Water, sanitation and hygiene interventions for acute childhood diarrhea: a systematic review to provide estimates for the Lives Saved Tool
Bibliographic record
Abstract
BACKGROUND: In the Sustainable Development Goals (SDGs) era, there is growing recognition of the responsibilities of non-health sectors in improving the health of children. Interventions to improve access to clean water, sanitation facilities, and hygiene behaviours (WASH) represent key opportunities to improve child health and well-being by preventing the spread of infectious diseases and improving nutritional status. METHODS: We conducted a systematic review of studies evaluating the effects of WASH interventions on childhood diarrhea in children 0-5 years old. Searches were run up to September 2016. We screened the titles and abstracts of retrieved articles, followed by screening of the full-text reports of relevant studies. We abstracted study characteristics and quantitative data, and assessed study quality. Meta-analyses were performed for similar intervention and outcome pairs. RESULTS: Pooled analyses showed diarrhea risk reductions from the following interventions: point-of-use water filtration (pooled risk ratio (RR): 0.47, 95% confidence interval (CI): 0.36-0.62), point-of-use water disinfection (pooled RR: 0.69, 95% CI: 0.60-0.79), and hygiene education with soap provision (pooled RR: 0.73, 95% CI: 0.57-0.94). Quality ratings were low or very low for most studies, and heterogeneity was high in pooled analyses. Improvements to the water supply and water disinfection at source did not show significant effects on diarrhea risk, nor did the one eligible study examining the effect of latrine construction. CONCLUSIONS: Various WASH interventions show diarrhea risk reductions between 27% and 53% in children 0-5 years old, depending on intervention type, providing ample evidence to support the scale-up of WASH in low and middle-income countries (LMICs). Due to the overall low quality of the evidence and high heterogeneity, further research is required to accurately estimate the magnitude of the effects of these interventions in different contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".