Comparison of childhood asthma incidence in 3 neighbouring cities in southwestern Ontario: a 25-year longitudinal cohort study
Bibliographic record
Abstract
Background: Air pollution is a known trigger for exacerbations among individuals with asthma, but its role in the development of new-onset asthma is unclear. We compared the rate of new asthma cases in Sarnia, a city with high pollution levels, with the rates in 2 neighbouring regions in southwestern Ontario, Canada. Methods: Using a population-based birth cohort design and linked health administrative data, we compared the hazard of incident asthma among children 0 to 10 years of age between those born in Lambton (Sarnia) and those born in Windsor and London–Middlesex, for the period Apr. 1, 1993, to Mar. 31, 2009. We used Cox proportional hazards models to adjust for year of birth and exposure to air pollutants (nitrogen dioxide, sulphur dioxide [SO2], ozone and small particulate matter [PM2.5]), as well as maternal, geographic and socioeconomic factors. Results: Among 114 427 children, the highest incidence of asthma was in Lambton, followed by Windsor and London–Middlesex (30.3, 24.4 and 19.8 per 1000 person-years, respectively; p < 0.001). Relative to Lambton, the hazard of asthma, adjusted for socioeconomic and perinatal factors, was lower in Windsor (hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.67–0.77) and London–Middlesex (HR 0.65, 95% CI 0.61–0.69). Inclusion of air pollutants attenuated this relative difference in both Windsor (HR 0.79, 95% CI 0.62–1.01) and London–Middlesex (HR 0.89, 95% CI 0.64–1.24). Interpretation We identified a higher incidence of asthma among children born in Lambton (Sarnia) relative to 2 other regions in southwestern Ontario. Higher levels of air pollution (particularly SO2 and PM2.5) in this region, as experienced by children in their first year of life, may be contributory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".