Impact of the Tobacco Products Directive on self-reported exposure to e-cigarette advertising, promotion and sponsorship in smokers—findings from the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
BACKGROUND: Advertising, promotion and sponsorship of electronic cigarettes (ECAPS) have increased in recent years. Since May 2016, the Tobacco Products Directive 2014/40/EU (TPD2) prohibits ECAPS in various advertising channels, including media that have cross-border effects. The objective of this study was to investigate changes in exposure to ECAPS in a cohort of smokers from six European Union member states after implementation of TPD2. METHODS: Self-reported exposure to ECAPS overall and in various media and localities was examined over two International Tobacco Control Policy Evaluation survey waves (2016 and 2018) in a cohort of 6011 adult smokers from Germany, Greece, Hungary, Poland, Romania and Spain (EUREST-PLUS Project) using longitudinal generalized estimating equations models. RESULTS: Self-reported ECAPS exposure at both timepoints varied between countries and across examined advertising channels. Overall, there was a significant increase in ECAPS exposure [adjusted odds ratio (aOR): 1.25, 95% CI: 1.09-1.44]. Between waves, no consistent patterns of change in ECAPS exposure across countries and different media were observed. Generally, ECAPS exposure tended to decline in some channels regulated by TPD2, particularly on television and radio, while exposure tended to increase in some unregulated channels, such as at points of sale. CONCLUSIONS: The findings suggest that the TPD2 was generally effective in reducing ECAPS in regulated channels. Nonetheless, further research is warranted to evaluate its role in reducing ECAPS exposure, possibly by triangulation with additional sources of data.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".