Association between water, sanitation and hygiene (WASH) and child undernutrition in Ethiopia: a hierarchical approach
Bibliographic record
Abstract
BACKGROUND: Undernutrition is a significant public health challenge and one of the leading causes of child mortality in a wide range of developing countries, including Ethiopia. Poor access to water, sanitation, and hygiene (WASH) facilities commonly contributes to child growth failure. There is a paucity of information on the interrelationship between WASH and child undernutrition (stunting and wasting). This study aimed to assess the association between WASH and undernutrition among under-five-year-old children in Ethiopia. METHODS: A secondary data analysis was undertaken based on the Ethiopian Demographic and Health Surveys (EDHS) conducted from 2000 to 2016. A total of 33,763 recent live births extracted from the EDHS reports were included in the current analysis. Multilevel logistic regression models were used to investigate the association between WASH and child undernutrition. Relevant factors from EDHS data were identified after extensive literature review. RESULTS: The overall prevalences of stunting and wasting were 47.29% [95% CI: (46.75, 47.82%)] and 10.98% [95% CI: (10.65, 11.32%)], respectively. Children from households having unimproved toilet facilities [AOR: 1.20, 95% CI: (1.05,1.39)], practicing open defecation [AOR: 1.29, 95% CI: (1.11,1.51)], and living in households with dirt floors [AOR: 1.32, 95% CI: (1.12,1.57)] were associated with higher odds of being stunted. Children from households having unimproved drinking water sources were significantly less likely to be wasted [AOR: 0.85, 95% CI: (0.76,0.95)] and stunted [AOR: 0.91, 95% CI: (0.83, 0.99)]. We found no statistical differences between improved sanitation, safe disposal of a child's stool, or improved household flooring and child wasting. CONCLUSION: The present study confirms that the quality of access to sanitation and housing conditions affects child linear growth indicators. Besides, household sources of drinking water did not predict the occurrence of either wasting or stunting. Further longitudinal and interventional studies are needed to determine whether individual and joint access to WASH facilities was strongly associated with child stunting and wasting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".