Menu
Menu
Menu
Menu
Understanding the gaps in missed opportunities for vaccination (MOV) in sub-Saharan Africa would inform interventions for improving immunisation coverage to achieving universal childhood immunisation. We aimed to conduct a multicountry analyses to decompose the gap in MOV between poor and non-poor in SSA. We used cross-sectional data from 35 Demographic and Health Surveys in SSA conducted between 2007 and 2016. Descriptive statistics used to understand the gap in MOV between the urban poor and non-poor, and across the selected covariates. Out of the 35 countries included in this analysis, 19 countries showed pro-poor inequality, 5 showed pro-non-poor inequality and remaining 11 countries showed no statistically significant inequality. Among the countries with statistically significant pro-illiterate inequality, the risk difference ranged from 4.2% in DR Congo to 20.1% in Kenya. Important factors responsible for the inequality varied across countries. In Madagascar, the largest contributors to inequality in MOV were media access, number of under-five children, and maternal education. However, in Liberia media access narrowed inequality in MOV between poor and non-poor households. The findings indicate that in most SSA countries, children belonging to poor households are most likely to have MOV and that socio-economic inequality in is determined not only by health system functions, but also by factors beyond the scope of health authorities and care delivery system. The findings suggest the need for addressing social determinants of health.
Data for this cross-sectional study were obtained from Demographic and Health Surveys (DHS), which are nationally representative household surveys conducted in low- and middle-income countries. This study used data from 35 recent DHS surveys conducted between 2007 and 2016 in sub-Saharan Africa available as of December 2017. The DHS uses a multi-stage, stratified sampling design with households as the sampling unit.18 Within each sample household, all women and men meeting the eligibility criteria are interviewed. Because the surveys are not self-weighting, weights are calculated to account for unequal selection probabilities as well as for non-response. With weights applied, survey findings represent the full target population. The DHS surveys include a household questionnaire, a women’s questionnaire, and in most countries, a men’s questionnaire. All three DHS questionnaires are implemented across countries with similar interviewer training, supervision, and implementation protocols. We used the World Health Organisation (WHO) definition of missed opportunity for vaccination (MOV) as the outcome variable, categorized as a binary variable that takes the value of ‘1’ if a child aged 12–23 months had any contact with health services who is eligible for vaccination (e.g. unvaccinated or partially vaccinated and free of contraindications to vaccination), which does not result in the child receiving one or more of the vaccine doses for which he or she is eligible, (and ‘0’ if otherwise). Contact with health services were defined using the following six variables: skilled birth attendance, baby postnatal check within 2 months, received vitamin A dose in first 2 months after delivery, has health card and medical treatment of diarrhea/ fever/cough We limited the analysis to one child per woman in order to minimise over-representation of women with more than one child in the age category. DHS did not collect direct information on household income and expenditure. We used DHS wealth index as a proxy indicator for socioeconomic position. The methods used in calculating DHS wealth index have been described elsewhere.19-20 Briefly, an index of economic status for each household was constructed using principal components analysis based on the following household variables: number of rooms per house, ownership of car, motorcycle, bicycle, fridge, television and telephone as well as any kind of heating device. From these criteria the DHS wealth index quintiles (poorest, poorer, middle, richer and richest) are derived. The bottom two quintiles (lower 40%) were considered as ‘poor’ and remaining three were classified as ‘non-poor’. The following factors were included in the models: child’s age, sex of the child (male versus female), high birth order (> 4 birth order), number of under five children in the household, maternal age completed years (15 to 24, 25 to 34, 35 or older), maternal education (no education, primary or secondary or higher), employment status (working or not working), and media access (radio, television or newspaper). The analytical approach included descriptive statistics, univariable analysis and Blinder-Oaxaca decomposition techniques using logistic regressions. We used the descriptive statistics to show the distribution of respondents by the key variables. Values were expressed as absolute numbers (percentages) and mean (standard deviation) for categorical and continuous variables respectively. We calculated the risk difference in missed opportunities between the two groups, from poor or non-poor households. A risk difference greater than 0 suggests that missed opportunities are prevalent among children from poor households (pro-poor inequality). Conversely, a negative risk difference indicates that missed opportunities for vaccination is prevalent among children from non-poor households (pro-non-poor inequality). Finally, we adopted logistic regression method using the pooled cross-sectional data to conduct the Blinder-Oaxaca decomposition analysis. The Blinder-Oaxaca decomposition21-22 is a counterfactual method with an assumption that “what the probability of missed opportunities for vaccination would be if children from poor households had the same characteristics as their non-poor counterparts?”. The Blinder-Oaxaca method allows for the decomposition of the difference in an outcome variable between 2 groups into 2 components. The first component is the “explained” portion of that gap that captures differences in the distributions of the measurable characteristics (referred to as “compositional” or “endowments”) of these groups. This illustrates the portion of the gap in missed opportunities for vaccination that is attributed to the differences in observable, measurable characteristics between the two groups. Using this method, we can quantify how much of the gap the “advantaged” and the “disadvantaged” groups is attributable to these differences in specific measurable characteristics. The second component is the “unexplained” part, meaning the portion of the gap due to the differences in the estimated regression coefficients and the unmeasured variables between the two groups. This is also referred to in the literature as the “structural” component or the “coefficient” portion of the decomposition. This reflects the remainder of the model not explained by the differences in measurable, objective characteristics. The “unexplained” portion arises from differentials in how the predictor variables are associated with the outcomes for the two groups. This portion would persist even if the disadvantaged group were to attain the same average levels of measured predictor variables as the advantaged group.
