Secondhand Smoke Exposure in European Countries With Different Smoke-Free Legislation: Findings From the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: Exposure to secondhand smoke (SHS) poses serious and extensive health and economic-related consequences to European society and worldwide. Smoking bans are a key measure to reducing SHS exposure but have been implemented with varying levels of success. We assessed changes in the prevalence of self-reported SHS exposure and smoking behavior in public places among smokers in six European countries and the influence of the country's type of smoking ban (partial or total ban) on such exposure and smoking behavior. AIMS AND METHODS: The EUREST-PLUS ITC Europe Surveys were conducted among adult smokers in Germany, Greece, Hungary, Poland, Romania, and Spain in 2016 (Wave 1, n = 6011) and 2018 (Wave 2, n = 6027). We used generalized estimating equations models to assess changes between Waves 1 and 2 and to test the interaction between the type of smoking ban and (1) self-reported SHS exposure, (2) self-reported smoking in several public places. RESULTS: A significant decrease in self-reported SHS exposure was observed in workplaces, from 19.1% in 2016 to 14.0% in 2018 (-5.1%; 95% CI: -8.0%; -2.2%). Self-reported smoking did not change significantly inside bars (22.7% in Wave 2), restaurants (13.2% in Wave 2) and discos/nightclubs (34.0% in W2). SHS exposure in public places was significantly less likely (adjusted odds ratio = 0.35; 95% CI: 0.26-0.47) in the countries with total bans as compared to those countries with partial bans. CONCLUSION: The inverse association between smoking in public places and smoking bans indicates an opportunity for strengthening smoke-free legislation and protecting bystanders from exposure to SHS in public places. IMPLICATIONS: Prevalence of smokers engaging in and being exposed to smoking in public places varied by type of smoke-free legislation across six European Union countries in our study; those with total smoke bans reported significantly less exposure to SHS than those with partial or no bans. Our results indicate room for improvement, not only to decrease the prevalence of exposure to SHS in Europe but also to diminish the variability between countries through common, more restrictive smoke-free legislation, and importantly, strong and sustained enforcement.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".