\n\npneumonia among children aged 2-59\n\n2011;29:3564\u201370.\n\nmonths in western Kenya: A case control\n\nDOI:10.1016/j.vaccine.2011.02.093.\n\nstudy. Pan Afr Med J. 2012;13:13:45.\n\n14. Favin M, Steinglass R, Fields R, Banerjee\n\n4. Uganda Bureau of Statistics. The National\n\nK, Sawhney M. Why children are not\n\nPopulation and Housing Census 2014.\n\nvaccinated\u202f: A review of the grey literature.\n\nArea Specific Profile Series -Butaleja\n\nInt Health. 2012;4:229\u201338.\n\nDistrict. Kampala, Uganda; 2017.\n\nDOI:10.1016/j.inhe.2012.07.004.\n\n12\n\nNabwana et al.; IJTDH, 39(3): 1-13, 2019; Article no.IJTDH.52839\n\n15. Kamanda BC. Immunization coverage and 19. Kiptoo E. Factors Influencing Low\n\nfactors associated with failure to complete\n\nImmunization Coverage Among Children\n\nchildhood immunization in Kawempe\n\nBetween 12 - 23 Months in East Pokot,\n\nDivision, Uganda. University of the\n\nBaringo Country, Kenya. Int J Vaccines\n\nWestern Cape; 2010.\n\nVaccin. 2015;1:1\u20136.\n\n16. Mukungwa T. Factors Associated with full\n\nDOI:10.15406/ijvv.2015.01.00012.\n\nImmunization Coverage amongst children 20. Abdulrahman SA, Olaosebikan MO.\n\naged 12 \u2013 23 months in Zimbabwe. African\n\nMhealth: A narrative synthesis of evidence\n\nPopul Stud. 2015;29:1761\u201374.\n\nof its application in improving childhood\n\nDOI:10.11564/29-2-745.\n\nimmunization coverage. J Hosp Manag\n\n17. Bbaale E. Factors affecting Childhood\n\nHeal Policy. 2017;1:6.\n\nImmunization in Uganda. J Heal,\n\nDOI: 10.21037/jhmhp.2017.10.01.\n\nPopulation Nutr. 2015;31:118\u201329.\n\n21. Legesse E, Dechasa W. An assessment of\n\n18. Babirye JN, Engebretsen IMS, Makumbi F,\n\nchild immunization coverage and its\n\nFadnes LT, Wamani H, Tylleskar T, et al.\n\ndeterminants in Sinana District, Southeast.\n\nTimeliness of Childhood Vaccinations in\n\nBMC Pediatr. 2015;15:31.\n\nKampala Uganda\u202f: A Community-Based\n\nDOI: 10.1186/s12887-015-0345-4\n\nCross-Sectional Study. PLoS One. 2012;7: 22. Singh A, PJ, Divya N. Factors influencing\n\ne35432.\n\nchildhood immunization. Int J Adv Sci Res.\n\nDOI:10.1371/journal.pone.0035432\n\n2017;2:81\u20134.\n\n_________________________________________________________________________________\n\n\u00a9 2019 Nabwana et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\n\nPeer-review history: The peer review history for this paper can be accessed here:\nhttp://www.sdiarticle4.com/review-history/52839\n\n13\n\n\n",
+    "authors": [
+      "B. W. Nabwana",
+      "S. S. Namayanja",
+      "C. Kemigisha",
+      "E. Kisakye",
+      "A. K. Kusetula",
+      "S. Wakabi",
+      "I. Wambi",
+      "I. Musiime",
+      "R. Nekaka",
+      "Y. Gavamukulya"
+    ],
+    "doi": "10.9734/ijtdh/2019/v39i330209",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "Z88TH8HL",
+    "title": "Assessing Potential Exemplars in Reducing Zero-Dose Children: A Novel Approach for Identifying Positive Outliers in Decreasing National Levels and Geographic Inequalities in Unvaccinated Children",
+    "abstract": "BACKGROUND: Understanding past successes in reaching unvaccinated or \"zero-dose\" children can help inform strategies for improving childhood immunization in other settings. Drawing from positive outlier methods, we developed a novel approach for identifying potential exemplars in reducing zero-dose children. METHODS: Focusing on 2000-2019, we assessed changes in the percentage of under-one children with no doses of the diphtheria-tetanus-pertussis vaccine (no-DTP) across two geographic dimensions in 56 low- or lower-middle-income countries: (1) national levels; (2) subnational gaps, as defined as the difference between the 5th and 95th percentiles of no-DTP prevalence across second administrative units. Countries with the largest reductions for both metrics were considered positive outliers or potential 'exemplars', demonstrating exception progress in reducing national no-DTP prevalence and subnational inequalities. Last, so-called \"neighborhood analyses\" were conducted for the Gavi Learning Hub countries (Nigeria, Mali, Uganda, and Bangladesh), comparing them with countries that had similar no-DTP measures in 2000 but different trajectories through 2019. RESULTS: From 2000 to 2019, the Democratic Republic of the Congo, Ethiopia, and India had the largest absolute decreases for the two no-DTP dimensions-national prevalence and subnational gaps-while Bangladesh and Burundi registered the largest relative reductions for each no-DTP metric. Neighborhood analyses highlighted possible opportunities for cross-country learning among Gavi Learning Hub countries and potential exemplars in reducing zero-dose children. CONCLUSIONS: Identifying where exceptional progress has occurred is the first step toward better understanding how such gains could be achieved elsewhere. Further examination of how countries have successfully reduced levels of zero-dose children-especially across variable contexts and different drivers of inequality-could support faster, sustainable advances toward greater vaccination equity worldwide.",
+    "full_text": "Article\nAssessing Potential Exemplars in Reducing Zero-Dose Children: A Novel Approach for Identifying Positive Outliers in Decreasing National Levels and Geographic Inequalities in Unvaccinated Children\nNancy Fullman 1,*, Gustavo C. Correa 2, Gloria Ikilezi 1, David E. Phillips 1 and Heidi W. Reynolds 2\n\n1 Exemplars in Global Health, Gates Ventures, 2401 Elliott Ave, Seattle, WA 98121, USA 2 Gavi, the Vaccine Alliance, Chemin du Pommier 40, Le Grand-Saconnex, 1218 Geneva, Switzerland\n* Correspondence: nancy.fullman@gatesventures.com\n\nCitation: Fullman, N.; Correa, G.C.; Ikilezi, G.; Phillips, D.E.; Reynolds, H.W. Assessing Potential Exemplars in Reducing Zero-Dose Children: A Novel Approach for Identifying Positive Outliers in Decreasing National Levels and Geographic Inequalities in Unvaccinated Children. Vaccines 2023, 11, 647. https://doi.org/10.3390/ vaccines11030647\nAcademic Editors: Ahmad Reza Hosseinpoor, M. Carolina Danovaro, Devaki Nambiar, Aaron Wallace and Hope Johnson\n\nAbstract: Background: Understanding past successes in reaching unvaccinated or \u201czero-dose\u201d children can help inform strategies for improving childhood immunization in other settings. Drawing from positive outlier methods, we developed a novel approach for identifying potential exemplars in reducing zero-dose children. Methods: Focusing on 2000\u20132019, we assessed changes in the percentage of under-one children with no doses of the diphtheria\u2013tetanus\u2013pertussis vaccine (no-DTP) across two geographic dimensions in 56 low- or lower-middle-income countries: (1) national levels; (2) subnational gaps, as de\ufb01ned as the difference between the 5th and 95th percentiles of no-DTP prevalence across second administrative units. Countries with the largest reductions for both metrics were considered positive outliers or potential \u2018exemplars\u2019, demonstrating exception progress in reducing national no-DTP prevalence and subnational inequalities. Last, so-called \u201cneighborhood analyses\u201d were conducted for the Gavi Learning Hub countries (Nigeria, Mali, Uganda, and Bangladesh), comparing them with countries that had similar no-DTP measures in 2000 but different trajectories through 2019. Results: From 2000 to 2019, the Democratic Republic of the Congo, Ethiopia, and India had the largest absolute decreases for the two no-DTP dimensions\u2014national prevalence and subnational gaps\u2014while Bangladesh and Burundi registered the largest relative reductions for each no-DTP metric. Neighborhood analyses highlighted possible opportunities for cross-country learning among Gavi Learning Hub countries and potential exemplars in reducing zero-dose children. Conclusions: Identifying where exceptional progress has occurred is the \ufb01rst step toward better understanding how such gains could be achieved elsewhere. Further examination of how countries have successfully reduced levels of zero-dose children\u2014especially across variable contexts and different drivers of inequality\u2014could support faster, sustainable advances toward greater vaccination equity worldwide.\nKeywords: immunization; vaccines; zero-dose children; equity\n\nReceived: 2 February 2023 Revised: 9 March 2023 Accepted: 10 March 2023 Published: 14 March 2023\nCopyright: \u00a9 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).\n\n1. Introduction\nThe expansion of routine immunization is heralded as a global success story [1], enabling greater survival and improved child health worldwide [2]. Nevertheless, an estimated 25 million children were un- or under-vaccinated in 2021 [3], with many facing compounding barriers in vaccine access, availability, and demand. The ongoing COVID-19 pandemic has contributed to at least some of today\u2019s gaps in childhood vaccination [3], with estimates of under-one children without any doses of the diphtheria\u2013tetanus\u2013pertussis vaccine (no-DTP) rising from 10% prevalence in 2019 to 14% in 2021 [3]. Communities with high levels of unvaccinated or \u201czero-dose children\u201d often face myriad vulnerabilities [4\u20137], such as residing in highly remote areas or informal settlements in cities [7\u20139]; being affected by displacement and/or prolonged con\ufb02ict or unrest [7,8]; longstanding poverty and/or societal neglect [4]; or some constellation of these factors. Subsequently,\n\nVaccines 2023, 11, 647. https://doi.org/10.3390/vaccines11030647\n\nhttps://www.mdpi.com/journal/vaccines\n\nVaccines 2023, 11, 647\n\n2 of 19\noptimally identifying where and how to better reach zero-dose children will likely require a combination of context-speci\ufb01c strategies and broader investments to address persisting structural challenges.\nOver the last few years, a growing body of research has sought to assess characteristics of zero-dose children and their families or households, as well as potential drivers of high zero-dose prevalence at different geographic levels [4\u20138,10\u201317]. Past work has found that zero-dose children experience a higher odds of missing or lacking access to other types of primary care services [6,11,12], while their mothers were more likely to have no antenatal care visits and not deliver at a health facility [11,12]. Lower levels of household wealth, educational attainment, and measures of women\u2019s empowerment also have been associated with higher levels of zero-dose children [4,10,16,17]. Gender-based inequalities, which span from differential rates of immunization by infant gender and gender-related barriers related to who can seek or provide vaccination services [16,18], emphasize the complex yet crucial role that gender plays in a country and/or community [19]. Prior studies have found ethnic disparities [15], as well as differences by religious af\ufb01liation [13,20], among children who have received no doses of DTP, though the exact nature of these relationships varied by country. Quantifying these risk factors and determinants of zero-dose children can provide critical program inputs, spanning from identifying key barriers to service access [7,18] to honing in on what sociocultural forces may be negatively affecting vaccine sentiments and trust [18\u201320]. However, exclusively focusing on zero-dose risk pro\ufb01les and factors associated with higher rates of unvaccinated children may miss important lessons around successful approaches to addressing inequalities in childhood immunization. Accordingly, also understanding what has worked to improve childhood vaccination can inform program and policy adaptations tailored for reaching zero-dose children.\nPositive outlier, or so-called \u2018positive deviance\u2019, methodologies have been used at the unit or organizational level in healthcare settings [21\u201323], as well as for more populationlevel contexts [24\u201328], to generate or strengthen the evidence base around what works to improve key health priorities. While the exact approaches toward this type of research and synthesis vary, they usually espouse a shared premise: knowledge and implementation strategies around achieving success or progress exist from places or contexts where such success or progress have been previously attained [21]. As a result, identifying and then examining what contributes to exceptional performance or progress can offer actionable insights into what policies and practice could be adapted for similar impact elsewhere. For instance, the Good Health at Low Cost case studies \ufb01rst in 1985 [26] and then in 2013 [27], sought to synthesize how and why countries or regions achieved substantial advances in several health indicators compared to their peers with similar income and demographic pro\ufb01les; in 2018, the World Bank took a similar approach for understanding rapid progress on universal health coverage measures and facilitating shared learning opportunities across countries [28]. In 2016, the Global Burden of Disease study developed analyses to compare country-level performance on various health metrics relative to changes in sociodemographic development [29\u201331]; such \ufb01ndings emphasized that important health program and policy lessons could be learned from countries where achievements exceeded expected levels or trends on the basis of sociodemographic improvements alone. Lastly, the Exemplars in Global Health (EGH) program has sought to synthesize key lessons and strategies used by countries that attained exceptional progress in health\u2014exemplars\u2014 through mixed-methods research and engagement with partners [32\u201335]. As highlighted by past and current work on positive outliers, such analyses can foster opportunities for cross-country learning and exchange around successful policy or programmatic approaches for a given health challenge. With more learning agendas and priority-setting around zero-dose children for both national and global initiatives (e.g., Immunization Agenda 2030 [IA2030] [36] and Gavi 5.0 [37]), adopting a positive outlier lens toward country progress in reducing zero-dose children could further inform key immunization program and policy efforts.\n\nVaccines 2023, 11, 647\n\n3 of 19\n\nCountry\nAfghanistan Angola Bangladesh Benin Burkina Faso Burundi Cambodia Cameroon Central African Rep\n\nWith this study, we develop a novel approach for identifying positive outliers in reducing zero-dose children over time. This analysis currently takes a geographic focus, one of many important dimensions of inequality, by comparing patterns in both national and subnational declines in the percentage of under-one children with no doses of DTP (noDTP) from 2000 to 2019 among 56 low- and lower-middle-income countries (LMICs). Based on this approach, identi\ufb01ed \u2018exemplar\u2019 countries or subnational locations that substantially reduced zero-dose children could be targeted for further examination into the policy or program factors behind such gains.\n2. Materials and Methods 2.1. Data\nWe used estimates of DTP1 among children under 1 year of age at the national and second administrative levels from the Institute for Health Metrics and Evaluation (IHME). The methods used to estimate DTP1 at different geospatial resolutions are detailed elsewhere [38]; in brief, DTP1 coverage estimates were derived from georeferenced household surveys and modeled using Bayesian geostatistical methods for 106 countries at the \ufb01rst and second administrative levels from 2000 to 2019. We opted to use these spatially modelled estimates over alternative sources (e.g., administrative data) to maximize both the potential number of countries included and comparability of estimates across locations. We subtracted DTP1 estimates from 100% to re\ufb02ect the percentage of under-one children with no doses of DTP, or no-DTP prevalence\u2014a commonly used indicator for zero-dose children [10,36].\nFor this analysis, we focused on 56 LMICs (Table 1). These countries were selected on the following criteria: (1) designation of low- or lower-middle income for \ufb01scal year 2020 by the World Bank [39] or having received support from Gavi, the Vaccine Alliance as of 2018 [40]; (2) availability of both national and subnational no-DTP estimates at the second administrative level from 2000 to 2019; (3) not being classi\ufb01ed as a post-transition middle-income country by Gavi and inclusion as part of Gavi\u2019s zero-dose segmentation country groups [41]. Supplementary Table S1 includes the full list of initially considered countries and those excluded from the current analysis.\n\nTable 1. Included countries for identifying potential exemplars in reducing zero-dose children. * Gavi-supported indicates that the country received Gavi support as of 2018 or had a dedicated country hub page. ** Countries with national and subnational DTP1 estimates (for both \ufb01rst and second administrative units) as modeled by the Institute for Health Metrics and Evaluation. Supplementary Table S1 provides the list of initial countries considered but excluded due to not meeting inclusion criteria.\n\nWorld Bank FY20 Income Group\nLow-income Lower-middle income\nLower-middle income\nLow-income Low-income Low-income\nLower-middle income\nLower-middle income Low-income\n\nGavi-Supported *\nYes Yes Yes Yes Yes Yes Yes Yes Yes\n\nNational and Subnational DTP1 Estimates Available, 2000\u20132019 ** Yes Yes\nYes\nYes Yes Yes\nYes\nYes Yes\n\nGavi zero-Dose Segmentation Grouping\nCon\ufb02ict/fragile\nCore\u2014ESA (Priority)\nCore\u2014Rest of World (Priority)\nCore\u2014WCA (Priority)\nCore\u2014WCA (Priority)\nCore\u2014ESA (Standard)\nCore\u2014Rest of World (Standard)\nCore\u2014WCA (Priority)\nCon\ufb02ict/fragile\n\nVaccines 2023, 11, 647\n\n4 of 19\n\nTable 1. Cont.\n\nCountry\n\nWorld Bank FY20 Income Group\n\nChad Comoros Congo C\u00f4te d\u2019Ivoire Dem Rep of the Congo Djibouti Eritrea Ethiopia Gambia Ghana Guinea Guinea-Bissau Haiti India Kenya\n\nLow-income Lower-middle income Lower-middle income Lower-middle income Low-income Lower-middle income Low-income Low-income Low-income Lower-middle income Low-income Low-income Low-income Lower-middle income Lower-middle income\n\nKyrgyzstan\n\nLower-middle income\n\nLaos\nLesotho Liberia Madagascar Malawi Mali Mauritania Mozambique\nMyanmar\n\nLower-middle income\nLower-middle income Low-income Low-income Low-income Low-income Lower-middle income Low-income\nLower-middle income\n\nNepal\n\nLow-income\n\nNiger Nigeria Pakistan Papua New Guinea Rwanda S\u00e3o Tom\u00e9 and Pr\u00edncipe Senegal Sierra Leone Somalia South Sudan Sudan\n\nLow-income Lower-middle income Lower-middle income Lower-middle income Low-income Lower-middle income Lower-middle income Low-income Low-income Low-income Lower-middle income\n\nGavi-Supported *\nYes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes\nYes\nYes\nYes Yes Yes Yes Yes Yes Yes\nYes\nYes\nYes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes\n\nNational and Subnational DTP1 Estimates Available, 2000\u20132019 ** Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes\nYes\nYes\nYes Yes Yes Yes Yes Yes Yes\nYes\nYes\nYes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes\n\nGavi zero-Dose Segmentation Grouping Con\ufb02ict/fragile Core\u2014ESA (Standard) Core\u2014WCA (Priority) Core\u2014WCA (Priority) High impact Core\u2014ESA (Priority) Core\u2014ESA (Standard) High impact Core\u2014WCA (Standard) Core\u2014WCA (Priority) Core\u2014WCA (Priority) Core\u2014WCA (Priority) Con\ufb02ict/fragile High impact Core\u2014ESA (Priority) Core\u2014Rest of World (Standard) Core\u2014Rest of World (Priority) Core\u2014ESA (Standard) Core\u2014WCA (Standard) Core\u2014ESA (Priority) Core\u2014ESA (Priority) Con\ufb02ict/fragile Core\u2014WCA (Standard) Core\u2014ESA (Priority) Core\u2014Rest of World (Priority) Core\u2014Rest of World (Priority) Con\ufb02ict/fragile High impact High impact Con\ufb02ict/fragile Core\u2014ESA (Standard) Core\u2014WCA (Standard) Core\u2014WCA (Standard) Core\u2014WCA (Standard) Con\ufb02ict/fragile Con\ufb02ict/fragile Con\ufb02ict/fragile\n\nVaccines 2023, 11, 647\nCountry\nTajikistan Tanzania Timor-Leste Togo Uganda Uzbekistan Vietnam Yemen Zambia Zimbabwe\n\n5 of 19\n\nTable 1. Cont.\n\nWorld Bank FY20 Income Group\nLow-income Low-income Lower-middle income Low-income Low-income Lower-middle income\nLower-middle income Low-income Lower-middle income Lower-middle income\n\nGavi-Supported *\nYes Yes Yes Yes Yes Yes\nYes Yes Yes Yes\n\nNational and Subnational DTP1 Estimates Available, 2000\u20132019 **\nYes\nYes\nYes\nYes Yes\nYes\nYes\nYes Yes Yes\n\nGavi zero-Dose Segmentation Grouping\nCore\u2014Rest of World (Standard)\nCore\u2014ESA (Priority)\nCore\u2014Rest of World (Standard)\nCore\u2014WCA (Priority)\nCore\u2014ESA (Priority)\nCore\u2014Rest of World (Standard)\nCore\u2014Rest of World (Standard)\nCon\ufb02ict/fragile\nCore\u2014ESA (Priority)\nCore\u2014ESA (Standard)\n\n2.2. Analysis\nWe conducted three analyses to characterize potential exemplars in reducing zero-dose children over time, as summarized below. R version 4.2.1 was used for data processing, analyses, and visualizations [42].\nQuantifying changes in zero-dose children across geographies. We assessed changes in the percentage of under-one children without any doses of DTP (no-DTP) between 2000 and 2019 across two geographic dimensions: (1) national levels; (2) subnational gaps among second-level administrative units. For the latter\u2014subnational gaps\u2014we used the 5th and 95th percentile values of the prevalence of no-DTP children estimated across second-level administrative units and computed the difference for a given country\u2013year. We opted to use the 5th and 95th percentiles rather than absolute minimum and maximum values of no-DTP prevalence to offset the potential for undue in\ufb02uence of outliers for a given subnational unit\u2013year. Furthermore, how countries de\ufb01ne second-level administrative units widely varies (e.g., 10 or fewer units in Comoros, S\u00e3o Tom\u00e9 and Pr\u00edncipe, and Lesotho to 774 local government areas (LGAs) in Nigeria); using percentiles to de\ufb01ne subnational gaps may also help mitigate the degree to which having more (or fewer) administrative units could affect measures of subnational inequality.\nIdentifying potential exemplars in reducing zero-dose children. Second, countries with the largest declines for both no-DTP metrics between 2000 and 2019 were considered as potential exemplars in reducing zero-dose children. Prior research conducted under the EGH program has typically used one progress measure per geographic unit [32,34,35], and then benchmarked changes against indicators of sociodemographic development. Because many locations with the highest levels of unvaccinated children face compounding vulnerabilities [4], any investments in reaching zero-dose children should also correspond with action to address disparities in immunization rates. Our approach to operationalizing this pro-equity lens from a geographic perspective was equally weighting reductions at the national level and subnational differences for no-DTP. In other words, a country that achieved marked national reductions in no-DTP prevalence without corresponding declines in subnational gaps should not be considered a potential exemplar in reducing zero-dose children.\nWe ranked each country ordinally, 1 to 56, based on their national and subnational reductions in no-DTP prevalence from 2000 to 2019, with 1 being the largest reduction and\n\nVaccines 2023, 11, 647\n\n6 of 19\n56 being the smallest reduction or, if applicable, the largest increase since 2000. We took the mean of those rankings to identify which countries had achieved the most progress across both geographic dimensions. We applied these rankings and calculations for absolute and relative progress separately: computing percentage point changes for absolute progress from 2000 to 2019 and then percentage change from 2000 to 2019 for relative progress. We opted to consider both progress metrics\u2014absolute and relative progress\u2014as they could better represent a range of successful approaches used to reduce no-DTP prevalence from different starting points (i.e., higher and lower absolute levels of no-DTP children in 2000), and thus likely mirror different stages of immunization delivery needs and strategies.\nComparing divergent no-DTP trajectories since 2000 for select locations. Third, we conducted so-called \u201cneighborhood analyses\u201d for select countries, comparing them to other countries that had similar levels for both no-DTP measures in 2000 but different trajectories through 2019. Such analyses are thought to be supportive of potential crosslocation learning and knowledge translation around what could work to address zero-dose challenges when starting from similar baseline levels of no-DTP prevalence. At the countrylevel, we focused on Nigeria, Mali, Uganda, and Bangladesh\u2014the four countries selected for the Gavi Learning Hubs [43] and sought to match a \u201cneighbor\u201d exemplar to each country. Further detail on the Gavi\u2019s Learning Hub initiative is available elsewhere [43]; in brief, these four countries were selected on the basis of zero-dose metrics (i.e., high absolute numbers or prevalence of zero-dose children) as well as variations in zero-dose prevalence across geographic locations and among key populations that experience higher rates of no vaccination (i.e., rural, urban poor, refugeed, or con\ufb02ict settings). A primary objective of the Learning Hubs is to support deeper assessment and engagement to improve monitoring and measurement systems, and to enable learning about what works programmatically to reach unvaccinated children and missed communities.\n3. Results 3.1. Quantifying Changes in No-DTP Children from 2000 to 2019\nAmong the 56 LMICs included in this analysis, 44 (78.6%) had some kind of reduction in both national levels of no-DTP and subnational gaps in no-DTP prevalence between 2000 and 2019 (Figure 1; Table 2). In contrast, \ufb01ve countries\u2014Benin, Kenya, Guinea, Papua New Guinea, and Uzbekistan\u2014had at least some increase in both estimated national and subnational gaps in no-DTP prevalence. Five countries decreased national no-DTP levels between 2000 and 2019, but in tandem saw subnational gaps increase to some degree: Congo (an 8.4 percentage-point rise); Tajikistan (2.1 percentage points); Djibouti (0.7 percentage points); Central African Republic (0.6 percentage points); and S\u00e3o Tom\u00e9 and Pr\u00edncipe (0.4 percentage points). Two countries\u2014Haiti and Myanmar\u2013 had the national percentage of under-one children with no DTP doses at least somewhat increase since 2000 while subnational gaps declined; this was particularly pronounced for Myanmar (a 6.1 percentage-point rise).\n\nVaccines 2023, 11, 647\n\n7 of 19\n\nQuadrant IV: Increasing national no-DTP, increasing subnational gap\n\n>10 0\n\u221210\n\nAbsolute change, 2000\u22122019\n\nPapua New Guinea\n\nCongo\n\nQuadrant I: Decreasing national no-DTP, increasing subnational gap\n\nGuinea\n\nKenya\n\nUzbekistan Myanmar\n\nTajikistan Djibouti\n\nLesotho\n\nAngola\n\nZambia\n\nPakistan\n\nComoros Gambia\n\nTimor\u2212Leste\n\nGuinea\u2212Bissau Mali Nigeria\n\nCambodia Eritrea\n\nSudan Afghanistan\n\nDifference in the 5th\u221295th percentile subnational gap in no\u2212DTP prevalence, 2000\u22122019\n\n\u221220\n\nSomalia\n\nNepal Ghana\n\nBurkina Faso\n\nCameroon\n\nMauritania\n\nUganda\n\nSierra Leone\n\nLiberia\n\nEthiopia\n\n\u221230\n\nQuadrant III: Increasing national no-DTP, decreasing subnational gap\n\n>10\n\n0\n\nIndia\n\u221220\n\nDem Rep of the Congo\n\u221240\n\nQuadrant II: Decreasing national no-DTP, decreasing subnational gap\n\nPapua New Guinea\n>30\nUzbekistan\nBenin Kenya\n\nRelative change (%), 2000\u22122019\nTajikistan\nCongo\n\nQuadrant I: Decreasing national no-DTP, increasing subnational gap\n\nGuinea\n\nSao Tome and Principe\n\n0 \u221250 Myanmar\n\nSomalia Haiti\n\nDjibouti\n\nCentral African Republic Angola\n\nPakistan\n\nNigeria\n\nChad\n\nYemen\n\nMali Niger\n\nLaos Zambia\n\nTimor\u2212Leste\n\nLesotho\n\nKyrgyzstan\n\nAfghanistan\nCambodia Ethiopia\n\nGuinea\u2212Bissau\n\nCameroon\n\nMadagascar\n\nVietnam Gambia\n\nSenegal\n\nTanzania Nepal Togo\n\nComoros\n\nLiberia\n\nMalawi\n\nGhana\n\nSierra Leone\n\nEritrea\n\nRwanda\n\nQuadrant III: Increasing\n\n\u2212100\n\nnational no-DTP, decreasing subnational gap\n\n>30\n\n0\n\n\u221250\n\nDifference in the national no\u2212DTP prevalence, 2000\u22122019\n\nNational no-DTP, 2019\n\n\u226525%\n\n15\u221224%\n\n10\u221214%\n\n5\u22129%\n\n<5%\n\nCote d'Ivoire Burundi\nBangladesh\n\u2212100\nQuadrant II: Decreasing national no-DTP, decreasing subnational gap\n\nFigure 1. Comparing changes in no-DTP prevalence, nationally and for subnational gaps, from 2000 to 2019 for 56 LMICs. Countries are color-coded by national estimates of no-DTP in 2019.