Decomposing the gap in missed opportunities for vaccination between poor and non-poor in sub-Saharan Africa: A Multicountry Analyses
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".