Equity in vaccine coverage in Uganda from 2000 to 2016: Revealing the multifaceted nature of inequity.

doi:10.21203/rs.3.rs-2002082/v1

Background

This study analyses vaccine coverage and equity among children under five years of age in Uganda based on the 2016 Uganda Demographic and Health Survey (UDHS) dataset. Understanding equity in vaccine access and the determinants is crucial for the redress of emerging as well as persistent inequities.

Methods

Applied to the UDHS for 2000, 2006, 2011, and 2016, the Vaccine Economics Research for Sustainability and Equity (VERSE) Equity Toolkit provides a multivariate assessment of immunization coverage and equity by (1) ranking the sample population with a composite direct unfairness index, (2) generating quantitative measure of efficiency (coverage) and equity, and (3) decomposing inequity into its contributing factors. The direct unfairness ranking variable is the predicted vaccination coverage from a logistic model based upon fair and unfair sources of variation in vaccination coverage. Our fair source of variation is defined as the child’s age – children too young to receive routine immunization are not expected to be vaccinated. Unfair sources of variation are the child’s region of residence, and whether they live in an urban or rural area, the mother’s education level, the household’s socioeconomic status, the child’s sex, and their insurance coverage status. For each unfair source of variation, we identify a “more privileged” situation.

Results

DPT3 and MCV1 coverage and equity – two vaccines indicative of performance – improved significantly since 2000, from 49.7–76.8% and 67.8–82.7%, respectively, and there are fewer zero-dose children: from 8.4–2.2%. Improvements in retaining children in the program so that they complete the immunization schedule are more modest (from 38.1–40.8%). Except for MCV1, the main drivers for the lack of vaccination shifted from supply-side factors (e.g., availability of vaccines at clinics, disparities between regions) to more demand-side factors – primarily maternal education.

Conclusion

The past two decades have seen significant improvements in vaccine coverage and equity, thanks to the efforts to strengthen routine immunization and ongoing SIA such as the Family Health Days. While maintaining the regular provision of vaccines to all regions, efforts should be made to alleviate the impact of low maternal education and literacy on vaccination uptake.

vaccine equity

vaccine coverage

vaccine delivery

access to healthcare

Uganda

determinants of health

national medical stores

immunization schedule

zero-dose

Vaccination is one of the most successful cost-effective interventions for controlling vaccine-preventable diseases, thereby reducing childhood morbidity and mortality (Kamya et al., 2021). Although remarkable improvement has been made since 1974 when the World Health organization introduced the expanded program for immunization, access to vaccines still remains a challenge in many developing countries with the lowest improvements reported among disadvantaged and marginalized groups. Furthermore, global coverage for vaccines has not changed markedly in the past decade. It dropped from 86% in 2019 to 83% in 2020. The drop in 2020 was largely attributed to disruptions caused by the Covid 19 pandemic. Uganda is one of the Gavi priority countries where the improvement of vaccination coverage and equitable uptake is a key concern. In the recent past, there has been a renewed call for governments and development partners to include strategic goals related to coverage and equity in their plans (Kamya et al., 2017). For instance, the Gavi and the Vaccine Alliance 5.0 (2021–2025) strategy has special focus on the unreached communities and under-immunized children with equity at the center of the strategy (Gavi 5.0). This is also reflected in the broad vision of the Immunization Agenda 2030 (IA2030) which states that: “A world where everyone, everywhere, at every age, fully benefits from vaccines for good health and well-being.”(WHO, 2018)

The Uganda Expanded Programme on Immunization (UNEPI) has the mandate to ensure full immunization of children (aged 0–12 months) and women of childbearing age (15–45 years both pregnant and non-pregnant), and other high-risk groups as might be determined by the epidemiological patterns (MoH, 2012). In Uganda, vaccination services are provided by MOH through UNEPI using the healthcare system structures. Furthermore, vaccination services in Uganda are managed at national, regional, district and community levels, with different stakeholders involved at each level. The Uganda National Expanded Programme on Immunization (UNEPI) and the Global Vaccine Action Plan set coverage goals of 90% at national level and 80% at district level by 2020. Overall, Uganda’s vaccination coverage estimates suggest good performance in 2019, especially for third dose Diphtheria, Tetanus and Pertussis (DPT3) at 93%, Pneumococcal Conjugate vaccine (PCV3) at 92%, Polio 3 at 92%, BCG at 88%, Rotavirus vaccines completed dose (RotaC) at 87%, and Measles-containing-vaccine first-dose (MCV1) at 87% (World Health Organization, 2019).

