Effect of smoke-free legislation on respiratory health services use in children with asthma: a population-based open cohort study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: This study will add to existing literature by examining the impact of smoke-free legislation in outdoor areas among children with asthma. We aimed to examine the effect of the 2015 Smoke-Free Ontario Act (SFOA) amendment, which prohibited smoking on patios, playgrounds and sports fields, on health services use (HSU) rates in children with asthma. METHODS: We conducted a population-based open cohort study using health administrative data from the province of Ontario, Canada. Each year, all Ontario residents aged 0-18 years with physician diagnosed asthma were included in the study. Annual rates of HSU (emergency department (ED) visits, hospitalisations and physician office visits) for asthma and asthma-related conditions (eg, bronchitis, allergic rhinitis, influenza and pneumonia) were calculated. Interrupted time-series analysis, accounting for seasonality, was used to estimate changes in HSU following the 2015 SFOA. RESULTS: The study population ranged from 618 957 individuals in 2010 to 498 812 in 2018. An estimated average increase in ED visits for asthma in infants aged 0-1 years of 0.42 per 100 individuals (95% CI: 0.09 to 0.75) and a 57% relative increase corresponding to the 2015 SFOA was observed. A significant decrease in ED visits for asthma-related conditions of 0.19 per 100 individuals (95% CI: -0.37 to -0.01) and a 22% relative decrease corresponding to the 2015 SFOA was observed. CONCLUSION: Based on the observed positive effect of restricting smoking on patios, playgrounds and sports fields on respiratory morbidity in children with asthma, other jurisdictions globally should consider implementing similar smoke-free policies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".