Early Life Exposure to Greenspace and Autism Spectrum Disorder and the Mediating Effects of Air Pollution in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND AIM Early life exposure to greenspace is associated with health benefits on childhood development, but the pathways of this relationship are not completely understood. This study aimed to evaluate early life exposure to different greenspace metrics on the development on autism spectrum disorder (ASD) and whether these associations are mediated by reductions in ambient air pollution. METHODS This case-control study included 1,801 ASD cases and 9002 controls less than 5 years of age identified from 2012 to 2017 using health administrative databases in the province of Ontario, Canada. Greenspace metrics were estimated using the Normalized Difference Vegetation Index (NDVI), Green View Index (GVI) and percent tree canopy coverage using values within 250 m of participants’ residential postal codes at birth. Conditional logistic regression was used to investigate associations between greenspace exposure and ASD while adjusting for maternal age, parity, maternal comorbidities, substance use (i.e. smoking and alcohol), socioeconomic status and urbanicity. We estimated the mediation effects of nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) using causal mediation analyses. RESULTS In the adjusted model, we found that one interquartile range (IQR) increase in percentage tree canopy was associated with a 6% reduction in the odds of ASD (Odds ratio = 0.94; 95% CI: 0.90, 0.98). No associations were found for NDVI and GVI in relation to ASD. We found that 81.8% (77.6 – 85.9), 20.1% (16.6 – 23.6) and 13.6% (11.1 – 16.0) of the association between tree canopy exposure and ASD was mediated through reductions in NO2, PM2.5, and O3, respectively. CONCLUSIONS Early life exposure to greenspace might reduce the risk of ASD through reductions in ambient air pollution, in particular traffic pollution from NO2. Our findings may provide support to communities on the potential health benefits of greenspaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".