However despite this overall relatively high coverage, inequities in access to vaccination has been noted and remains one of the biggest challenges in several districts of Uganda (Mupere et al., 2020). Such inequities should not be ignored, because persisting inequities in vaccine coverage can contribute to the frequent occurrence of outbreaks of vaccine preventable diseases (Wiyeh, Cooper, Nnaji, & Wiysonge, 2018). Whereas studies done elsewhere have attributed similar inequities in vaccine coverage to factors such as household wealth, geographic location, maternal education, and child characteristics (Arsenault, Harper, et al., 2017; Geweniger & Abbas, 2020), there is still a dearth of evidence in Uganda about the factors that contribute to inequities in vaccine coverage. Furthermore, there are also gaps related to the extent and types of inequity in vaccine coverage. Lack of evidence on the types of inequities and the explanatory factors that propagate such inequities constrains the development of appropriate and synchronized mitigation strategies and is therefore only likely to lead to persisting inequities in access to vaccines and poor child health outcomes that could have been prevented. This situation is further compounded by the fact that the administration of different vaccines and different doses of vaccines reflect different attributes of the immunization programme. For example, whereas DPT1 coverage is an indicator of health care access, DPT3 is an indicator of both health care access and the capacity to utilize immunization services on multiple visits.

Based upon this backdrop, this paper focuses on analysing vaccine coverage and equity among children under five years of age in Uganda based on data from the 2000, 2006, 2011, and 2016 Uganda Demographic and Health Surveys (UDHS). It will expand on the results from Okello et al. (Okello et al., 2022), which focused on the completion rate for children aged 12–23 months old. Strengthening equitable access to vaccines has potential to not only save lives, but also reduce morbidity, in addition to subsequently advancing economic development at both household and national levels. This will further facilitate the design and implementation of targeted programmes to overcome the identified indicators that explain variations in vaccine coverage and equity in Uganda.

As detailed in our more extensive methodological paper (Patenaude et al., 2022) the VERSE composite vaccination equity assessment metric is derived from the work of Wagstaff and Erreygers on socioeconomic equity combined with measures of direct unfairness in healthcare access outlined in the works of Fleurbaey, Schokkaert, Cookson, and Barbosa (Barbosa & Cookson, 2019; Fleurbaey & Schokkaert, 2011; Adam Wagstaff, 2011; Williams & Cookson, 2006).

The VERSE metric is a concentration index of vaccination coverage where individuals are ranked by multi-dimensional unfairness in access instead of using a measure of wealth alone. The direct unfairness ranking variable is the predicted vaccination coverage from a logistic (binary outcomes) or generalized linear (continuous) models based upon fair and unfair sources of variation in vaccination coverage. Our fair source of variation is defined as the child’s age – children too young to receive routine immunization are not expected to be vaccinated. Unfair sources of variation are the child’s region of residence, and whether they live in an urban or rural area, the mother’s education level, the household’s socioeconomic status, the child’s sex, and their insurance coverage status. For each unfair source of variation, we identify a “more privileged” situation.

The produced equity metrics include the Wagstaff concentration index and its Erreyger correction, which denote whether the vaccine coverage benefits most more privileged people (when the index is positive; 0 < CI < 1) or disadvantaged people (when it is negative; -1 < CI < 0), and the Absolute Equity Gap (AEG), which quantifies the difference in vaccine coverage between the 20% most privileged and the 20% most disadvantaged people. A regression decomposition is performed to determine the relative share each unfair parameter has on overall inequality in vaccination status. We also computed concentration indices using socioeconomic status as the population ranking instead of the composite index to create equity measures comparable to other studies.

The UDHS is one of the major data sources for evidence regarding vaccine equity in Uganda. This analysis uses data from Uganda’s last four Demographic Health Survey (UDHS) from 2000 to 2016 to assess equity for several statuses as part of Uganda’s immunization schedule. These are immunization by Bacillus Calmette–Guérin vaccine (BCG), diphtheria-pertussis-tetanus vaccine doses 1 to 3 (DPT1-3), oral polio vaccine 1 to 3 (OPV1-3), measles containing vaccine first dose (MCV1), zero dose, fully immunized, and completely immunized. Zero dose status refers to a child who has received no vaccines on the national immunization schedule. Fully immunized means that a child has been fully immunized relative to their age. Complete immunization indicates that the child is older than 24 months of age and has completed their routine pediatric immunization schedule.

We focused the results below on the equity metrics generated for full immunization (i.e., the child received all vaccines scheduled for their age), zero-dose status (i.e., the child did not receive any vaccine), and DPT3 and MCV1 (i.e., the child received their third dose of diphtheria-tetanus-pertussis or first dose of measles-containing vaccine). We recognize that coverage levels for DPT3 and MCV1 are generally widely accepted as standard indicators for performance of immunization system (Sodha & Dietz, 2015).

Table 1 summarizes the efficiency (coverage) and equity metrics (concentration indices and absolute equity gap) for 2000 and 2016. Additional data for 2000, 2006, 2011, and 2016 is featured in Tables 1 to 4 in the supplementary material, and subnational data in tables 5 to 16. Figure 1 shows the coverage and equity by region. Figure 2 tracks the evolution of national coverage and equity for all vaccines and health outcomes from 2000 to 2016. Figure 3 tracks the evolution of subnational coverage and equity for being fully immunized for age from 2000 to 2016. Figure 4 presents the decomposition analysis for zero-dose status, fully immunized for age, DPT3, and MCV1.

