Illicit purchasing and use of flavour accessories after the European Union menthol cigarette ban: findings from the 2020–21 ITC Netherlands Surveys
Bibliographic record
Abstract
BACKGROUND: The 2020 European Union (EU) menthol cigarette ban increased quitting among pre-ban menthol smokers in the Netherlands, but some reported continuing to smoke menthol cigarettes. This study examined three possible explanations for post-ban menthol use-(i) illicit purchasing, (ii) use of flavour accessories and (iii) use of non-menthol replacement brands marketed for menthol smokers. METHODS: Data were from the ITC Netherlands Cohort Surveys among adult smokers before the menthol ban (Wave 1: February-March 2020, N = 2067) and after the ban (Wave 2: September-November 2020, N = 1752; Wave 3: June-July 2021, N = 1721). Bivariate, logistic regression and generalized estimating equation model analyses were conducted on weighted data. RESULTS: Illicit purchasing remained low from pre-ban (2.4%, 95% CI: 1.8-3.2, Wave 1) to post-ban (1.7%, 1.2-2.5%, Wave 3), with no difference between menthol and non-menthol smokers from Wave 1 to Wave 3. About 4.4% of post-ban menthol smokers last purchased their usual brand outside of the EU and 3.6% from the internet; 42.5% of post-ban menthol smokers and 4.4% of smokers overall reported using flavour accessories, with greater odds among those aged 25-39 years vs. 55+ (aOR = 3.16, P = 0.002). Approximately 70% of post-ban smokers who reported using a menthol brand were actually using a non-menthol replacement brand. CONCLUSIONS: There was no increase in illicit purchasing or of smuggling outside the EU among menthol and non-menthol smokers in the Netherlands 1 year after the EU menthol cigarette ban. Use of flavour accessories and non-menthol replacement brands best explain post-ban menthol use, suggesting the need to ban accessories and ensure industry compliance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.002 | 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".