Maternal Exposure to Air Pollutions and Risk of Autism in Children: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
TPS 731: Neurological effects in children, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Background: The number of children diagnosed with autism spectrum disorder (ASD) has increased. This systematic review and meta-analysis aimed to summarize the association between maternal exposure to outdoor air pollution and ASD in children. Methods: A systematic literature search in three databases (Medline, Embase, and Web of Science) was performed using subject headings related to ASD and maternal exposure to air pollution. Eligible studies were screened based on predetermined criteria, and risk of bias was assessed by the Newcastle-Ottawa Scale. 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 due to the small number of studies. Results: The initial search identified 1,302 papers, of which 20 studies remained for final analysis after duplicates and ineligible studies were removed. Of the 20 studies, 9, 11, 10, and 6 studies, respectively, investigated the association of ASD with PM2.5, PM10, NO2, and ozone. The frequentist and Bayesian random effects models resulted in different statistical significance. For prenatal period, frequentist meta-analysis returned significant pooled ORs for PM2.5 (1.073 with 95% confidence interval (1.008, 1.142)) and ozone (1.010: (1.002, 1.017)), but Bayesian meta-analysis showed ORs with wider uncertainty for PM2.5 (1.075 with 95% posterior interval (0.966, 1.222)) and ozone (1.010: (0.907, 1.133)). Trimester 3 appeared to have higher pooled ORs for PM2.5, PM10, NO2, and ozone. Patterns in the time-varying associations over the trimester were inconsistent, as between-study differences were apparent. Conclusions: There is marginal evidence for positive associations between maternal exposure to PM2.5, but not PM10, NO2, and ozone, and ASD development in children during pregnancy. However, trends in associations over trimesters were inconsistent between studies and between 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| 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".