An Investigation of the Association between Maternal and Early Life Exposure to Urban Greenness and the Incidence of Childhood Asthma
Bibliographic record
Abstract
Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the development of childhood asthma.These studies have yielded inconsistent findings, and not all have explored the relevance of timing of exposure.This research was done to address gaps in this area.The role of residential surrounding greenness on the risk of incident asthma was studied using a retrospective cohort study design that consisted of 982,131 singleton births in Ontario, Canada between 2006 and 2013.Two measures of greenness, the Normalized Different Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential addresses of these infants.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.An interquartile range increase (0.08) of the NDVI during childhood, within a residential buffer of 250m was associated with a 4% (95%CI=0.95-0.96)reduced risk of asthma, however, no association was noted after adjusting for ambient concentrations of air pollution (HR=0.99;95%CI=0.99-1.01).These findings suggest that greenness is not associated with the development of asthma, and those investigations of this topic account for the possible confounding role of air pollution.However, greenness may reduce the risk of developing asthma for children diagnosed at older age, and for those children born during the spring and summer seasons.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".