Arsenic exposure and young adult's mortality risk: A 13-year follow-up study in Matlab, Bangladesh
Bibliographic record
Abstract
BACKGROUND: Widespread arsenic contamination in underground water is a well-documented public health concern that threatens millions of lives worldwide. We investigated the risk of young-adult mortality due to high chronic exposure to arsenic through years of drinking arsenic contaminated water. METHODS: A prospective cohort study of 58,406 individuals was enrolled who were 4-18 years at baseline. Since Matlab HDSS (Health and Demographic Surveillance System) has an active surveillance system, all individuals were included in the follow up. Each individual's arsenic exposure was calculated at (1) baseline As level as current exposure (2) time-weighted lifetime (average or lifetime average) and (3) cumulative arsenic exposure. Age, sex, educational attainment and SES were adjusted during the analysis. In this 13 years closed-cohort study (2003-2015), all young-adult deaths were captured through verbal autopsy (VA) using International Classification of Diseases (ICD-10) to define the causes. RESULTS: Although, girls had higher values of cumulative arsenic exposure via tube well water than boys (median: 1858.5 μg/year/L vs. 1798.8 μg/year/L) but higher mortality due to cancers and due to cerebro-vascular disease, cardio-vascular disease, and respiratory disease (7.0 vs. 5.7 per 100,000 person-years and 6.4 vs. 4.2 per 100,000 person-years respectively). Higher risk of deaths among young adults (Adjusted HR: 2.7, 1.3-5.8) due to all cancers among those who were exposed to As > 138.7 compared to As ≤ 1.1 μg/L. For cerebro-vascular disease, cardio-vascular disease, and respiratory disease deaths, average arsenic in well water (>223.1 μg/L vs. ≤90.9 μg/L) and cumulative arsenic in well water (>2711.0 μg/year/L vs. ≤1013.3 μg/year/L) had 4.8 (1.8-12.8) and 5.1 (1.7-15.1) times higher risks of mortality than to those lowest exposed. CONCLUSION: Higher concentration of, and chronic exposure to arsenic in drinking water, increases the mortality risk among the young adults, regardless of gender.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".