Improving Water, Sanitation and Hygiene Practices, and Housing Quality to Prevent Diarrhea among Under-Five Children in Nigeria
Bibliographic record
Abstract
Sub-Saharan Africa as a region accounts for the bulk of the global under-five mortality rate, to which diarrhea is major contributor. Millions of children die from diarrheal diseases each year and those who survive often do so facing suboptimal growth. Preventing the common pathways of transmission for diarrhea-causing pathogens, including improved water, sanitation, and hygiene (WASH) are regarded as the most cost-effective measures for tackling this life-threatening disease. This study aimed to quantitatively assess the quality of living arrangement and access to WASH, and their impact on diarrheal outcomes among under-five children in Nigeria. Methods: Data were collected from the 2013 Nigeria Demographic and Health survey (NDHS). Study participants included 28,596 mother-child pairs. Household construction material for wall, floor, and ceiling, access to electricity, and improved water and toilet, were included as the main explanatory variables. Data were analyzed using descriptive and multivariable regression methods. Results: The prevalence of diarrhea was 11.3% (95% CI = 10.2–12.6), with the rate being markedly higher in rural (67.3%) as compared to urban areas (32.7%). In the regression analysis, lacking access to improved toilet and water facilities were associated with 14% and 16% higher odds, respectively, of suffering from diarrhea as compared to those who had improved access. Conclusion: There is evidence of a weak, but statistically significant, relationship between the quality of living environment, including water and sanitation facilities, and diarrhea among under-five children in Nigeria. The study concludes that investing in living conditions and WASH may have potential benefits for child mortality prevention programs in the country.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".