Inequalities in full vaccination coverage based on maternal education and wealth quintiles among children aged 12–23 months: further analysis of national cross-sectional surveys of six South Asian countries
Bibliographic record
Abstract
OBJECTIVE: This study was conducted to compare full vaccination coverage and its inequalities (by maternal education and household wealth quintile). DESIGN: This further analysis was based on the data from national-level cross-sectional Demographic and Health Survey (DHS) from six countries in South Asia. SETTING: We used most recent DHS data from six South Asian countries: Nepal, India, Pakistan, Bangladesh, Afghanistan and the Maldives. The sample size of children aged 12-23 months ranged from 6697 in the Maldives to 628 900 in India. PRIMARY AND SECONDARY OUTCOME MEASURES: To measure absolute and relative inequalities of vaccination coverage, we used regression-based inequality measures, slope index of inequality (SII) and the relative index of inequality (RII), respectively, by maternal education and wealth quintile. RESULTS: Full vaccination coverage was the highest in Bangladesh (84%) and the lowest in Afghanistan (46%), with an average of 61.5% for six countries. Pakistan had the largest inequalities in coverage both by maternal education (SII: -50.0, RII: 0.4) and household wealth quintile (SII: -47.1, RII: 0.5). Absolute inequalities were larger by maternal education compared with wealth quintile in four of the six countries. The relative index of inequality by maternal education was lower in Pakistan (0.5) and Afghanistan (0.5) compared with Nepal (0.7), India (0.7) and Bangladesh (0.7) compared with rest of the countries. By wealth quintiles, RII was lower in Pakistan (0.5) and Afghanistan (0.6) and higher in Nepal (0.9) and Maldives (0.9). CONCLUSIONS: The full vaccination coverage in 12-23 months old children was below 85% in all six countries. Inequalities by maternal education were more profound than household wealth-based inequalities in four of six countries studied, supporting the benefits of maternal education to improve child health outcome.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".