Children with access to improved sanitation but not improved water are at lower risk of stunting compared to children without access: a cohort study in Ethiopia, India, Peru, and Vietnam
Bibliographic record
Abstract
BACKGROUND: This study's purpose was to understand associations between water, sanitation, and child growth. METHODS: We estimated stunting (height-for-age Z score <-2 SD) and thinness (BMI-Z <-2 SD) risk ratios using data from 7,715 Ethiopian, Indian, Peruvian, and Vietnamese children from the Young Lives study. RESULTS: In unadjusted models, household access to improved water and toilets was often associated with reduced stunting risk. After adjusting for child, household, parent, and community variables, access to improved water was usually not associated with stunting nor thinness except in Ethiopia where access to improved water was associated with reduced stunting and thinness at 1y and 5y. In contrast, in both unadjusted and adjusted models, stunting at 1y was less common among children with good toilet access than among those without access and this difference persisted when children were 5y and 8y. For example, in adjusted estimates, Vietnamese 5y olds with access to improved toilets had relative stunting risk at 8y 0.62-0.68 that of 5y olds with no access to improved toilets. Water and toilets were rarely associated with thinness. CONCLUSIONS: Results from our study indicate that access to improved sanitation is more frequently associated with reduced stunting risk than access to improved water. However, additional studies are needed before drawing definitive conclusions about the impact of toilets relative to water. This study is the first to our knowledge to demonstrate the robust and persistent importance of access to improved toilets in infancy, not only during the first year but continuing into childhood. Additional longitudinal investigations are needed to determine concurrent and long-term associations of WASH with stunting and thinness.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".