Drinking Water Arsenic and Adverse Reproductive Outcomes in Men and Women: A Systematic PRISMA Review
Bibliographic record
Abstract
Infertility is a worldwide health issue, but mechanisms of both male and female reproductive toxicity remain to be elucidated. So far, a limited focus has been on potentially harmful environmental factors such as arsenic, which is naturally occurring in groundwater. The objective of this review was to systematically investigate the association between arsenic in drinking water and adverse reproductive outcomes in men and women of fertile age. We conducted a systematic literature search and included case-control studies and cohort studies reporting on decreased semen quality characteristics, increased time to pregnancy, infertility, or spontaneous abortion. In total, 433 articles were screened and ultimately, eight studies were included. Included literature was quality assessed with the Newcastle-Ottawa Scale. Findings were reported in a narrative synthesis. Only one study investigated male fertility. An association between increasing arsenic exposure and decreasing semen quality characteristics was found, as well as an indication of arsenic accumulation in seminal plasma. These findings are, however, at high arsenic levels (>1000 µg/L). No consistent evidence was found to support the hypothesis that arsenic exposure from drinking water is a cause of longer waiting time to pregnancy or spontaneous abortion, being the only endpoints investigated in the included literature. In conclusion; the evidence is sparse and of varying quality, however, it does warrant attention, as it conflicts with existing evidence, mainly from cross-sectional or ecologic studies.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".