Table 1: National-level coverage data.

Vaccine or health outcome ¹	Efficiency metric		Equity metrics
	Coverage or prevalence		Concentration index						Absolute Equity Gap (multivariate)
	Coverage or prevalence		Wealth only (Wagstaff) ²		Multivariate (Wagstaff)		Multivariate (Erreyger) ³		Absolute Equity Gap (multivariate)
	2000	2016	2000	2016	2000	2016	2000	2016	2000	2016
BCG	77.4%	94.2%	0.167	-0.071	0.072	0.020	0.226	0.077	0.269	0.098
DPT1	76.8%	92.7%	0.157	-0.062	0.070	0.020	0.211	0.071	0.234	0.106
DPT2	65.6%	86.8%	0.135	-0.052	0.101	0.033	0.260	0.106	0.305	0.146
DPT3	49.7%	76.8%	0.127	-0.042	0.150	0.054	0.295	0.150	0.358	0.183
POLIO1	83.0%	89.7%	0.173	-0.058	0.051	0.023	0.166	0.077	0.185	0.107
POLIO2	73.5%	82.5%	0.144	-0.051	0.073	0.039	0.209	0.120	0.261	0.153
POLIO3	56.0%	65.9%	0.117	-0.048	0.120	0.071	0.257	0.169	0.317	0.185
PCV1	N/A	81.4%	N/A	-0.049	N/A	0.044	N/A	0.137	N/A	0.177
PCV2	N/A	74.6%	N/A	-0.043	N/A	0.058	N/A	0.161	N/A	0.215
PCV3	N/A	64.1%	N/A	-0.035	N/A	0.071	N/A	0.164	N/A	0.197
MCV1	67.8%	82.7%	0.123	-0.029	0.087	0.055	0.200	0.133	0.234	0.180
ZERO	8.4%	2.2%	0.236	-0.078	0.427	0.425	0.106	0.022	0.123	0.027
FULL	38.1%	40.8%	0.133	-0.043	0.163	0.110	0.260	0.181	0.291	0.230
COMPLETE	47.9%	39.0%	0.133	-0.044	0.154	0.140	0.309	0.218	0.363	0.267

Notes: ^1. ZERO, the child didn’t receive any vaccine by 12 months old; FULL, the child is under 24 months old and is fully immunized for their age; COMPLETE, the child is above 24 months and completed the routine pediatric immunization schedule.
^2. Concentration index based on households ranked by socioeconomic status (as defined in the DHS) only.
^3. See (Erreygers, 2009; A. Wagstaff, 2011) for details on the Erreyger’s correction.

Full immunization for age

A child was considered fully immunized if they received all scheduled vaccines for their age. Overall, there was an improvement in the proportion of children who were fully vaccinated for age from 38% (UDHS 2000) to 41% (UDHS 2016). However, there was a sharp drop in the percentage of children that were fully vaccinated for age in UDHS 2006, falling to just 13%. We also noted that there was a decline in the proportion of children that were completely vaccinated from 48% (UDHS 2000) to 39% (UDHS 2016).

The Wagstaff and Erreyger corrected concentration indices were 0.111 and 0.157, both indicating inequity in full vaccination status, as this status is more prevalent amongst more privileged people. The AEG of 0.188 implied that the 20% most disadvantaged households would need to increase full immunization status by 18.8% to have similar levels to the top 20% most privileged households. Similarly, an AEG of 0.233 for immunization completion implied that the 20% most disadvantaged households would need to increase immunization completion status by 23.3% to have similar levels to the top 20% most privileged households. Overall, there was an improvement in equity indices for full vaccination between UDHS 2000 and that of 2016 rising from 0.84 to 0.89 respectively. In addition, there was an improvement in the equity indices across the different vaccine antigens between UDHS 2000 and that of 2016, with the highest improvement registered for DPT3 from 0.85 to 0.95 respectively.

Decomposition of the inequity for UDHS 2016 showed that the region where the household resides (19.1%) and maternal education level (11.5%) were major contributors as to whether a child is fully immunized for their age. On the other hand, decomposition of the inequity indices for UDHS 2000 showed that the region where the household resides (47.5%), maternal education level (14.7%) and urban/rural household setting type (12.9%) were major contributors as to whether a child is fully immunized for their age.

Settings (urban/rural; 0%), sex of the child (1.1%), health insurance coverage (1.4%) and socioeconomic status (0.1%) had a limited influence. On the other hand, unexplained variation increased between 2000 to 2016 accounting for 22.9% and 67.5% of the variation in full immunization status respectively, indicating either random noise or the presence of factors not captured in the model.

