Association of Arsenic with Adverse Pregnancy Outcomes/Infant Mortality: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Exposure to arsenic is one of the major global health problems, affecting > 300 million people worldwide, but arsenic's effects on human reproduction are uncertain. OBJECTIVES: We conducted a systematic review and meta-analysis to examine the association between arsenic and adverse pregnancy outcomes/infant mortality. METHODS: We searched PubMed and Ovid MEDLINE (from 1946 through July 2013) and EMBASE (from 1988 through July 2013) databases and the reference lists of reviews and relevant articles. Studies satisfying our a priori eligibility criteria were evaluated independently by two authors. RESULTS: Our systematic search yielded 888 articles; of these, 23 were included in the systematic review. Sixteen provided sufficient data for our quantitative analysis. Arsenic in groundwater (≥ 50 μg/L) was associated with increased risk of spontaneous abortion (6 studies: OR = 1.98; 95% CI: 1.27, 3.10), stillbirth (9 studies: OR = 1.77; 95% CI: 1.32, 2.36), moderate risk of neonatal mortality (5 studies: OR = 1.51; 95% CI: 1.28, 1.78), and infant mortality (7 studies: OR = 1.35; 95% CI: 1.12, 1.62). Exposure to environmental arsenic was associated with a significant reduction in birth weight (4 studies: β = -53.2 g; 95% CI: -94.9, -11.4). There was paucity of evidence for low-to-moderate arsenic dose. CONCLUSIONS: Arsenic is associated with adverse pregnancy outcomes and infant mortality. The interpretation of the causal association is hampered by methodological challenges and limited number of studies on dose response. Exposure to arsenic continues to be a major global health issue, and we therefore advocate for high-quality prospective studies that include individual-level data to quantify the impact of arsenic on adverse pregnancy outcomes/infant mortality.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".