The Association between Water, Sanitation, Hygiene, and Child Underweight in Punjab, Pakistan: An Application of Population Attributable Fraction
Bibliographic record
Abstract
Background: Access to safe drinking water, sanitation, and hygiene (WASH) facilities is crucial for health and human rights, impacting nutrition and weight. Methods: Multiple Indicators Cluster Survey (MICS) 2017-18 has been used in this study to examine the association between WASH and underweight, alongside other factors. Analysis included descriptive statistics, association tests, logistic regression, and population-attributable fractions (PAF). Results: According to results child were 1.8, 1.1 and 1.04 times less likely to be underweight if they had access to improved source of drinking water, improved sanitation and hygiene facilities respectively. The likelihood of child being underweight reduces by 1.4, 1.89, 2.01 and 2.55 times if the household wealth status increases from poorest to second, middle, fourth and richest wealth quintiles, respectively. As the mothers' education level increases from no schooling to primary, middle, secondary, and higher level, the possibility of child being underweight reduces by 1.22, 1.24, 1.60 and 2.01 times, respectively. Moreover, the likelihood of a child being underweight decreases as the education level of the household head improves. If maternal age is less than 20 or more than 35 years the likelihood of the child being underweight is increased by 1.074 and 1.121 times, respectively. A child is 1.1 times more likely to be underweight if birth spacing is less than 2 years. A child's risk of being underweight decreases by 1.1 times if they have not experienced diarrhea. A child who has never been breastfed has 1.3 times higher risk of being underweight. The results of Population Attributable Fraction (PAF) indicate that holding the other factors constant, approximately 36.46% burden of underweight was preventable by access to improved drinking water, sanitation, and hygiene practices. Conclusion: Comprehensive strategy is needed that focuses on improving access to safe drinking water, sanitation infrastructure, and hygiene behaviors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".