\n\nVaccines 2023, 11, 647\n\n8 of 19\n\nCountry\n\n2000 (%)\n\nAfghanistan 53.4\n\nAngola\n\n46.3\n\nBangladesh 8.8\n\nBenin\n\n11.3\n\nBurkina Faso\n\n23.8\n\nBurundi\n\n18.4\n\nCambodia\n\n22.8\n\nCameroon\n\n26.4\n\nCentral\n\nAfrican\n\n44.0\n\nRep\n\nChad\n\n59.7\n\nComoros\n\n19.1\n\nCongo\n\n48.5\n\nCote d\u2019Ivoire\n\n17.9\n\nDem Rep\n\nof the\n\n51.9\n\nCongo\n\nDjibouti\n\n34.3\n\nEritrea\n\n10.6\n\nEthiopia\n\n63.4\n\nGambia\n\n8.6\n\nGhana\n\n12.5\n\nGuinea\n\n35.6\n\nGuineaBissau\n\n19.5\n\nHaiti\n\n20.5\n\nIndia\n\n30.9\n\nKenya\n\n9.6\n\nKyrgyzstan 10.3\n\nLaos\n\n30.8\n\nLesotho\n\n13.1\n\nLiberia\n\n21.2\n\nMadagascar 25.7\n\nMalawi\n\n4.8\n\nMali\n\n36.4\n\nMauritania 28.1\n\nMozambique 12.7\n\nMyanmar\n\n11.5\n\nNepal\n\n13.5\n\nNiger\n\n52.6\n\nNigeria\n\n55.5\n\nPakistan\n\n24.0\n\nPapua New Guinea\n\n17.3\n\nRwanda\n\n5.7\n\nTable 2. Comparing levels and changes in no-DTP prevalence, nationally and for subnational gaps, from 2000 to 2019 for 56 LMICs. Bolded countries are those with the largest progress in reducing zero-dose children for both national and subnational gaps, based on the difference between the 5th and 95th percentiles for no-DTP prevalence at the second-level administrative unit, for either absolute or relative declines from 2000 to 2019. pp = percentage-points.\n\nNational Prevalence of No-DTP\n\n2019 (%)\n14.5 24.8 0.2 13.6\n4.6\n2.3 7.3 17.4\n\nAbsolute Change, 2000\u20132019 (pp)\n\u221239.0 \u221221.5 \u22128.6\n2.4\n\u221219.2\n\u221216.0 \u221215.6 \u22129.0\n\nRelative Change, 2000\u20132019 (%)\n\u221272.9 \u221246.4 \u221298.0 21.1\n\u221280.6\n\u221287.3 \u221268.2 \u221234.1\n\nSubnational Gap in No-DTP Prevalence (5\u201395th Percentile Difference across Districts)\n\n2000 (pp)\n\n2019 (pp)\n\nAbsolute Change, 2000\u20132019 (pp)\n\nRelative Change, 2000\u20132019 (%)\n\n39.0\n\n31.8\n\n38.6\n\n37.2\n\n14.4\n\n0.0\n\n16.9\n\n19.9\n\n\u22127.2 \u22121.3 \u221214.4 3.0\n\n\u221218.5 \u22123.5 \u221299.9 18.0\n\n31.2\n\n10.7\n\n\u221220.5\n\n\u221265.8\n\n17.1\n\n2.4\n\n27.2\n\n18.6\n\n48.7\n\n25.8\n\n\u221214.7 \u22128.6 \u221222.9\n\n\u221285.9 \u221231.6 \u221247.0\n\n28.9\n\n\u221215.1\n\n\u221234.3\n\n19.1\n\n19.7\n\n0.6\n\n3.1\n\n24.0\n\n\u221235.6\n\n6.8\n\n\u221212.3\n\n33.2\n\n\u221215.3\n\n2.3\n\n\u221215.6\n\n\u221259.7 \u221264.6 \u221231.5\n\u221287.1\n\n40.4\n\n36.4\n\n7.5\n\n3.2\n\n22.2\n\n30.6\n\n20.5\n\n4.4\n\n\u22124.0 \u22124.3 8.4\n\u221216.1\n\n\u221210.0 \u221257.5 38.1\n\u221278.4\n\n6.9\n\n\u221245.0\n\n26.1\n\n\u22128.2\n\n1.1\n\n\u22129.4\n\n11.7\n\n\u221251.6\n\n3.8\n\n\u22124.8\n\n6.5\n\n\u22126.0\n\n38.4\n\n2.8\n\n6.6\n\n\u221212.9\n\n21.0\n\n0.6\n\n7.2\n\n\u221223.6\n\n10.0\n\n0.4\n\n5.0\n\n\u22125.3\n\n24.6\n\n\u22126.1\n\n9.6\n\n\u22123.5\n\n11.2\n\n\u221210.0\n\n10.7\n\n\u221215.0\n\n3.4\n\n\u22121.4\n\n17.1\n\n\u221219.3\n\n6.5\n\n\u221221.6\n\n3.6\n\n\u22129.1\n\n17.6\n\n6.1\n\n8.7\n\n\u22124.7\n\n18.2\n\n\u221234.5\n\n28.9\n\n\u221226.6\n\n8.4\n\n\u221215.6\n\n44.4\n\n27.1\n\n1.9\n\n\u22123.8\n\n\u221286.8\n\u221224.0 \u221289.2 \u221281.5 \u221255.7 \u221248.2\n7.9\n\u221266.3\n2.8 \u221276.5\n4.4 \u221251.5 \u221219.9 \u221226.9 \u221247.2 \u221258.5 \u221229.2 \u221253.1 \u221276.8 \u221271.8 52.9 \u221235.2 \u221265.4 \u221248.0 \u221265.1\n156.4\n\u221266.9\n\n46.8\n\n16.0\n\n17.2\n\n18.0\n\n15.3\n\n3.8\n\n47.9\n\n30.8\n\n9.4\n\n4.2\n\n26.2\n\n5.6\n\n33.2\n\n39.3\n\n13.4\n\n7.8\n\n17.9\n\n9.7\n\n52.5\n\n19.7\n\n16.6\n\n20.8\n\n7.0\n\n5.0\n\n28.4\n\n21.5\n\n4.3\n\n3.3\n\n37.9\n\n12.3\n\n34.0\n\n17.1\n\n12.0\n\n2.6\n\n47.8\n\n40.5\n\n33.0\n\n10.1\n\n23.5\n\n7.6\n\n25.4\n\n13.7\n\n24.4\n\n7.7\n\n23.5\n\n20.4\n\n71.7\n\n64.9\n\n37.2\n\n36.5\n\n13.7\n\n32.5\n\n6.9\n\n1.3\n\n\u221230.8\n0.7 \u221211.4 \u221217.2 \u22125.2 \u221220.6\n6.1\n\u22125.6\n\u22128.2 \u221232.7\n4.1 \u22122.1 \u22126.9 \u22121.1 \u221225.6 \u221216.9 \u22129.4 \u22127.3 \u221222.8 \u221215.9 \u221211.7 \u221216.7 \u22123.2 \u22126.8 \u22120.7\n18.8\n\u22125.6\n\n\u221265.8\n4.4 \u221274.8 \u221235.8 \u221255.7 \u221278.7 18.4\n\u221241.7\n\u221245.7 \u221262.4 24.8 \u221229.3 \u221224.4 \u221224.6 \u221267.6 \u221249.8 \u221278.4 \u221215.2 \u221269.3 \u221267.5 \u221246.0 \u221268.4 \u221213.4 \u22129.5 \u22122.0\n137.7\n\u221281.5\n\nVaccines 2023, 11, 647\n\n9 of 19\n\nCountry\nS\u00e3o Tom\u00e9 and Pr\u00edncipe Senegal Sierra Leone Somalia South Sudan Sudan Tajikistan Tanzania TimorLeste Togo Uganda Uzbekistan Vietnam Yemen Zambia Zimbabwe\n\n2000 (%)\n14.6\n17.3\n31.1\n51.2\n64.1\n33.1 8.9 8.3\n51.3\n23.1 21.9 1.3 7.8 27.1 7.5 20.1\n\nTable 2. Cont.\n\nNational Prevalence of No-DTP\n\n2019 (%)\n\nAbsolute Change, 2000\u20132019 (pp)\n\nRelative Change, 2000\u20132019 (%)\n\n3.0\n\n\u221211.6\n\n2.2\n\n\u221215.1\n\n9.0\n\n\u221222.1\n\n51.1\n\n\u22120.1\n\n33.0\n\n\u221231.1\n\n3.4\n\n\u221229.7\n\n6.4\n\n\u22122.5\n\n5.7\n\n\u22122.7\n\n27.0\n\n\u221224.3\n\n13.1\n\n\u221210.1\n\n6.7\n\n\u221215.2\n\n7.2\n\n5.9\n\n4.6\n\n\u22123.2\n\n22.4\n\n\u22124.7\n\n5.8\n\n\u22121.7\n\n5.5\n\n\u221214.7\n\n\u221279.5\n\u221287.1\n\u221271.0\n\u22120.2\n\u221248.5\n\u221289.6 \u221228.0 \u221232.0\n\u221247.4\n\u221243.5 \u221269.2 441.9 \u221241.0 \u221217.2 \u221222.3 \u221272.9\n\nSubnational Gap in No-DTP Prevalence (5\u201395th Percentile Difference across Districts)\n\n2000 (pp)\n\n2019 (pp)\n\nAbsolute Change, 2000\u20132019 (pp)\n\nRelative Change, 2000\u20132019 (%)\n\n3.6\n\n4.0\n\n23.9\n\n9.9\n\n30.5\n\n7.5\n\n44.0\n\n27.9\n\n25.2\n\n22.5\n\n14.4\n\n4.8\n\n6.3\n\n8.4\n\n20.7\n\n8.5\n\n18.6\n\n15.3\n\n26.1\n\n11.1\n\n29.2\n\n6.3\n\n2.9\n\n5.1\n\n17.3\n\n8.7\n\n43.4\n\n35.4\n\n10.2\n\n7.1\n\n19.5\n\n6.6\n\n0.4\n\u221214.0\n\u221223.0\n\u221216.1\n\u22122.7\n\u22129.6 2.1 \u221212.2\n\u22123.3\n\u221215.0 \u221222.9\n2.2 \u22128.7 \u22128.1 \u22123.1 \u221212.9\n\n11.4\n\u221258.4\n\u221275.4\n\u221236.5\n\u221210.6\n\u221266.9 32.7 \u221259.0\n\u221217.9\n\u221257.5 \u221278.5 77.5 \u221250.0 \u221218.5 \u221230.5 \u221266.0\n\n3.2. Identifying Potential Exemplars in Reducing Zero-Dose Children since 2000\nAbsolute progress. The DRC, Ethiopia, and India registered the largest absolute reductions in national no-DTP prevalence and subnational gaps from 2000 to 2019 (Figure 2; Table 2). Supplementary Figure S1A\u2013C show both national and subnational no-DTP trends over time for each country.\nIn 2000, 51.9% of under-one children had no doses of DTP in the DRC nationally, with the country experiencing a 46.8 percentage-point gap between the territories with 5th and 95th percentiles for no-DTP prevalence (i.e., 28.5% to 75.3%). By 2019, national no-DTP prevalence levels fell to 6.9%, a 45.0 percentage-point decline. The DRC\u2019s subnational gaps narrowed by 30.8 percentage-points by 2019, decreasing to a total of 16.0 percentage points across the 5th and 95th percentiles of territories (i.e., 1.5% to 17.5%). As highlighted by Figrues 2 and S1A, subnational gaps started narrowing faster from 2015\u20132019 than in previous time periods.\nFor Ethiopia nationally, 63.4% of under-one children lacked any doses of DTP in 2000, but no-DTP prevalence fell 51.6 percentage points to 11.7% by 2019. Across its zones, Ethiopia had a 47.9 percentage-point gap between the 5th and 95th percentile levels of no-DTP prevalence in 2000, spanning from 39.6% to 87.5%. This subnational gap decreased by 17.2 percentage points by 2019, to a 30.8 percentage-point difference between the 5th and 95th percentile no-DTP levels across zones (i.e., 1.6% to 32.4%). However, amid such marked gains over the last 19 years, Ethiopia\u2019s reductions in subnational gaps have stagnated from 2016\u20132019 (Figure 2 and Figure S1B).\nIn India, national no-DTP prevalence was 30.9% in 2000 with a 52.5 percentage-point gap between the 5th and 95th percentile for no-DTP levels across districts (i.e., 7.2% to 59.7%). By 2019, 7.2% of under-one children had no doses of DTP in India nationally, a 23.6 percentage-point decline. Subnational gaps in India decreased 32.7 percentage points between 2000 and 2019, falling to 19.7 percentage-point difference in 2019 (i.e., 1.6% to 21.3%). Although overall subnational gaps have narrowed (Figure 2 and Figure S1C), several districts still exceeded 30% of under-one children with no doses of DTP in 2019.\n\nVaccines 2023, 11, 647\n\n10 of 19\n\nDifference in the 5th\u221295th percentile subnational gap in no\u2212DTP prevalence\n\nDem Rep of the Congo\n\nEthiopia\n\nIndia\n\n80\n\n70\n\n60\n\n50\n\n40\n\n2000\n\n2000\n\n30\n\n20\n\n10\n\n2019\n\n0\n\nBangladesh\n\n2019\nBurundi\n\n2000\n2019\n60 50 40 30 20 10 0\n\n80\n\nDecreasing subnational gap in\n\nno-DTP\n\n70\n\n60\n\n50\n\n40\n\n30\n\n20\n\n2000\n\n2019\n\n2000\n\n10\n\n0\n\nDecreasing national no-DTP\n\n2019\n\n60 50 40 30 20 10 0\n\n60 50 40 30 20 10 0\n\nNational no\u2212DTP prevalence (%)\n\nYear\n2019 2015 2010 2005 2000\n\nFigure 2. Comparing no-DTP trajectories for potential exemplars in reducing zero-dose children, 2000 to 2019. National no-DTP prevalence is represented on the x-axis and the subnational gap (as measured by the difference between the 5th and 95th percentile no-DTP prevalence across secondlevel administrative units) is represented on the y-axis. Each corner represents an extreme for each of these no-DTP metrics, with the lower right-hand corner\u2014low national no-DTP prevalence and low subnational inequality\u2014being the direction in which every location should strive to reach to equitably reduce no-DTP prevalence. Trends in the two no-DTP metrics for the potential exemplars are highlighted in black, with each circle representing a year from 2000 to 2019 that is color-coded from orange (2000) to blue (2019). The light gray trajectories represent the other 51 countries in this analysis.\n\nRelative progress. As measured by the percentage change in no-DTP metrics between 2000 and 2019, Bangladesh and Burundi achieved the largest relative reductions in no-DTP prevalence (Figure 2; Table 2). National and subnational no-DTP trends are illustrated in Supplementary Figure S1D,E.\nIn 2000, estimated national prevalence of no-DTP was 8.8% in Bangladesh, already below the 10% target set forth by the Global Vaccine Action Plan for 2020 [44]. However, by 2019, the percentage of under-one children with no doses of DTP fell to 0.8% in Bangladesh, a 98.0% decline since 2000. Subnational gaps in Bangladesh fell by 99.9% since 2000, narrowing from a 14.4 percentage-point difference for the 5th and 95th percentiles across districts in 2000 (i.e., 3.3% to 17.7%) to approximately 0.01 percentage-points in 2019. Absolute subnational gaps began narrowing faster after about 2012 (Supplementary Figure S1D).\nFor Burundi, national no-DTP estimates were 18.4% in 2000, but decreased to 2.3% in 2019\u2014an 87.3% reduction. Across Burundi\u2019s communes in 2000, there was a 17.1 percentagepoint difference for the 5th and 95th percentiles in no-DTP prevalence (i.e., 10.2% to 27.3%). By 2019, this subnational gap fell to 2.4 percentage points, representing an 85.9% decline\n\nVaccines 2023, 11, 647\n\n11 of 19\n\nDifference in the 5th\u221295th percentile subnational gap in no\u2212DTP prevalence\n\nacross communes at the 5th and 95th percentile (i.e., 1.5% to 3.9%). Progress accelerated after 2005 (Figure 2, Supplementary Figure S1E), when the country\u2019s 13-year civil war ended [45].\nTable 2 details these estimates for 2000, 2019, and across change metrics for all 56 countries, while Figure 2 depicts trajectories for no-DTP across national levels and subnational gaps for potential exemplars in reducing zero-dose children for each year between 2000 to 2019.\n3.3. Comparing Divergent No-DTP Trajectories since 2000 for Select Locations\nFocusing on the four Gavi Learning Hub countries\u2014Nigeria, Mali, Uganda, and Bangladesh\u2014we mapped their no-DTP trajectories from 2000 to 2019 against potential exemplars in reducing zero-dose children (Figure 3); the exception was Bangladesh, which achieved among the largest relative reductions in national no-DTP prevalence and subnational gaps since 2000. Accordingly, Figure 3 excludes Bangladesh.\n\n80\n2000\n70 60 Ethiopia\n50\n2000\n40\n30\n20\n10\n0 60 50\n\nNigeria\n2019\n40 30 20\n\n2019\n\n10\n\n0\n\nMali\nDem Rep of the Congo\n\nUganda\n\nDecreasing subnational gap in\nno-DTP\n\n2000\n\n2000\n\n2019\n\n2019\n\n60 50 40 30 20 10\n\n0\n\nNational no\u2212DTP prevalence (%)\n\nDecreasing national no-DTP\n60 50 40\n\n2000 2000\n\nBurundi 30 20\n\n2019\n10\n\n2019\n0\n\nYear 2019 2015 2010 2005 2000\n\nFigure 3. Comparing Gavi Learning Hub country no-DTP trajectories since 2000 to potential exemplars in reducing zero-dose children. Bangladesh, a Gavi Learning Hub country, was identi\ufb01ed as potential exemplar based on its marked progress on relative no-DTP metrics of change (Figure 2). Accordingly, we focus on Nigeria, Mali, and Uganda here. National no-DTP prevalence is represented on the x-axis and the subnational gap (as measured by the difference between the 5th and 95th percentile no-DTP prevalence across second-level administrative units) is represented on the y-axis. Each corner represents an extreme for each of these no-DTP metrics, with the lower right-hand corner\u2014low national no-DTP prevalence and low subnational inequality\u2014being the direction in which every location should strive to reach to equitably reduce no-DTP prevalence. Trends in the two no-DTP metrics for the Gavi Learning Hub countries are highlighted in black, with each circle representing a year from 2000 to 2019 that is color-coded from orange (2000) to blue (2019). The teal trends represent trajectories for the potential exemplars in reducing zero-dose children based on their absolute or relative progress since 2000. The light gray trajectories represent the other countries in this analysis.\n\nFor Nigeria, Ethiopia was its closest \u2018neighbor\u2019 in terms of national no-DTP prevalence in 2000\u201455.5% in Nigeria and 63.4% in Ethiopia\u2014with diverging no-DTP trajectories through 2019 (i.e., 28.9% in Nigeria and 11.7% in Ethiopia). From 2000 to 2019, Nigeria consistently had among the highest subnational no-DTP disparities in the world; even in 2000, when Ethiopia had the fourth highest subnational gap in no-DTP among included countries (47.9 percentage points; Table 2), Nigeria\u2019s subnational gap was more than 20 percentage points higher (71.7; Table 2). Nonetheless, given how Ethiopia markedly reduced no-DTP subnational gaps at the same time trends in Nigeria\u2019s subnational disparities more or less stagnated, they may be well-aligned for cross-country learning.\nFor Mali, the DRC was its closest \u2018neighbor\u2019 for subnational no-DTP prevalence gaps in 2000, with Mali experiencing a 47.8 percentage-point gap and the DRC having a 46.8 percentage-point disparity. By 2019, Mali still had a subnational gap exceeding\n\nVaccines 2023, 11, 647\n\n12 of 19\n40 percentage points (40.5, Table 2) while the DRC reduced its subnational gap to 16.0 (Table 2). National no-DTP prevalence was more variable for Mali and the DRC, with Mali\u2019s national no-DTP levels registering far lower than the DRC\u2019s in 2000 (36.4% and 51.9%, respectively) but then only moderately declining to 17.1% by 2019. In contrast, the DRC\u2019s national no-DTP prevalence decreased to 6.9% in 2019. Yet the DRC\u2019s no-DTP metrics from 2010\u20132015\u2014the time before the country accelerated no-DTP reductions\u2014parallel Mali\u2019s 2019 no-DTP measures. Accordingly, this more recent time period may support optimal cross-country learning for Mali.\nFor Uganda, Burundi aligned most closely to its 2000 no-DTP measures but showed divergences by 2019. In 2000, national no-DTP prevalence was 21.9% in Uganda and 18.4% in Burundi; by 2019, their no-DTP estimates were 6.7% and 2.3%, respectively (Table 2). Subnational gap trends were less similar for these two countries, with Burundi\u2019s no-DTP subnational gap in 2000 being narrower (17.1 percentage points) than that of Uganda\u2019s (29.2 percentage points). Each country recorded sizeable declines in subnational no-DTP gaps, with Uganda\u2019s falling to 6.3 percentage points and Burundi\u2019s to 2.4. In many ways, both Uganda and Burundi could offer meaningful lessons around reducing subnational disparities among unvaccinated children.\n4. Discussion\nWith this analysis, we offer a novel application of positive-outlier methods for identifying potential exemplars in reducing zero-dose children since 2000. The DRC, Ethiopia, and India showed among the largest absolute declines in both national no-DTP prevalence and subnational gaps between 2000 and 2019, while Bangladesh and Burundi demonstrated the largest percentage decreases in national no-DTP prevalence and subnational gaps during that time. Given the range of starting points, local contexts, and health system structures in these \ufb01ve countries, it is quite possible that the strategies used, and corresponding lessons learned in improving childhood vaccination\u2014speci\ufb01cally around expanding service reach to unvaccinated children\u2014may be applicable (or at least adaptable) to other settings. As highlighted by the so-called \u201cneighborhood\u201d analysis, comparing divergent no-DTP trajectories among peer locations could support deeper study around what catalyzed faster progress for some places\u2014and how those lessons could be applied elsewhere. The combination of this positive outlier methodology and cross-country platforms supported by the Gavi Learning Hubs offers unique opportunities to better understand \u2018what works\u2019 for accelerating progress in reaching unvaccinated children worldwide.\nConsidering positive outliers\u2014or potential exemplars\u2014in reducing no-DTP prevalence for both absolute and relative progress can better re\ufb02ect the range of successful strategies implemented from a range of different no-DTP prevalence starting points. After all, the types of programmatic and policy decisions that may occur when more than 30\u201350% of under-one children have had no doses of DTP could differ from those occurring when high zero-dose communities are more clustered and national levels of no-DTP are well below 10%. In health service delivery, these differences may unfold around more widespread intervention introduction and scale-up activities (e.g., addressing key infrastructure and personnel gaps that would otherwise impede adoption; mass mobilization and campaign-style outreach efforts) versus more tailored service provision to individuals or communities who still lack access to or demand for an intervention (e.g., hard-to-reach and hard-to-vaccinate populations [46]). For instance, in 2000, the DRC and Ethiopia started among the highest national levels of no-DTP observed across included countries in this study, as well as moderate-to-high levels of subnational gaps. Better understanding how the DRC and Ethiopia substantially reduced no-DTP metrics by 2019 could strengthen strategies adapted for countries that started at similar no-DTP measures in 2000 but had minimal or less pronounced reductions (e.g., Nigeria, Chad, Somalia).\nDespite their marked progress since 2000, further improvements in vaccination reach and uptake are needed in the DRC, Ethiopia, India, and other countries still experiencing large populations of unvaccinated children. Accordingly, it is possible that lessons learned\n\nVaccines 2023, 11, 647\n\n13 of 19\nfrom countries with exceptional relative reductions from 2000 to 2019 could be applicable to countries such as the DRC, Ethiopia, and India today; after all, 2019 no-DTP estimates for the latter countries are quite similar to the 2000 estimates for countries such as Bangladesh and Burundi. For Bangladesh, reductions in national no-DTP and subnational gaps nearly paralleled each other time, charting a path toward nearly 0% no-DTP nationally and negligible subnational differences by 2019. These trends may re\ufb02ect the country\u2019s concerted efforts to better reach rural communities with lower levels of vaccination [47], among other immunization and primary care strengthening interventions. For Burundi, levels of and subnational gaps in no-DTP markedly declined after the end of its civil war in 2005 [45]. From 2006 to 2010, Burundi adopted nationwide performance-based \ufb01nancing initiatives focused on improving child and maternal care [48], actions that have been associated with higher vaccination rates, particularly among the poor [49]. To better understand how different interventions and strategies may optimally align with current needs and barriers to vaccination, it is crucial to more deeply examine the programs and contexts in which past gains have occurred.\nThere is ample opportunity\u2014and need\u2014to characterize what drives successful vaccine delivery and uptake across the spectrum of past and current challenges, particularly around vaccination inequalities. One key consideration that emerged from this analysis involves the pathways by which no-DTP changed both nationally and sub-nationally from 2000 to 2019. Particularly among countries that started with higher levels of noDTP (e.g., Nigeria and Mali; Ethiopia, and the DRC), subnational gaps often remained unchanged or increased while national no-DTP prevalence began improving. Such pathways suggest that explicit equity program targets and implementation practices may not occur until later. Other countries, including India and Bangladesh, had more consistent declines for both metrics from 2000 to 2019\u2014a potential signal into the ways in which countries are concurrently addressing both national vaccination priorities and at least geographic inequalities. Nonetheless, it is also possible that countries such as India and Bangladesh experienced similar pathways of minimal changes in or rising subnational inequality amid decreasing national no-DTP prior to 2000. Developing a more formalized characterization or framework around \u2018pathways of progress\u2019 toward greater vaccination equity should be considered in future studies, both by geography and across other crucial factors (e.g., gender, wealth, education, religion, ethnicity).\nWhile assessing progress metrics is a necessary \ufb01rst step to better identify potential exemplars in reducing zero-dose burdens, they alone cannot shed light on what countries have executed and how such actions were associated with further improvements. Formally applying methods such as that of the EGH program, with qualitative examination of policy and programs alongside quantitative analyses around drivers of progress [32], should be prioritized for countries and/or subnational locations with notable advances in reducing zero-dose children. Furthermore, the learning and evaluation platform offered through the Gavi Learning Hubs [43], wherein characteristics of immunization programs and factors contributing to their impact will be examined in prospective manner with country researchers and leadership, will enable greater cross-country or subnational engagement around what works to reach unvaccinated children across contexts. This is particularly important for larger countries where subnational locations started at similar starting points but experienced different trajectories over time. For instance, in Nigeria, bordering states Kaduna and Plateau had fairly high levels of no-DTP prevalence in 2000 (69.4% and 50.6%, respectively; Supplementary Figure S2A). By 2019, Plateau decreased its no-DTP prevalence to 14.5%, whereas Kaduna reduced no-DTP prevalence to 31.9%. Another pair of bordering states\u2014Kogi and Enugu\u2014had no-DTP prevalence of 48.1% and 40.7% in 2000; by 2019, Engu recorded a much larger decline by 2019 (to 7.5%) whereas Kogi still exceeded 20% no-DTP prevalence. In Ethiopia, three regions\u2014Afar, Somali, Benshangual-Gomez\u2014had the country\u2019s highest no-DTP prevalence in 2000, at 75% or higher, followed by Oromia (69.3%) (Supplementary Figure S2B). By 2019, Benshangual-Gomez and Oromia reduced regional levels of no-DTP to 8.3% and 11.1%. Although Afar and Somali also recorded\n\nVaccines 2023, 11, 647\n\n14 of 19\nsubstantive declines in overall no-DTP prevalence, each region still had no-DTP prevalence exceeding 25%\u2014and experienced widening gaps in no-DTP among zones. Given these trends and patterns, it is likely that many countries\u2014especially larger ones\u2014could bene\ufb01t from so-called neighborhood analyses and positive outlier research at the subnational level.\nPast work has sought to synthesize and/or assess particular characteristics of successful immunization programs; nonetheless, few studies have expressly focused on both zero-dose children and incorporating mixed-methodologies with a positive outlier lens. For example, qualitative research in Senegal, Zambia, and Nepal points to factors including strong community engagement, integrated delivery, adaptive service provision, and robust data systems as central to improving and/or maintaining high levels of DTP1 and/or DTP3 [35,50\u201352]. However, the degree to which these approaches are fully transferable to communities with high zero-dose burdens remains unclear. Integrated service delivery, particularly for key primary care interventions for mothers and infants, may have an important role in addressing zero-dose burdens given the high overlap of missing vaccine doses with other essential health services [11]. Strengthening community engagement may require taking a longer-term lens and multifaceted investments, especially in areas of prolonged con\ufb02ict and/or distrust of health systems and providers. Innovative programs such as the DRC\u2019s Mashako Plan, which was launched in 2018 and has sought to improve vaccination completion rates among select provinces through a mixture of supervision support, supply chain improvements, and monitoring efforts [53], may also provide valuable implementation lessons for countries with equally large and/or dispersed populations.\nIt is worth noting that declines in no-DTP prevalence\u2014and thus increased coverage of DTP1, a marker of program reach\u2014do not inherently equate to gains in broader program retention or complete immunization. For instance, in much of Ethiopia, DTP3 coverage has not improved in parallel amid sizeable increases in DTP1 [7,54,55]. This means that while more children are being reached by vaccination services\u2014an unequivocally crucial milestone\u2014an increasing percentage of them remain under-vaccinated and thus may still be vulnerable to preventable disease. Although some parts of immunization programs can support both vaccination initiation and completion well (e.g., suf\ufb01cient availability of quali\ufb01ed health workers, strong supply, and cold chain systems), other factors can differentially affect how or whether children \ufb01nish vaccination series after receiving their \ufb01rst doses [7,56,57]: the availability of defaulter tracking systems, provider-client relationships and trust, \ufb02exibility in scheduling for multiple vaccine doses and/or other health services, among others. As global immunization agendas such as IA2030 [36] and Gavi 5.0 [37] rightly bring more attention to zero-dose populations and programs strategies to reach them, it is crucial that political and funding commitments around addressing gaps in under-vaccination also are maintained.\nLimitations\nThis analysis is subject to a number of limitations. First, this study focuses on changes in geographic inequalities at the second administrative level or higher, which results in representing only one of many critical factors that contribute to inequities in immunization delivery [19]. While geographic location can serve as a proxy for determinants also associated with location (e.g., district-level program funding levels, relative remoteness) [58], geography on its own cannot appropriately approximate the mechanisms by which gender, ethnicity, education, wealth, religious af\ufb01liation, and other individual, household, or community characteristics affect childhood vaccination [4,8,12\u201314,16,18]. It is also very possible that reductions in geographic inequalities do not consistently correspond with decreases in vaccination inequalities by these other key drivers of disparities across locations or do so consistently over time. Accordingly, it is critical to prioritize future research and analyses that explicitly assess how these trends in inequality may correlate with each other.\nSecond, focusing on the second-administrative level likely masks important differences experienced at more granular levels (e.g., within communities), [38,54] and thus potentially could obscure a more nuanced understanding of the localized sociocultural and/or\n\nVaccines 2023, 11, 647\n\n15 of 19\neconomic contributors to higher levels of zero-dose children. Future analyses should explore alternative geographic levels or areal operationalizations (e.g., 5 \u00d7 5 km pixel estimates rather than administrative boundaries) to further characterize the distribution and magnitude of vaccination inequalities in a given location.