Estimated burden of disease from arsenic in drinking water supplied by domestic wells in the United States
Bibliographic record
Abstract
Well water around the world can be contaminated with arsenic, a naturally occurring geological element that has been associated with myriad adverse health effects. Persons obtaining their drinking water from private wells are often responsible for well testing and water treatment. High levels of arsenic have been reported in well water-supplied areas of the United States. We quantified - in cases and dollars - the potential burden of disease associated with the ingestion of arsenic through private well drinking water supplies in the United States. To estimate cancer and cardiovascular disease burden, we developed a Monte Carlo model integrating three input streams: (1) regional concentrations of arsenic in drinking water wells across the United States; (2) dose-response relationships in the form of cancer slope factors and hazard ratios; and (3) economic cost estimates developed for morbidity endpoints using 'cost-of-illness' methods and for mortality using 'value per statistical life' estimates. Exposure to arsenic in drinking water from U.S. domestic wells is modeled to contribute 500 annual premature deaths from ischemic heart disease and 1,000 annual cancer cases (half of them fatal), monetized at $10.9 billion (2017 USD) annually. These considerable public health burden estimates can be compared with the burdens of other priority public health issues to assist in decision-making.
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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 | 0.001 |
| 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) | 0.002 | 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".