Effects of and challenges to bans on menthol and other flavors in tobacco products
Bibliographic record
Abstract
In May 2020, the European Tobacco Products Directive (TPD), which bans characterizing flavors in cigarettes and roll-your-own tobacco (RYO) in the European Union (EU), extended its application to menthol 1,2 .Countries which were early adopters of flavor bans include Brazil, Canada, Ethiopia, the United Kingdom (UK), amongst others 3 .Two main regulatory approaches exist: a ban on characterizing flavors that allows for the presence of additives but not at detectable sensory levels (e.g.EU, UK), and a total ban on flavor additives that eliminates their presence altogether (e.g.Brazil, Canada).As more countries work towards adopting tobacco flavor bans, it is critical to understand how these policies are implemented, ascertain their population-level impact, and identify the regulatory challenges.Initial evaluation of these bans has provided evidence for their positive impact as well as the challenges.Population-level data from the International Tobacco Control (ITC) Surveys in Canada and Europe have demonstrated that banning menthol and other flavors in cigarettes can lead to positive public health outcomes including increased quitting, without significant unintended consequences such as illicit purchasing 4,5 .The menthol cigarette ban in Canada led to 7.5% additional quitting among menthol smokers compared to nonmenthol smokers 4 .Findings from the EUREST-PLUS ITC Europe Surveys, before and after the flavor ban, but prior to the menthol ban, found a reduction in menthol use 5 as well as other flavors and improved health knowledge and beliefs among menthol smokers.However, a majority of menthol smokers in Canada switched to non-menthol cigarettes rather than quitting, and a substantial proportion of EU smokers continued to smoke menthol cigarettes prior to the ban and intended to either continue or switch to non-menthol cigarettes after the menthol ban, rather than quit 4,5 .This is not surprising, given the high addictiveness of cigarettes, coupled with the lack of promotion and availability of smoking cessation support, and the measures taken by the tobacco industry to circumvent and undermine menthol bans 6 .In response to menthol bans, the industry has introduced menthol products that remain legal post-ban 6 , such as menthol cigarillos, menthol accessories sold separately (e.g.menthol-infused cards, filter capsules, and RYO papers and filters) and new cigarette blends with low levels of menthol 6 .This raises the question of whether a characterizing flavor ban, compared to a total additive ban, allows a gap where the industry-desirable properties of additives, such as the cooling effect of menthol, could operate at a subliminal level.Some EU Member States, such as Germany and Finland, have gone beyond the current TPD by prohibiting menthol as an additive at any level, based on the evidence that it facilitates inhalation 7 .Other questions remain: 'To what extent can current methodologies for determining the presence of characterizing flavors
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".