\nThird, country-to-country comparisons of subnational gaps and changes in these gaps over time may be affected by a country\u2019s total number of second-level administrative units rather than meaningful differences in vaccination equity at comparable areal units. For instance, subnational gaps in no-DTP may seem higher among in a country divided into more second-level administrative units than those with fewer units [59]. At least for the present analysis, having more (or fewer) second-level administrative units does not appear to be strongly related to 5th/95th percentile gap measures (i.e., r = 0.47 in 2000 and r = 0.38 in 2019) or change metrics from 2000 to 2019 (i.e., r = \u22120.17 for absolute change and r = 0.03 for percentage change). Since \ufb01rst- or second-level administrative units are often meaningful for health program implementation (e.g., district health authorities), we viewed using country administrative units as having more bene\ufb01ts and relevance than the potential drawbacks around variable subnational geographies. However, exploring alternative units of analysis (e.g., standardized pixel units) could be bene\ufb01cial for future work.\nFourth, we opted to use estimates from IHME for this analysis rather than administrative data sources (e.g., DHIS2) or alternative sources (e.g., WUENIC estimates). Because the primary goal of this study was to be able to directly compare national and subnational levels and trends in no-DTP across countries, IHME estimates provided the greatest number of countries with subnational estimates for the full time period (2000\u20132019).\nFifth, DTP estimates draw from household surveys and other data sources in which groups or communities with higher rates of unvaccinated children may be systematically under-represented (e.g., displaced or highly mobile populations). Accordingly, current no-DTP estimates may not fully capture the \u2018true\u2019 magnitude or trends in zero-dose children among populations with disproportionately high vulnerabilities and risks for not being vaccinated.\nSixth, the time period of analysis focused on 2000 to 2019, and thus the identi\ufb01cation of potential exemplars may be sensitive to estimated levels of childhood vaccination at either end of the 19-year range. Importantly, this analysis does not account for the ongoing effects of the COVID-19 pandemic, of which has had differential impacts across countries and communities since March 2020 [3,60,61]. Relatedly, these analyses do not re\ufb02ect improvements in or worsening of con\ufb02ict since 2019, such as in the Tigray region in Ethiopia [62].\nLastly, these analyses currently lack deeper contextual information from and by the communities most affected by higher rates of un- and under-vaccination. Our aim is to receive critical feedback on the potential applications of these positive-outlier methods for cross-country learning and synthesis around what works to reduce high rates of zero-dose prevalence, and to work with country and regional leadership to improve these approaches going forward.\n5. Conclusions\nRecognizing where exceptional progress in reducing zero-dose children has occurred is the \ufb01rst step toward better understanding what countries did to attain such improvements. Such insights then can inform strategy adaptions to other settings, and further reinforce successful strategies in places that achieved large reductions historically but still have large populations of unvaccinated children today. Characterizing pathways to greater vaccination equity, as well ensuring mechanisms by which effective knowledge translation and cross-country learning can be supported, will strengthen efforts toward ensuring all children can fully bene\ufb01t from vaccines.\nSupplementary Materials: The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/vaccines11030647/s1, Table S1. Initial countries considered for the present\n\nVaccines 2023, 11, 647\n\n16 of 19\n\nanalysis but were excluded for not meeting inclusion criteria; Figure S1. National and subnational trends in the prevalence of no-DTP children, 2000-2019, in the Democratic Republic of the Congo (A), Ethiopia (B), India (C), Bangladesh (D), and Burundi (E); Figure S2. Subnational no-DTP prevalence in Nigeria (A) and Ethiopia (B), 2000\u20132019.\nAuthor Contributions: Conceptualization and Methodology: N.F., G.C.C., G.I., D.E.P. and H.W.R. Formal Analysis: N.F. Writing\u2014Original Draft Preparation: N.F.; Writing\u2014Review and Editing: N.F., G.C.C., G.I., D.E.P. and H.W.R. Visualization: N.F. and D.E.P. Supervision: G.I. and D.E.P. All authors have read and agreed to the published version of the manuscript.\nFunding: This research received no external funding.\nInstitutional Review Board Statement: Not applicable.\nInformed Consent Statement: Not applicable.\nData Availability Statement: Vaccine estimates used in this analysis are currently unpublished estimates from the Institute for Health Metrics and Evaluation (IHME). Data may be shared upon request to the corresponding author and IHME.\nAcknowledgments: We thank Jonathan Mosser, Emily Hauser, Sam Rolfe, and Jason Nguyen from the Institute for Health Metrics and Evaluation for providing the estimates of childhood vaccination used in this study. We thank the following individuals from Gates Ventures, for their programmatic support and assistance: Patrick Y Liu, Jordan-Tate Thomas, Rowan Hussein, and Ryan Fitzgerald.\nCon\ufb02icts of Interest: N.F., G.I. and D.E.P. are paid employees of Gates Ventures, which coordinates the Exemplars in Global Health (EGH) program. N.F., G.I., and D.E.P. previously worked at the Institute for Metrics and Evaluation (IHME): September 2008\u20132011 and February 2013\u2013June 2022 for N.F.; August 2014\u2013July 2020 for G.I.; and June 2009\u2013February 2019 for D.E.P., G.C.C. and H.W.R. are employed by Gavi, the Vaccine Alliance. 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Gesesew, H.; Berhane, K.; Siraj, E.S.; Siraj, D.; Gebregziabher, M.; Gebre, Y.G.; Gebreslassie, S.A.; Amdeslassie, F.; Tesema, A.G.; Siraj, A.; et al. The Impact of War on the Health System of the Tigray Region in Ethiopia: An Assessment. BMJ Global Health 2021, 6, e007328. [CrossRef]\nDisclaimer/Publisher\u2019s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\n\n\n",
+    "authors": [
+      "N. Fullman",
+      "G. C. Correa",
+      "G. Ikilezi",
+      "D. E. Phillips",
+      "H. W. Reynolds"
+    ],
+    "doi": "10.3390/vaccines11030647",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "B2VIAABJ",
+    "title": "Factors associated with the utilization of inactivated polio vaccine among children aged 12 to 23 months in Kalungu District, Uganda",
+    "abstract": "Uganda officially introduced the inactivated polio vaccine (IPV) in May 2016 as part of the polio eradication strategy and integrated it into its routine immunization programme in addition to the oral polio vaccine. The current coverage stands at 60% as of July 2017. We therefore aimed to determine factors associated with the uptake of IPV among children in Kalungu District so as to inform the implementation of the vaccine policy. A community-based cross-sectional study was conducted among caregivers of 406 eligible children aged 12-23\u2009months through multi-stage systematic sampling and a standardized semi-structured questionnaire. Nine key informant interviews were conducted through purposive selection of health care providers and members of Village Health Teams (VHTs) based on their expertize. Modified Poisson regression and thematic content analysis were used to determine factors significant to IPV uptake among children. 71% of sampled children aged 12-23\u2009months had received IPV in Kalungu District. The survey found that being encouraged by health workers and VHTs was significant to children's uptake of IPV (Adjusted PR 1.24, 95% CI; 1.22-3.47). Distance to the immunization point (Adjusted PR 0.32,95% CI; 0.16-0.62) and caregiver's education level (Adjusted PR 1.16,95% CI; 1.05-2.22) were also associated with IPV uptake. Qualitative findings from health workers and VHT members further confirmed the perception that distance to the immunization post was important, and VHTs also stated that being encouraged by health workers was critical to IPV uptake. The current prevalence of IPV uptake among children aged 12-23 months in Kalungu is 71%, higher than the last reported national coverage (60%), though still below the recommended national coverage of 95%. Efforts should be focused on sensitization of caregivers through health workers and VHTs. Immunization outreach should be strengthened so as to bring services closer to patients.",
+    "full_text": "Health Policy and Planning, 35, 2020, i30\u2013i37 doi: 10.1093/heapol/czaa099 Supplement Article\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nFactors associated with the utilization of inactivated polio vaccine among children aged 12 to 23 months in Kalungu District, Uganda\nMirembe Rachel Faith1,*, Babirye Juliet2, Nathan Tumuhamye2, Tumwebaze Mathias3 and Emma Sacks4\n1Ministry of Health, Uganda Sanitation Fund Programme, Kampala, PO Box 7272, Uganda 2Makerere University, School of Public Health, Kampala, Uganda 3Bishop Stuart University, Mbarara, Uganda 4Johns Hopkins School of Public Health, Baltimore, Maryland, USA\n*Corresponding author. Ministry of Health, Uganda Sanitation Fund Programme, Kampala, PO Box 7272, Uganda. E-mail: rachellefaith@ymail.com\nAccepted on 12 August 2020\nAbstract\nUganda of\ufb01cially introduced the inactivated polio vaccine (IPV) in May 2016 as part of the polio eradication strategy and integrated it into its routine immunization programme in addition to the oral polio vaccine. The current coverage stands at 60% as of July 2017. We therefore aimed to determine factors associated with the uptake of IPV among children in Kalungu District so as to inform the implementation of the vaccine policy. A community-based cross-sectional study was conducted among caregivers of 406 eligible children aged 12\u201323 months through multi-stage systematic sampling and a standardized semi-structured questionnaire. Nine key informant interviews were conducted through purposive selection of health care providers and members of Village Health Teams (VHTs) based on their expertize. Modi\ufb01ed Poisson regression and thematic content analysis were used to determine factors signi\ufb01cant to IPV uptake among children. 71% of sampled children aged 12\u201323 months had received IPV in Kalungu District. The survey found that being encouraged by health workers and VHTs was signi\ufb01cant to children\u2019s uptake of IPV (Adjusted PR 1.24, 95% CI; 1.22\u20133.47). Distance to the immunization point (Adjusted PR 0.32,95% CI; 0.16\u20130.62) and caregiver\u2019s education level (Adjusted PR 1.16,95% CI; 1.05\u20132.22) were also associated with IPV uptake. Qualitative \ufb01ndings from health workers and VHT members further con\ufb01rmed the perception that distance to the immunization post was important, and VHTs also stated that being encouraged by health workers was critical to IPV uptake. The current prevalence of IPV uptake among children aged 12\u201323 months in Kalungu is 71%, higher than the last reported national coverage (60%), though still below the recommended national coverage of 95%. Efforts should be focused on sensitization of caregivers through health workers and VHTs. Immunization outreach should be strengthened so as to bring services closer to patients.\nKeywords: IPV, polio, vaccinations, immunization, children, Uganda\n\nIntroduction\nOver the last two decades, eradicating polio has been a key global health goal (Parker et al., 2015; WHO, 2016a). Between 2013 and 2014, the number of wild polio cases reported globally reduced from 256 to 171 and, in 2018, further reduced to 33 reported cases (WHO, 2018). Most\n\nof these outstanding cases are from three countries that have never stopped polio transmission (Nigeria, Afghanistan and Pakistan). Tackling the last 1% of polio cases has still proven difficult due to reasons such as conflict, political instability, hard-to-reach populations, community misconceptions and poor infrastructure (Omole et al., 2015).\n\nVC The Author(s) 2020. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.\n\nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits\n\nunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.\n\ni30\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\ni31\n\nKEY MESSAGES\n\u2022 Over the last two decades, eradicating polio has been a key global health goal, and the existence of wildtype virus with use of oral polio has catalysed countries to add inactivated polio vaccine (IPV) to their schedules. Uganda introduced IPV in 2016, but vaccination coverage continues to be substandard and limited studies have been conducted in regard to IPV utilization.\n\u2022 Our study \ufb01nds that 71% of sampled children aged 12\u201323 months had received IPV in Kalungu District, which is well below the recommended 95%.\n\u2022 The household survey found that being encouraged by health workers and Village Health Teams (VHTs) was signi\ufb01cant to children\u2019s utilization of IPV; distance to the immunization point and caregiver\u2019s education level were also associated with IPV utilization.\n\u2022 Qualitative \ufb01ndings from health workers and village health team members further con\ufb01rmed the perception that distance to the immunization post was important, and village health teams also stated that being encouraged by health workers to immunize was critical to IPV utilization.\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nIn May 2012, the World Health Assembly declared the eradication of poliovirus (WHO, 2012) as a programmatic emergency for global public health. To achieve and sustain a polio-free world, WHO recommended the introduction of at least one dose of inactivated polio vaccine (IPV) given in addition to the oral polio vaccine (OPV). IPV is a systemic immunity booster that eliminates the risk of paralytic polio and circulating vaccine-derived polioviruscVDPV, which exist with OPV (Tevi-Benissan et al., 2017). Countries are expected to shift to IPV-only schedules; the double vaccine process is expected to finish by the end of 2020. The introduction of IPV in national programmes has been largely successful despite initial global scepticism; all countries using OPV have now formally committed to adding IPV to their vaccine schedules. Current global polio immunization coverage stands at 85%, against the 90% target (WHO, 2016b), while the overall IPV coverage in sub-Saharan Africa stands at 73% (Anand et al., 2014). As long as a single child remains infected, children in all countries are at risk of contracting polio, and failure to eradicate polio from these last remaining strongholds could result in as many as 200 000 new cases every year within 10 years all over the world (WHO, 2017a).\nUganda is at high risk for polio outbreaks due to the frequent cross-border movements of populations, mainly due to insecurity in neighbouring countries. Wild polio virus may exist in such countries; therefore, the risk of importing and re-establishing these polio cases in Uganda is high (Ministry of Health, 2017). Uganda officially introduced IPV vaccine in May 2016 and integrated it into its routine immunization programme (WHO, 2017b). The current national IPV coverage stands at 60% as of July 2017 (WHO-UNICEF, 2017) while the OPV coverage is recorded at 106%, thanks to efforts to reach recent refugees who were not counted in the initial target population.\nThe introduction of IPV in Uganda, like elsewhere, resulted in the delivery of multiple injections during a single visit, with infants receiving IPV alongside pentavalent vaccine (for diphtheria, tetanus and whole-cell pertussis; hepatitis B; Haemophilus influenza type b) and pneumococcal vaccine at 14 weeks (Preza et al., 2017). Infants also receive the third and final OPV dose at 14 weeks. Unanticipated concerns have emerged from other countries over the acceptability of multiple injections, sites of administration and safety. It has also raised common caregiver and provider concerns about the pain experienced by the child, worry about potential side effects, uncertainty about vaccine effectiveness or other misunderstandings about the IPV vaccine (Wallace et al., 2014; Ughasoro et al., 2015).\nIn Uganda, limited studies have been conducted in regard to IPV uptake since its introduction in May 2016; therefore, understanding the factors associated with IPV uptake among children will help\n\ninform policy makers and implementers on better ways to support scaling up of the new vaccine to ensure high coverage and achieve the goal of polio eradication.\nMaterials and methods\nThis was a cross-sectional study, which involved the use of both qualitative and quantitative data collection methods. The aims of this study were to determine the current proportion of children aged 12\u201323 months utilizing IPV in Kalungu District, and the individual and health service factors associated with IPV uptake.\nStudy setting\nKalungu District is one of the new districts that were created in Uganda in 2010, having originally been part of Masaka District. It is a 2 1=2 hour drive from Kampala. It has a population of approximately 184 134 people, with 54 360 women of reproductive age and 11 910 children aged 12\u201323 months, according to the 2014 Uganda National Population and Housing Census (Uganda Bureau of Statistics, 2014). The major economic activities are agriculture, livestock and fish farming; and many people carry out trading on a small scale. The district contains two Health Sub-Districts (HSDs), served by one hospital (offering specialized services), two health centre-level IVs (offering out-patient and in-patient services, maternity, plus an operational theatre), 12 health centre-level IIIs (offering comprehensive out-patient and maternity services) and 15 health centre-level IIs (basic out-patient clinics). All of the hospitals, health centre IVs and health centre IIIs offer vaccination services, as do a few health centre IIs.\nThe study population was children aged 12\u201323 months in the selected community in Kalungu District, and respondents were the caregivers of children aged 12\u201323 months in the community who were 18 years or older, as well as Village Health Teams (VHTs) and health workers at the selected health facilities who had administered vaccinations within the past two years. Caregivers and VHT members who were ill, drunk or inactive (no longer actively carrying out their duties, according to clinic records) were ineligible for participation.\nSampling\nFor the quantitative data, sample size determination was done using the Leslie Kish formula since it was a cross-sectional study; it was determined that 410 respondents were required for the study. Multistage sampling was used to select the study participants across the two HSDs. A complete list of the sub-counties in each HSD was\n\ni32\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nobtained from the District Planning Unit. A randomizer using computer-generated numbers was used to randomly select two subcounties from each of the two HSDs. A list of all parishes was obtained from the two selected sub-counties. From these parishes, three were randomly selected from each HSD for a total of six parishes. All of the villages in the selected six parishes were listed, and 20 villages were randomly selected, with 10 selected from each parish representing each HSD.\nWith the help of the local council leader and VHT, the research team established the number of households with children aged 12\u2013 23 months. Then the total number of households in each village was divided by the respective samples to calculate the intervals for household selection. Thereafter, every fifth eligible household was selected. At the household level, the mother or father or other caregiver was selected as a respondent. In total, 21 households were randomly selected from the 20 villages. For the qualitative data, a complete list of the health centres and their levels was obtained from the District Health Office. Then one health centre IV, three health centre IIIs and two health centre IIs were selected because of their high volume capacities in regard to immunization. Interview participants were then selected who were associated with these facilities.\nParticipants were purposively selected based on their expert opinion and work experience in relation to immunization activities. A total of nine respondents were selected. These included three VHT members who were actively involved in the immunization activities in the selected parishes, four nurses who were directly responsible for immunization activities (two from each selected HSD and subcounty), a District Vaccines Focal Officer and an Assistant District Health Officer responsible for Maternal Child Health (ADHOMCH).\nData collection\nHome-based records (vaccination cards) were used as evidence for immunization and in case of missing or incomplete home-based vaccination record, recall or verbal history of vaccination by the caregiver was used. The caregiver was asked questions about the site of injection, when the vaccine was received and number of injections administered; answers to two questions were considered sufficient as record of vaccination. A semi-structured questionnaire was used by an interviewer to collect information on the demographics, knowledge and perceptions of the respondents about IPV, and factors related to IPV uptake. A caregiver was considered to have a favourable attitude towards IPV if they agreed with three or more positive statements about it. Interviews of health care providers and VHTs were conducted using a key informant guide. They were audio-recorded and lasted approximately 30 minutes each. Interviews with health workers were conducted in English; interviews with VHT members were conducted in the local language, Luganda, by the trained research assistants, and later translated into English for analysis.\nPre-testing of tools was done in one of the health centres not selected to participate in the study, to check for clarity, content validity and ability to generate required data. After pre-testing, tools were slightly revised before actual data collection.\nData analysis\nQuantitative data were coded and analysed at univariate, bivariate and multivariate levels. Categorical variables were summarized using frequencies and proportions. Continuous variables were summarized using medians with their interquartile ranges (IQR). Chisquare test was used to examine the associations between the\n\nselected independent variables and uptake of IPV. Modified Poisson regression with robust variances was used for bivariate and multivariate analysis to identify factors associated with uptake of IPV. Associations were tested at a 95% confidence interval (CI). Prevalence ratios (PR) were used instead of odds ratio because the prevalence of the outcome of interest (uptake of IPV) was >10%, therefore use of an odds ratio would overestimate the strength of association (Wilber and Fu, 2010).\nFactors that had a p-value of >0.05 at bivariate analysis were included in the multivariate analysis to obtain adjusted prevalence ratios. Multivariate analysis was conducted to identify the factors associated with uptake of IPV for caregivers with children aged 12\u2013 23 months after controlling for background factors. Model-building was done by a stepwise elimination process that involved adding the variables that qualified for the multivariate level one at a time while dropping those which did not have any significance. This was done to obtain the best model with smaller Akaike\u2019s information criterion (AIC) and the log likelihood ratio closer to zero. Crude prevalence ratios, adjusted prevalence ratios and their 95% confidence intervals (CIs) and p-values are reported in tables.\nFor interview data, thematic content analysis using a deductive approach was used. Each of the transcripts was carefully read to initially become familiar with the content. A sample of the transcripts were chosen and re-read to identify the key points raised so as to enhance data coding. All transcripts were then read again and coding was applied independently by two reviewers, who met to discuss disagreements and come to consensus.\nBased on the findings emerging from the data, similar codes were grouped into sub-themes. Similar sub-themes were then merged together and aligned with our initial codes from the conceptual framework. A master sheet containing the themes, subthemes and issues raised was developed and used to obtain the frequently mentioned items. Descriptive statistics were then used to summarize responses from all the interviews in each theme. The most outstanding quotes that were representative of the responses from all interviews are presented.\nEthical considerations\nEthical approval for this study was obtained from the Makerere University School of Public Health Higher Degrees Research and Ethics Committee (HDREC). Permission was granted from the District Health Officer-Kalungu to do research in the lower health facilities and from the in-charge health officers at each health centre. Written informed consent was sought from participants. Participant confidentiality was ensured using assigned study identity numbers instead of names.\nResults\nBackground characteristics of the respondents\nA total of 406 eligible respondents completed the survey, the majority (86.9%) of whom were female. Their median age was 27 years (IQR 23\u201332). More than half (59.3%) of the respondents were aged 20\u201329 years. 52% of the respondents had only a primary level of education (see Table 1). Of the 406 respondents, 84.6% had child health cards and 71.0% of the study children had received the single dose IPV.\nBivariate analysis of individual factors associated with IPV uptake\nIn bivariate analysis, caregiver age and education level appeared significantly associated with IPV uptake among children. The\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\ni33\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nTable 1 Background characteristics of the respondents (n \u00bc 406)\n\nVariable\n\nFrequency (n) Percentage (%)\n\nSex\n\nMale\n\n53\n\nFemale\n\n353\n\nAge\n\n15\u201319\n\n27\n\n20\u201329\n\n241\n\n30\u201339\n\n104\n\n40\u00fe\n\n34\n\nReligion\n\nCatholic\n\n219\n\nProtestant\n\n64\n\nMuslim\n\n103\n\nOthers (Pentecostal and Seventh\n\n20\n\nDay Adventists)\n\nMarital status\n\nMarried\n\n334\n\nNot married\n\n72\n\nEducation\n\nNone\n\n11\n\nPrimary\n\n209\n\nSecondary\n\n162\n\nTertiary\n\n20\n\nPossession of an immunization card\n\nYes\n\n244\n\nNo\n\n44\n\n13.1 86.9\n6.65 59.36 25.62\n8.37\n53.9 15.8 25.4\n4.93\n82.27 17.73\n2.7 52.0 40.3\n5.0\n84.6 15.4\n\nprevalence of IPV uptake among children whose caregivers were aged 40 years and above was 2.54 times higher compared with those whose any education whose care givers had not attained any form of education (unadjusted PR 1.51, 95% CI 1.3\u20132.10). Prevalence of IPV uptake among children whose caregivers had attained postprimary education (secondary and tertiary) was 1.51 times higher than that among children whose caregivers had not attained any form of education (unadjusted PR 1.51,95% CI 1.3\u20132.10). Prevalence of IPV uptake among children whose caregivers had negative attitudes to IPV was 0.41 times lower compared with that for those whose caregivers had favourable attitudes (unadjusted PR 0.41 95% CI 0.29\u2013059); see Table 3.\nBivariate analysis of health service factors associated with IPV uptake\nIn bivariate analysis, incurring transport costs to reach the immunization site and being encouraged by health workers or VHTs to take the child for IPV were significantly associated with IPV uptake (Table 3).\nMultivariate analysis of factors associated with IPV uptake\nAfter controlling for the education level of caregivers and encouragement by health workers and VHTs to take children for IPV, the prevalence of IPV uptake among children whose caregivers resided >5 km away from an immunization post was 0.32 times lower than those who resided <5 km from the immunization post (Adjusted PR 0.32,95% CI; 0.16\u20130.62) (Table 4).\nAfter controlling for distance to the immunization post and encouragement by health workers or VHTs to take children for IPV, the prevalence of IPV uptake among children whose caregivers had attained post-primary (secondary and tertiary) education was 1.16 times higher than among children whose caregivers had not attained\n\nany education (Adjusted PR 1.16,95% CI; 1.05\u20132.22). Children whose caregivers were encouraged by health workers or VHTs to take children for IPV were 1.24 times more likely to utilize IPV vaccines than those whose caregivers were not encouraged to do so.