Mind the Gap: What explains the education-related inequality in missed opportunities for vaccination in sub-Saharan Africa? Compositional and structural characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".