Association between exposure to ambient particulate matters and risks of autism spectrum disorder in children: a systematic review and exposure-response meta-analysis
Bibliographic record
Abstract
Abstract The impact of prenatal and early childhood exposure of ambient particulate matters (PMs) on the risk of autism spectrum disorder (ASD) in children remained inconclusive, particularly at low levels below current National Ambient Air Quality Standards. The study summarizes the epidemiological association between PM exposure and risks of ASD in children. PubMed, Embase, Web of Science, Cochrane Library, Compendex, Biosis Previews, and Agricultural & Environmental Science Databases for studies published before February 2020. Original studies with the following information were included: (a) exposure of ambient PM (including PM2.5 and/or PM10); (b) ASD as the outcome of interest in children; (c) effect estimates of relative risk (RR), odds ratio (OR), or hazard ratio. The risks of ASD are summarized at different exposure windows (i.e. first, second, third trimesters, and early childhood period) by using a random-effects model. Exposure-response meta-regression was performed across various background levels of PM2.5. We used Newcastle–Ottawa Scale for quality assessment. Eleven studies (two cohort and nine case-control studies) and 313 301 children were enrolled. Overall, the risk of ASD increased by 64% (pooled RR = 1.64, 95% CI = 1.16–2.34) and 31% (pooled RR = 1.31, 95% CI = 1.08–1.58), with exposure to 10 μg m−3 increment of PM2.5 during early childhood and prenatal periods, respectively. Stratifying by three trimesters of prenatal period, the risk of ASD increased 35% per 10 μg m−3 difference of PM2.5 exposure during the third trimester (pooled RR = 1.35, 95% CI = 1.18–1.55), but not during the first and second trimesters. The risks of ASD persisted at the background PM2.5 levels from 8 μg m−3 (pooled RR = 1.30, 95% CI = 1.02–1.66) and onward. Our findings suggested an association between PM2.5 exposure and risks of ASD, particularly within specific exposure windows, even at low background levels of PM2.5.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".