Responses to Real-World and Hypothetical E-Cigarette Flavor Bans Among US Young Adults Who Use Flavored E-Cigarettes
Bibliographic record
Abstract
INTRODUCTION: E-cigarette flavor bans could reduce or exacerbate population health harms. To determine how US e-cigarette flavor restrictions might influence tobacco use behavior, this study assesses responses to real-world and hypothetical flavor bans among young adults who use flavored e-cigarettes. AIMS AND METHODS: An online, national survey of young adults ages 18-34 who use flavored e-cigarettes was conducted in 2021 (n = 1253), oversampling states affected by e-cigarette flavor restrictions. Participants were asked about their responses to real-world changes in the availability of flavored e-cigarettes. Unaffected participants were asked to predict their responses under a hypothetical federal e-cigarette flavor ban. RESULTS: The most common response to real-world changes in flavored e-cigarettes availability was to continue vaping (~80%). Among those who exclusively vaped, 12.5% switched to combustible tobacco. Quitting all forms of tobacco was selected by 5.3% of those exclusively vape versus 4.2% who dual use. Under a hypothetical federal ban, more than half of respondents stated they would continue vaping; 20.9% and 42.5% of those who exclusively vape versus dual use would use combustible tobacco. Quitting all tobacco products was endorsed by 34.5% and 17.2% of those who exclusively vape versus dual use. CONCLUSIONS: Young adults who vape flavored e-cigarettes have mixed responses to e-cigarette flavor bans. Under both real-world and hypothetical e-cigarette flavor bans, most who use flavored e-cigarettes continue vaping. Under a real-world ban, the second most common response among those who exclusively vape is to switch to smoking; under a hypothetical federal ban, it is to quit all tobacco. IMPLICATIONS: This is the first national survey to directly ask young adults who use flavored e-cigarettes about their responses to real-world changes in flavored e-cigarette availability due to state and local flavor restrictions. The survey also asked individuals to predict their responses under a hypothetical federal e-cigarette flavor ban. Most who use flavored e-cigarettes would continue vaping following e-cigarette flavor restrictions, but many would switch to or continue using combustible tobacco, highlighting potential negative public health consequences of these policies. Policymakers must consider the impact of e-cigarette flavor bans on both e-cigarette and cigarette use.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".