All the regions of Uganda had an achievement level for full vaccination below 50%. The regions of Kigezi (47%), Acholi (45%), and Karamoja (43%) had the highest level of coverage, whereas North Buganda, Lango, and Tooro had the lowest rates, all at 29%. South Buganda shortly follows with 30% coverage. Based on the Wagstaff concentration index, the coverage for full immunization status was most equitable in Kigezi, Lango, and Karamoja, and least equitable in the regions of Bugisu and South Buganda.

Zero-dose status

Zero-dose refers to a child not receiving a single dose of national immunization schedule vaccine by 12 months of age. A very small proportion (2.2%) of all children in Uganda have never received any vaccine according to the UDHS 2016, as compared to 8% captured in the UDHS 2000. The AEG showed an achievement gap of 0.027%, which shows a modest inequity between the 20% most disadvantaged and 20% most privileged households in Uganda. The extreme value for the Wagstaff concentration index (0.425; significant inequity) indicates that nearly all children with zero-dose status are in the most disadvantaged group[1]. On the other hand, it was observed that equity indices for zero-dose status between UDHS 2000 and 2016 was comparable at 0.57 and 0.58 respectively. An all-time low equity index for zero-dose status was registered in the UDHS 2011 at 0.47.

Decomposition of the inequity for UDHS 2016 found that the region (13.7%) where the household resides affected most whether a child had ever received any vaccine, closely followed, by maternal education (12.2%), and whether the household resided in an urban or rural community (4.7%). Being underaged for vaccination (a fair contributor) also contributed to 5.1% of the variation and here refers to children who are younger than 12 months old and have not received any vaccines yet: they are at risk of becoming “zero-dose” as per Gavi’s definition. On the other hand, the decomposition of the inequity for UDHS 2000 found that maternal education (21.8%), the setting where the household is located in an urban or rural community (17.2%), and the region (9.8%) were the major contributors of a child ever receiving any vaccine. Neither the sex of the child, nor the household’s socioeconomic status or health insurance coverage had any impact on whether the child had a zero-dose status. About 64% of the variation was left unexplained in UDHS 2016 compared to 45.7% for UDHS 2000.

South (5%) and North Buganda (4%) reported the highest prevalence of zero-dose children, with an unequal distribution, with a Wagstaff concentration index of 0.312 and 0.254 respectively. Most other regions had a zero-dose prevalence ranging between 0% and 3%, but with a large range of concentration indices: 0 to 0.553. The lowest level of equity was found in Bunyoro (0.553), and this was also the only region with a higher index than South Buganda. Two regions had negative concentration indices, indicating that less children among the most disadvantaged households were zero-dose compared to more privileged households: Busoga, with a zero-dose prevalence of 3% and a concentration index of -0.142, and Teso, with a prevalence of 1% and an index of -0.055.

DPT3 coverage

Generally, National DPT3 coverage improved from 50% (UDHS 2000) to 76.8% (UDHS 2016). However, the lowest DPT3 coverage was experienced in UDHS 2006 at 25%. Six of the fifteen UDHS regions in UDHS 2016 had a DPT3 coverage level at or above 80% (none above 90%), with Teso and Karamoja displaying the highest DPT3 coverage levels (85% and 89%, respectively). South and North Buganda, Tooro, and Busoga had the lowest DPT3 coverage, estimated between 71% and 73%.

There was an improvement in equity indices from 0.85 (UDHS 2000) to 0.95 (UDHS 2000). Due to its lower coverage, DPT3 vaccination distribution is less equitable than for DPT1, BCG, and OPV1. The Wagstaff and Erreyger concentration indices were respectively 0.054 and 0.150. The AEG was 0.183: the 20% most disadvantaged people would need to increase DPT3 vaccine coverage by 18.3% to have similar levels to the top 20% most privileged. Based on the Wagstaff index, Kampala (0.066), followed by South and North Buganda (both 0.059) had lower levels of distributional equity than the other regions.

Considering UDHS 2016, several unfair factors of variation also had a dominant influence: the level of maternal education (28.6%), followed by being underaged to receive the DPT3 vaccine (22.8%), and the region of residence (12.7%). On the other hand, in the UDHS 2000, region (43.4%), level of maternal education (24.4%) and urban/rural setting where household is located (21.4%) were the major unfair factors of variation in DPT3 coverage.

MCV1 coverage

Generally, the coverage for the MCV1 increased from 68% (UDHS 2000) to 82.7% (UDHS 2016). The national estimate for MCV1 coverage (82.7%) was lower than that of DPT1 (93%), but higher than DPT3 (77%). The MCV1 coverage equity indices improved from 0.91 (UDHS 2000) to 0.95 (UDHS 2016). However, the MCV1 coverage equity indices were similar for the UDHS rounds of 2006, 2011 and 2016. The level of equity for MCV1 is also worse than for DPT1, but similar to DPT3 with Wagstaff and Erreyger concentration indices at 0.055 and 0.133, respectively, and an AEG of 0.180, signifying that 20% of the most disadvantaged people would need to increase MCV1 vaccine coverage by 18% to have similar levels to the top 20% most privileged. Level of maternal education (45.3%) and region (29.9%) were the biggest contributors of variation in equity for the MCV1 in the UDHS 2016. Similarly, region (43%), level of maternal education (26.7%) and urban/rural location for the households (15.7%) contributed most to the variation in equity for the MCV1 in the UDHS 2000.

