Zinc in Well Water and Infant Mortality in Bangladesh: A Report from Gonoshasthaya Kendra
Bibliographic record
Abstract
Zinc supplementation reduces the duration, severity and recurrence of diarrhoea in young children. This study examines whether zinc, found naturally in drinking water, reduced infant deaths from diarrhoea in rural Bangladesh. Information was compiled for births over two calendar years with follow-up for deaths within one year of birth. The study included 29,744 live births and 934 deaths in some 600 villages under the care of Gonoshasthaya Kendra (GK), grouped into 15 health centre regions within 12 upazillas. Individual matching of death to birth data was not possible, but information on exposures through well water and on potential confounders was available for each upazilla. Average concentration of zinc in well water, reported by the British Geological Survey, was grouped into high (>0.07 mg/L), moderate (0.020-0.070 mg/L) and low (<0.020 mg/L) concentrations. Odds ratios (OR) were calculated for zinc by age and cause of death. Zinc concentration was unrelated to all-cause mortality but a decrease in deaths from diarrhoea (N = 50) was seen in areas with high zinc (OR = 0.30; 95% CI 0.13-0.69). No relation to diarrhoeal deaths was found with other well contaminants (arsenic, manganese) having accounted for zinc. Upazillas with a high proportion of women without education had higher rates of death from diarrhea, but the decrease in risk with high zinc remained (OR adjusted = 0.41; 95% CI 0.20-0.84). It is concluded that exposure to zinc through drinking water may reduce risk of diarrhoeal deaths.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".