Residential surrounding greenness and the incidence of childhood asthma: Findings from a population-based cohort in Ontario, Canada
Bibliographic record
Abstract
Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the incidence of childhood asthma. These findings have been inconsistent, and few studies explored the relevance of timing of exposure. We investigated the role of residential surrounding greenness on the risk of incident asthma using a population-based retrospective cohort study. We included 982,131 singleton births in Ontario, Canada between 2006 and 2013. Two measures of greenness, the Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential histories of these infants from pregnancy through to age 12. Longitudinally-based diagnoses of asthma were determined by using provincial administrative health data. The extended Cox hazards model was used to characterize associations between greenness measures and asthma (up to age 12 years) while adjusting for several risk factors. In a fully adjusted model, that included a term for traffic-related air pollution (NO2), we found no association between an interquartile range increase (0.08) of the NDVI during childhood and asthma incidence (HR = 0.99; 95 % CI = 0.99–1.01). In contrast, we found that an 0.08 increase in NDVI during childhood reduced the risk of asthma in children 7–12 years of age by 14 % (HR = 0.86, 95 % CI:0.79–0.95). Seasonal differences in the association between greenness and asthma were noted. Our findings suggest that residential proximity to greenness reduces the risk of asthma in children aged 7–12.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".