The highest performing regions for MCV1 coverage were Kigezi (97%), Karamoja (93%), Tooro (89%), and Acholi (89%). Regions with the lowest coverage included Busoga at 76%, North Buganda at 77%, and South Buganda and Lango both at 78%. Wagstaff’s CI method found Karamoja (-0.008) and Bugisu (0.006) as experiencing the most equitable distribution of all regions in MCV1 vaccination status. South Buganda (0.076), North Buganda (0.064), and Acholi (0.063) displayed the lowest level of equity.

The selected VERSE parameters (detailed before the methods section) seem to explain well inequity in DPT3 and MCV1 vaccination status as the unexplained variation was found to be contributing only 33.9% and 5.1% (respectively) to the variation.

When computing the (more traditional) concentration index based on wealth only, it returned positive, or “pro-rich”, values for all vaccines and statuses in the earliest UDHS in 2000, and negative, or “pro-poor”, values afterward. This indicator reveals that the distribution of vaccines and their associated outcomes in Uganda benefited most wealthier people in 2000, and that this trend was reversed in the following years: now poorer Ugandans benefit most (although only slightly more) from it. While it displays an encouraging trend, using such wealth-based indicators may skew our assessment of distributional equity: adding the other demographical, social, and economic factors through the VERSE Equity Toolkit (detailed in the Methods) brings the concentration indices back in positive values, highlighting the adverse effect of compounding disadvantages on coverage.

[1] For mathematical reasons, when the prevalence/coverage outcome is low (in this case, 2.2%), the Wagstaff and Erreyger indices may produce conflicting results in terms of order of magnitude: the Wagstaff index reported a value of 0.425 (significant inequity) whereas the Erreyger corrected index was 0.022 (very equitable distribution). Both indices are positive: privileged people benefit most.

We conducted an analysis to estimate variations in vaccine coverage and equity in Uganda, based on data collected from the UDHS of 2000, 2006, 2011 and 2016. We found that hardly a third of all the children under 24 months old in Uganda were fully immunized for age, and that there was inequity in full vaccination status, with the under-privileged populations being most affected as portrayed by the high AEG estimate. On the other hand, we found that a very small proportion of all children in Uganda had not received any of the nationally scheduled immunization vaccines by 12 months of age. It was also observed that the influence of the household’s residence (the district and whether urban or rural) on the inequitable distribution of vaccines significantly reduced between 2000 to 2016, suggesting improvements in logistics (most can be credited to the National Medical Stores) and in the vaccine supply chain at the sub-national level, translating into better availability and access to the vaccines. Supply constraints remain a considerable issue for specific vaccines such as the measles vaccine (MCV) and such shortcomings reduce the parents’ ability to get their children fully immunized.

From 2000 to 2016, improvements in vaccine delivery increased equity and shifted the dominant contribution to inequity from largely supply-side constraints to demand-side constraints, where the households’ behaviour, whether guided by financial and social constraints, awareness, or beliefs, seems to play a stronger role in immunization. Our findings are consistent with the findings from two other studies conducted in Uganda and Ethiopia that reported that maternal education, wealth status, region of residence, and the household head’s sex were strongly associated with full vaccination coverage (Geweniger & Abbas, 2020; Okello et al., 2022). Strengthening the capacity of the health systems to vaccinate priority populations is critical to improving coverage and equity. The kind of evidence generated by our study and other similar studies on vaccine equity is therefore critical for improving the coverage and impact of immunization programs (Memirie, Nigus, & Verguet, 2021).

Inequity in full immunization for age versus complete immunization - Children dropping out of the EPI

Childhood vaccination is an essential public health intervention that can significantly lower disparity levels towards a perfect equity stance (Kakama, 2021). Completion of the immunization schedule is crucial to achieving low childhood morbidity and mortality. Although the Ugandan Government has taken steps to increase access to vaccination services by providing them free to the clients, as noted above inequities in full and complete immunization persist. Generally, there was an improvement in the proportion of children who were fully vaccinated for age (“FULL” in Table 1) and those 24 months old and older that had completed their vaccination schedules (“COMPLETE”) comparing UDHS 2000 and UDHS 2016. Our analysis noted that there was a drop in the proportion of children that were fully immunized for age (45.7%) and the proportion that completed the routine paediatric immunization schedule by the age of 24 months (43.4%) implying that there are additional barriers that hinder completion of immunization. Furthermore, the Wagstaff and Erreyger corrected concentration indices both indicated inequity in full vaccination status with the status more prevalent amongst more privileged people. The sharp drop in the proportion of children that were fully vaccinated for age during UDHS 2006 could have been influenced by a number of factors including aspects of cost of implementation of the vaccination programs, increment in the facility catchment populations and shortages in human resources for health (Phillips, Dieleman, Shearer, & Lim, 2018). This drop cannot be well explained by the typical contributors to inequity included in the VERSE toolkit (see Fig. 7 in the Supplemental Material). Similarly to children who do not receive any vaccine (zero-dose), the low proportion of children fully immunized for their age could be partly explained by variations relating to access to vaccination services and missed opportunities for vaccination (Geweniger & Abbas, 2020).

