Association between maternal exposure to oil and gas extraction process with adverse birth outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Oil and gas extraction plays a critical role in global energy supply and economic development, but it is increasingly associated with adverse health outcomes, particularly during pregnancy. This systematic review and meta-analysis evaluates the relationship between maternal exposure to oil and gas pollutants and selected adverse birth outcomes. METHODS: A comprehensive literature search was conducted across MEDLINE/PubMed, Scopus, CINAHL, Web of Science, and the Cochrane Library up to June 16, 2024. We included studies assessing the effects of maternal exposure to oil and gas extraction processes on preterm birth (PTB), miscarriage, stillbirth, birth defects, small for gestational age (SGA), low birth weight (LBW), and birth weight (BW). Study quality was assessed using the Newcastle-Ottawa Scale and GRADE framework. From 4,235 screened articles, 24 studies met inclusion criteria. RESULTS: Our analyses revealed statistically significant associations between maternal exposure and increased odds of PTB (pooled OR: 1.07, 95% CI: 1.01-1.13), miscarriage (OR: 2.03, 95% CI: 1.60-2.58), SGA (OR: 1.23, 95% CI: 1.05-1.45), and reduced BW (mean difference: -30.36 g, 95% CI: -43.98 to -17.28). No significant associations were observed for stillbirth (OR: 0.98, 95% CI: 0.68-1.40), birth defects (OR: 1.14, 95% CI: 0.84-1.53), or LBW (OR: 1.07, 95% CI: 0.93-1.24). Substantial heterogeneity was present across most outcomes, and publication bias could not be ruled out in several analyses. CONCLUSIONS: These findings suggest possible associations between maternal exposure to oil and gas extraction processes and several adverse birth outcomes, including PTB, miscarriage, SGA, and LBW. However, due to methodological variability, potential biases, and high heterogeneity among studies, these results should be interpreted with caution. Further research with standardized exposure assessments and larger, population-based cohorts is needed to confirm and refine these associations.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".