Trihalomethanes in Drinking Water and Bladder Cancer Burden in the European Union
Bibliographic record
Abstract
BACKGROUND: Trihalomethanes (THMs) are widespread disinfection by-products (DBPs) in drinking water, and long-term exposure has been consistently associated with increased bladder cancer risk. OBJECTIVE: We assessed THM levels in drinking water in the European Union as a marker of DBP exposure and estimated the attributable burden of bladder cancer. METHODS: We collected recent annual mean THM levels in municipal drinking water in 28 European countries (EU28) from routine monitoring records. We estimated a linear exposure-response function for average residential THM levels and bladder cancer by pooling data from studies included in the largest international pooled analysis published to date in order to estimate odds ratios (ORs) for bladder cancer associated with the mean THM level in each country (relative to no exposure), population-attributable fraction (PAF), and number of attributable bladder cancer cases in different scenarios using incidence rates and population from the Global Burden of Disease study of 2016. RESULTS: [standard deviation (SD) of 11.2]. The estimated bladder cancer PAF was 4.9% [95% confidence interval (CI): 2.5, 7.1] overall (range: 0-23%), accounting for 6,561 (95% CI: 3,389, 9,537) bladder cancer cases per year. Denmark and the Netherlands had the lowest PAF (0.0% each), while Cyprus (23.2%), Malta (17.9%), and Ireland (17.2%) had the highest among EU26. In the scenario where no country would exceed the current EU mean, 2,868 (95% CI: 1,522, 4,060; 43%) annual attributable bladder cancer cases could potentially be avoided. DISCUSSION: Efforts have been made to reduce THM levels in the European Union. However, assuming a causal association, current levels in certain countries still could lead to a considerable burden of bladder cancer that could potentially be avoided by optimizing water treatment, disinfection, and distribution practices, among other possible measures. https://doi.org/10.1289/EHP4495.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".