\nQualitative findings\nFindings from the surveys were supported by qualitative interviews. Data were organized into key themes: distance to immunization post; and encouragement to utilize IPV services.\nRespondents reported that children whose caregivers resided far from the immunization post were not effectively utilizing IPV immunization services, as revealed in the quotes below:\n. . . our health facilities are not evenly distributed; caregivers in hard to reach areas are really struggling since they have to incur high transport costs. This deters the children from utilizing these services, but we ensure that outreaches are conducted routinely amidst the financial and human resources challenges (ADHOMCH).\n. . . The mothers who stay far find it a challenge to take their children for immunization (VHT, HC III).\n. . . caregivers staying in hard to reach areas such as landing sites [collection and trading centres for fish] sometimes fail to make it to the immunization point due to constraints in transport as they try to cross the water (VHT, HC III).\nRespondents also stated that children whose caregivers had been encouraged by the health workers and VHTs were utilizing the vaccine more than those who had not been, as illustrated in the quotes below:\n. . . we start our health education when the mothers come for antenatal care. The VHTs help us to follow them up and we encourage those mothers in our neighbourhood. This helps us to ensure that the children utilize the IPV immunization services (Nurse, HC II).\n. . . some caregivers still have misconceptions about the polio vaccine of course, some wonder why the change from oral to injectable; but we continuously health educate and encourage them to have their children immunized for their good. Surely most of them adhere since they trust us and some have even witnessed the consequences of not immunizing against polio (District focal person for Expanded Programme on Immunization).\nDiscussion\nProportion of caregivers to children aged 12\u201323 months utilizing IPV\nThis study found out that the current proportion of IPV uptake in Kalungu District stands at 71%. This is slightly higher than the last reported national and district coverage figures in 2017, which were at 60% and 54.4%, respectively (KDLG, 2017; WHO and UNICEF, 2017). However, the proportion of IPV uptake observed in this study was slightly lower than the overall IPV coverage in subSaharan Africa, which is at 73% (Anand et al., 2014), and is lower than many other regional coverage figures such as 80% for Rwanda (UNICEF, 2018b) and 90% for Kenya (UNICEF, 2018a). In an earlier study in Kenya, the high prevalence of IPV uptake was attributed to the mobile tracking system, whereby mothers are sent SMS/ text reminders for their next immunization date, coupled with the traditional home visits, which are believed to reduce dropout rates. However, the increasing IPV prevalence in Kalungu from 54.4% in\n\ni34\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nTable 2 Bivariate analysis of individual factors associated with IPV uptake.\n\nFactor\n\nIPV use\n\nYes n (%)\n\nNo n (%)\n\nAge 15\u201319 20\u201329 30\u201339 40\u00fe Sex Male Female Religion Catholic Protestant Muslim Others* Marital status Married Not married Education level No education Primary Post-primary Knowledge of IPV Knowledgeable Poor knowledge Attitude towards IPV Favourable attitude Negative attitude\n\n22(81.5) 185(76.7)\n64(61.5) 18(52.9)\n34(64.2) 255(72.2)\n162(74.0) 41(64.1) 71(68.9) 15(75.0)\n238(71.3) 51(70.8)\n3(27.3) 119(55.9) 167(91.8)\n92(93.9) 144(96.6)\n7(35.0) 282(73.3)\n\n5(18.5) 56(23.2) 40(38.5) 16(47.1)\n19(35.9) 98(27.8)\n57(26.0) 23(35.9) 32(31.1)\n5(25.0)\n96(28.7) 21(29.2)\n94(44.1) 15(8.2)\n6(6.1) 5(3.4)\n13(65) 103(26.7)\n\nUPR 95% CI\n1.25(0.55\u20132.86) 2.08(0.91\u20134.76) 2.54(1.06\u20136.06)*\n1 0.77(0.52\u20131.15)\n1 1.38(0.93\u20132.05) 1.19(0.83\u20131.72) 0.96(0.43\u20132.12)\n1 1.01(0.68\u20131.51)\n8(72.7) 1.45(1.12\u20131.71)* 1.51(1.3\u20132.10)***\n1.82(0.57\u20135.83) 1\n1 0.41(0.29\u20130.59)***\n\n*P < 0.05; **P < 0.01; ***P < 0.001. UPR \u00bc unadjusted prevalence ratio. CI \u00bc con\ufb01dence interval.\n\nTable 3 Bivariate analysis of health service factors associated with IPV uptake\n\nFactor\n\nIPV use\n\nYes n(%)\n\nDistance to nearest immunization site <5 km >5 km Transport costs to the immunization site (price ranges in ugandan shillings) No cost 1000/\u00bc to 4000/\u00bc 5000/\u00bc to 10 000/\u00bc Encouraged to take child for IPV Never By health worker By VHT By others (peer, husband, close relative) Vaccines available at all times at the immunization site Yes No Health workers available at all times at the immunization site Yes No Informed about the vaccination schedule Yes No Ever turned away from the vaccination site due to long queues Yes No Health workers receptive at the vaccination site Yes No\n\n200(82.0) 65(40.1)\n132(62.0) 111(80.4)\n17(30.9)\n5(9.3) 154(90.6)\n86(86) 73(89)\n247(84.8) 72(62.6)\n232(81.9) 93(75.6)\n220(79.7) 90(69.2)\n126(77.3) 199(81.8)\n224(87.5) 120(80)\n\nNo n(%)\n44(18.0) 97(59.8)\n81(38.0) 27(19.6) 38(69)\n49(90.7) 16(9.4) 14(14)\n9(10.9)\n44(15.1) 43(37.4)\n51(18) 30(24.3)\n56(20.3) 40(30.7)\n37(22.6) 44(18.1)\n32(12.5) 30(20)\n\nUPR 95% CI\n1 0.52(0.26\u20131.02)\n1 0.51(0.35\u20130.75)** 0.60(0.27\u20131.32)\n1 2.20(2.0\u20132.27)** 1.11(1.01\u20131.31)** 0.14(0.01\u20130.26)*\n1 1.54(0.67\u20133.56)\n1 1.43(0.67\u20133.05)\n1 0.65(0.26\u20131.62)\n1 2.17(0.96\u20134.90)\n1 0.95(0.37\u20132.45)\n\n*P < 0.05; **P < 0.01; ***P < 0.001. UPR \u00bc unadjusted prevalence ratio. CI \u00bc con\ufb01dence interval.\n\np-value 1 0.590 0.084 0.035\n0.208\n0.111 0.341 0.921\n0.943 1 0.013 <0.001 0.310\n<0.001\np-value\n0.059\n0.001 0.202\n0.01 0.01 0.02\n0.312\n0.353\n0.353\n0.061\n0.920\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\nTable 4 Multivariate analysis of factors associated with IPV uptake among children aged 12\u201323 months\n\nFactor\n\nIPV use\n\nCaregivers\u2019 age 15\u201319 20\u201329 30\u201339 40\u00fe Caregivers\u2019 education level No education Primary Post-primary Caregivers\u2019 attitude towards IPV Favourable attitude Negative attitude Distance to the nearest immunization site <5 km >5 km Transport cost to immunization site (price ranges in ugandan shillings) No cost 1000/\u00bc to 4000/\u00bc 5000/\u00bc to 10 000/\u00bc Encouraged to take child for IPV Never By health worker By VHT By others (peer, husband, close relative)\n\nYes n(%)\n22(81.5) 185(76.7)\n64(61.5) 18(52.9)\n3(27.3) 119(55.9) 167(91.8)\n7(35.0) 282(73.3)\n266(85.8) 45(46.8)\n165(73.4) 141(96.5)\n17(38.6)\n11(18.3) 110(87.3) 126(90)\n71(88.7)\n\nNo n(%)\n5(18.5) 56(23.2) 40(38.5) 16(47.1)\n8(72.7) 94(44.1) 15(8.2)\n13(65) 103(26.7)\n44(14.2) 51(53.1)\n51(23.6) 5(3.4)\n27(61.4)\n49(81.6) 16(12.6) 14(10)\n9(11.3)\n\n*P < 0.05; **P < 0.01; ***P < 0.001. APR \u00bc adjusted prevalence ratio. CI \u00bc con\ufb01dence interval.\n\nAPR 95% CI\n1 1.05(0.47\u20132.31) 1.04(0.42\u20132.61) 1.07(0.35\u20133.24)\n1 0.59(0.22\u20131.63) 1.16(1.05\u20132.22)**\n1 0.45(0.26\u20130.78) *\n1 0.32(0.16\u20130.62)**\n1 0.34(0.29\u20134.1)* 1.55(0.91\u20132.65)\n1 1.24(1.22\u20133.47)***\n1.1(1.02\u20131.58)** 0.45(0.23\u20131.89)\n\ni35\np-value\n0.908 0.930 0.905\n0.311 <0.001\n0.005\n0.001\n0.009 0.109\n<0.001 0.01 0.61\n\n2017 to the current 71% indicates a positive trend and could be attributed to the increasing awareness about the vaccine among the caregivers, coupled with the routine and supplementary immunization activities conducted at both health centres and outreaches.\nIndividual factors associated with IPV uptake\nThis study found that the education levels of caregivers were significantly associated with IPV uptake. This is consistent with a study conducted in Nigeria where caregivers with lower education levels were linked to low vaccine uptake among children. Similar findings from a study conducted in Kenya and Ethiopia (Sullivan et al., 2010) on the factors associated with immunization completion rates indicated that children whose caregivers had attained some level of education had higher chances of following and completing the immunization schedules compared with those whose caregivers had not attained any level of education (Abuya et al., 2011). This could be attributed to the fact that caregivers who have attained some form of education can more easily comprehend health information and may be more aware of the benefits of child immunization.\nThis suggests that education has a strong influence on the uptake of public health interventions in general, since many public health topics are taught in schools, and may empower future caregivers to protect their children from preventable diseases. Therefore, there is a need to reinforce sharing of health information about immunization in schools so as to widen the knowledge base of future parents.\nFindings from this study show that caregiver religion was not significantly associated with IPV uptake among children, which contrasts earlier studies conducted in other African countries, like Nigeria, where religious beliefs in relation to polio immunization negatively affected vaccine uptake (Obadare, 2005). These studies found that many in the Muslim community in Nigeria believed that\n\npolio campaigns were a Western conspiracy to control the Muslim population and that polio vaccination was used as a tool to cause sterility in children. Some studies have also suggested that poor uptake of immunization services in the Muslim community may have cultural underpinnings in addition to the mistrust of vaccinations (Ophori et al., 2014). However, while the finding was different in this study, there may still be a benefit in the active involvement of the different religious leaders to increase their awareness about the vaccine and advocate for its uptake among children.\nHealth service factors associated with IPV uptake\nDistance to the immunization site, along with incurring transport costs, was found to be significantly associated with IPV uptake among children aged 12\u201323 months. This is consistent with findings from a study conducted in Bangladesh where caregivers close to health centres had higher uptake of IPV immunization services compared with those who resided far away (Breiman et al., 2004). Further, comparable findings from studies conducted in Uganda and India revealed that rural areas, which have fewer immunization posts, had lower vaccination coverage than urban areas (Babirye et al., 2012; Bbaale, 2013; Obrego\u00b4 n et al., 2009). Having to travel long distances may be challenging for many caregivers if it impacts on their productive time needed for domestic or commercial duties. Caregivers may be discouraged by the long distances or by the transport costs they have to incur in order to access the health centres. There is a need to reduce transport costs, as well as to improve outreaches to serve the children who are far from the health facilities and bring services closer to them for easier uptake.\nIn this study, being encouraged by health workers or VHTs to take children for IPV vaccination was found to be significant. These findings are consistent with earlier studies conducted in Nigeria\n\ni36\n\nHealth Policy and Planning, 2020, Vol. 35, Suppl. 1\n\nDownloaded from https://academic.oup.com/heapol/article/35/Supplement_1/i30/5960435 by guest on 19 August 2024\n\n(Tabana et al., 2016; Osadebe et al., 2017) where children utilized immunization services more after their caregivers were encouraged by health workers. In a study in Bangladesh, caregivers (about 48%) attributed their acceptance of IPV to the fact that health care practitioners had encouraged them to immunize their children, stating that the health workers knew best in regard to the wellbeing of their children (Estivariz et al., 2017). This is also similar to findings from a post-IPV introduction study conducted in Albania, where caregiver acceptance of IPV was attributed to trust in the immunization system due to health workers\u2019 recommendations; therefore, having a strong trust in health workers or other community health workers is an important enabling factor towards uptake of IPV among children (Platt, 2015).\nThe study findings are also consistent with several other studies conducted in Tanzania, Bangladesh and other developing countries on the influence of families and peers on immunization uptake: namely, having friends and family members with positive immunization views resulted in improved immunization uptake, underscoring the role of social support in improving immunization outcomes (Keoprasith et al., 2012; Stockwell et al., 2014; Brunson, 2015; Mazige et al., 2016). When caregivers are confident that health worker or VHT recommendations are for the good of their children, they may have less hesitancy to follow such recommendations. Therefore, health caregivers and VHTs have a vital role in promoting such public health interventions, not only to caregivers of young children but to the entire community.\nThere is therefore a need to intensify the health education at health facilities and in the community along with strengthening the traditional home visit system so as to continue encouraging caregivers to take their children for IPV immunization.\nConclusions\nThe current proportion of IPV uptake among children aged 12\u2013 23 months in Kalungu District, Uganda is higher than the last reported national coverage, but more progress needs to be made. Caregivers\u2019 education status was significantly associated with IPV uptake, as was distance to the immunization post. Health workers and VHTs in the community have a critical role in promoting uptake of the vaccine. Strengthening multiple aspects of the health system together can have a beneficial impact on IPV uptake and improve the health of Uganda\u2019s children.\nConflict of interest statement. None declared.\nEthical approval. Ethical approval for this study was obtained from the Makerere University School of Public Health Higher Degrees Research and Ethics Committee (HDREC). Permission was granted from the District Health Officer-Kalungu to do research in the lower health facilities and from the in-charge health officers at each health centre. Written informed consent was sought from participants. Participant confidentiality was ensured using assigned study identity numbers instead of names.\nAcknowledgements\nWe would like to thank the academic staff of Makerere University School of Public Health, our families and the participants of the study. This study was funded as part of the \ufb01rst author\u2019s graduate studies at Makerere. Thanks also to the Health Systems Global mentorship programme for supporting the publication of this article.\n\nReferences\nAbuya EO, Kimani Onsomu JK et al. 2011. In\ufb02uence of maternal education on child immunization and stunting in Kenya. Maternal and Child Health Journal 15: 1389\u201399.\nAnand A, Pallansch MA, Estivariz CF, Gary H, Wassilak SGF. 2014. Estimating the likely coverage of inactivated poliovirus vaccine in routine immunization: Evidence From demographic and health surveys. The Journal of Infectious Diseases 210: S465\u201374.\nBabirye JN, Engebretsen IM, Makumbi F et al. 2012. Timeliness of childhood vaccinations in Kampala Uganda: A community-based cross-sectional study. PloS One 7: e35432.\nBbaale E. 2013. Factors in\ufb02uencing childhood immunization in Uganda. 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Rwanda: WHO and UNICEF estimates of immunization coverage.\nWadaani FA. 2013. The knowledge attitude and practice regarding diabetes and diabetic retinopathy among the \ufb01nal year medical students of King Faisal University Medical College of Al Hasa region of Saudi Arabia: A cross sectional survey. Nigerian Journal of Clinical Practice 16: 164\u201368.\nWallace AS, Mantel C, Mayers G et al. 2014. Experiences with provider and parental attitudes and practices regarding the administration of multiple injections during infant vaccination visits: Lessons for vaccine introduction. Vaccine 32: 5301\u201310.\nWorld Health Organization (WHO). 2012. Immunization, Vaccines and Biologicals.\nWorld Health Organization (WHO). 2016a. Training guide for Operational Level Health workers.\nWorld Health Organization (WHO). 2016b. Polio vaccines: WHO position paper. World Health Organization (WHO). 2017a. WHO fact sheet on polio. World Health Organization (WHO). 2017b. Introduction of IPV in Uganda. World Health Organization (WHO). 2018. WHO Vaccination Coverage\nCluster Surveys: Reference Manual. WHO and UNICEF. 2015. WHO and UNICEF estimates of immunization\ncoverage: 2015 revision. WHO and UNICEF. 2017. Estimates of immunization coverage in Uganda. Wilber ST, Fu R. 2010. Risk ratios and odds ratios for common events in\ncross-sectional and cohort studies. Academic Emergency Medicine 17: 649\u201351. Zaman K, Anh DD, Victor JC et al. 2010. Ef\ufb01cacy of pentavalent rotavirus vaccine against severe rotavirus gastroenteritis in infants in developing countries in Asia: A randomised, double-blind, placebo-controlled trial. The Lancet 376: 615\u201323.\n\n\n",
+    "authors": [
+      "M. R. Faith",
+      "B. Juliet",
+      "N. Tumuhamye",
+      "T. Mathias",
+      "E. Sacks"
+    ],
+    "doi": "10.1093/heapol/czaa099",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "DKJX2FV2",
+    "title": "Recovering from the Unprecedented Backsliding in Immunization Coverage: Learnings from Country Programming in Five Countries through the Past Two Years of COVID-19 Pandemic Disruptions",
+    "abstract": "Between 2020 and 2021, the COVID-19 pandemic severely strained health systems across countries, leaving millions without access to essential healthcare services. Immunization programs experienced a 'double burden' of challenges: initial pandemic-related lockdowns disrupted access to routine immunization services, while subsequent COVID-19 vaccination efforts shifted often limited resources away from routine services. The latest World Health Organization (WHO) and United Nations Children's Fund (UNICEF) estimates suggest that 25 million children did not receive routine vaccinations in 2021, six million more than in 2019 and the highest number witnessed in nearly two decades. Recovering from this sobering setback requires a united push on several fronts. Intensifying the catch-up of routine immunization services is critical to reach children left behind during the pandemic and bridge large immunity gaps in countries. At the same time, we must strengthen the resilience of immunization systems to withstand future pandemics if we hope to achieve the goals of Immunization Agenda 2030 to ensure vaccinations are available for everyone, everywhere by 2030. In this article, leveraging the key actions for sustainable global immunization progress as a framework, we spotlight examples of strategies used by five countries-Cambodia, Cameroon, Kenya, Nigeria, and Uganda-who have exhibited exemplar performance in strengthening routine immunization programs and restored lost coverage levels in the last two years of the COVID-19 pandemic. The contents of this article will be helpful for countries seeking to maintain, restore, and strengthen their immunization services and catch up missed children in the context of pandemic recovery and to direct their focus toward building back a better resilience of their immunization systems to respond more rapidly and effectively, despite new and emerging challenges.",
+    "full_text": "Opinion\nRecovering from the Unprecedented Backsliding in Immunization Coverage: Learnings from Country Programming in Five Countries through the Past Two Years of COVID-19 Pandemic Disruptions\nAnithasree Athiyaman *, Tosin Ajayi *, Faith Mutuku, Fredrick Luwaga, Sarah Bryer, Omotayo Giwa, Shadrack Mngemane, Nnang Nadege Edwige and Leslie Berman\n\nClinton Health Access Initiative (CHAI), Global Vaccines Delivery Team and Country Of\ufb01ces, Boston, MA 02127, USA * Correspondence: aathiyaman@clintonhealthaccess.org (A.A.); tajayi@clintonhealthaccess.org (T.A.)\n\nCitation: Athiyaman, A.; Ajayi, T.; Mutuku, F.; Luwaga, F.; Bryer, S.; Giwa, O.; Mngemane, S.; Edwige, N.N.; Berman, L. Recovering from the Unprecedented Backsliding in Immunization Coverage: Learnings from Country Programming in Five Countries through the Past Two Years of COVID-19 Pandemic Disruptions. Vaccines 2023, 11, 375. https:// doi.org/10.3390/vaccines11020375\nAcademic Editors: Ahmad Reza Hosseinpoor, M. Carolina Danovaro, Devaki Nambiar, Aaron Wallace and Hope Johnson\nReceived: 14 November 2022 Revised: 16 December 2022 Accepted: 26 December 2022 Published: 7 February 2023\nCopyright: \u00a9 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).\n\nAbstract: Between 2020 and 2021, the COVID-19 pandemic severely strained health systems across countries, leaving millions without access to essential healthcare services. Immunization programs experienced a \u2018double burden\u2019 of challenges: initial pandemic-related lockdowns disrupted access to routine immunization services, while subsequent COVID-19 vaccination efforts shifted often limited resources away from routine services. The latest World Health Organization (WHO) and United Nations Children\u2019s Fund (UNICEF) estimates suggest that 25 million children did not receive routine vaccinations in 2021, six million more than in 2019 and the highest number witnessed in nearly two decades. Recovering from this sobering setback requires a united push on several fronts. Intensifying the catch-up of routine immunization services is critical to reach children left behind during the pandemic and bridge large immunity gaps in countries. At the same time, we must strengthen the resilience of immunization systems to withstand future pandemics if we hope to achieve the goals of Immunization Agenda 2030 to ensure vaccinations are available for everyone, everywhere by 2030. In this article, leveraging the key actions for sustainable global immunization progress as a framework, we spotlight examples of strategies used by \ufb01ve countries\u2014Cambodia, Cameroon, Kenya, Nigeria, and Uganda\u2014who have exhibited exemplar performance in strengthening routine immunization programs and restored lost coverage levels in the last two years of the COVID-19 pandemic. The contents of this article will be helpful for countries seeking to maintain, restore, and strengthen their immunization services and catch up missed children in the context of pandemic recovery and to direct their focus toward building back a better resilience of their immunization systems to respond more rapidly and effectively, despite new and emerging challenges.\nKeywords: routine immunization; COVID-19 pandemic; coverage backsliding; recovery; Cambodia; Cameroon; Kenya; Nigeria; Uganda\n1. Introduction Between 2020 and 2021, the COVID-19 pandemic severely strained health systems\nacross countries, leaving millions without access to essential healthcare services. Routine Immunization programs experienced a \u2018double burden\u2019 of challenges due to the COVID19 pandemic and associated disruptions and to the subsequent COVID-19 vaccination efforts shifting often limited resources away from routine services. The latest World Health Organization (WHO) and United Nations Children\u2019s Fund (UNICEF) estimates suggest that 25 million children missed routine vaccinations in 2021, six million more than in 2019 and the highest number witnessed in nearly two decades.\nRecovering from this sobering setback requires a united push on several fronts. Intensifying the catch-up of routine immunization services is critical to reach children left behind\n\nVaccines 2023, 11, 375. https://doi.org/10.3390/vaccines11020375\n\nhttps://www.mdpi.com/journal/vaccines\n\nVaccines 2023, 11, 375\n\n2 of 7\nduring the pandemic and bridge large immunity gaps in countries. At the same time, we must strengthen the resilience of immunization systems to withstand future pandemics if we hope to achieve the goals of Immunization Agenda 2030 to ensure vaccinations are available for everyone, everywhere by 2030.\n2. Materials and Methods\nThis article spotlights examples of strategies used by \ufb01ve countries\u2014Cambodia, Cameroon, Kenya, Nigeria, and Uganda\u2014that have exhibited exemplar performance in strengthening routine immunization programs and that have restored lost coverage levels back to pre-pandemic levels to identify country-level learnings and inform and support other countries to adopt similar practices. No personal or patient-level health information was gathered for this work, and this work is not considered human subjects research. Coverage data was obtained from WHO-UNICEF and reported at the country-level.\n3. Discussions 3.1. The Challenge: Historic Backsliding in Routine Immunization Coverage\nAcross the world, the COVID-19 pandemic has disrupted essential health services, adversely impacting signi\ufb01cant gains in health outcomes achieved in recent decades. This is particularly so for routine immunizations, with recently published WHO-UNICEF estimates showing historic reductions in immunization coverage in 2021, with 25 million children missing out on life-saving vaccines, the highest number since 2006 [1]. The number of \u2018zero dose children\u2019 (those who did not receive any dose of Diphtheria, Tetanus and Pertussis (DTP) containing vaccines) [2] increased sharply from 13 to 18 million during the pandemic period, a shocking 37% increase since 2019. While immunization coverage dropped in all WHO regions, some regions were more affected than others, with coverage dips ranging between 1% in Europe to as high as 9% in South-East Asia.\nAs a result of this monumental drop in coverage, a disproportionate number of children in low- and middle-income countries are left most vulnerable to vaccine-preventable diseases. With rising immunity gaps, the risk of large outbreaks is imminent, with cases of measles already reported in Africa and Eastern Mediterranean, and wild polio virus 1 (WPV1) detected outside the endemic countries in Asia [3]. This threatens the lives of unprotected children and could be severely disruptive to already over-stretched health services. Furthermore, some speci\ufb01c newly introduced antigens like Human Papillomavirus (HPV) vaccines are also seen to be more adversely impacted and less resilient to these shocks. Over the last two years, HPV vaccine coverage dropped by 15% [1]. Since 2019, 3.5 million eligible girls have not yet received their \ufb01rst dose of HPV vaccine, the highest decline since 2010. The trend is compounded by the fact that 59% of cervical cancer cases occur in countries that have not yet introduced HPV vaccination into their national programs, leaving millions of adolescent girls unprotected against cervical cancer.\nRoutine immunization programs were impacted by a \u2018double burden\u2019 of managing the disruptive effect of the COVID-19 pandemic as well as the subsequent historic COVID-19 vaccination efforts to improve population immunity against the virus. Nearly all countries reported some form of disruption to routine immunization (RI) services in 2020 and early 2021, with primary and community care among the most affected service delivery settings [4]. In many countries, several planned outreaches and even Supplementary Immunization Activity (SIA) were either suspended or postponed. In early 2021, more than one third of countries (37%) participating in surveys monitoring pandemic impact on health systems still reported disruptions to their routine immunization services in comparison to early 2020 [4].\nDespite these sobering trends, some countries improved vaccination coverage during the pandemic\u201439 countries recovered or almost recovered to pre-pandemic levels in 2021. But over two years, only 24 countries achieved higher coverage in 2021 than in 2019 [5]. This notable progress is attributable to the intensi\ufb01cation and mitigation efforts of country programs to maintain and/or resume vaccine delivery to catch up on missed populations.\n\nVaccines 2023, 11, 375\n\nDespite these sobering trends, some countries improved vaccination coverage d the pandemic\u201439 countries recovered or almost recovered to pre-pandemic leve 2021. But over two years, only 24 countries achieved higher coverage in 2021 than in [5]. This notable progress is attributable to the intensification and mi3tiogfa7tion effo country programs to maintain and/or resume vaccine delivery to catch up on missed ulations. In some cases, these countries have managed to also attain high COVID-19 In some cases, tchineasetiocnoucnovtreireasghea\u2014vtehme faansategsetdantodaolnsoe oaftttahine mhiogsht cCoOmVpIlDex-1g9lovbaaclcvinaacctionne campaig coverage\u2014the fhaisstteosrtya.nd one of the most complex global vaccine campaigns in history.