Who Smokes in Europe? Data From 12 European Countries in the TackSHS Survey (2017–2018)
Bibliographic record
Abstract
BACKGROUND: Population data on tobacco use and its determinants require continuous monitoring and careful inter-country comparison. We aimed to provide the most up-to-date estimates on tobacco smoking from a large cross-sectional survey, conducted in selected European countries. METHODS: Within the TackSHS Project, a face-to-face survey on smoking was conducted in 2017-2018 in 12 countries: Bulgaria, England, France, Germany, Greece, Ireland, Italy, Latvia, Poland, Portugal, Romania, and Spain, representing around 80% of the 432 million European Union (EU) adult population. In each country, a representative sample of around 1,000 subjects aged 15 years and older was interviewed, for a total of 11,902 participants. RESULTS: Overall, 25.9% of participants were current smokers (31.0% of men and 21.2% of women, P < 0.001), while 16.5% were former smokers. Smoking prevalence ranged from 18.9% in Italy to 37.0% in Bulgaria. It decreased with increasing age (compared to <45, multivariable odds ratio [OR] for ≥65 year, 0.31; 95% confidence interval [CI], 0.27-0.36), level of education (OR for low vs high, 1.32; 95% CI, 1.17-1.48) and self-rated household economic level (OR for low vs high, 2.05; 95% CI, 1.74-2.42). The same patterns were found in both sexes. CONCLUSIONS: These smoking prevalence estimates represent the most up-to-date evidence in Europe. From them, it can be derived that there are more than 112 million current smokers in the EU-28. Lower socio-economic status is a major determinant of smoking habit in both sexes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".