Assessing the association between lifetime exposure to greenspace and early childhood development and the mediation effects of air pollution and noise in Canada: a population-based birth cohort study
Bibliographic record
Abstract
Background Exposure to greenspace is associated with improved childhood development, but the pathways behind this relationship are insufficiently understood. Therefore, we aimed to investigate the association between lifetime residential exposure to greenspace and early childhood development and evaluate the extent to which this association is mediated by reductions in traffic-related air pollution and noise. Methods This population-based birth cohort study comprised singleton births in Metro Vancouver, BC, Canada, between April 1, 2000, and Dec 31, 2005. Children and mothers had to be registered with the mandatory provincial health insurance programme, Medical Services Plan, and have lived within the study area from the child's birth to the time of outcome assessment. Early childhood development was assessed via teacher ratings on the Early Development Instrument (EDI), and we used the total EDI score as the primary outcome variable. We estimated greenspace using percentage vegetation derived from spectral unmixing of annual Landsat satellite image composites. Lifetime residential exposure to greenspace was estimated as the mean of annual percentage vegetation values within 250 m of participants' residential postal codes. Multilevel modelling, adjusted for eight covariates, was used to investigate associations between greenspace exposure and EDI scores. We estimated the mediation effects of nitrogen dioxide (NO 2 ), fine particulate matter (PM 2·5 ), and noise levels using causal mediation analyses. Findings Of the 37 745 children born in Metro Vancouver between April 1, 2000, and Dec 31, 2005, 27 372 were included in our final study sample. In the adjusted model, 1 IQR increase in percentage vegetation was associated with a 0·16 (95% CI 0·04–0·28; p=0·0073) increase in total EDI score, indicating small improvements in early childhood development. We estimated that 97·1% (95% CI 43·0–396·0), 29·5% (12·0–117·0), and 35·2% (17·9–139·0) of the association was mediated through reductions in NO 2 , PM 2·5 , and noise, respectively. Interpretation Increased exposure to residential greenspace might improve childhood development by reducing the adverse developmental effects of traffic-related exposures, especially NO 2 air pollution. Our study supports the implementation of healthy urban planning and green infrastructure interventions. Funding Canadian Institutes of Health Research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".