Risk of bladder and kidney cancer from exposure to low levels of arsenic in drinking water, Nova Scotia – Canada
Bibliographic record
Abstract
Introduction: Arsenic (As) in drinking water affects the health of hundreds of millions of people worldwide. Although West Bengal, Bangladesh and Taiwan are among the most affected regions, with As concentration as high as 4,700 μg/L and where levels >300 μg/L are common, As contamination of well water is also prevalent across the US and Canada. Studies of populations exposed to such high levels show strong associations and dose-response relationships with As in drinking water and a wide range of illness, including, skin, lung, bladder, and kidney cancer. The threshold at which these diseases develop is uncertain at lower levels of exposure. Recent evidence suggests possible health effects at levels as low as 10 μg/L, current World Health Organization advisory limit. This study models the risk of developing bladder/kidney cancer in those exposed to As around current guideline levels. Methods: A Bayesian spatial autoregressive model was used to model risk at 3 levels of As exposure (0–2 μg/L; 2–5 μg/L; >5 μg/L—based on 10,498 private well samples) in 864 bladder and 525 kidney cancer cases diagnosed in Nova Scotia Canada, between 1998-2010. The model accounted for spatial dependencies and included covariates (e.g. smoking proxies). Results: The risk of developing bladder cancer (both sex) was on average 16 % (2–5 µg/L) and 18 % (> 5 µg/L) greater than that of the referent group (< 2 µg/L), with associated posterior probabilities of 0.88 and 0.93 for these risks being greater than the reference rate. Effect sizes for kidney cancer were lower; the risk being on average 5 % (2–5 µg/L) and 14 % (> 5 µg/L) greater than that of the referent group, with associated probabilities of 0.66 and 0.87. Stratified analyses by sex, showed larger posterior probabilities for an increased risk amongst males– a result likely due to low female case counts. Conclusion: This study provides evidence for health effects at low level of As exposure in drinking water.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".