Impact of state cigarette and e-cigarette flavors bans on smoking, vaping and dual use in the United States
Bibliographic record
Abstract
BACKGROUND AND AIMS: Some states have banned flavors in various tobacco products. This can reduce use of banned products and induce substitution towards non-banned products. The net impact must be determined empirically. The aim of this study is to evaluate the impacts of these bans on both use and substitution. SETTING: US tobacco market. PARTICIPANTS: 3220 individuals aged 18-41 in the United States who smoked and/or vaped (past 30-day use) completed an online survey. MEASUREMENTS: Multinomial logistic models regressed changes in tobacco product use between two time periods on: states with and without bans on flavored e-cigarettes and menthol cigarettes, individuals' characteristics, and other state-level tobacco policies. Estimated models were used to simulate impacts of flavor bans for people who dual use. RESULTS: Policies' impacts were only observed for those who dual use cigarettes and e-cigarettes. Most who dual use did not change their tobacco product use regardless of state policy. Some significant differences were found by states for those who quit both products. Massachusetts, with bans on both flavored e-cigarettes and menthol cigarettes, had the greatest predicted rate of quitting both products (9 %) compared to states without (3 %). States with e-cigarette flavors bans had higher cessation of e-cigarette use among those who dual use. CONCLUSIONS: Flavor bans on cigarettes and e-cigarettes were associated with reduced vaping among those who dual use. Massachusetts saw a higher proportion of quitting all tobacco products, likely because people who smoked in Massachusetts could not substitute with flavored e-cigarettes which had been banned.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".