An ecological quantification of the relationships between water, sanitation and infant, child, and maternal mortality
Bibliographic record
Abstract
BACKGROUND: Water and sanitation access are known to be related to newborn, child, and maternal health. Our study attempts to quantify these relationships globally using country-level data: How much does improving access to water and sanitation influence infant, child, and maternal mortality? METHODS: Data for 193 countries were abstracted from global databases (World Bank, WHO, and UNICEF). Linear regression was used for the outcomes of under-five mortality rate and infant mortality rate (IMR). These results are presented as events per 1000 live births. Ordinal logistic regression was used to compute odds ratios for the outcome of maternal mortality ratio (MMR). RESULTS: Under-five mortality rate decreased by 1.17 (95%CI 1.08-1.26) deaths per 1000, p < 0.001, for every quartile increase in population water access after adjustments for confounders. There was a similar relationship between quartile increase of sanitation access and under-five mortality rate, with a decrease of 1.66 (95%CI 1.11-1.32) deaths per 1000, p < 0.001. Improved water access was also related to IMR, with the IMR decreasing by 1.14 (95%CI 1.05-1.23) deaths per 1000, p < 0.001, with increasing quartile of access to improved water source. The significance of this relationship was retained with quartile improvement in sanitation access, where the decrease in IMR was 1.66 (95%CI 1.11-1.32) deaths per 1000, p < 0.001. The estimated odds ratio that increased quartile of water access was significantly associated with increased quartile of MMR was 0.58 (95%CI 0.39-0.86), p = 0.008. The corresponding odds ratio for sanitation was 0.52 (95%CI 0.32-0.85), p = 0.009, both suggesting that better water and sanitation were associated with decreased MMR. CONCLUSIONS: Our analyses suggest that access to water and sanitation independently contribute to child and maternal mortality outcomes. If the world is to seriously address the Millennium Development Goals of reducing child and maternal mortality, then improved water and sanitation accesses are key strategies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".