There was a reduction in the equity gap between the UDHS 2000 and UDHS 2016. Uganda is one of the countries globally implementing the highest number of pro-equity immunization strategies (over 38 strategies on both the demand- and supply-side) (Dadari et al., 2021), which have subsequently been a large contribution in bridging the equity gaps in immunization programming between UDHS 2000 and 2016. Hence, Uganda is currently ranked as a Gavi Tier-1 country with initial self-financing (Gavi, 2020).

Looking at the coverage for BCG, DPT1 and OPV1 which are given at birth and DPT3 and OPV3 which are given at 14 weeks, we see a clear drop for the later doses, particularly between the 2nd and 3rd dose for both vaccines. This highlights the fact that whereas access for the first dose which is given at birth is relatively good, several challenges hinder continued access to the vaccines leading to the observed dropouts seen. As reported by Sodha et al., this is likely to be the result of supply side challenges related to poor infrastructure for vaccines regarding the supply chain and human resource as well as demand side factors that lead to vaccine hesitancy stemming from factors such as fears about vaccine safety and side effects as well as administration of multiple vaccines (Sodha & Dietz, 2015). The measles vaccine which is received at 9 months however has a higher coverage than OPV3 and DPT3. The higher measles vaccination may be attributed to mass vaccination campaigns that are not usually done for the other two vaccines (Arsenault, Johri, et al., 2017).

The variables that strongly contribute to inequity for fully immunized children for age included the district of residence and maternal education. Other studies report that employment, ante-natal care follow-up, wealth quintile, and delivery at the health facility also do contribute to full immunization (Tamirat & Sisay, 2019). Instead of these specific parameters for predicting vaccination inequity among the children who were fully immunized for age, our study used maternal education as it is documented to impact both the mother and the child’s health: lower maternal education is associated with decreased used of preventive care and childcare, slower development of motor and cognitive skills, and generally higher mortality risk (Andriano & Monden, 2019; Bbaale & Guloba, 2011; Kakwangire et al., 2021). The significant unexplained variation (66%) of inequity for fully immunized children for age implicates the existence of other factors unrelated to maternal education and the other selected parameters. A factor that was raised by district health officers (private communications) surround increases in vaccine hesitancy among Ugandans of all sociodemographic status and education level. While we cannot easily draw conclusions on whether the household was influenced by anti-vaccine beliefs through the DHS data, we should consider it as a likely significant contributor to demand-side constraints.

If vaccine hesitancy were associated with maternal education, the most salient example of it from the DHS data would be the strong association between the measles vaccine (MCV1) coverage and maternal education, particularly in 2016. The region of residence forms the most (in 2000) and then second most (2016) dominant factor, which can indicate either shortcomings in vaccine supply or a population cluster effect on coverage. Efforts to improve MCV1 coverage in specific districts or target population would reduce the occurrence of outbreaks that have been noted despite high vaccination coverages and attributed to inequity and drops in coverage among other factors (Salathé & Bonhoeffer, 2008).

High and low performing regions

The north and south Buganda had the lowest rates of achievement for full vaccination. The central region (north and south Buganda), includes Kampala, the capital city of Uganda which grapples with unique challenges including having a large population of urban informal settlement dwellers (especially the urban-poor) as well as new populations with widely varying health seeking behaviour and limited access to public health services (Mupere et al., 2020). For example, it is sometimes argued that given the nature of their work, several caretakers fail to apportion time to take their children for immunization (Mupere et al., 2020) leading to inequitable access to immunization services (Mutua, Kimani-Murage, & Ettarh, 2011). In addition to strengthening routine immunization, the government in partnership with UNICEF and other NGOs implemented supplementary immunization activities, such as Family Health Days, which significantly improved the coverage of DPT3 and MCV1 in the last decade (Mupere et al., 2020).