\n\n3.2. The Opportu3n.2it.yT: hReeOstporpionrgtuCnoitvye:rRageestaonridnSgtCreonvgetrhaegneianngdPSrtorgernagmthReneisnilgiePnrcoegram Resilience\n\nThe vulnerabiliTtyheofvgullonbearal bimilimtyuonfigzalotiboanl ismysmteumniszdateimononsysstrteamtes dtheemuorngsetnrattneseetdhetourgent ne\n\nmaintain, restorme,aainntdaisnt,rreensgtothree,narnodutsitnreenigmthmeunnriozuattiinoenismymsteumniszaintioonrdseyrstteomasddinreosrsdtehreto addres\n\nwidening immuwniidtyenginagp iimn mthuencitoyntgeaxpt oinf tthhee pcoanntdeexmt oicf tahnedpbaunidldemreicsialinedncbeufioldr fruestuilrieence for f\n\nshocks. Despitesthhoecckosm. Dpleesxpiittieesthoef mcoamnapgleinxgitiiems mofumniaznaatigoinngpriomgmraumnsizdautiroingparocgornatminsudalulyring a con\n\nevolving pandeamlliyc,esveovlevrianlgcpoaunndtreimesich,asveeveshraolwconutnhtartieismhparvoevsinhgowimnmthuantiizmatpiroonvcinogveimramgeunization\n\nis possible. Doceurmageenitsinpgoassnidblesp. Dotolicguhmtienngtilnegarannindgsspfortolimghtthinegselecaorunnintrgiessfrwomill thheelspeoctohuenrtries will\n\ncountries to adooptht esirmcoiluanr tprrieascttioceasdtoopitnscirmeailsaer vparacccitnicaetsiotno ienqcureitays.e vaccination equity.\n\nThis article presTehnitssaerxtiacmlepplreessfernotms e\ufb01xvame spelleescftreodmcofiuvnetrsieelse\u2014cteCdacmobuondtriiae,sC\u2014aCmaemroboond,ia, Came\n\nKenya, Nigeria,KaenndyaU, gNaingderai\u2014a, tahnadt hUagvaenediath\u2014etrhmatahinatvaeineeitdheorrminacirnetaasinededthoeririnDcriepahstehdertihae,ir Diphth\n\nTetanus, and PeTrettuasnsuiss\u2013, caonndtaPinerintugssvias\u2013cccoinneta(iDniTnPg3v) accocvineera(gDeTrPa3te) scoinve2r0a2g1e croamtespainre2d02to1 compar\n\n2019/20 (see Fig2u01re9/12)0. T(shereoFuigghursetr1o)n. gThpraorutngehrssthriopnwg ipthartthneerMshiinpiswtriitehs tohfeHMeianltihstriniesthoefsHe ealth in\n\ncountries, CHAcIoaunndtriimesm, CuHniAzaItaionnd pimarmtnuenrsizhaativoenwpaitrntnesesresdhaavnedwsuitpnpesosreteddanthdesirujpopuornrteeyd their jou\n\nto recovery. Usitnogrtehceovkeeryy.aUctsioinngs tfhoer ksueystaacintiionngsgfolorbsaulsitmaimniungnigzlaotbioanl ipmrmogurnesizsa[t6io],nwphriocghress [6], w\n\nhighlight the urhgiegnhtlisgthetptshneeucergsseanrtystfeoprssunsetcaeisnsianrgy ifmormsuusntiaziantiinogniamcmtivuintiiezsatgiloonbaaclltyivaitsieas globally\n\nconceptual framceownocerkp,tuwael ofruatmlineewsoervke,rwaleeoxuemtlipnlearsyevsetrraatleegxieesmapdloaprytesdtrbaytetghieesseacdooupntterdiesby these c\n\nover the past twtroieyseoavrserththaet cpoansttrtiwbuotyeedatros tfhaavtocroanbtleribimutmeduntoizfaatvioornaboluetcimommeusn\u2014izaaltsioonseoeutcomes\u2014\n\n[File S1].\n\nsee [File S1].\n\nFigure 1. Graph rFeipgruerseen1.tiGngraDphTPre1p/r3esceonvteinraggDe TlePv1e/l3s cinovseerlaegcteedlevperlosgirnamselceocutendtrpierso\u2014grCamamcbooudnitari,es\u2014Cam Cameroon, KenyCa,aNmiegreoroina,, KanendyUa,gNanigdear\u2014ia,baentdwUeegnan2d0a1\u20149 abnedtw2e0e2n12. 0I1t9haignhdl2ig0h21ts. Itthhaitgahclirgohstsstthheasteacross thes \ufb01ve countries, nacctoaiosuninnatglrictehosev, neimraatimgoenuoanlfizcDaotTvieoPrn3agsreyemsotfeamDinTse\u2019Pdr3ethrseielmiesanaimcneeetdootrhwienitscharsmetaaesneoddrsibnhecotrcwekaesseedndu2eb0et1tow9taehenendC22O001V291IaD, n-d1920p2a1n,d showcasing the imSmouurnciez:a2t0io2n1 sWysHteOm-Us\u2019NreIsCilEieFnEcesttiomwatieths.stand shocks due to the COVID-19 pandemic. Source: 2021 WHO-UNICEF Estimates.\n\n3.2.1. Conductin3g.2F.1r.eCqounedntuactnindgInFtreenqsui\ufb01enetdaCnadtcInht-eunpsiAficetdivCitaietcsh-up Activities\n\nClosing immunCitlyosgianpgsimanmdurneaitcyhginagpsmainsdserdeaccohminmgumniistiseesdrceoqmuimreuinnitteienssir\ufb01eeqduierfefoinrttsensified e\n\nthat and\n\naUrgeawnedlal ,pmlatrunhonlaotetinpd,alreaaennrdwdoueiUnnllfgdopasrlnmaodnfeancd,eabmdtcyuhael-ntvudipipdlieenancfrotcoirevum.inItenidedssCbwaoymfeecrbveaotidcdcohiean-n,udcKpeue.canItcneytdaiCv,tiaNotmiiesgsbeeorrvdiwaiic,aeeC,rehKamiecgnoehynr-oadri,ousnNkc,tiegdertioa,\n\nC se\n\ncinocmlumduednitaiems itxhhTroihogfeuhsign-erhtiieosnnkucsltcuio\ufb01tdhmeeedmdpauaannnmdidtiiexteamsortgfihceirnttoeotuednagdsohiudofiutreretdestashacnechodpevsateanarrdangegedmteecigdcaamtopoupsatardaeindgadrncehissenstsyecqaloenuvdpietreicaeragsimo.edTpgiahacpiegissnnea-sntdylienepqeur\n\ntensi\ufb01cation of routine immunization (PIRIs) activities e.g., integrated child health days\n\n(ICHDs) and local immunization days (LIDs). In Cameroon, districts in con\ufb02ict-affected regions were serviced through three rounds of periodic intensi\ufb01cations of routine immu-\n\nnizations (PIRI), which resulted in 28% improvement in DTP3 coverage in the South West Region in 2020/2021. To support with planning these outreach efforts, Clinton Health Access Initiative (CHAI) Cameroon helped track on a quarterly basis district level service\n\nVaccines 2023, 11, 375\n\n4 of 7\ndelivery and immunization coverage indicators. We used an Excel/power BI-based dashboard, which enabled the rapid identi\ufb01cation and prioritization of districts that required intensi\ufb01ed catch-up activities. Similarly, in Cambodia, CHAI supported with data review and \ufb01eld assessments to assess and update the Expanded Programme for Immunization (EPI) list of high-risk communities with recent pockets of missed children, while in Kenya, CHAI supported the roll-out of tracking tools to monitor PIRI microplanning progress and review the accuracy and completion of immunization micro plans. In Nigeria, CHAI supported program planning for a catch-up campaign and identifying zero-dose children in 145 districts across the country, including redesigning microplanning and other data tools to accommodate infection prevention and control (IPC) needs in the context of the pandemic.\n3.2.2. Strengthening Health Information Systems to Routinely Capture Immunization Coverage and Ongoing Disease Surveillance\nDespite challenges brought on by the pandemic, these countries continued to strengthen health information system capacities to capture and use routine immunization data for planning and implementation. CHAI provided technical support to EPI across all levels in Cambodia, Uganda, and Kenya to strengthen data management and review capacities to promote immunization data use for planning and decision making. By promoting a systemic approach that leverages existing data, underserved communities can be identi\ufb01ed and necessary resources allocated for rapid course correction. In Cambodia, CHAI supported the development of a new visualization dashboard that provided easy access and review of coverage gaps at all levels (national, provincial, district, and service delivery point) to enable prompt follow-up and action. The dashboard was made available in English and the local language, Khmer, for ease of access and user-friendliness. In Kenya, CHAI was instrumental in supporting the Health Management Information System (HMIS) team in separating PIRI and RI indicators within the DHIS2 platform to enable clear performance monitoring for supplementary immunization activities. In Cameroon, CHAI supported the identi\ufb01cation and characterization of zero-dose communities using triangulation of demographic, geographic, and immunization data. Through this effort, health areas with the highest risk or probability of zero-dose children were prioritized for targeted action. CHAI also supported mentoring activities to improve data completeness, timeliness, and quality into DHIS2, resulting in an increase of 18% in timeliness, 5% in data quality, and 5% in completeness in the Adamawa region in Cameroon. In Nigeria, to inform decision making, CHAI was instrumental in the roll out of the PowerBI tool for the visualization of real-time campaign immunization coverage and reach. In Uganda, CHAI strengthened identi\ufb01cation of underserved areas within health facility catchment areas through monthly reviews of health facility immunization registration data, resulting in in ~50% increase in the number of children from underserved villages vaccinated against DTP3 and MR1 [7].\n3.2.3. Finding Synergies with the COVID-19 Vaccine Roll-out\nAcross the spectrum of activities undertaken to plan, implement, and monitor the COVID-19 vaccine roll-out, many of the countries who demonstrated resilience in the last two years capitalized on these activities for the mutual bene\ufb01t of routine immunizations.\n\u2022 To improve integrated delivery of services and promote a life course approach to vaccination, the Cambodian Ministry of Health, with support from CHAI and other partners, developed policies to integrate routine immunization into the COVID-19 outreach strategy in 2021, with a focus on prioritizing hard-to-reach communities. CHAI\u2019s support included the following:\nThe design and implementation of an integrated NCD screening and COVID19 vaccination pilot in two provinces, which was initially implemented at mass vaccination sites and later shifted to health center \ufb01xed sites after the acute emergency phase (as booster dose delivery picked up pace). In the\n\nVaccines 2023, 11, 375\n\n5 of 7\n\ufb01rst \ufb01ve months during implementation at mass vaccination sites, the pilot successfully resulted in the referral or linkage to care for approximately ~1600 adults with previously undiagnosed diabetes or hypertension. By the end of the second phase of the pilot (February 2022), which has now been transitioned to government implementation, a total of ~2700 adults had been referred for appropriate follow-up care. To optimize healthcare worker capacity and national health budgets to provide hard-to-reach communities with immunization services, CHAI supported with analyzing COVID-19 and routine immunization coverage data at the provincial, district, and village levels to inform the implementation of such integrated outreach in previously underserved communities.\n\u2022 The Ugandan Ministry of Health, with support from partners, developed and disseminated operational guidelines to support the symbiotic delivery of health services alongside COVID-19 vaccination, care, and treatment. This included models that encourage task shifting among healthcare workers and hybrid offsite and onsite approaches to supportive supervision to better balance workload and delivery of health services at the district level.\n\u2022 Data reporting helped countries recognize and react to the cannibalistic effect COVID19 vaccination was having on healthcare workers\u2019 ability to deliver other primary healthcare (PHC) services, including routine immunization. The government of Nigeria decided to separate the service delivery and data collection functions of COVID-19 vaccination to support front line healthcare workers to prioritize the dual duty of COVID vaccinations and other PHC services. CHAI provided guidance in developing strategies and adopting technical guidelines for the integration of COVID-19 vaccination with other PHC services, including immunization, Vitamin-A, and ante-natal care (ANC) services. Three main strategies were promoted, which combined to boost coverage by 70% by October 2022. The strategies included the following:\nTEACH, which combines traditional microplanning methods with appropriate technology. A family-centered integrated PHC approach that translates into the national strategy of improving access to basic health services. SCALES 3.0 (Supervision, Communication, Accountability, Logistics, Immunization data and Service delivery), a strategy that incorporates integration of services, performance-based incentives, data use for action, and decentralized demand generation.\n\u2022 In Cameroon, Kenya, and Nigeria, COVID-19 vaccination training was also used to refresh frontline healthcare worker knowledge on routine immunization and promote broader immunization best practices.\n\u2022 In Kenya, through support from CHAI, several county-level EPIs leveraged COVID-19 vaccine outreach services, targeting teachers in schools to co-deliver HPV vaccinations to eligible adolescent girls enrolled in the schools.\n3.2.4. Mobilizing Resources for Sustaining Immunization Services\nEarly intervention by governments to provide clear directives to health facilities was instrumental in minimizing service delivery disruptions. For example, the mid-2020 nationwide directive in Nigeria to continue routine outreach sessions resulted in an 11.6% increase [8] in service provision since the early disruptions. In Uganda, by engaging non-traditional health stakeholders, particularly at the district level, the immunization program was able to mobilize additional \ufb01nancing and resources, including human resources to support data management and operational aspects of delivering vaccinations, which tremendously eased pressure on system capacities and helped sustain routine immunization services.\n\nVaccines 2023, 11, 375\n\n6 of 7\n3.2.5. Restructuring Health Systems to Build Resilience Learning from the largest disruption to immunization service delivery in three decades\nand the largest vaccination rollout in history, a few countries are prioritizing investments in systems that enable multisectoral collaboration with strong community participation for agile decision making. For example, the Cambodian government is in the process of developing a PHC booster strategy to ensure all individuals can access a quality package of care in the public sector\u2014from prevention to early diagnosis and management across the life course, encompassing maternal and child health, communicable diseases, selected non-communicable diseases, mental health, and other ageing-related illnesses. This strategy aims to emphasize stronger community engagement and new models of service delivery that could strengthen the resilience of health systems. CHAI is supporting by updating the community participation policy, which will rede\ufb01ne the governance, roles, and responsibilities for community heath workers within existing community structures. In Uganda, the government is adapting routine immunization for the use of Smart Paper Technology, which enables individual-level tracking of COVID-19 vaccine recipients and includes a reminder function for subsequent doses. CHAI is supporting the country to develop systems and processes that reduce missed opportunities for vaccination at every encounter with the health system, which has had a demonstrable impact on all antigens of note, including sustained increase in the number of vaccinated (11% in DTP3, 4% in MCV1, 72% in HPV2) in the supported districts in 2021 [7]. These health system capacities and processes will facilitate improved preparedness and operations to rapidly respond to emergent system shocks while maintaining the effectiveness of routine programs.\n4. Conclusions\nThe importance of integrating routine immunization into primary healthcare systems has never been clearer. As countries continue to recover and adopt lessons from the last few years, to not only mitigate the effects of backsliding but to reach those who were previously unreached, integrated and holistic PHC systems offer the best way forward for supporting resilient and sustainable routine immunization programs. Investing in broader health system strengthening and improving linkages with communities will help advance immunization goals. With the future of the COVID-19 pandemic remaining uncertain, it is vital to focus on building back better the resilience of our immunization systems to respond more rapidly and effectively to challenges, and ensure all children continue to have access to lifesaving vaccinations despite new and emerging concerns. Learnings from the exemplary countries presented in this article provide insights into the various possibilities that can be unlocked with the right commitment and support.\nSupplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/vaccines11020375/s1, File S1: Enablers for RI Recovery in CHAI focal countries.\nAuthor Contributions: Conceptualization, writing\u2014original draft preparation, A.A. and T.A.; Resources\u2014F.M., F.L., S.B., O.G., S.M. and N.N.E.; Supervision\u2014L.B. All authors have read and agreed to the published version of the manuscript.\nFunding: The development of this commentary received no additional external funding, however the CHAI-supported work highlighted in this article was made possible through funding from Bill & Melinda Gates Foundation and Gavi, the Vaccine Alliance.\nInstitutional Review Board Statement: Not applicable. This work reports on country-level program policies and is not considered human subjects research.\nInformed Consent Statement: Not applicable. This work reports on country-level program policies and is not considered human subjects research.\nData Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.\n\nVaccines 2023, 11, 375\n\n7 of 7\n\nAcknowledgments: Many thanks to CHAI country team colleagues from Cambodia, Cameroon, Kenya, Nigeria, and Uganda, who provided insights during this commentary development. We extend our gratitude and appreciation to our Ministry of Health counterparts and health partners in countries who continue to make signi\ufb01cant progress in immunization access.\nCon\ufb02icts of Interest: The authors declare no con\ufb02ict of interest.\nReferences\n1. 2021 WHO-UNICEF Estimates (WUENIC), as of 15 July 2022. Available online: https://www.who.int/teams/immunization-vac cines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimate s-of-national-immunization-coverage (accessed on 25 January 2023).\n2. Gavi, the Vaccine Alliance, The Zero Dose Children: Explained, April 2021. Available online: https://www.gavi.org/vaccineswork/ zero-dose-child-explained (accessed on 25 January 2023).\n3. GPEI. Wild Polio (WP1) Remains Endemic Only in Two Countries Afghanistan and Pakistan. Malawi Con\ufb01rmed Detection of WP1 in February 2022, 17 February 2022. Available online: https://polioeradication.org/polio-today/polio-now/ (accessed on 25 January 2023).\n4. WHO. Third Round of the Global Pulse Survey on Continuity of Essential Health Services during the COVID-19 Pandemic; WHO: Geneva, Switzerland, 2022.\n5. Immunization Services Begin Slow Recovery from COVID-19 Disruptions, though Millions of Children Remain at Risk from Deadly Diseases. WHO, 26 April 2021. Available online: https://www.who.int/news/item/26-04-2021-immunization-services -begin-slow-recovery-from-covid-19-disruptions-though-millions-of-children-remain-at-risk-from-deadly-diseases-who-uni cef-gavi (accessed on 25 January 2023).\n6. Shet, A.; Carr, K.; Danovaro-Holliday, M.C.; Sodha, S.V.; Prosperi, C.; Wunderlich, J.; Wonodi, C.; Reynolds, H.W.; Mirza, I.; Gacic-Dobo, M.; et al. Impact of the SARS-CoV-2 pandemic on routine immunization services: Evidence of disruption and recovery from 170 countries and territories. Lancet 2021, 10, e186\u2013e194. [CrossRef]\n7. CHAI. Review of Administrative Data for Immunization; CHAI: Kampala, Uganda, 2021. 8. Ministry of Health. Analysis of Weekly SMS Reporting; Ministry of Health: Abuja, Nigeria, 2020.\nDisclaimer/Publisher\u2019s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\n\n\n",
+    "authors": [
+      "A. Athiyaman",
+      "T. Ajayi",
+      "F. Mutuku",
+      "F. Luwaga",
+      "S. Bryer",
+      "O. Giwa",
+      "S. Mngemane",
+      "N. N. Edwige",
+      "L. Berman"
+    ],
+    "doi": "10.3390/vaccines11020375",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "EPT6QJZA",
+    "title": "Can routine health facility data be used to monitor subnational coverage of maternal, newborn and child health services in Uganda?",
+    "abstract": "BACKGROUND: Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level. Uganda has been using district health statistics from facility data for many years. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions. METHODS: Annual data from the Uganda routine health facility information system 2015-2019 for all 135 districts were used, as well as national surveys for external comparison and the identification of near-universal coverage interventions. The quality of reported data on antenatal and delivery care and child immunization was assessed through completeness of facility reporting, presence of extreme outliers and internal data consistencies. Adjustments were made when necessary. The denominators for the coverage indicators were derived from population projections and health facility data on near-universal coverage interventions. The coverage results with different denominators were compared with the results from household surveys. RESULTS: Uganda's completeness of reporting by facilities was near 100% and extreme outliers were rare. Inconsistencies in reported events, measured by annual fluctuations and between intervention consistency, were common and more among the 135 districts than the 15 subregions. The reported numbers of vaccinations were improbably high compared to the projected population of births or first antenatal visits - and especially so in 2015-2016. There were also inconsistencies between the population projections and the expected target population based on reported numbers of antenatal visits or immunizations. An alternative approach with denominators derived from facility data gave results that were more plausible and more consistent with survey results than based on population projections, although inconsistent results remained for substantive number of subregions and districts. CONCLUSION: Our systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring.",
+    "full_text": "Agiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512 https://doi.org/10.1186/s12913-021-06554-6\n\nRESEARCH\n\nOpen Access\n\nCan routine health facility data be used to monitor subnational coverage of maternal, newborn and child health services in Uganda?\nGeraldine Agiraembabazi1, Jimmy Ogwal2, Christine Tashobya1, Rornald Muhumuza Kananura1,3,4* , Ties Boerma5 and Peter Waiswa1,3,6\n\nAbstract\nBackground: Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level. Uganda has been using district health statistics from facility data for many years. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions.\nMethods: Annual data from the Uganda routine health facility information system 2015\u20132019 for all 135 districts were used, as well as national surveys for external comparison and the identification of near-universal coverage interventions. The quality of reported data on antenatal and delivery care and child immunization was assessed through completeness of facility reporting, presence of extreme outliers and internal data consistencies. Adjustments were made when necessary. The denominators for the coverage indicators were derived from population projections and health facility data on near-universal coverage interventions. The coverage results with different denominators were compared with the results from household surveys.\nResults: Uganda\u2019s completeness of reporting by facilities was near 100% and extreme outliers were rare. Inconsistencies in reported events, measured by annual fluctuations and between intervention consistency, were common and more among the 135 districts than the 15 subregions. The reported numbers of vaccinations were improbably high compared to the projected population of births or first antenatal visits \u2013 and especially so in 2015\u20132016. There were also inconsistencies between the population projections and the expected target population based on reported numbers of antenatal visits or immunizations. An alternative approach with denominators derived from facility data gave results that were more plausible and more consistent with survey results than based on population projections, although inconsistent results remained for substantive number of subregions and districts.\n\n* Correspondence: mk.rornald@mushp.ac.ug 1Department of health policy planning and Management, Makerere University School of Public Health, Mulago New-Complex, Kampala, Uganda 3Makerere University Centre of Excellence for Maternal, Newborn and Child Health, Mulago New-Complex, Kampala, Uganda Full list of author information is available at the end of the article\n\u00a9 The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 2 of 10\n\nConclusion: Our systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring.\nKeywords: Health facility data, District health information system, Data quality, Maternal health, Child health, Uganda\n\nBackground Household surveys are the most common and trusted source for monitoring service coverage in the population, but the frequency and sample size of surveys limits the ability to assess annual progress at national and subnational levels. Annual estimates of coverage are required to monitor national and local plans and programs for regular reviews which are used to adjust program priorities at national and subnational levels [1, 2].\nRoutine health facility data are potentially a useful source of more frequent estimates of service coverage at the local level, as has been shown in several countries [3\u20136]. Such facility-data derived estimates of coverage may focus on indicators of maternal, newborn and child health (MNCH), child immunization [7], malaria [8] and other infectious disease programs such as HIV and TB. In recent years, many countries have stepped up their investments in their routine health facility information systems, using a web-based data platform (District Health Information System, DHIS2) [9] leading to improvements in completeness of reporting and advances in data quality assessment [10, 11].\nData quality is often the greatest impediment to the use of routine health facility data for coverage estimates. The numerator of the coverage statistic may be affected by poorly designed or shortages of data collection instruments, recording errors, incomplete and inaccurate reporting [11\u201314]. WHO has published a set of methods and indicators to assess the quality of reported data on services which is available as a module within DHIS2, and can be used to detect and investigate outliers and inconsistencies [10, 15].\nCoverage estimates are further hampered by the lack of accurate data for denominators or target populations such as births, especially for subnational units. Census population projections are the most common source of such denominators. If the most recent census was conducted several years ago, the likelihood that population projections are well-off the actual target populations increases. Geospatial analyses or alternative methods to improve target population estimates have been proposed [5, 16].\nUganda has a strong tradition of monitoring district performance to inform annual reviews. In 2003, the ministry of health introduced a system of annual district\n\nleague tables which is still in use to assess district progress and performance as of today. The system currently uses an index based on 14 indicators of coverage, quality, human resources and reporting process, with RMNC H accounting for the largest share (six indicators) [17]. The system has been extensively reviewed, showing both the potential and limitations of district assessments with routine data [18, 19]. In addition, many countries including Uganda are increasingly using district scorecards based on routine facility data.\nThis paper examines the ability to generate statistics for selected MNCH coverage indicators at the subnational level using routine health facility data and assesses the extent to which such statistics can be used to monitor local progress.\nMethods The focus is on districts as the main unit of health planning and program implementation in Uganda. Uganda\u2019s 135 districts are grouped into 15 subregions and six regions. These (sub)regional classifications, however, are used for surveys such as the Uganda Demographic and Health Survey (UDHS) [20] but are not an operational part of the health system.\nThe routine health facility data are collected as part of service provision at all health facilities. The system is paper-based in most health facilities. Data are entered into the computer as part of a DHIS2 web-based software system at the district level. Compilation, quality check and initial analysis of the health facility data from the districts, are done by the Ministry of Health at the national level.\nThe Ministry of Health uses the population projections from the Uganda Bureau of Statistics (UBOS) within DHIS2 to obtain denominators for coverage estimates. The last population census was conducted on 27 August 2014. UBOS provides official district population projections for single years 2015\u20132020 including total population and population by single years and sex for districts, including for the new districts formed since the census. We also used the United Nations Population Division projections of total population and crude birth rates to estimate live births [21].\nWe analyzed the DHIS2 data for 2015\u20132019 at the national, sub-regional and district levels. The subregions\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 3 of 10\n\nhave the advantage that the facility data derived coverage results can be compared with the results from the national surveys. In comparisons of facility data derived coverage estimates with survey results, the districts were given the same survey results as the subregion.