Secondhand exposure to e-cigarette aerosols among smokers:A cross-sectional study in six European countries of theEUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: Electronic cigarette (e-cigarette) use has grown significantly in some European Union (EU) Member States (MS). A better understanding of the exposure to secondhand e-cigarette aerosols (SHA) is necessary to develop and implement comprehensive regulations on e-cigarette use in public places. This study aims to assess the observation of e-cigarette use in public places, the self-reported exposure to SHA, and the level of users' comfort using e-cigarettes in the presence of others. METHODS: This is a cross-sectional study of the Wave 1 International Tobacco Control 6 European Countries Survey recruiting adult smokers (n=6011) across six EU MS: Germany, Greece, Hungary, Poland, Romania, and Spain, within the EURESTPLUS Project. A descriptive analysis was conducted to estimate the prevalence (%) of observed e-cigarette use in different places, frequency of self-reported exposure to SHA, and level of comfort using e-cigarettes in the presence of others. RESULTS: In all, 31.0% of smokers observed others using e-cigarette in public places, 19.7% in indoor places where smoking is banned, and 14.5% indoors at work. Almost 37% of smokers reported to be ever exposed to SHA, ranging from 17.7% in Spain to 63.3% in Greece. The higher prevalence of observed e-cigarette use and passive exposure to SHA was reported by smokers of younger age, of higher educational level and those being current or former e-cigarette users. Part (8.8%) of the smokers who were also e-cigarette users reported feeling uncomfortable using e-cigarettes in the presence of others. CONCLUSIONS: A third of smokers from six EU MS reported being exposed to SHA. Prevalence differences were observed among the countries. In the context of scarce evidence on long-term health effects of exposure to SHA, precautionary regulations protecting bystanders from involuntary exposure should be developed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".