O5-1.2 Can smoking bans lead to sustained improvements in population health? An overview of the evidence
Bibliographic record
Abstract
Over the past decade, bans on smoking in enclosed public places have been introduced in many states in the US and provinces in Canada, and a growing number of European countries, including Scotland. Evidence from Scotland and other jurisdictions clearly indicates that when compliance is high, implementation of comprehensive legislation is accompanied by dramatic reductions in worker exposure to secondhand smoke and improvements in respiratory symptoms among both non-smoking and smoking workers alike. Studies have also found population level reductions in secondhand smoke exposure among both adults and children and this has been accompanied by measurable improvements in population health including reductions in hospital admissions for acute myocardial infarction and asthma. The magnitude of the health improvement varies, but a recent meta-analysis of 17 studies found a pooled risk reduction for acute myocardial infarction of 10% (95% CI 6 to 14%) following implementation of smoke-free legislation. While some of the observed risk reduction for heart attack is likely to be associated with behaviour change among smokers (a number of studies report both a reduction in smoking prevalence in the general population and tobacco consumption in those who continue to smoke, post-legislation), a prospective study of admissions for acute coronary syndrome, found that 67% of heart attacks averted were among non-smokers. In this paper we present an overview of the evidence about the health effects of smoking bans and then go on to consider whether these are simply short-term improvements or are sustained for a number of years after implementation of legislation.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".