\nThe most recent nationally representative populationbased surveys were the UDHS 2016 and the Uganda Malaria Indicator Survey (UMIS) 2018/19 [22] which were used for external comparison and to obtain crude birth rates. The survey samples allowed for estimation of coverage indicators for the 15 subregions. The first antenatal visit (ANC1), first pentavalent (penta1) and BCG vaccinations had near universal coverage according to the Uganda DHS 2016: 97% of pregnant women, 95 and 96% of children 12\u201323 months respectively (Table 1). ANC1 coverage was equally high in the 2018/19 UMIS (96%) and all three indicators were well over 90% in the Uganda DHS 2011, indicating that these services have been used by nearly all Ugandan pregnant women and infants for a long period. Coverage was high in all 15 subregions in the UDHS for the three interventions. Therefore, assuming no decline in coverage for the three interventions, we expect that the numbers of ANC1 visits, penta1 and BCG vaccinations would increase exclusively because of population growth.\nWe assessed the quality of the reported health facility data and resulting coverage estimates by\n\nconsidering (1) completeness of facility reporting (2) presence of extreme outliers (3) consistency of reported events over time; (4) consistency between reported numbers for antenatal care and immunization (5) assessment of target population estimates and (6) external consistency with survey coverage estimates, in line with the WHO Data Quality toolkit and related applications [5, 10, 11].\nFirst, completeness of reporting of RMNCH events, which is reported on a single form, was based on the proportion of expected reports from all listed public, private-not-for profit and private health facilities. The extreme outliers were defined as at least 3.5 standard deviations from the expected value of data element for a particular year based on the median of the five-year period and were corrected if errors (e.g. an additional digit increasing the number by a factor 10) appeared the likely cause.\nSecond, consistency over time for ANC1, penta1 and BCG was assessed by comparing the reported numbers trend with expected numbers based on a log transformed regression analysis (ln(y) = ax + b, where y is the reported number and x the year). In case of the nearuniversal coverage interventions, the increases are only driven by population growth. We expected the slope a of the regression to be about 3% (within the range 1\u20134.9%) and the year-to-year fluctuations, as measured by the\n\nTable 1 General characteristics, crude birth rate per 1000 population (CBR), coverage (%) of antenatal care first visit (ANC1), first pentavalent vaccination (penta1) and BCG vaccination, Uganda DHS (UDS) and Uganda Malaria Indicator Survey (UMIS), by subregion\n\nSubregion Population Population\n\n2015\n\ngrowth rate (%)\n\nNumber of CBR (UDHS CBR (MIS\n\ndistricts\n\n2016)\n\n2018/9)\n\nANC1 (UDHS 2016)\n\nANC1 (UMIS Penta1\n\nBCG\n\n2018/9)\n\n(UDHS 2016) (UDHS\n\n2016)\n\nAcholi\n\n1,535,100 2.8\n\n8\n\n38.0\n\n34.6\n\n97.3\n\n99.0\n\n98.7\n\n96.9\n\nAnkole 2,946,700 2.1\n\n12\n\n34.5\n\n27.1\n\n96.9\n\n94.9\n\n96.7\n\n85.9\n\nBugisu 1,803,800 3.1\n\n9\n\n35.8\n\n33.4\n\n97.1\n\n100.0\n\n97.9\n\n96.6\n\nBukedi 1,928,200 3.0\n\n7\n\n39.8\n\n35.5\n\n96.8\n\n88.5\n\n95.6\n\n94.4\n\nBunyoro 2,102,300 4.3\n\n8\n\n42.3\n\n39.7\n\n92.3\n\n98.6\n\n94.4\n\n87.7\n\nBusoga 3,663,700 2.7\n\n11\n\n39.8\n\n33.5\n\n97.8\n\n97.7\n\n93.1\n\n90.1\n\nKampala 1,528,800 1.7\n\n1\n\n38.2\n\n28.1\n\n97.9\n\n96.9\n\n94.8\n\n92.3\n\nKaramoja 991,600\n\n3.3\n\n9\n\n45.5\n\n49.3\n\n97.3\n\n88.9\n\n98.5\n\n96.5\n\nKigezi\n\n1,390,500 1.2\n\n6\n\n32.0\n\n34.1\n\n99.8\n\n99.4\n\n98.3\n\n96.2\n\nLango\n\n2,111,400 2.9\n\n9\n\n35.2\n\n33.5\n\n97.1\n\n99.0\n\n95.5\n\n95.4\n\nNorth Central\n\n3,773,500 2.7\n\n12\n\n37.5\n\n29.3\n\n98.8\n\n90.3\n\n92.0\n\n86.0\n\nSouth Central\n\n4,474,500 3.9\n\n13\n\n39.9\n\n29.2\n\n95.8\n\n94.9\n\n90.9\n\n84.7\n\nTeso\n\n1,870,600 3.3\n\n10\n\n43.0\n\n38.0\n\n98.9\n\n98.5\n\n97.9\n\n96.6\n\nTooro\n\n2,644,000 3.3\n\n9\n\n40.4\n\n34.2\n\n98.0\n\n93.6\n\n93.7\n\n92.7\n\nWest Nile 2,727,400 3.0\n\n11\n\n40.8\n\n34.4\n\n98.7\n\n98.8\n\n97.6\n\n94.5\n\nNational 35,492,100 3.0\n\n135\n\n38.7\n\n32.7\n\n97.3\n\n95.5\n\n94.9\n\n96.3\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 4 of 10\n\nstandard errors of the regression line, to be small if data quality is good.\nThird, we assessed the internal consistency between interventions for immunization and antenatal care. The reported number of third doses of pentavalent vaccination (penta3) must be lower than that for penta1. We used the subregional ratios of the penta1 to penta3 vaccination coverage rates from the Uganda DHS 2016 by subregion to obtain an expected value for each subregion and district. We arbitrarily assumed that ratios within a range of plus or minus 15% (relative to the expected ratio), and a value not less than 1, presented plausible reporting of penta1 and penta3 vaccinations. The internal consistency of the reported numbers was further assessed by comparing annual ANC1 and penta1 reported numbers. As a reference, we used an expected ANC1 to penta1 numbers ratio of 1.11, ranging from 1.07 to 1.18 by subregion. This ratio was derived from the ANC1 and penta1 coverage rates in the UDHS 2016, and the loss of pregnancies between first ANC visit and first immunization after the neonatal period using the following assumptions: pregnancy wastage estimated at 8%, a twinning rate of 2% [23] and early infant mortality of 3 per 100 live births (based on the results of the UDHS 2016 [20]). Pregnancy wastage includes abortion between the first ANC visit (median 4.7 months of pregnancy duration in the UDHS 2016, estimated at 6%) and stillbirths (from end of the 7th month, estimated at about 2% [24])). We marked district and subregional ratios within the range of plus or minus 15% of the expected ratio as plausible (corresponding with at least five standard deviations).\nFourth, survey data, population projections and health facility data-based methods (5) were used to assess different denominators, and obtain the best estimate of the target populations, for the calculation of population coverage of interventions. Population projections are generally the primary source of denominators such as total population, live births or children eligible for immunization. We assessed the consistency of the population projections for live births, based on the UBOS total population projection and crude birth rates obtained from surveys (Table 1), with the estimated number of live births obtained from the reported number of events for near-universal coverage interventions of ANC1, BCG and penta1 vaccination. The computations were done for national, subregional and district level.\nFor the national level, we used a crude birth rate of 38.7 per 1000 population, obtained from the UDHS 2016 (and from StatCompiler for the subregions [25]), which we considered a more complete estimate than the UMIS 2018/19, given its larger sample and greater focus on the birth history data collection. The UDHS 2016 subregional crude birth rates were used for the\n\nsubregions and districts (Table 1). In addition to the UBOS projections, we applied the United Nations Population Division population projections which estimated a population size which was 8% greater in 2015 and 10% greater in 2019 than the UBOS projection. The extent to which the use of population projections led to coverage estimates close to the expected coverage value for nearuniversal interventions of ANC1, penta1 and BCG was evaluated for districts and subregions, considering the absolute difference from the expected coverage as an indication.\nFinally, we computed coverage rates for five interventions (four or more ANC visits, IPT 2 for pregnant women, institutional delivery care, third pentavalent and measles vaccination) using two methods of estimating the denominators: the population projections, using the UNPD data, and a health facility data derived denominator. The latter was computed from ANC1 reported numbers for the ANC4, IPT2 and institutional delivery indicators, and from penta1 for penta3 and measles vaccination indicators. The denominators include a small adjustment for non-coverage, based on the results of the most recent survey (e.g. if coverage was 95% add 5% for non-coverage), and take into account pregnancy loss, multiple births and early infant mortality as described above.\nWe assessed the performance of the two methods by assessing the difference with the survey values (UDHS except UMIS 2018/19 also for IPT2) and the variation over time. This included a comparison of the results for 2016\u201317 with the UDHS 2016, assuming that smaller differences indicate better data quality, while recognizing that coverage may have changed in the year following the UDHS 2016 for these interventions and that most survey data are retrospective. Absolute fluctuations greater than 20% of the previous year were considered less plausible.\nResults Table 2 summarizes the different methods and results on the different dimensions of data quality.\nCompleteness and outliers of the reported health facility data Reporting completeness for RMNCH has been high during 2015\u20132019, hovering around 98% nationally. The completeness of facility reporting was also above 95% for all years in all subregions, except North Central (94% in 2016) and Kampala (90, 94 and 85% in 2017, 2018 and 2019 respectively). Among the 135 districts, eight districts had reporting rates below 90% in 2018 and in 2019 (Supplementary materials Figure S1). Since reporting rates were high and consistent over time, no adjustments were made for possible service provision by non-\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 5 of 10\n\nTable 2 Summary of data quality measures at national, subregional and district levels, Uganda, 2015\u20132019\n\nData quality measure\n\nSelected definitions and thresholds\n\nNational\n\nSubregions\n\nDistricts\n\nCompleteness of Monthly facility MCH reports received out of 98% facility reporting total expected MCH reports\n\n> 95% in all years 2015\u20132019, with 4 exceptions\n\n8/135 districts below 90% in 2018 and 2019\n\nExtreme outliers More than 3.5 SD from expected value\n\nNone\n\nNone\n\n2 district values adjusted for a single year\n\nConsistency over ANC1, expected 3% annual increase time\n\n2.1% per year\n\n53% of subregions with annual increase 1.0\u20134.9%\n\n49% of districts with annual increase 1.0\u2013.4.9%\n\nPenta1 expected 3% annual increase\n\n0.5% per year\n\n40% of sub-regions with annual increase 1.0\u20134.9%\n\n28% of districts with annual increase 1.0\u20134.9%\n\nConsistency between interventions\n\nPenta1 to penta3 ratio: +/\u2212 15% from expected Range from 1.07\u20131.09 80% within plausible\n\nratio of 1.20\n\nduring 2015\u20132019\n\nrange in 2017\u20132019\n\n75\u201379% within plausible range in 2017\u201319\n\nANC1 to penta1: +/\u2212 15% from expected ratio Range from 1.00\u20131.07 67\u201380% within plausible\n\nof 1.10\n\nduring 2015\u20132019\n\nrange in 2017\u20132019\n\n63\u201371% in plausible range in 2017\u201319\n\nDenominators\n\nPopulation projection method (UN): coverage 2017\u201319 within +/\u2212 10% difference with survey results\n\nANC1 4% higher;\n\nsubregions within range districts within range\n\npenta1 same as survey; for ANC1\u201343%; penta1\u201373%; for ANC1\u201352%, penta1\u2013\n\nBCG 3% lower than BCG \u2013 67%\n\n55%, BCG \u2013 42%\n\nsurvey\n\nreporting facilities which could affect levels and trends. Extreme outliers in the annual data by subregion and district were few. Two extreme outliers for 2015 were corrected, using the expected value rather than the reported value which was likely a data entry error.\nConsistency over time The national rate of average annual increase for ANC1 numbers was 2.1% during 2015\u20132019, lower than the expected 3%. Variation over time during 2015\u20132019 was greater for districts compared to subregions or national level. For ANC1, the annual increase was within the range 1.0\u20134.9% for eight subregions (53%) and 66 (49%) districts. The average standard error of the regression line, indicative of year-to-year fluctuations, was much greater for districts compared to subregions (0.061 and 0.035, respectively) (Supplementary materials Table S1).\nFor penta1 reported numbers, there was little increase at the national level (slope of 0.5% per year), mainly due to the higher numbers reported in 2015 and 2016. In six subregions (40%) and 38 districts (28%) the annual increase was within the range of 1.0\u20134.9%. The standard errors of the regression line were lower at subregional than at district level (mean 0.040 and 0.073 respectively), indicating that annual coverage estimates will be less stable at the district level than at the subregional level.\nConsistency between interventions The national ratio penta1 to penta3 was almost constant over time at 1.07\u20131.09 during 2015\u20132019 (Supplementary materials Table S2). This was lower than the expected ratio based on the UDHS 2016 (1.20), possibly\n\ndue to overreporting of penta3 overreporting. By subregion, the survey-based penta1/penta3 ratio ranged from 1.07 to 1.34. In 2019, the observed ratio was within a plausible range of the expected ratio in 80% of the 15 subregions, up from 67% in 2015. For the 135 districts, 75% had plausible values in 2019.\nThe consistency between ANC1 and penta1 reported numbers improved over time (Table S2). The greatest inconsistencies were observed in subregions of Kampala and South Central (all years) and North Central (2015\u2013 2018). Kampala had a much higher number of ANC1 visits than penta1 visits, presumably due to women coming to the capital city for services (e.g. for 2019 the ratio was 1.52). South Central region had a major deficit in ANC1 visits compared to penta1 only in one of the 13 districts: Wakiso which accounts for more than half of the population in the South-Central subregion and borders Kampala. This suggests that pregnant women are using ANC services in Kampala (e.g. the ANC1/penta1 ratio in Wakiso in 2019 was 0.80).\nProjected target populations Figure 1 compares the national number of live births for 2015\u201319, based on five estimation methods. The UBOS population projection with the crude birth rate obtained from the UDHS 2016 results in 1.56 million live births in 2019. Compared to the UN population estimates, there were 1.71 million live births in 2019. The reported numbers of ANC1 (1.76 million) and penta1 (1.91 million in DHIS2) were higher. It is likely that penta1 vaccinations were overreported, as shown above. The estimate of live births based on BCG vaccinations was\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 6 of 10\n\nFig. 1 Number of live births, estimated from population projections and from health facility data, 2015\u20132019\n\nclose to the UN population-based estimate for 2017\u2013 2018 but reported numbers in 2015\u201316 were problematic.\nTo obtain coverage estimates using population projections for the denominator we used the UN total population estimates, while maintaining the crude birth rate at 3.87 per 1000 population. Figure 2 presents the average difference of the estimated (based on the projected live births) and expected (survey) coverage for ANC1, penta1 and BCG (which should all be above 90%) for the period 2017\u20132019 for districts (left) and for subregions (right panel). The distribution of the 135 districts shows that just over 50% of districts fall in a +/\u2212 10 percentage points range for penta1 and ANC1 and 42% of districts for BCG. Just under one-tenth of districts had deviations exceeding 30%, and the remaining 40% were in between. Similar proportions were too high (often well over\n\n100%) and too low. The deviations from the expected near universal coverage were greater for districts than for the subregions.\nKampala was an outlier with coverage rates over 150% in all 5 years for ANC1 and also had unlikely high coverage estimates for penta1 and BCG. This implies either severe underestimation of the target population, or issues with the reported number which may be true (service users from other regions/districts) or false (overreporting, but less likely given the consistency over the years).\nCoverage indicators We examined coverage estimates for five indicators, using either population projections or facility dataderived denominators. Figure 3 compares the relative difference between coverage estimates for institutional\n\nFig. 2 Percent distribution of absolute difference between estimated and expected coverage for ANC1, penta1 and BCG for 2017\u20132019 (mean), 135 districts (left panel) and 15 subregions (right panel), Uganda\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 7 of 10\n\nFig. 3 Population coverage of deliveries in health facilities and pentavalent vaccination (third dose) by subregion in the Uganda DHS 2016 (bar with 95% confidence intervals), and coverage derived from health facility reported data according to denominator method (population projection \u2013 dash; health facility data derived - dots)\n\ndeliveries and penta3 vaccination by subregion for 2016/ 17 according to the UDHS 2016 and according to the reported health facility data. For these two indicators the facility-based denominator method performed considerably better than the population projections method. For deliveries, nine subregional estimates fell within the 95%\n\nconfidence interval of the survey statistic with the facility data method, compared to five subregions with the population projection method. The mean gaps between the UDHS delivery coverage and the facility-based and population projection-based estimates were 16 and 13%, respectively. For penta3 vaccination, the 95% confidence\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 8 of 10\n\nintervals of the survey coverage included three subregions with the facility-based estimate and none with the population projection-based estimate. The mean gaps with the survey coverage were 12 and 31% for the facility and population projection-based methods, respectively. These patterns were also observed for measles coverage, but less so for IPT2 coverage among pregnant women and ANC4 where both denominators performed equally poor compared to the UDHS or UMIS results (Supplementary Figure S2).\nThe same comparison between the survey statistics and the estimates based on the health facility data using the two denominators was done for districts and showed similar results (Supplementary Figure S3). For many districts, the computed coverage for 2016\u201317 differed considerably (more than 20%) from the UDHS 2016 value (or UMIS 2018/19 value in case of IPT2). For four of the five indicators, the health facility-based denominator method gave more consistent results than the population projections, especially for the immunization indicators. We also compared year-to-year fluctuations in coverage based on both denominator methods for districts. For most years, the percent of districts with less plausible values is higher for the coverage rates derived from the population projection method compared to the health facility-based denominators.\nDiscussion Routine facility data for monitoring of subnational coverage of MNCH interventions are critical for local and national resource allocation and targeting of interventions. Monitoring of coverage requires good quality reported data on specific interventions as well as accurate target population sizes. Our systematic assessment of the quality of routine reports of key events for RMNCH shows that data quality is a major challenge for the computation of subnational health statistics. On the positive side, Uganda\u2019s completeness of reporting by facilities was near 100% and extreme outliers were rare. On the other hand, major annual fluctuations in reported events and inconsistencies between numbers of reported events were common at district, subregional and national level. Less plausible and inconsistent reported numbers were more common for the 135 districts than for the 15 subregions.\nThe numbers of reported penta1 vaccinations appeared improbably high \u2013 higher than projected population of births or first antenatal visits \u2013, especially in 2015\u20132016, and even more so for penta3 than for penta1. This may be due to overreporting which has been an issue in other contexts as well, sometimes associated with incentives for vaccinating infants [7, 15, 26].\nThe estimation of population denominators for coverage estimates is challenging. At the national level, a\n\nplausible target population estimates could be made after upward adjustment of the census projections, using the projection by the United Nations Population Division, although several inconsistencies remained. Subnational estimates were more problematic and more so at the district level than the subregional level. Even though working with fixed annual denominators is preferable, also for target setting in districts, the fluctuations in the numbers of reported events rendered large proportions of districts with improbable coverage values within the five-year period. Our analysis showed that an alternative approach based on denominators derived from facility data gives better results but was still unsatisfactory for a substantive number of subregions and districts. This was mainly due to the inconsistencies between the numbers of first antenatal visits and first vaccination, most likely a data quality issue.\nThe Uganda data illustrate the additional challenges for estimating coverage with health facility data for large urban districts. Kampala and the adjacent district of Wakiso had the greatest inconsistencies. Even though the government health facility reporting system is supposed to cover all types of facilities, there may be challenges with the privately owned facilities. A larger private sector with poorer reporting is often a major factor contributing to such inconsistencies, but also user preferences for the use of, for example, antenatal services in the city of Kampala may play a role. In addition, the quality of reporting of multi-visit indicators, such as ANC and multi-dose vaccinations, may further affect data quality especially in settings where users have multiple choices.\nThe findings have important implications for efforts to track coverage in individual districts using health facility data. Given the considerable noise in reported events and uncertainty of the target populations, district coverage estimates for MNCH and other health indicators must be interpreted cautiously. More accurate reporting of numerators is critical as it will also help the estimation of target populations, using the near-universal coverage intervention-based methods. This requires greater investments in training and supervision of health workers and data quality control [27\u201329].\nThese results also affect the Uganda district league tables where incorrect population denominators lead to systematically over- or under-rating of a district\u2019s performance and where errors in the numerators result in major shifts in district rankings from year to year. The league tables system deals with coverage estimates over 100% by truncating at 100%, but the problem of underestimation of coverage is much harder to detect and correct. In this situation, it may be best to focus on \u201cwithin reported data\u201d coverage and quality indicators, such as proportion of women who receive ANC4 visits among\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 9 of 10\n\nthose who made the first visit or proportion of children who receive measles vaccination among those who received the first pentavalent or BCG vaccination, with or without a correction for non-users of the specific health services. This approach is still highly dependent on the accuracy of reporting by health facilities.\nLimitations Our approach has several limitations. The external comparison leans heavily on survey-based coverage estimates. The retrospective nature of survey-based estimation, the differences in year of observation with the facility data and sampling errors are all drawbacks on the accuracy of the assumed \u201cgold standard\u201d. On the other hand, our analyses rely heavily on first antenatal visit and first immunizations for which there is little evidence that shows that the coverage rates are not well over 90% almost everywhere in Uganda. We did not have survey-based estimates for districts and used subregional values. Within subregions there may be considerable variation between districts. Furthermore, we did not consider possible declines in fertility, which would imply that the excess reported numbers of nearuniversal coverage events and expected target populations would even be larger. Population census and surveys however have shown that the fertility decline prior to 2016 was slow.\nConclusion Regular reliable estimates of coverage for key MNCH indicators in subnational units including districts are urgently needed for local planning, district monitoring. The quality of routine health facility data however still needs considerable improvement before it can become a reliable instrument for planning, monitoring, and targeting at the district level. In addition, multiple ways of assessing target population sizes for coverage estimates need to be explored, including the use of interventions with potentially near-universal coverage such as first antenatal visit and child immunizations such as first dose of pentavalent vaccination and BCG. Systematic assessment of data quality and transparent adjustments are critical steps towards improving the quality of national and subnational coverage statistics derived from health facility data.\nAbbreviations ANC1/4: First or fourth antenatal care visit; DHIS2: District Health Information System 2; IPT2: Intermittent preventive therapy second dose; MNCH: Maternal, newborn and child health; Penta1: Pentavalent vaccine (Diphteria, Pertusssis, Tetanus, type b haemophilus influenza, hepatitis B), first dose; UBOS : Uganda Bureau of Statistics; UDHS: Uganda Demographic and Health Survey; UMIS: Uganda Malaria Indicator Survey\n\nSupplementary Information\nThe online version contains supplementary material available at https://doi. org/10.1186/s12913-021-06554-6.\nAdditional file 1: Table S1. Summary of results of regression of reported annual numbers of ANC1 and penta1 by national, subregional and district levels. Table S2. Absolute difference between two health facility data coverage estimates with either population projection denominator or health facility data derived denominator and the UDHS 2016 results (or UMIS 2018 in case of IPT2). Figure S1. Completeness of reporting for MCH by district, 2015\u20132019, DHIS2, Uganda. Figure S2. Population coverage of measles vaccination among infants, intermittent preventive therapy second dose (IPT 2) among pregnant women and deliveries in health facilities and antenatal care 4th visit by subregion according to Uganda DHS 2016 (bar), and derived from health facility reported data according to denominator method (population projection \u2013 dash and health facility data derived - dots). Figure S3. Percent of districts with less plausible coverage estimates according to denominator method (population projection and health facility data derived).\nAcknowledgements This study was made possible by a grant for the Bill and Melinda Gates foundation to the Countdown to 2030 for Women\u2019s, Children\u2019s and Adolescents\u2019 Health and the School of Public Health of Makerere University, Uganda.\nAbout this supplement This article has been published as part of BMC Health Services Research Volume 21 Supplement 1 2021: Health facility data to monitor national and subnational progress. The full contents of the supplement are available at https://bmchealthservres.biomedcentral.com/articles/supplements/ volume-21-supplement-1.\nAuthors\u2019 contributions TB, GA and PW conceived the study. GA, RKM, JO and TB contributed to study design. JO extracted the data from the Ministry of Health databases. TB and GA produced initial drafts of the paper. All authors contributed to the data analysis, interpretation, and manuscript preparation, and approved the final version.\nAvailability of data and materials The data used in this study are either publicly available or can be requested directly from the Ministry of Health.\nDeclarations\nEthics approval and consent to participate Ethical approval was obtained by the University of Makerere of this study which was conducted as part of the endline review of the Uganda national sharpened plan for reproductive, maternal, child and adolescent health 2015/16\u20132019/20. The analytical work was deemed non-human subject research as only anonymous and aggregated secondary data available in the public domain or accessible on request were used in the analyses.\nConsent for publication Not applicable.\nCompeting interests The authors declare that they had no financial or non-financial competing interests in conducting and publishing the study.