Arsenic in drinking water and lung cancer: A systematic review of 35 years of evidence
Bibliographic record
Abstract
The association between higher arsenic concentrations in drinking water and lung cancer is well-established. However, the risk associated with lower levels of arsenic exposure remains uncertain. This systematic review and meta-analysis summarizes the evidence on the relationship between exposure to arsenic in drinking water and lung cancer outcomes as measured over a broad range of exposures, including lower levels. A total of 51 studies were included in the review and 15 met criteria for inclusion in meta-analysis. Risk estimates for lung cancer incidence and mortality were pooled and analyzed separately using Bayesian hierarchical random-effects models with a Gaussian observation submodel for log(Risk), computed using the "brms" R package. For lung cancer incidence, the predicted posterior mean relative risks (RRs) at arsenic concentrations of 10, 50 and 150 μg/L were 1.11 (0.86-1.43), 1.67 (1.27-2.17) and 2.21 (1.61-3.02), respectively, with posterior probabilities of 79%, 100% and 100%, respectively, for the RRs to be >1. The posterior mean mortality ratios at 20, 50 and 150 μg/L were 1.22 (0.83-1.78), 2.10 (1.62-2.71) and 2.41 (1.88-3.08), respectively, with posterior probabilities being above 80%. In addition to observing the dose-response relationship, these findings demonstrate that individuals exposed to low to moderate levels of arsenic (<150 μg/L) were at an elevated risk of developing or dying from lung cancer. Given the widespread exposure to lower levels of arsenic, there is an urgent need for vigilance and potential revisions to regulatory guidelines to protect people from the cancer risks associated with arsenic exposure.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".