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Missed opportunities for vaccination (MOV) is an important barrier hindering full immunisation coverage among eligible children. Though factors responsible for MOV are well documented in literature, little attention has been paid to the role of inequalities. The aim of this study is to examine the association between structural or compositional factors and education inequalities in MOV. Blinder-Oaxaca decomposition technique was used to explain the factors contributing to the average gap in missed opportunities for vaccination between uneducated and educated mothers in sub-Saharan Africa using DHS survey data from 35 sub Saharan African countries collected between 2007 and 2016. The sample contained 69,657 children aged 12 to 23 months. We observed a wide variation and inter-country differences in the prevalence of missed opportunity for vaccination across populations and geographical locations. Our results show that the prevalence of MOV in Zimbabwe among uneducated and educated mothers was 9% and 21% respectively while in Gabon corresponding numbers were 85% and 89% respectively. In 15 countries, MOV was significantly prevalent among children born to uneducated mothers (pro-illiterate inequality) while in 5 countries MOV was significantly prevalent among educated mothers (pro-educated inequality). Our results suggest that education-related inequalities in missed opportunities for vaccination are explained by compositional and structural characteristics; and that neighbourhood socio-economic status was the most important contributor to education-related inequalities across countries followed by either the presence of under-five children, media access or household wealth index. The results showed that differential effects such as neighbourhood socio-economic status, presence of under-five children, media access and household wealth index, primarily explained education-related inequality in MOV. Interventions to reduce gaps in education-related inequality in MOV should focus on social determinants of health.
This study included data from 35 recent Demographic and Health Surveys (DHS) surveys conducted between 2007 and 2016 in sub-Saharan Africa available as of December 2017. DHS data collected every five years in low- and middle-income countries are nationally representative multi-stage, stratified sampling designs with households as the sampling unit.14 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 populations. 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 WHO definition of MOV as the outcome variable. It is defined as a binary variable that takes the value of 1 if the child 12–23 months had any contact with health services who is eligible for vaccination but does not result in the child receiving one or more of the vaccine doses for which he or she is eligible. Contact with health services is 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 a health card and medical treatment of diarrhoea/ fever/cough. Maternal education was categorized as no formal education or educated (at least completed primary education). The following individual-level factors were included in the models: child’s age, sex of the child (male versus female), birth order, number of under five children in the household, maternal age in completed years (15 to 24, 25 to 34, 35 to 49), occupation (working or not working), and media access (radio, television or newspaper). 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.15,16 An index of economic status for each household were 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) were calculated and used in the subsequent modelling. We used the term neighbourhood to describe clustering within the same geographical living environment. Neighbourhoods were based on sharing a common primary sample unit within the DHS data. The sampling frame for identifying primary sample unit in the DHS is usually the most recent census. This unit of analysis was chosen for two reasons. First, primary sample unit is the most consistent measure of neighbourhood across all the surveys,17 and thus the most appropriate identifier of neighbourhood for this cross-region comparison. Second, for most of the DHS conducted, the sample size per cluster meets the optimum size with a tolerable precision loss.18 We considered neighbourhood socioeconomic disadvantage as a community-level variable in this study. Neighbourhood socioeconomic disadvantage was operationalized with a principal component comprised of the proportion of respondents with: no education (illiterate), unemployed, rural resident, and living below the poverty level (asset index below 20% poorest quintile). A standardized score with mean score of 0 and standard deviation 1 was generated from this index; with higher scores indicative of lower social economic position and vice versa. We divided the resultant scores into five quintiles to allow for nonlinear effects and to enable us provide results that were more readily interpretable in the policy arena. The analytical approach included descriptive statistics, univariable analysis and Blinder-Oaxaca decomposition techniques using logistic regressions. We used descriptive statistics to show the distribution of respondents by the key variables. Values were expressed as absolute numbers (percentages) and means (standard deviation) for categorical and continuous variables respectively. We calculated the risk difference in missed opportunities between the two groups, children born to uneducated or educated mothers. A risk difference greater than 0 suggests that missed opportunities are prevalent among children born to uneducated mothers (pro-illiterate inequality). Conversely, a negative risk difference indicates that missed opportunities for vaccination are prevalent among children born to educated mothers (pro-educated inequality). Finally, we adopted logistic regression method using the pooled cross-sectional data to conduct the Blinder-Oaxaca decomposition analysis. The Blinder-Oaxaca decomposition19,20 was a counterfactual method with an assumption that children born to uneducated mothers had the same characteristics as their educated counterparts. The Blinder-Oaxaca method allows for the decomposition of the differences 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 the “compositional” or “endowments”) of these groups. Using this method, we can quantify how much of the gap between the “advantaged” and the “disadvantaged” groups is attributable to differences in specific measurable characteristics. The second component is the “unexplained” part, or structural component which captures the gap due to the differences in the regression coefficients and the unmeasured variables between the two groups. 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.
