Wealth-related inequalities in the coverage of reproductive, maternal, newborn and child health interventions in 36 countries in the African Region
Bibliographic record
Abstract
OBJECTIVE: To investigate whether sub-Saharan African countries have succeeded in reducing wealth-related inequalities in the coverage of reproductive, maternal, newborn and child health interventions. METHODS: We analysed survey data from 36 countries, grouped into Central, East, Southern and West Africa subregions, in which at least two surveys had been conducted since 1995. We calculated the composite coverage index, a function of essential maternal and child health intervention parameters. We adopted the wealth index, divided into quintiles from poorest to wealthiest, to investigate wealth-related inequalities in coverage. We quantified trends with time by calculating average annual change in index using a least-squares weighted regression. We calculated population attributable risk to measure the contribution of wealth to the coverage index. FINDINGS: We noted large differences between the four regions, with a median composite coverage index ranging from 50.8% for West Africa to 75.3% for Southern Africa. Wealth-related inequalities were prevalent in all subregions, and were highest for West Africa and lowest for Southern Africa. Absolute income was not a predictor of coverage, as we observed a higher coverage in Southern (around 70%) compared with Central and West (around 40%) subregions for the same income. Wealth-related inequalities in coverage were reduced by the greatest amount in Southern Africa, and we found no evidence of inequality reduction in Central Africa. CONCLUSION: Our data show that most countries in sub-Saharan Africa have succeeded in reducing wealth-related inequalities in the coverage of essential health services, even in the presence of conflict, economic hardship or political instability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".