Arsenic in Drinking Water and Urinary Tract Cancers: A Systematic Review Update
Bibliographic record
Abstract
Problem: There remains uncertainty around cancer risk at lower levels of arsenic in drinking water. This study updates evidence from our previous review on the relationship between arsenic in drinking water and urinary bladder and kidney cancers (updated search January 2013 to February 2023). Method: Thirty-four studies were retained for review; six met criteria for inclusion in meta-analysis. Risk estimates for bladder and kidney cancer incidence and mortality were analyzed separately using Bayesian multilevel linear models. Results: For bladder cancer incidence, the estimated posterior mean relative risks (RRs) were 1.25 (0.92–1.73), 2.11 (1.18–4.22) and 3.01 (1.31–8.17) at arsenic concentrations of 10, 50 and 150 μg/L, respectively, with posterior probabilities of 92%, 99% and 100%, respectively, for the RRs to be >1. The corresponding RRs for kidney cancer were 1.37 (1.07–1.77), 1.95 (1.44–2.65) and 2.47 (1.74–3.52), with posterior probabilities of 100%. For bladder cancer, the posterior mean mortality ratios were 1.36 (0.35–6.39), 2.92 (1.24–7.82) and 4.88 (2.83–9.03) with posterior probabilities of 72%, 99% and 100%, respectively. Conclusions: The findings show increased bladder and kidney cancer risks at lower levels of arsenic in drinking water. Given that many people worldwide are exposed to lower levels of arsenic in drinking water, the public health impacts are substantial.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".