Testing the socioeconomic and environmental determinants of better child-health outcomes in Africa: a cross-sectional study among nations
Bibliographic record
Abstract
OBJECTIVE: We sought to test hypotheses regarding the principal correlates of child-health performance among African nations based on previous evidence collected at finer spatial scales. DESIGN: Retrospective, cross-sectional study. SETTING: All countries in Africa, excluding small-island nations. PRIMARY AND SECONDARY OUTCOME MEASURES: We defined a composite child-health indicator for each country comprising the incidence of stunting, deaths from respiratory disease, deaths from diarrhoeal disease, deaths from other infectious disease and deaths from injuries for children aged under 5 years. We also compiled national-level data for Africa to test the effects of country-level water quality, air pollution, food supply, breast feeding, environmental performance, per capita wealth, healthcare investment, population density and governance quality on the child-health indicator. RESULTS: Across nations, child health was lowest when water quality, improved sanitation, air quality and environmental performance were lowest. There was also an important decline in child health as household size (a proxy for population density) increased. The remaining variables had only weak effects, but in the directions we hypothesised. CONCLUSIONS: These results emphasise the importance of continued investment in clean water and sanitation services, measures to improve air quality and efforts to restrict further environmental degradation, to promote the UN's Sustainable Development Goal 3 target to '… end preventable deaths of newborns and children under 5' and Goal 6 to '… ensure access to water and sanitation for all' by 2030.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".