On the other hand, Bugisu and south Buganda regions were the least equitable. Some of the reasons that could have contributed to this finding include the fact that South Buganda has several Island districts which are hard-to-reach areas, including Kalangala and Buvuma districts. The populations in these districts are largely underserved, due to limited resources (such as boats, fuel, health workers, outreach staff to mobilize and do health education, etc.) coupled with the long travel time required to traverse the islands and unpredictable weather patterns on the lake. These bottlenecks consequently pose enormous challenges for equitable access to vaccination services for the island communities compared to their counterparts on the mainland. On the other hand, Bugisu region also has hard-to-reach populations in the mountainous terrains in some districts located in the Elgon Mountain ranges, such as Manafwa, Bududa and Sironko, among others. These districts are also faced with unique supply-related bottlenecks that include inadequate health facilities and health workers in the hard-to-reach areas as well as poor access roads for ease of navigation within and between communities in addition to limited access to appropriate safe transport especially in the mountainous areas. Mass campaigns may be better capable of serving under-privileged populations in such hard to reach areas compared to the routine immunization system which is challenged by several bottlenecks (Arsenault, Johri, et al., 2017).

We also noted that the Acholi region had fairly good coverage for MCV1, but rather low equity indices, which implies that the under-privileged populations in that region have low access to vaccination services. Some of the factors that could have contributed to this variation include aspects of maternal education, district and residence type (urban/rural) in addition to supply side challenges such as long distance and poor access to healthcare services.

On the other hand, the Karamoja region which is a typically hard to reach region with nomadic pastoralists had consistently good coverage and equity for the MCV1, which is an indication that the interventions that were introduced to address supply and demand side bottlenecks have been successfully implemented. Facing a resurgence of vaccine-preventable human (e.g., yellow fever) and animal diseases (e.g., Peste des petits ruminants) due to increased sedentarism, efforts were recently made to deliver both human and animal vaccines to Karamoja (Cummings, Wamala, Komakech, Malimbo, & Lukwago, 2014; Hopp, Clementinah, Verdick, & Napyo, 2022). According to Mupere et al. (Mupere et al., 2020), strengthened routine immunization in Karamoja allowed the region to achieve good coverage before Family Health Days were implemented in 2011 – a notable difference from other Ugandan regions.

Maternal education and vaccine coverage

Maternal level of education is a powerful predictor of higher full vaccination and completion of the immunization schedule (Mutua et al., 2011; Nankabirwa, Tylleskär, Tumwine, & Sommerfelt, 2010). Our analysis suggests that maternal education, to a larger extent, explains variations of inequity in vaccine coverage, based on the UDHS data. These findings concur with those reported in a systematic review done by Rainey et al., who also reported that low education levels for the maternal caregiver was one of the most commonly identified reasons for under-vaccination (Rainey et al., 2011). Vaccination coverage has been observed by some scholars to have a regressive distribution with respect to maternal education, with relatively higher vaccination coverage among children whose mothers had higher maternal education compared to those whose mothers had lower maternal education (Geweniger & Abbas, 2020; Lakew, Bekele, & Biadgilign, 2015). This therefore suggests that vaccination programs need to give critical attention to those with low formal education (Nankabirwa et al., 2010).

Our findings were also in conformity with a systematic review done in six countries that reported that maternal education inequalities were, in several instances, more profound than household wealth-based inequalities (Acharya, Dharel, Subedi, Bhattarai, & Paudel, 2022). For instance, the systematic review reported that variations in maternal education created much larger inequalities in vaccine coverage as compared to household wealth differences in the countries of Nepal, Afghanistan and Pakistan. It is also important to note that higher levels of maternal education provide opportunity to raise awareness about the importance of vaccination thereby boosting uptake and vaccination coverage (Geweniger & Abbas, 2020). Low maternal education levels could be responsible for the poor attitude and low uptake of immunization services, especially in the regions faced with the large inequities (Mupere et al., 2020).

Other similar studies report that disparities in full immunization coverage varied considerably by maternal education status, from 31% among those with no education at all, to 72% among those with secondary education or a higher level of education (Memirie et al., 2021).

Policymakers and implementing partners need to prioritize policies and interventions to reduce inequities in vaccination coverage, with special focus on improving maternal education, and narrow equity gaps relating to variations in districts, residence type as well as region (between and within) in an effort to improve full vaccination coverage.

Limitations of the analysis

We were not able to triangulate or deepen our findings with additional interviews which would have provided more insights into some of the unexplained variations. For instance, qualitative interviews would have provided some additional information on the system level indicators/bottlenecks that could have influenced the inequities in vaccine coverage that were observed. Furthermore, the UDHS data collected information mainly on caregiver, child, and household related indicators and much less information about the healthcare system level indicators. We attempted to relate/triangulate our findings with literature from similar studies conducted in other settings, in an attempt to bridge this gap.

Significant improvements in both vaccine coverage and equity occurred in the past two decades, thanks to the government and her partners’ efforts to strengthen routine immunization, and the various supplementary immunization activities. The latter, through vaccination campaigns and the Family Health Days started in 2011, improved coverage for DTP3 and MCV1 in several districts. In others, like Karamoja, an opportunity to strengthen routine immunization came sooner with an increased sedentarism, low vaccine coverage and the need to respond to ensuing outbreaks.

