Association of anti-smoking legislation with rates of hospital admission for cardiovascular and respiratory conditions
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the impact of anti-smoking legislation on respiratory or cardiovascular conditions other than acute myocardial infarction. We studied rates of hospital admission attributable to three cardiovascular conditions (acute myocardial infarction, angina, and stroke) and three respiratory conditions (asthma, chronic obstructive pulmonary disease, and pneumonia or bronchitis) after the implementation of smoking bans. METHODS: We calculated crude rates of admission to hospital in Toronto, Ontario, from January 1996 (three years before the first phase of a smoking ban was implemented) to March 2006 (two years after the last phase was implemented. We used an autoregressive integrated moving-average (ARIMA) model to test for a relation between smoking bans and admission rates. We compared our results with similar data from two Ontario municipalities that did not have smoking bans and with conditions (acute cholecystitis, bowel obstruction and appendicitis) that are not known to be related to second-hand smoke. RESULTS: Crude rates of admission to hospital because of cardiovascular conditions decreased by 39% (95% CI 38%-40%) and admissions because of respiratory conditions decreased by 33% (95% CI 32%-34%) during the ban period affecting restaurant settings. No consistent reductions in these rates were evident after smoking bans affecting other settings. No significant reductions were observed in control cities or for control conditions. INTERPRETATION: Our results serve to expand the list of health outcomes that may be ameliorated by smoking bans. Further research is needed to establish the types of settings in which smoking bans are most effective. Our results lend legitimacy to efforts to further reduce public exposure to tobacco smoke.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".