Association of anti-smoking legislation with rates of hospital admission for cardiovascular and respiratory conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it