Those efforts tackled supply constraints and significantly reduced differences across districts in vaccine coverage or, at least, their contribution to the remaining inequity in coverage. Most vaccines, with the notable exception of MCV1, saw a shift in the determinants of inequity where the factors generally associated with supply-side constraints – district and settings of residence – decreased, while demand-side constraints increased or remained unchanged, thus becoming the main contributor to vaccine coverage inequity. Maternal education by far has a dominant influence on equity, suggesting that developing appropriate public health communication and facilitating access to healthcare for populations with lower literacy levels may have the greatest effect.

The specific opportunities identified in this equity analysis for reducing inequity included the need to target the districts/regions with highest inequity, a focus on increasing maternal education as a fulcrum for boosting vaccine coverage and equity, as well as prioritizing delivery of vaccines to the urban-poor populations. Implementers should also consider expanding the scope of variables collected during immunization coverage surveys to broaden the range of independent variables that can be included during analysis to explain observed variations in inequity to vaccines.

Authors:
AS	Mr. Anthony Ssebagereka
EK	Dr. Elizabeth Ekirapa Kiracho
RM	Dr. Rornald Muhumuza Kananura
AM	Mr. Aloysius Mutebi
BP	Dr. Bryan Patenaude
GD	Mr. Gatien de Broucker
JM	Mr. Joshua Mak

In-text abbreviations:
BCG	Bacille Calmette-Guérin Vaccine
DPT	Diphtheria-Pertussis-Tetanus Vaccine
DPT1	First dose of Diphtheria-Pertussis-Tetanus Vaccine
DPT2	Second dose of Diphtheria-Pertussis-Tetanus Vaccine
DPT3	Third dose of Diphtheria-Pertussis-Tetanus Vaccine
EPI	Expanded Program for Immunization
IPV	Inactivated Polio Vaccine
MCV	Measles-Containing Vaccine
MCV1	First dose of Measles-Containing Vaccine
NGO	Non-Governmental Organization
OPV	Oral Poliovirus Vaccine
PCV	Pneumococcal Conjugate Vaccine
PCV1	First dose of Pneumococcal Conjugate Vaccine
PCV2	Second dose of Pneumococcal Conjugate Vaccine
PCV3	Third dose of Pneumococcal Conjugate Vaccine
POLIO	Poliovirus Vaccine (either IPV or OPV)
POLIO1	First dose of Poliovirus Vaccine (either IPV or OPV)
POLIO2	Second dose of Poliovirus Vaccine (either IPV or OPV)
POLIO3	Third dose of Poliovirus Vaccine (either IPV or OPV)
UDHS	Uganda Demographic and Health Survey
UNEPI	Uganda National Expanded Program for Immunization
VERSE	Vaccine Economics Research for Sustainability and Equity project

ETHICAL APPROVAL AND CONSENT TO PARTICIPATE: Not applicable, the data were derived from public Demographic and Health Survey records.

CONSENT TO PUBLISH: Not applicable.

AVAILABILITY OF DATA AND MATERIALS: The datasets supporting the conclusions of this article are available in the Demographic and Health Surveys repository at https://dhsprogram.com (last accessed on 18 July 2022). The VERSE Equity Toolkit used for the analysis is available in open access:

Patenaude, Bryan, de Broucker, Gatien, Mak, Joshua, Sriudomporn, Salin, Odihi, Deborah. 2022, "Official VERSE Equity Toolkit GitHub Repository to analyze DHS data", https://github.com/VERSE-Equity/Toolkit-DHS, GitHub, last accessed on 18 July 2022.

COMPETING INTERESTS: All co-authors received funding from the Bill & Melinda Gates Foundation to conduct this study.

FUNDING: This article is part of the Vaccine Economics Research for Sustainability and Equity (VERSE) project, funded under a grant by the Bill & Melinda Gates Foundation, Seattle, WA (USA). The project includes secondary data analyses conducted by the International Vaccine Access Center at Johns Hopkins Bloomberg School of Public Health, and Makerere University School of Public Health. In addition to providing funding, the Bill & Melinda Gates Foundation provided feedback and coordination support during the planning phase of the study. The funders did not influence the data collection, analysis and interpretation of the data, the development of this paper, or the decision to submit it for publication.

AUTHORS CONTRIBUTIONS: All authors designed the study, analyzed the data, reviewed the results and wrote this paper. All authors have read and approved the manuscript.

ACKNOWLEDGMENTS: We wish to acknowledge the contribution of Ms. Salin Sriudomporn and Ms. Deborah Odihi to the development of the VERSE Equity Toolkit.

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AppendixEquityinvaccinecoverageinUganda.docx
File name: Supplementary material.docxTitle: Supplementary materialDescription: Supplementary tables and figures featuring all model outputs for 2000, 2006, 2011, and 2016.

Equity in vaccine coverage in Uganda from 2000 to 2016: Revealing the multifaceted nature of inequity.

Status:

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Results

Full immunization for age

Zero-dose status

DPT3 coverage

MCV1 coverage

Discussion

High and low performing regions

Maternal education and vaccine coverage

Limitations of the analysis

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1