\nAuthor details 1Department of health policy planning and Management, Makerere University School of Public Health, Mulago New-Complex, Kampala, Uganda. 2Ministry of Health, Kampala, Uganda. 3Makerere University Centre of Excellence for Maternal, Newborn and Child Health, Mulago New-Complex, Kampala, Uganda. 4Department of International Development, London School of Economics and Political Science, London, UK. 5Institute for Global Public Health, University of Manitoba, Winnipeg, Canada. 6Global Health\n\nAgiraembabazi et al. BMC Health Services Research 2021, 21(Suppl 1):512\n\nPage 10 of 10\n\nDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.\nReceived: 18 May 2021 Accepted: 2 June 2021 Published: 13 September 2021\nReferences 1. World Health Organization. Monitoring, evaluation and review of national\nhealth strategies: a country-led platform for information and accountability. Geneva: International Health Partnership and World Health Organization; 2011. Available from: http://www.who.int/healthinfo/country_monitoring_ evaluation/1085_IER_131011_web.pdf?ua=1 2. Mboera LE, Ipuge Y, Kumalija CJ, et al. Midterm review of national health plans: an example from the United Republic of Tanzania. Bull World Health Organ. 2015;93(4):271\u20138. https://doi.org/10.2471/BLT.14.141069. 3. Wagenaar BH, Sherr K, Fernandes Q, Wagenaar AC. Using routine health information systems for well-designed health evaluations in low- and middle-income countries. Health Policy Plan. 2016;31(1):129\u201335. https://doi. org/10.1093/heapol/czv029. 4. Health Systems Trust. District health barometer 2018/19. Durban; 2019. https://www.hst.org.za/publications/Pages/DISTRICT-HEALTHBAROMETER-2 01819.aspx. Accessed 12 Nov 2020. 5. Maina I, Wanjala P, Soti D, Kipruto H, Droti B, Boerma T. Using health-facility data to assess subnational coverage of maternal and child health indicators, Kenya. Bull World Health Organ. 2017;95(10):683\u201394. https://doi.org/10.2471/ BLT.17.194399. 6. Mutale W, Chintu N, Amoroso C, et al. Improving health information systems for decision making across five sub-Saharan African countries: implementation strategies from the African Health Initiative. BMC Health Serv Res. 2013;13(Suppl 2):S9. 2. 7. Burton A, Monasch R, Lautenbach B, Gacic-Dobo M, Neill M, Karimov R, et al. WHO and UNICEF estimates of national infant immunization coverage: methods and processes. Bull World Health Organ. 2009;87(7):535\u201341. 8. Gething PW, Noor AM, Goodman CA, Gikandi PW, Hay SI, Sharif SK, et al. Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics. BMC Med. 2007;5(1):37. https://doi.org/10.1186/1741-7015-5-37. 9. DHIS2. https://www.dhis2.org/. Accessed 2 Oct 2020. 10. WHO. Data quality review toolkit. Geneva. https://www.who.int/healthinfo/ tools_data_analysis/en/. Accessed 2 Oct 2020. 11. Ma\u00efga A, Jiwani SS, Mutua MK, Porth TA, Taylor CM, Asiki G, et al. Generating statistics from health facility data: the state of routine health information systems in Eastern and Southern Africa. BMJ Glob Health. 2019; 4(5):e001849. https://doi.org/10.1136/bmjgh-2019-001849. 12. Dunkle SE, Wallace AS, MacNeil A, Mustafa M, Gasasira A, Ali D, et al. Limitations of using administratively reported immunization data for monitoring routine immunization system performance in Nigeria. J Infect Dis. 2014;210(suppl 1):S523\u201330. https://doi.org/10.1093/infdis/jiu373. 13. Gimbel S, Micek M, Lambdin B, et al. An assessment of routine primary care health information system data quality in Sofala Province, Mozambique. Popul Health Metr. 2011;9:1\u20139. 14. Mphatswe W, Mate KS, Bennett B, Ngidi H, Reddy J, Barker PM, et al. Improving public health information: a data quality intervention in KwaZulu-Natal, South Africa. Bull World Health Organ. 2012;90(3):176\u201382. https://doi.org/10.2471/BLT.11.092759. 15. Bhattacharya AA, Umar N, Audu A, Felix H, Allen E, Schellenberg JRM, et al. Quality of routine facility data for monitoring priority maternal and newborn indicators in DHIS2: a case study from Gombe state, Nigeria. PLoS One. 2019;14(1):e0211265. https://doi.org/10.1371/journal.pone.0211265. 16. Matthews Z, Rawlins B, Duong J, Molla YB, Moran AC, Singh K, et al. Geospatial analysis for reproductive, maternal, newborn, child and adolescent health: gaps and opportunities. BMJ Glob Health. 2019;4(Suppl 5):e001702. https://doi.org/10.1136/bmjgh-2019-001702. 17. Ministry of Health. Annual Health Sector Performance report. Financial year 2018/19. Kampala; 2019. http://library.health.go.ug/publications/performa nce-management/annual-health-sector-performance-report-20182019. Accessed 2 Oct 2020. 18. Tashobya CK, Criel B, Marchal B, et al. The waxing and waning of the Uganda district league table: using historical and policy analysis to study implementation of a health system performance assessment framework. Stud Health Serv Organ Policy. 2016;32:1\u2013112.\n\n19. Tashobya KC, et al. A critique of the Uganda district league table using a normative health system performance assessment framework. BMC Health Serv Res. 2018;18:355.\n20. Uganda Bureau of Statistics (UBOS) and ICF. Uganda Demographic and Health Survey 2016. Kampala and Rockville: UBOS and ICF. 2018.\n21. United Nations, Department of Economic and Social Affairs, Population Division (2019). World population prospects 2019. Online Edition. Rev. 1.\n22. Uganda Malaria Indicator Survey 2018\u201319. Kampala and Rockville: Ministry of Health National Malaria Control Division, UBOS, and ICF. 2020.\n23. Smits J, Monden C. Twinning across the developing world. PLoS One. 2011; 6(9):e25239. https://doi.org/10.1371/journal.pone.0025239.\n24. UN Inter-agency Group for Child Mortality Estimation. https://childmortality. org/data/Uganda. Accessed 3 Feb 2021.\n25. DHS program STATcompiler. www.Statcompiler.com. Accessed 12 Mar 2021. 26. Bosch-Capblanch X, Ronveaux O, Doyle V, Remedios V, Bchir A. Accuracy\nand quality of immunization information systems in forty-one low income countries. Tropical Med Int Health. 2009;14(1):2\u201310. https://doi.org/10.1111/ j.1365-3156.2008.02181.x. 27. Wagenaar BH, Gimbel S, Hoek R, Pfeiffer J, Michel C, Manuel JL, et al. Effects of a health information system data quality intervention on concordance in Mozambique: time-series analyses from 2009-2012. Popul Health Metrics. 2015;13(1):9. https://doi.org/10.1186/s12963-015-0043-3. 28. Braa J, Heywood A, Sahay S. Improving quality and use of data through data-use workshops: Zanzibar, United Republic of Tanzania. Bull World Health Organ. 2012;90(5):379\u201384. https://doi.org/10.2471/BLT.11.099580. 29. Bhattacharya AA, Allen E, Umar N, Usman AU, Felix H, Audu A, et al. Monitoring childbirth care in primary health facilities: a validity study in Gombe state, northeastern Nigeria. J Glob Health. 2019;9(2):020411. https:// doi.org/10.7189/jogh.09.020411.\nPublisher\u2019s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n\n\n",
+    "authors": [
+      "G. Agiraembabazi",
+      "J. Ogwal",
+      "C. Tashobya",
+      "R. M. Kananura",
+      "T. Boerma",
+      "P. Waiswa"
+    ],
+    "doi": "10.1186/s12913-021-06554-6",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  },
+  {
+    "key": "JIZ4YN4Y",
+    "title": "COVID-19 Impact on DTP Vaccination Trends in Africa: A Joinpoint Regression Analysis",
+    "abstract": "BACKGROUND: Deaths due to vaccine-preventable diseases are one of the leading causes of death among African children. Vaccine coverage is an essential measure to decrease infant mortality. The COVID-19 pandemic has affected the healthcare system and may have disrupted vaccine coverage. METHODS: DTP third doses (DTP3) Vaccine Coverage was extracted from UNICEF databases from 2012 to 2021 (the last available date). Joinpoint regression was performed to detect the point where the trend changed. The annual percentage change (APC) with 95% confidence intervals (95% CI) was calculated for Africa and the regions. We compared DTP3 vaccination coverage in 2019-2021 in each country using the Chi-square test. RESULT: During the whole period, the vaccine coverage in Africa increased with an Annual Percent change of 1.2% (IC 95% 0.9-1.5): We detected one joinpoint in 2019. In 2019-2021, there was a decrease in DTP3 coverage with an APC of -3.5 (95% -6.0; -0,9). (p < 0.001). Vaccination rates decreased in many regions of Sub-Saharan Africa, especially in Eastern and Southern Africa. There were 26 countries (Angola, Cabo Verde, Comoros, Congo, C\u00f4te d'Ivoire, Democratic Republic of the Congo, Djibouti, Ethiopia, Eswatini, The Gambia, Guinea-Bissau, Liberia, Madagascar, Malawi, Mauritania, Mauritius, Mozambique, Rwanda, Senegal, Seychelles, Sierra Leone, Sudan, Tanzania, Togo, Tunisia, Uganda, and Zimbabwe) where the vaccine coverage during the two years decreased. There were 10 countries (Angola, Cabo Verde, Comoros, Democratic Republic of the Congo, Eswatini, The Gambia, Mozambique, Rwanda, Senegal, and Sudan) where the joinpoint regression detected a change in the trend. CONCLUSIONS: COVID-19 has disrupted vaccine coverage, decreasing it all over Africa.",
+    "full_text": "Article\nCOVID-19 Impact on DTP Vaccination Trends in Africa: A Joinpoint Regression Analysis\nInes Aguinaga-Ontoso 1,2,* , Sara Guillen-Aguinaga 1 , Laura Guillen-Aguinaga 1,3, Rosa Alas-Brun 1, Luc Onambele 4, Enrique Aguinaga-Ontoso 5 and Francisco Guillen-Grima 1,2,6,*\n\n1 Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain;\nsguillen.4@alumni.unav.es (S.G.-A.); lauraguillencole@gmail.com (L.G.-A.);\nrosamaria.alas@unavarra.es (R.A.-B.) 2 Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain 3 Sykepleieavdelingen, Suldal Sykehjem, 4230 Sand, Norway 4 School of Health Sciences, Catholic University of Central Africa, Yaounde 1110, Cameroon;\nonambele.luc@ess-ucac.org 5 Department of Sociosanitary Sciences, University of Murcia, 30120 Murcia, Spain; aguinaga@um.es 6 Department of Preventive Medicine, Cl\u00ednica Universidad de Navarra, 31008 Pamplona, Spain\n* Correspondence: ines.aguinaga@unavarra.es (I.A.-O.); f.guillen.grima@unavarra.es (F.G.-G.)\n\nCitation: Aguinaga-Ontoso, I.; Guillen-Aguinaga, S.; Guillen-Aguinaga, L.; Alas-Brun, R.; Onambele, L.; Aguinaga-Ontoso, E.; Guillen-Grima, F. COVID-19 Impact on DTP Vaccination Trends in Africa: A Joinpoint Regression Analysis. Vaccines 2023, 11, 1103. https:// doi.org/10.3390/vaccines11061103\nAcademic Editor: Giuseppe La Torre\nReceived: 30 April 2023 Revised: 24 May 2023 Accepted: 9 June 2023 Published: 15 June 2023\n\nAbstract: Background: Deaths due to vaccine-preventable diseases are one of the leading causes of death among African children. Vaccine coverage is an essential measure to decrease infant mortality. The COVID-19 pandemic has affected the healthcare system and may have disrupted vaccine coverage. Methods: DTP third doses (DTP3) Vaccine Coverage was extracted from UNICEF databases from 2012 to 2021 (the last available date). Joinpoint regression was performed to detect the point where the trend changed. The annual percentage change (APC) with 95% con\ufb01dence intervals (95% CI) was calculated for Africa and the regions. We compared DTP3 vaccination coverage in 2019\u20132021 in each country using the Chi-square test. Result: During the whole period, the vaccine coverage in Africa increased with an Annual Percent change of 1.2% (IC 95% 0.9\u20131.5): We detected one joinpoint in 2019. In 2019\u20132021, there was a decrease in DTP3 coverage with an APC of \u22123.5 (95% \u22126.0; \u22120,9). (p < 0.001). Vaccination rates decreased in many regions of Sub-Saharan Africa, especially in Eastern and Southern Africa. There were 26 countries (Angola, Cabo Verde, Comoros, Congo, C\u00f4te d\u2019Ivoire, Democratic Republic of the Congo, Djibouti, Ethiopia, Eswatini, The Gambia, Guinea-Bissau, Liberia, Madagascar, Malawi, Mauritania, Mauritius, Mozambique, Rwanda, Senegal, Seychelles, Sierra Leone, Sudan, Tanzania, Togo, Tunisia, Uganda, and Zimbabwe) where the vaccine coverage during the two years decreased. There were 10 countries (Angola, Cabo Verde, Comoros, Democratic Republic of the Congo, Eswatini, The Gambia, Mozambique, Rwanda, Senegal, and Sudan) where the joinpoint regression detected a change in the trend. Conclusions. COVID-19 has disrupted vaccine coverage, decreasing it all over Africa.\nKeywords: DTP vaccine; Africa; COVID-19; vaccine coverage; joinpoint regression; healthcare system; vaccination rates\n\nCopyright: \u00a9 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).\n\n1. Introduction\nVaccination is a pivotal public health intervention, substantially reducing morbidity and mortality associated with infectious diseases [1\u20133]. Immunization programs have contributed to a signi\ufb01cant decline in the global burden of vaccine-preventable diseases, thus enhancing millions of individuals\u2019 overall quality of life [4\u20136]. The diphtheria-tetanuspertussis (DTP) vaccine plays an essential role in childhood immunization programs, safeguarding against three major infectious diseases that can lead to severe illness and potential death in children [7]. In 2012, the World Health Organization (WHO) introduced the Global Vaccine Action Plan (GVAP) 2011\u20132020 to endorse routine vaccination for all\n\nVaccines 2023, 11, 1103. https://doi.org/10.3390/vaccines11061103\n\nhttps://www.mdpi.com/journal/vaccines\n\nVaccines 2023, 11, 1103\n\n2 of 22\nchildren worldwide [8]. Achieving high vaccination coverage is crucial to protect children and facilitate herd immunity.\nConsequently, the WHO established a 90% vaccination coverage target for three doses of DTP (DTP3) by 2015. However, despite progress in recent years, achieving vaccine coverage remains a formidable challenge in many African countries [9]. Resource constraints, inadequate infrastructure, and sociocultural factors contribute to persistent disparities in vaccine access [10]. As per the WHO, vaccine-preventable diseases are a leading cause of child mortality, with over 300,000 pertussis-induced deaths reported annually [11]. Across different countries, vaccine coverage varies with factors such as urban versus rural residence, wealth status, education level, and frequency of maternal prenatal visits [12].\nThe COVID-19 pandemic has exerted unprecedented pressure on global healthcare systems, potentially affecting routine immunization programs [13\u201315]. The crisis has led to disruptions in vaccine supply chains, diversion of healthcare resources to manage the outbreak, and increased reluctance among communities to visit healthcare facilities due to fear of infection and vaccine hesitancy [16\u201321]. These challenges might have led to a decline in routine immunization coverage, putting millions of children at risk of vaccinepreventable diseases [22]. Understanding the magnitude of this disruption is critical for informing public health policy, formulating intervention strategies, and preparing for future sanitary crises [23]. Furthermore, it is essential to safeguard the health and well-being of children in Africa. A study reported global reductions of 7.7% and 7.9% in the coverage of DTP3 and the \ufb01rst dose of the Measles-Containing Vaccine (MCV1), respectively [24]. However, this study\u2019s limitation is that it only includes data up to December 2020. Our study utilizes the percentage of infants who received the third DTP vaccine (DTP3) as the primary indicator for DTP vaccination coverage. As per the WHO, an unimmunized child is one who is between 12 and 23 months old and has not received DTP3 [25]. This metric is a reliable and widely accepted measure of the performance of an immunization program [23,26].\nThe administration of the third dose of the DTP vaccine is crucial as it completes the primary immunization series and ensures optimal protection against diphtheria, tetanus, and pertussis [27]. By focusing on this indicator, we can assess the effectiveness of immunization programs in reaching their target populations. The coverage rates of the \ufb01rst and third doses of the DTP vaccine serve as vital markers in gauging the ef\ufb01cacy of an immunization program. DTP1 coverage is often used to assess accessibility, re\ufb02ecting the extent of health services\u2019 reach and the program\u2019s initial engagement with the population [28]. In contrast, DTP3 coverage demonstrates the program\u2019s sustainability and effectiveness in ensuring that the target population receives all necessary doses [29]. A meta-analysis estimated that the prevalence of vaccination dropout in Africa was 26.06%, with Nigeria recording the highest immunization dropouts (33.59%) [30]. Discrepancies between DTP1 and DTP3 coverages can reveal issues with patient retention, hinting at potential barriers such as cost, transportation, and health education. Therefore, both metrics offer valuable insights for fortifying immunization programs [31].\nBased on available data, the COVID-19 pandemic has signi\ufb01cantly impacted DTP vaccination trends worldwide. According to the WHO and UNICEF, there has been a substantial decline in childhood vaccinations globally [32]. The proportion of children receiving DTP3 globally decreased by 5% between 2019 and 2021, representing the most signi\ufb01cant decline in the past 30 years. In 2021, more than 25 million children worldwide missed one or more doses of DTP, over 6 million more than in 2019. UNICEF reports that vaccine coverage fell in every region, with the East Asia and Paci\ufb01c region experiencing the most drastic decline in DTP3 coverage, dropping nine percentage points within just two years. The data from the CDC further illustrate the extent of this impact. In 2021, the estimated global coverage with three doses of diphtheria-tetanus-pertussis\u2013containing vaccine (DTPcv3) decreased to 81%, marking the lowest point since 2008 [33]. Consequently, these children are at a heightened risk of developing vaccine-preventable infectious diseases.\n\nVaccines 2023, 11, 1103\n\n3 of 22\nThis study aims to examine DTP3 vaccination coverage trends in Africa between 2012 and 2021, focusing on the impact of the COVID-19 pandemic. We hypothesize that the COVID-19 pandemic may have disrupted routine vaccination programs [34].\n2. Materials and Methods\nVaccination rates were extracted from the UNICEF databases, which cover the period from 2000 to 2017, with the latest data update available in 2021 [35]. Western Sahara was excluded from the analysis due to its absence on the UNICEF database, while British and French territories were also excluded because they are not members of the African Union. We sourced regional estimations from the UNICEF database as well. As there were no estimations for North Africa, we calculated the DTP3 coverage estimation, weighted by population. Adhering to the African Union scheme, we included Algeria, Egypt, Libya, Mauritania, Morocco, and Tunisia in North Africa. However, we excluded the Sahrawi Arab Democratic Republic (Western Sahara) and South Sudan due to the lack of available data. The population of each country for every year of the study period was obtained from the World Bank [36].\nWe utilized joinpoint regression analysis, a widely recognized method for analyzing regional and country trends and detecting changes in various data types. This method allows the detection of periods of signi\ufb01cant changes in incidence rates and quanti\ufb01cation of the magnitude of change in each trend. Joinpoint regression is a statistical technique that identi\ufb01es trend change points, termed \u201cjoinpoints.\u201d It has been used previously in Africa to study the evolution of regional maternal mortality trends [37]. Employing joinpoint regression, the annual percentage change (APC) and the corresponding 95% con\ufb01dence intervals (95% CI) were estimated to quantify the magnitude of change in each trend. In these models, vaccine coverage was the dependent variable, while the year was the independent variable.\nWe addressed autocorrelation in the time series data using the Durbin\u2013Watson test [38]. Accounting for autocorrelation in our models offers several advantages, such as improved model \ufb01t due to recognizing dependencies within the series [39], reduced bias in parameter estimates, and adjusted signi\ufb01cance levels, providing more accurate standard errors and con\ufb01dence intervals. Nonetheless, it is important to note that employing autocorrelation models can result in more complex models, potentially leading to interpretation challenges and a risk of over\ufb01tting the data. This could compromise the model\u2019s ability to generalize to new data or accurately represent the underlying trend.\nWe compared the DTP3 coverage for each country in 2019 with its coverage in 2021. A Paired Samples Test was performed to compare the means of DTP coverage in these two related groups, speci\ufb01cally for 2019 and 2021. We evaluated DTP3 vaccination coverage in Africa by country, comparing the coverage rates across different countries for 2019 and 2021. This analysis was conducted using the Chi-square test, wherein the coverage rates were weighted by the number of newborns in each country for the respective year. The data on the number of newborns per country per year were sourced from the UNICEF database [40].\nAll joinpoint computations were conducted using the joinpoint regression software (Joinpoint Regression Program, Version 4.9.1.0, National Cancer Institute, Bethesda, MD, USA). This software is a widely recognized tool for analyzing trends and detecting changes in various data types. It facilitated the effective implementation of joinpoint regression analysis and the assessment of autocorrelation in the time series data [41,42]. The joinpoint software automatically selects the number of joinpoints, using statistical tests to determine the optimal number. The statistical analysis comparing rates from 2019 to 2021 was conducted using IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY, USA). The data were checked for normality using the Shapiro\u2013Wilk test. The level of signi\ufb01cance was set at \u03b1 = 0.05. The results were presented as the mean difference (MD) mean \u00b1 standard deviation (SD).\n\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, 1103\n\n4 of 23 4 of 22\n\n3. Results\n3. Results\nFroFmro2m012201to2 t2o022102, 1v,avcaccinciantaiotinonrartaetseswwitihthththeetthhiirrdd ddoossee ooff DDTTPPiinnAAfrfircicaaexepxpereireinecnecded an annaunalapnnerucaelnpteargceenchtaagnegceh(aAngPeC()AoPfC0.)5o%f .0A.5%jo.inApjooiinnpt woinast wdeatsedcteetdecitned20in192.0F1r9o. mFro2m01220t1o22019, the tvoa2c0ci1n9a, ttihoenvraactceinhaatdioannraintecrheaadsianngitnrcernedas. iFnrgomtren20d2. 0Froonmwa20rd20s,oandweacrrdesa,saindgectrreenasdinbgegan. Fromtre2n0d19beognawn.arFdrso,mth2e0A19PConwwaasrdnse,gtahteivAeP, wC iwthaasnneagnantuivael,dwecitrheaasne aonfn\u2212u2a.9l%d,ecarlethasoeugohf this\n\nchan\u2212g2e.9i%s ,naolthsotautgishtitchailslychsaignngeificsannottdstuaetitsotictahlelyshsiogrnti\ufb01pcearniotddu(Teatbolteh1e,sFhiogrutrpee1ri)o. d (Table 1,\n\nFigure 1).\n\nTable 1. Joinpoint analysis for the third doses DTP vaccination rates in Africa, 2000\u20132017.\n\nTable 1. Joinpoint analysis for the third doses DTP vaccination rates in Africa, 2000\u20132017.\n\nPeriods\n\nYears\n\nAPC (95% CI)\n\nTotalPPereiroidosd\n\n2Y0e1a2r\u2013s2021\n\nAPC (95%0C.5I)(0; 1.1)\n\np\n\nPTeortiaoldPe1riod\nPeriod 1\nPerPieordiod2 2\n\n2201021\u201322\u20130221019\n2012\u20132019\n2201091\u201392\u20130221021\n\n0.5 (0; 11..11) (0.7; 1.6)\n1.1 (0.7; 1.6)\n\u22122.9 (\u2212\u22126.29.;91.(3\u2212)6.9; 1.3)\n\n0.052 0.001 0.134\n\np 0.052 0.001 0.134\n\nFiguFriegu1r.eT1h. iTrdhirDdTDPTdPodsoessesvavcaccicninaattiioonn rraattee ttrreennddssininAAfrfirciac(a20(21021\u2013220\u20132210)2i1n)diincadtiincagtjionignpjooiinntpso. ints.\n\nThe joinpoint regression did not show any joinpoint in North Africa during 2012\u20132021\n(TTabhlee j2o, iFnipguorient2)r.eDgurerisnsgiotnhedwidhonleotpesrhioodw, tahneyAPjoCinwpaosinsltigihntlNy onretghatAivferi\u2212ca0.3d%u.ring 2012\u2013 2021 (Table 2, Figure 2). During the whole period, the APC was slightly negative \u22120.3%.\n\nTable 2. Joinpoint analysis for Regional 3rd DTP dose coverage in Africa, 2012\u20132021.\n\nTable 2. Joinpoint analysis for Regional 3rd DTP dose coverage in Africa, 2012\u20132021.\n\nPeriods\n\nYears\n\nAPC (95% CI)\n\np\n\nPNeorritohds\n\nYears\n\nAPC (95% CI)\n\nTotaNl Poerrtihod\n\n2012\u20132021\n\n\u22120.3 (\u22120.4; \u22120.2)\n\n0.001\n\nSubT-SoathaalrPanerAiofrdica\n\n2012\u20132021\n\n\u22120.3 (\u22120.4; \u22120.2)\n\nSub-TSoatahlaPrearinodAfrica\n\n2012\u20132021\n\n0.5 (0; 1.0)\n\n0.053\n\nToPtearlioPde1riod\n\n20122\u20132001129\u20132021 1.0 (0.6; 10.4.5) (0; 1.0) 0.001\n\nPPeerrioiodd2 1 Period 2\n\n20202\u20132001221\u20132019 \u22122.4 (\u22125.12.;01.(40) .6; 1.4) 0.078\n\n2020\u20132021\n\n\u22122.4 (\u22125.2; 1.4)\n\np\n0.001\n0.053 0.001 0.078\n\nWest and Central Africa\n\nTotal Period\n\n2012\u20132021\n\n1.1 (0.5; 1.8)\n\n0.004\n\nEast and South Africa\n\nTotal Period\n\n2012\u20132021\n\n\u22120.6 (0; 0.5)\n\n0.835\n\nVaccines 2023, 11, 1103\nTable 2. Cont.\nPeriods Vaccines 2023, 11, x FOR PEER REVIEW West and Central Africa\nTotal Period East and South Africa\nToPtaelriPoedPrie1ordiod 1 PeriodPe2riod 2\n\n5 of 22\n\nYears\n\nAPC (95% CI)\n\np\n\n2012\u20132021\n\n1.1 (0.5; 1.8)\n\n0.004\n\n22001122\u2013\u2013220021192012\u2013201\u221209.50.(60(.20;;\n\n0.5) 0.8)\n\n0.5\n\n(0.2;\n\n0.008..08) 0365\n\n2020\u201320212020\u20132\u2212032.15 (\u22126.3; \u2212\u22120.35.)5 (\u22126.3; \u221200.0.52)9\n\n5 of 2\n0.006 0.029\n\nFFigiugruer2e. 2T.hTirhdirDdTDP TdPosdesovseasccvinaactciionnartaiotentrreantedstr(e2n01d2s\u20132(200211)2:\u2013N20o2rt1h):ANfroicrat.h Africa.\nIn Sub-Saharan Africa, one joinpoint was detected in 2019. From 2012 to 2019, the\nvaccinIantioSnurba-tSeaihnacrreaanseAdfwricitah, aonnAe PjoCinopf o1%in.t Twhaissindcerteeacstiendg itnren2d01w9.asFirnotmerr2u0p1te2dtoin2019, th 2v02a0ccwinhaentioandercarteeasinincgretraesneddbwegitahn awnithAaPnCAoPfC1o%f .\u2212T2h.4i%s i(nFcigreuaresi3n)g. trend was interrupted i 202R0ewgihoennalaandaelcyrseisaisninSgubt-rSeanhdarbaengAafnricwaiftohuannd nAoPjCoinopfo\u2212in2t.4in%W(Fesigt aunrde C3)e.ntral Africa.\nThroughout the whole period, coverage increased with an APC of 1.1% (Figure 4). In the Eastern and Southern Africa regions, there was a joinpoint in 2019. The APC\nwas positive until 2019 and negative from 2020, with an APC of \u22123.5% (Figure 5). The absolute decrease in vaccine coverage in Africa between 2019 and 2021 was \u22124%.\nThe coverage remained the same in North Africa, while a more signi\ufb01cant decrease occurred in Eastern and Southern Africa at \u22125% (Table 3).\nTable 4 presents the DTP third dose coverage in 2019 and 2021. The average country decrease was \u22123.19 (SD = 6.598), p < 0.001. Figure 3 presents the map of Africa, indicating the absolute differences in vaccine coverage between 2012 and 2021. Vaccination coverage declined in 26 countries, representing 51% of the countries. In countries where vaccination coverage decreased, the absolute decrease had a mean of \u22127.58 (SD = \u22126.64).\nThere were 14 countries where vaccination coverage was maintained. There were 11 countries with increased vaccination coverage. In countries where vaccination coverage decreased, the absolute decrease had a mean of \u22127.58 (SD = \u22126.64) (Table 4) (Figure 5).\n\nVaccines 2023, 11, 1103\n\nFigure 2. Third DTP doses vaccination rate trends (2012\u20132021): North Africa.\nIn Sub-Saharan Africa, one joinpoint was detected in 2019. From 2012 to 2019, th vaccination rate increased with an APC of 1%. This increasing trend was in6 toef r2r2upted in 2020 when a decreasing trend began with an APC of \u22122.4% (Figure 3).\n\nccines 2023, 11, x FOR PEER REVIEW\nFigure 3. Third DTP doses vaccination rate trends in Sub-Saharan Africa (2012\u20132021) in joinpoints.\nRegional analysis in Sub-Saharan Africa found no joinpoint in West and Cen rica. Throughout the whole period, coverage increased with an APC of 1.1% (Figu\nFigure 3. Third DTP doses vaccination rate trends in Sub-Saharan Africa (2012\u20132021) indicating joinpoints.\n\nFiguFirgeur4e. 4T. hTihridrdDDTTPPddoosessevsavccaincactiinonatriaotentrreantdest(r2e01n2d\u20132s0(2210):1W2\u2013es2t0a2n1d)C: eWnterasltAafnridca.Central Africa.\nIn the Eastern and Southern Africa regions, there was a joinpoint in 2019. T was positive until 2019 and negative from 2020, with an APC of \u22123.5% (Figure 5).\n\nVaccines 2023, 11, 1103\n\nFigure 4. Third DTP doses vaccination rate trends (2012\u20132021): West and Central Africa.\nIn the Eastern and Southern Africa regions, there was a joinpoint i7no2f 20219. The was positive until 2019 and negative from 2020, with an APC of \u22123.5% (Figure 5).