Maternal exposure to air pollution and risk of autism in children: A systematic review and meta-analysis
Bibliographic record
Abstract
The number of children diagnosed with autism spectrum disorder (ASD) has been increasing. Previous studies suggested potential association between pregnancy air pollution exposure and ASD. This systematic review and meta-analysis is intended to summarize the association between maternal exposure to outdoor air pollution and ASD in children by trimester based on recent studies. A systematic literature search in 3 databases (Medline, Embase, and Web of Science) was performed using subject headings related to ASD and air pollution since 2007. Eligible studies were screened and evaluated based on predetermined criteria. For meta-analyses, the studies were grouped by air pollutant and exposure time (prenatal period and trimesters). Within-group studies were standardized by log odds ratio (OR) and then combined by three meta-analysis methods: frequentist fixed and random effects models, and Bayesian random effects model. Initial search identified 1564 papers, of which 25 studies remained for final analysis after duplicates and ineligible studies were removed. Of the 25 studies, 13, 14, 12, and 7 studies investigated ASD in children associated with PM2.5, PM10, NO2, and ozone, respectively. The frequentist and Bayesian random effects models resulted in different statistical significance. For prenatal period, frequentist meta-analysis returned significant pooled ORs with 95% confidence intervals, 1.06(1.01,1.11) for PM2.5 and 1.02(1.01,1.04) for NO2, whereas Bayesian meta-analysis showed similar ORs with wider 95% posterior intervals, 1.06(1.00,1.13) for PM2.5 and 1.02(1.00,1.05) for NO2. Third trimester appeared to have higher pooled ORs for PM2.5, PM10, and ozone, but patterns in the time-varying associations over the trimester were inconsistent. For positive association between maternal exposure to ambient air pollution and ASD in children, there is some evidence for PM2.5, weak evidence for NO2 and little evidence for PM10 and ozone. However, patterns in associations over trimesters were inconsistent among studies and among air pollutants.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".