Early life exposure to air pollution and incidence of childhood asthma, allergic rhinitis and eczema
Bibliographic record
Abstract
Rationale There is growing evidence that air pollution may contribute to the development of childhood asthma and other allergic diseases. In this follow-up of the Toronto Child Health Evaluation Questionnaire (T-CHEQ) study, we examined associations between early life exposures to air pollution and incidence of asthma, allergic rhinitis and eczema from birth through adolescence. Methods 1286 T-CHEQ participants were followed from birth until outcome (March 31, 2016) or loss to follow-up, with a mean of 17 years of follow-up. Concentrations of nitrogen dioxide (NO2), ozone (O3) and particulate matter with a 50% cut-off aerodynamic diameter of 2.5 µm (PM2.5) from January 1, 1999 to December 31, 2012 were assigned to participants based on their postal codes at birth using ground observations, chemical/meteorological models, remote sensing and land-use regression models. Study outcomes included incidence of physician-diagnosed asthma, allergic rhinitis and eczema. Cox proportional hazard regression models were used to estimate hazard ratios per interquartile range of exposures and outcomes, adjusting for potential confounders. Results Hazard ratios of 1.17 (95% CI 1.05–1.31) for asthma and 1.07 (95% CI 0.99–1.15) for eczema were observed for total oxidants (O3and NO2) at birth. No significant increase in risk was found for PM2.5. Conclusions Exposures to oxidant air pollutants (O3and NO2) but not PM2.5were associated with an increased risk of incident asthma and eczema in children. This suggests that improving air quality may contribute to the prevention of asthma and other allergic disease in childhood and adolescence.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".