\n\nFFigiugruere5.5T.hTirhdirDdTDPTdPosdeocsoevecroavgeer(a2g01e2(\u201322001221\u2013) 2in0d2i1c)atiinndgijcoaintipnoginjotsininpEoainsttesrninaEndasStoeurnthaenrndASforiucath. ern Afri\n\nTable 3T. hAebsaoblustoelauntde rdeelactirveeacsheainngevsainccDinTeP cvoacvceinraatgioenicnovAerfargicea(%b)eatwcroesesnA2fr0ic1a9n arengdio2ns021 was bTethweeecno2v01e9raangde2r0e2m1. ained the same in North Africa, while a more significant decreas\n\ncurrRedegiionnEastern 2a0n1d9 South2e0r2n1 AfricaAabtso\u2212lu5t%e C(hTaanbglees 3). Relative Changes\n\nAfrica\n\n75\n\n71\n\n\u22124\n\nWest and Central Africa\n\n69\n\n67\n\n\u22122\n\nEastern and Southern Africa\n\n80\n\n75\n\n\u22125\n\nSub-Saharan Africa\n\n74\n\n71\n\n\u22123\n\nNorth Africa\n\n94\n\n94\n\n0\n\n\u22125.33% \u22122.90%\n\u22126.25%\n\u22124.05% 0.00%\n\nTable 4. Absolute and relative changes in DTP vaccination coverage (%) across African countries between 2019 and 2021.\n\nCountry\nAlgeria Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Republic Chad Comoros Congo C\u00f4te d\u2019Ivoire Democratic Republic of the Congo Djibouti\n\n2019\n91 57 76 95 91 93 96 67 42 50 91 79 81\n73\n85\n\n2021\n91 45 76 95 91 94 93 69 42 58 85 77 76\n65\n59\n\nAbsolute Changes\n0 \u221212\n0 0 0 1 \u22123 2 0 8 \u22126 \u22122 \u22125\n\u22128\n\u221226\n\nRelative Changes\n0.0% \u221221.1%\n0.0% 0.0% 0.0% 1.1% \u22123.1% 3.0% 0.0% 16.0% \u22126.6% \u22122.5% \u22126.2%\n\u221211.0%\n\u221230.6%\n\np*\nns <0.001\nns ns ns ns <0.001 <0.001 ns <0.001 <0.001 <0.001 <0.001\n<0.001\n<0.001\n\nVaccines 2023, 11, 1103\n\n8 of 22\n\nTable 4. Cont.\n\nCountry\n\n2019\n\nEgypt\n\n95\n\nEquatorial Guinea\n\n53\n\nEritrea\n\n95\n\nEthiopia\n\n68\n\nEswatini\n\n90\n\nGabon\n\n70\n\nThe Gambia\n\n88\n\nGhana\n\n97\n\nGuinea\n\n47\n\nGuinea-Bissau\n\n78\n\nKenya\n\n91\n\nLesotho\n\n87\n\nLiberia\n\n70\n\nLibya\n\n73\n\nMadagascar\n\n68\n\nMalawi\n\n95\n\nMali\n\n77\n\nMauritania\n\n80\n\nMauritius\n\n96\n\nMorocco\n\n99\n\nMozambique\n\n88\n\nNamibia\n\n87\n\nNiger\n\n81\n\nNigeria\n\n56\n\nRwanda\n\n98\n\nSao Tome and Principe 95\n\nSenegal\n\n95\n\nSeychelles\n\n99\n\nSierra Leone\n\n95\n\nSomalia\n\n42\n\nSouth Africa\n\n85\n\nSudan\n\n93\n\nTanzania\n\n89\n\nTogo\n\n84\n\nTunisia\n\n98\n\nUganda\n\n93\n\nZambia\n\n88\n\nZimbabwe\n\n90\n\nns = no signi\ufb01cant * Chi-square Test.\n\n2021\n96 53 95 65 77 75 82 98 47 67 91 87 66 73 55 93 77 68 92 99 61 93 82 56 88 97 85 94 92 42 86 84 81 83 97 91 91 86\n\nAbsolute Changes\n1 0 0 \u22123 \u221213 5 \u22126 1 0 \u221211 0 0 \u22124 0 \u221213 \u22122 0 \u221212 \u22124 0 \u221227 6 1 0 \u221210 2 \u221210 \u22125 \u22123 0 1 \u22129 \u22128 \u22121 \u22121 \u22122 3 \u22124\n\nRelative Changes\n1.1% 0.0% 0.0% \u22124.4% \u221214.4% 7.1% \u22126.8% 1.0% 0,0% \u221214,1% 0.0% 0.0% \u22125.7% 0.0% \u221219.1 \u22122.1% 0.0% \u221215.0% \u22124.2% 0.0% \u221230.7% 6.9% 1.2% 0.0% \u221210.2% 2.1% \u221210.5% \u22125.1% \u22123.2% 0.0% 1.2% \u22129.7% \u22121.2% \u22123.4% \u22121.0% \u22122.2% 9.0% 0.0%\n\np*\n<0.001 ns ns\n<0.001 <0.001 <0.001 <0.001 <0.001\nns <0.001\nns ns <0.001 ns <0.001 <0.001 ns <0.001 <0.001 ns <0.001 <0.001 <0.001 ns <0.001 <0.001 <0.001 <0.001 <0.001 ns <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001\n\nThe relative coverage decrease in Africa between 2019 and 2021 was \u22125.33%. The more signi\ufb01cant relative decrease was in Eastern and Southern Africa, with a relative decrease of \u22126.25% (Table 3). Mozambique and Djibouti were the countries with a large relative decrease in coverage, with reductions of \u221230.7% and \u221230.6%, followed by Angola (\u221221.1%) and Madagascar (\u221219.1%). Despite the crisis, Chad had a relative increase of more than 10%. Other countries, such as Cameroon, Zambia, Namibia, and Gabon, substantially increased their coverage (Table 4) (Figures 6 and 7).\nIn North Africa, in Egypt, DTP3 vaccine coverage slightly increased by 1% between 2019 and 2021. In three countries of North Africa, Algeria, Libya, and Morocco, the coverage remained the same, while it decreased slightly in Tunisia and more signi\ufb01cantly in Mauritania, where absolute coverage decreased by 12%.\nBy 2019, 21 African countries had reached the WHO target of 90% coverage for the third dose of DTP (Botswana, Burkina Faso, Burundi, Cabo Verde, Comoros, Egypt, Eswatini, Eritrea, Ghana, Kenya, Malawi, Mauritius, Morocco, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Sudan, Tunisia, Uganda, and Zimbabwe). Following the\n\nVaccines 2V02ac3c,in1e1s,2x02F3O, 1R1,P11E0E3R REVIEW\n\n9 of 22 9 of 23\n\npandemic, by 2021, six of these countries (Burkina Faso, Eritrea, Eswatini, Sao Tome and Principe, Sierra Leone, and Zimbabwe) had dropped off this list (Figures 8 and 9).\n\nFiguFriegu6r.eA6b. sAoblsuotleutCe hCahnagnegsesinin DDTTPP VVaacccicniantaiotinoCnoCveorvaegrea(g%e) (A%cr)oAsscArofrsiscaAn fCriocuanntrCieos ubenttwrieeesnbetween 20192a0n19da2n0d2210.21.\nThere were 10 countries with a joinpoint close to 2019: Angola, Cabo Verde, Comoros, Democratic Republic of the Congo, Eswatini, The Gambia, Mozambique, Rwanda, Senegal, and Sudan (Table 5, Figures 10\u201319). In four countries (Angola, Cabo Verde, Comoros, and The Gambia), there was a joinpoint in 2018 (95% CI 2017\u20132019), while, in six countries, the joinpoint was in 2019 (95% CI 2018\u20132020). In all these countries, there was a decrease in the APC after the joinpoint, ranging from \u22121.7% in Cabo Verde to \u221216.5% in Mozambique.\n\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, 1103\n\n10 of 23 10 of 22\n\nVaccines 2023, 11, x FOR PEER REVIEW\n\n11 of 23\n\nFiguFrieg7u.rRee7l.atRiveleactihvaencgheasnigneDs TinPD3 vTaPc3civnaactciionnatcioovnecroavger(a%g)ea(c%ro) sascrAofsrsicAafnriccoaunnctoriuenstbrieetswbeeetnw2e0e1n92019 and a2n02d12. 021.\nBy 2019, 21 African countries had reached the WHO target of 90% coverage for the third dose of DTP (Botswana, Burkina Faso, Burundi, Cabo Verde, Comoros, Egypt, Eswatini, Eritrea, Ghana, Kenya, Malawi, Mauritius, Morocco, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Sudan, Tunisia, Uganda, and Zimbabwe). Following the pandemic, by 2021, six of these countries (Burkina Faso, Eritrea, Eswatini, Sao Tome and Principe, Sierra Leone, and Zimbabwe) had dropped off this list (Figures 8 and 9).\n\nFiguFrieg8u.rAe f8r.icAafnricoanunctoruienstrmieesemtineegtitnhge tWheHWOH9O0%90D%TPD3TvPa3cvcainccaitnioantiocnovceorvaegreagtaertgaertg2et01290.19.\n\nVaccines 2023, 11, 1103\n\nFigure 8. African countries meeting the WHO 90% DTP3 vaccination coverage target 2019. 11 of 22\n\nFiguFriegu9.reA9fr. iAcafnriccaonuncotruinestrmieseemtienegtitnhgetWheHWOH9O0%90D%TDP3TPv3acvcaicncaitnioatniocnovcoervaegraegteartgaergt e2t022012. 1. Table 5. Joinpoint analysis 3rd DTP dose coverage in Africa, 2012\u20132021.\n\nCountry\nAngola Period 1 Period 2 Cabo Verde Period 1 Period 2 Comoros Period 1 Period 2 Democratic Republic of the Congo Period 1 Period 2 Eswatini Period 1 Period 2 The Gambia Period 1 Period 2 Mozambique Period 1 Period 2 Rwanda Period 1 Period 2 Senegal Period 1 Period 2\n\nYears\n2012\u20132018 2018\u20132021\n2012\u20132018 2018\u20132021\n2012\u20132018 2018\u20132021\n2012\u20132019 2019\u20132021\n2012\u20132019 2019\u20132021\n2012\u20132018 2018\u20132021\n2012\u20132019 2019\u20132021\n2012\u20132019 2019\u20132021\n2012\u20132019 2019\u20132021\n\nAPC (95% CI)\n2.6 (1.3; 4.1) \u22129.3 (\u221214.4; \u22126.1)\n0.8 (0.3; 1.2) \u22121.7 (\u22123.2; \u22120.1)\n1.2 (0.6; 1.8) \u22122.7 (\u22124.6; \u22120.8)\n0.2 (\u22120.5; 1.0) \u22124.5 (\u22129.5; 0.8)\n\u22121.2 (\u22122.6; 0.2) \u22126.8 (\u221215.7; 3.1)\n\u22121.0 (\u22121.6; \u22120.4) \u22124.0 (\u22125.9; \u22122.1)\n1.0 (0.0; 2.0) \u221216.5 (\u221224.4; \u22127.8)\n\u22120.1 (\u22120.2; 0.0) \u22125.5 (\u22127.4; \u22123.6)\n0.6 (\u22120.3; 1.6) \u22124.5 (\u221210.7; 2.1)\n\np\n0.013 0.007\n0.006 0.040\n0.004 0.014\n0.489 0.079\n0.078 0.134\n0.007 0.003\n0.047 0.006\n0.063 0.001\n0.141 0.138\n\nVaccines 2023, 11, 1103\n\nTable 5. Cont.\n\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, x FOR PEER REVIEW\n\nCountry\nSudan Period 1 Period 2\n\n12 of 22\n\nYears\n2012\u20132019 2019\u20132021\n\nAPC (95% CI)\n0.1 (\u22120.2; 0.5) \u22125.1 (\u22128.6; \u22121.4)\n\np\n0.294 0.017\n\n13 of 23 13 of 2\n\nFigure 10. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Angola. FigFuigruere101.0.TThhirirddDDTTPP ddoosseecocovevreargaeg(e20(1220\u20131220\u20132210) 2in1d)iicnatdinicgajtoiinngpojoinintspioniAntnsgionlaA. ngola.\n\nFigFuirgeur1e11. 1T.hTihrdirdDDTTPPddoossee ccoovveerraaggee(2(021021\u201322\u201302210)2i1n)diincadtiincagtjionignpjooiinntpsoininCtsabionVCeardbeo. Verde. Figure 11. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Cabo Verde.\n\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, x FOR PEER REVIEW\nVaccines 2023, 11, 1103\n\n14 of 23 14 of 23 13 of 22\n\nFigure 12. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Comoros. FiguFirgeu1re2.1T2.hTirhdirDd TDPTPddooseseccoovveerraaggee ((22001122\u2013\u201322002211) )inidnidcaictiantginjgoinjopionipnotsinintsCionmCooroms.oros.\nFigFuigruer1e31.3T. ThhiridrdDDTTPP ddoossee ccoovveerraaggee(2(021021\u201322\u2013022012)1in) dinicdaitcinagtinjoginjpooininptos iinnttshienDtehme oDcreamtioc cRreaptiucbRlicepublic Foigf uoCfroeCno1gn3og..oT.hird DTP dose coverage (2012\u20132021) indicating joinpoints in the Democratic Republic of Congo.\n\nVaccines 2023, 11, x FOR PEER REVIEW\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, 1103\n\n15 of 23\n15 of 23 14 of 22\n\nFigure 14. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Eswatini. FigFuigruer1e41.4T. ThhiridrdDDTTPP ddoosseeccoovveerargaege(2(021021\u2013220\u20132210)2i1n)diicnadtiincgatjioningpjooiinntps oininEtsswinatiEnsi.watini.\nFigFuigreur1e51.5T. hTihridrdDDTTPP ddoseeccoovveerargaege(2(021021\u201320\u20132210)2i1n)dicnadtiincgatjioningpjooiinntps oininTthseiGnaTmhbeiaG. ambia. Figure 15. Third DTP dose coverage (2012\u20132021) indicating joinpoints in The Gambia.\n\nVaccines 2023, 11, x FOR PEER REVIEW\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, 1103\n\n16 of 23\n16 of 23 15 of 22\n\nFigure 16. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Mozambique. FigFuirgeur1e6.16T.hTihrdirdDDTTPPddoosseeccoovveerraaggee ((22001122\u2013\u20132202012)1i)nidnidcaictiantginjoginjopionipntosinintsMinozMamobziaqmueb. ique.\n\nFigFuirgeur1e7.17T.hTihrdirdDDTTPPddoossee ccoovveerraaggee(2(201021\u201322\u2013022012) 1in)dinicdatiicnagtijnoignpjooiinnptsoiinnRtswinanRdwa. anda. Figure 17. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Rwanda.\n\nVaccines 2023, 11, x FOR PEER REVIEW\nVaccines 2023, 11, x FOR PEER REVIEW Vaccines 2023, 11, 1103\n\n17 of 23\n17 of 23 16 of 22\n\nFigure 18. Third DTP dose coverage (2012\u20132021) indicating joinpoints in Senegal. FigFuigreur1e81.8T. hTihridrdDDTTPPddoossee ccoovveerraaggee(2(021021\u201322\u201302210)2i1n)diincadtiicnagtjionignpjooiinntpsoininStesniengaSle.negal.\n\nFigFuigreur1e91.9T. hTihridrdDDTTPPddoossee ccoovveerraaggee(2(021021\u201322\u201302210)2i1n)diincadtiicnagtijonignpjooiinntpsoininStusdinanS.udan.\n\nFig4u. rDe i1s9c.uTsshiiordn DTP dose coverage (2012\u20132021) indicating joinpoints in Sudan.\n\n4. 4.\n\nDDUNiiTssIcchCTuuiEhsssFissssiitioossutnnwuddyidyueultytiillriiezzceeoddgnUUiNzNeIdICCEfEoFrFdcdoaltalaetactotitnoegveaavlnuadaltudeaiDstseTemDP TivnaPactcvininagcachtiiiongnhat-tqiroeunnadltistryeanccrhdoissldsaAhcreforailsctsha.Africa.\n\nUNinIdCTiEchaFitsoirstdwuaditdya,euliynticlrliuezcdeoidnggnUivNzaeIcdCciEnfoaFrtidocaontlalreactoetisne. gvTaahlnuediartedstiaDsnsTdePmarvdinaizacetcdiinnmgatehitoihgnohdt-roqeluonagdlyisteyanccsruhorislesds Ahefrailctah.\n\niUnNdcioIcCnasEtiosFtreidnscawytaii,dniendlcyaltuardeccionolglencvtiaizocencdi,npfaortoricocenosslrliaentcget,isna. ngTdhaenreidrpsodtraitsinnsdegmaarcidnrioazstesidncgomuhenittghrhioe-dsqoaulnaodlgitypyeercnihosiduldsr,ehsecaolnthsinisdftaeiccnailctiytoaritnidnagdtaacto,amincpcoallurleadctiitnvioegnav,napaclrcyoisnciesastoisofintrger,anatdenssd.. TDrehepesopirrittseitnapgnodtaeacnrtodiasilzselcidmoumitnaetttirohienossdasounlocdhgpyaesernidosadutasre, sfaccoilnitisttaaiysttaeitiannenvnngcagoidclccmHyaoopbpomimionalwisspptsdseyaaiivanbarretganlarae,tdtmiidcrvvpoeoeeeospsltlspaaoesAninicrtbfetatarilillenioUyycgnrasNseni,idpssIpnCoioosrarEfofcttFiirtcton\u2019rerensgeepssnsndsa,tiddpiannsnsrsccgo..driv,eeDDapisardee,adnesstnidshppzceaeiiirettderesep,dxlpptiotahaooertbeatntleeieesncnnxioagvtttlneiileaanedllcccsvtilrolviiiaoovmmelsnuecsiiartaotbaacanvlgottedeiiueroobarnnoaegtsspfseriosUissoeruuffNtsocicUrnahhIgNoCnaaudImEssrCFepaddEtndeaFaharttaodlaiaytdoaasaaodti,avvslse-,.aa,niifllcaaaocbbmiiillli-pitayosasginyn,dgdpamtoaosqsiutbaAlleiftrryiecapanondrntaiacnctgiuordnaicssy,cprderipsocvarniedpceiaesnsca,ietrhseelmiaeabxytleenmasneirvdgeevcadoluuveaetbroalegvaebraoiasftiiUsonfNosrIiCnoEureFrpadonartatialny,gseinsc. ompapssraiHnctgoicwmese,ovdsetartA,adfmreiacsnpaanigtenmaUteiNnotnI,Csa,nEpdFr\u2019hosevsaitldtahencsdaraerrdseyilzsiateebdmlesdaacntradoscvsoadlliueffcaetbrieolenntbcaaonsudinstrfreoieprsoo. rTuthirneagsneamdliyess-thiso.dol-\n\nogcyr,eHdpaaontwacieqsvuceaorlu,itldydeaasfnpfedictetaocUucrNuarInaCacElyyFsdi\u2019ssisascntrdaenpcdoanancrclduieiszsioemndsad[y3a5te]a.mMceoorlgrleeocvdtieuor,enthtaoenavdgagrreieapgtoaiotretndisnngiantmurreepthoortdinolg-\n\npograoycf,tUdicaNetsIa,CdqEauFtaadlaimttyaaaantnandgaetamicocneunarlta,acanyndddreihgsecioarneltpahlaclnaecrveieelsssymmstiaegymhtesomabcsercrougsrese dvuaifrefieatrtoieonvntascrioinautvniaotcnrcisiensian.tTiorhenepsoertdinisg-\n\ncprreapcatincceise,sdcaotaulmdaanfafegcetmoeunrt,aannadlyhsiesaaltnhdcacroenscylsutseimonssa[c3r5o]s.sMdiofrferoevnetrc, othuentargiegsr.eTghaetesde dnias-\n\ntcurerepaonfcUieNs IcCoEuFlddaaftfaecatt onuatrioanalyasnisdarnegdiocnonalcluevsieolns sm[i3g5h].t Mobosrceuorveevra, rtihaetiaogngsriengvataecdcina-\n\ntiuorne torfenUdNsIwCEitFhidnactoauanttnriaetsio, pnalrtaicnudlarrelgyioinnaalreleavsemlsamrkigedhtboybdscisupraervitaierisaitniohnesailnthvcaacrceinaac-\n\nctieosns otrresnodcsiowdietmhiongcroaupnhtirciefas,ctpoarrst.icularly in areas marked by disparities in healthcare ac-\n\nVaccines 2023, 11, 1103\n\n17 of 22\ntrends within countries, particularly in areas marked by disparities in healthcare access or sociodemographic factors.\nIt is important to mention that the data was last updated in 2021, excluding the most recent 2022 data. Consequently, the trends we observed might not fully encapsulate the ongoing impact of the COVID-19 pandemic on DTP vaccination rates in Africa [35]. Future studies accessing more recent data could provide further insight into these trends.\nWe employed various techniques, such as country comparisons between 2019 and 2021 of coverage and analysis of trends using the joinpoint. We revealed signi\ufb01cant decreases in vaccine coverage across many countries. We also showed that in Sub-Saharan Africa, a shift occurred in coverage trends, although this was not statistically signi\ufb01cant due to the limited number of years analyzed.\nThe reduction in vaccine coverage due to COVID-19 has been a global issue, affecting other vaccines as well. Between January and December 2020, it is estimated that 30 million children missed doses of DTP3 and 27.2 million missed MCV1 doses [24]. Global coverage of DTP3 experienced a decrease of \u22125.81% between 2019 and 2021. The coverage rate declined from 86% in 2019 to 81% in 2020 [35]. In Latin America and the Caribbean, the coverage of DTP3 saw a decrease of \u22125.06% between 2019 and 2021, dropping from 79% in 2019 to 75% in 2020 [35]. On the other hand, Europe experienced a minimal decrease of \u22121.05% between 2019 and 2021, with coverage rates reducing from 95% in 2019 to 94% in 2020 [35].\nOur study aimed to assess the impact of the COVID-19 pandemic on DTP vaccination trends in Africa. The results indicate that the pandemic has adversely affected vaccination coverage in various African countries, particularly Sub-Saharan Africa. The overall trend of DTP vaccination rates in Africa has shifted from a positive annual percentage change (APC) to a negative one, although the change is not statistically signi\ufb01cant due to the short study period. The decrease in DTP coverage is a global issue. The Global coverage of the third dose of DTP (DTP3) fell from 86% in 2019 to 83% in 2021 [43]. There may be discrepancies between the number of African countries that experienced a signi\ufb01cant decline in vaccination coverage between 2019 and 2021 (26 countries) and those in which a change in trend was detected that was smaller (only 10 of those 26 countries). This discrepancy is explained by the fact that although there has been a decrease in vaccination coverage rates, it is not yet of the magnitude to detect a trend change in some countries.\nThe decrease in vaccination rates can be attributed to several factors related to the COVID-19 pandemic, such as the diversion of healthcare resources towards managing the outbreak, lockdowns, and movement restrictions impeding access to vaccination services. Furthermore, fear of contracting the virus might have led to a reluctance among parents to bring their children to healthcare facilities for vaccination. A study in Africa found that the risks associated with suspension or vaccination outweigh the risk of contracting COVID-19 while receiving routine immunization services [44].\nWe found that DTP3 coverage remained relatively stable across most North African countries from 2019\u20132021, as depicted in Table 4. This observation warrants further exploration, as it contrasts with the 4% absolute decrease in vaccine coverage reported across Africa during the same period. Algeria, Libya, and Morocco demonstrated no absolute changes in their DTP3 vaccine coverage between 2019 and 2021. These \ufb01ndings may re\ufb02ect the effectiveness of their national immunization programs and robust healthcare systems, which allowed these countries to maintain their vaccination rates despite the challenges presented by the global COVID-19 pandemic. Egypt, on the other hand, observed a slight increase of 1% in vaccine coverage. This improvement might be attributed to concerted efforts by the Egyptian Government and international organizations to strengthen the national immunization services, particularly in rural and underserved areas [45,46].\nConversely, during this period, Tunisia and Mauritania experienced decreases in DTP3 vaccine coverage. The slight decrease of 1% in Tunisia might not be statistically signi\ufb01cant, considering the high baseline coverage of 98% in 2019. In contrast, Mauritania\u2019s absolute coverage decrease of 12% is substantial. This decrease may be attributed to the country\u2019s\n\nVaccines 2023, 11, 1103\n\n18 of 22\nongoing health system challenges, including vaccine supply chain inef\ufb01ciencies, which could have been exacerbated by the additional strain of the COVID-19 pandemic [3].\nThese \ufb01ndings highlight the need for tailored strategies to improve vaccine coverage, particularly in countries with decreasing trends. Continuous monitoring of immunization programs, addressing health system barriers, and community engagement are vital for maintaining and enhancing vaccine coverage in these settings.\nA recent meta-analysis has found that the prevalence of incomplete immunization (failing to receive any vaccination doses) in Africa was 35.5%. The main risk factors were home birth, rural residence, lack of antenatal care visits, knowledge of immunizations, and maternal illiteracy [47].\nOur study\u2019s \ufb01ndings align with other research suggesting that the COVID-19 pandemic has disrupted essential health services, including vaccination programs, worldwide.\nIn Sub-Saharan Africa, the decreasing trend was more prominent, with an APC of \u22122.4%. This decrease is consistent with previous studies that reported disruptions in routine immunization services in the region due to the COVID-19 pandemic. The most signi\ufb01cant decreases in vaccination coverage were observed in Eastern and Southern Africa, with a relative decrease of \u22126.25%. The interruption in the upward trend of vaccination rates in Sub-Saharan Africa is concerning, given the region\u2019s already low vaccination coverage and higher burden of vaccine-preventable diseases.\nInterestingly, despite the dif\ufb01culties the pandemic posed, some nations, including Chad, Cameroon, Zambia, Namibia, and Gabon, were able to increase their vaccination coverage. This increase may be attributed to effective immunization strategies, targeted interventions, or increased government support for vaccination programs during the crisis. It is crucial to examine and learn from the experiences of these countries to improve immunization services in the face of future crises.\nThe implications of reduced vaccination coverage are signi\ufb01cant, as it may lead to outbreaks of vaccine-preventable diseases such as diphtheria, tetanus, and pertussis. These outbreaks can strain already overwhelmed healthcare systems and further exacerbate the impact of the COVID-19 pandemic on public health in Africa. It is essential to address the decline in vaccination rates and implement strategies to ensure the continuity of immunization services.\nSeveral factors may explain the change in vaccine coverage. Unvaccinated children in Sub-Saharan Africa are more likely to be born to parents with no formal education and come from poorer households. They are often found in urban areas with high illiteracy rates and face limited maternal education and media access. Maternal health-seeking behaviors play a role in reducing the likelihood of children being unimmunized. Country-level factors, such as high fertility rates, and community-level factors, such as high illiteracy rates, contribute to the prevalence of unvaccinated children [25]. A systematic analysis revealed that, in Sub-Saharan Africa, the primary factors contributing to incomplete vaccination were time limitations faced by caregivers, insuf\ufb01cient understanding about vaccination, the absence of vaccines or personnel at healthcare facilities, missed opportunities for vaccination, concerns regarding minor side effects, limited access to vaccination services, and the vaccination beliefs held by caregivers [48]. Multiple missed vaccination opportunities exist among different populations and geographical regions.\nAddressing these factors through targeted interventions and policies is essential to improve immunization rates and ensure the health of these children. Geographical and economic factors, access to healthcare services, the grade of development of healthcare infrastructure, vaccine supply chain issues, sociocultural factors, political stability, and the government\u2019s commitment to vaccination may modulate the impact of the COVID\u201319 pandemic on DTP vaccination coverage [49,50]. One issue to be considered is that of inequalities within countries. National coverage rates may have held steady or even increased during the pandemic, masking regions where they have fallen due to the pandemic. That happened in Cameroon, where the increase in vaccination coverage masked the impact of COVIC\u221219 on the vaccination of children in COVID\u201319 hotspot regions [51]. A study\n\nVaccines 2023, 11, 1103\n\n19 of 22\nin Zimbabwe found that the prevalence of missed opportunities for vaccination was 9% among mothers with no education and 21% among educated mothers. In Gabon, the \ufb01gures were 85% and 89% [52]. A metanalyses in Ethiopia found that effective strategies to increase vaccination coverage included promoting institutional delivery, reducing travel time to vaccination sites, encouraging antenatal care visits, educating mothers about immunization, providing information on the immunization schedule, targeting urban areas, and conducting postnatal household visits by healthcare providers [53]. In Somalia, the integration of human and animal vaccination campaigns has been shown to be an effective method of reaching nomadic and pastoralist communities of Somalia [54].\nWe can observe that the countries that dropped out of the list in 2021 of those achieving WHO\u2019s DPT objectives are spread across different African regions. The decline in the number of countries reaching the target of 90% coverage for the third dose of DTP is observed across various African regions. It is essential to accurately assess the regional impact by comparing the countries that achieved the WHO target in 2019 and 2021. The decline in the number of countries reaching the target can be observed across multiple African regions. Another issue is that, even if vaccination programs improve and return to pre-pandemic levels, they might not do so to the extent required for catch-up immunizations, and this may take a long time [55]. Because of this delay, there may be an increased chance of outbreaks of diseases that could be prevented by vaccination [56].\nThe COVID\u201319 pandemic\u2019s disruption has brought attention to the need for resilient health systems that can continue providing essential medical services in times of need. [57]. Efforts should be made to strengthen vaccination programs and improve coverage across the continent, particularly in the regions most affected by the decrease in vaccination rates [58]. Ef\ufb01cient planning, organization, and swift implementation are crucial for expediting the vaccine distribution process and ensuring a speedy recovery of vaccine coverage [59]. Collaboration between governments, healthcare providers, and international organizations are vital to ensure that progress in achieving vaccination targets is not lost [60] and to protect the health of African populations from vaccine-preventable diseases. New and strengthened partnerships are being forged to address these challenges and work towards achieving universal access to immunizations [61].\n5. Conclusions\nThis study examined the repercussions of the COVID\u201319 pandemic on DTP3 vaccination trends in Africa from 2012 to 2021. The \ufb01ndings highlighted a negative annual percentage change (APC) of \u22122.9% starting in 2019, leading to an absolute decrease in vaccine coverage of \u22124% between 2019 and 2021. The most substantial relative reduction in coverage was observed in Eastern and Southern Africa, with a relative decline of \u22126.25%.\nA regional analysis of Sub-Saharan Africa disclosed a joinpoint detected in 2019, marking an increasing trend until that year and a decreasing trend from 2020 onward. In Eastern and Southern Africa, the APC transitioned from positive to negative in 2020, showcasing an APC of \u22123.5%. Notably, in 10 countries (Angola, Cabo Verde, Comoros, the Democratic Republic of the Congo, Eswatini, The Gambia, Mozambique, Rwanda, Senegal, and Sudan), the joinpoint regression detected a trend alteration.\nThe number of African nations achieving the World Health Organization\u2019s 90% coverage goal for the third DTP dose suffered a signi\ufb01cant setback of 22.7%, plummeting from 22 countries in 2019 to 17 in 2021. This decline provides compelling evidence of the detrimental impact of the COVID-19 pandemic on DTP vaccination trends in Africa, as half of the countries surveyed experienced a contraction in vaccination coverage.\nAuthor Contributions: Conceptualization, F.G.-G., L.O., E.A.-O. and I.A.-O.; methodology, S.G.-A., L.G.A. and E.A.-O.; software, S.G.-A., L.G.-A.; investigation, R.A.-B., E.A.-O. and L.O.; writing\u2014original draft preparation, F.G.-G., S.G.-A., L.G.-A., E.A.-O., I.A.-O., L.O. and R.A.-B.; writing\u2014review and editing, F.G.-G., S.G.-A., L.G.-A., I.A.-O., E.A.-O., L.O. and R.A.-B.; visualization, S.G.-A. and L.G.-A.; supervision, F.G.-G. and I.A.-O. All authors have read and agreed to the published version of the manuscript.\n\nVaccines 2023, 11, 1103\n\n20 of 22\n\nFunding: This research received no external funding.\nInstitutional Review Board Statement: Not applicable because the study was conducted with a publicly available database.\nInformed Consent Statement: Not applicable because the study was conducted with a publicly available database.\nData Availability Statement: Data are available from UNICEF database.\nCon\ufb02icts of Interest: The authors declare no con\ufb02ict of interest.\nReferences\n1. Ota, M.O.C.; de Moraes, J.C.; Vojtek, I.; Constenla, D.; Doherty, T.M.; Cintra, O.; Kirigia, J.M. Unveiling the Contributions of Immunization for Progressing towards Universal Health Coverage. Hum. Vaccin. Immunother. 2022, 18, e2036048. [CrossRef] [PubMed]\n2. Suffel, A.M.; Ojo-Aromokudu, O.; Carreira, H.; Mounier-Jack, S.; Osborn, D.; Warren-Gash, C.; McDonald, H.I. 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MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.\n\n\n",
+    "authors": [
+      "I. Aguinaga-Ontoso",
+      "S. Guillen-Aguinaga",
+      "L. Guillen-Aguinaga",
+      "R. Alas-Brun",
+      "L. Onambele",
+      "E. Aguinaga-Ontoso",
+      "F. Guillen-Grima"
+    ],
+    "doi": "10.3390/vaccines11061103",
+    "year": null,
+    "item_type": "journalArticle",
+    "url": ""
+  }
+]
\ No newline at end of file