Assessing Exposure and Health Consequences of Chemicals in Drinking Water: Current State of Knowledge and Research Needs
Bibliographic record
Abstract
BACKGROUND: Safe drinking water is essential for well-being. Although microbiological contamination remains the largest cause of water-related morbidity and mortality globally, chemicals in water supplies may also cause disease, and evidence of the human health consequences is limited or lacking for many of them. OBJECTIVES: We aimed to summarize the state of knowledge, identify gaps in understanding, and provide recommendations for epidemiological research relating to chemicals occurring in drinking water. DISCUSSION: Assessing exposure and the health consequences of chemicals in drinking water is challenging. Exposures are typically at low concentrations, measurements in water are frequently insufficient, chemicals are present in mixtures, exposure periods are usually long, multiple exposure routes may be involved, and valid biomarkers reflecting the relevant exposure period are scarce. In addition, the magnitude of the relative risks tends to be small. CONCLUSIONS: Research should include well-designed epidemiological studies covering regions with contrasting contaminant levels and sufficient sample size; comprehensive evaluation of contaminant occurrence in combination with bioassays integrating the effect of complex mixtures; sufficient numbers of measurements in water to evaluate geographical and temporal variability; detailed information on personal habits resulting in exposure (e.g., ingestion, showering, swimming, diet); collection of biological samples to measure relevant biomarkers; and advanced statistical models to estimate exposure and relative risks, considering methods to address measurement error. Last, the incorporation of molecular markers of early biological effects and genetic susceptibility is essential to understand the mechanisms of action. There is a particular knowledge gap and need to evaluate human exposure and the risks of a wide range of emerging contaminants. CITATION: Villanueva CM, Kogevinas M, Cordier S, Templeton MR, Vermeulen R, Nuckols JR, Nieuwenhuijsen MJ, Levallois P. 2014. Assessing exposure and health consequences of chemicals in drinking water: current state of knowledge and research needs. Environ Health Perspect 122:213–221; http://dx.doi.org/10.1289/ehp.1206229
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".