Association of Canada’s Provincial Bans on Electronic Cigarette Sales to Minors With Electronic Cigarette Use Among Youths
Bibliographic record
Abstract
Importance: Banning electronic cigarette (e-cigarette) sales to minors has been a key policy to protect children from e-cigarettes in the United States and Canada, but to date little is known about the outcome of such a ban. Objectives: To investigate the association of banning e-cigarette sales to minors in Canada with e-cigarette use among youths and the mechanisms through which a ban might be associated with their e-cigarette use. Design, Setting, and Participants: This quasi-experimental difference-in-differences and triple-differences study used data from the nationally representative Canadian Tobacco, Alcohol and Drugs Survey (2013-2017) and Canadian Student Tobacco, Alcohol and Drugs Survey (2014-2017). Study samples consisted of respondents aged 15 to 18 years (in difference-in-differences analysis; n = 8212) and aged 15 to 25 years (in triple-differences analysis; n = 20 934) in the Canadian Tobacco, Alcohol and Drugs Survey, and students in grades 6 to 12 (in difference-in-differences analysis; n = 78 650) in the Canadian Student Tobacco, Alcohol and Drugs Survey. Interventions: Canada's provincial bans on e-cigarette sales to youths younger than 18 or 19 years (depending on province) implemented between 2015 and 2017. Main Outcomes and Measures: The primary outcome was past 30-day e-cigarette use among youths. Secondary outcomes were difficulty of access to e-cigarettes, perception of e-cigarette harm, and use of social sources of e-cigarettes. Results: After the bans, e-cigarette use among youths increased in all provinces, but the increase was 3.1 percentage points (95% CI, 0.2-6.0; P = .04), or 79%, lower in provinces with a ban than in provinces without a ban. Youths in provinces with a ban were 2.6 percentage points (95% CI, 1.5-3.7; P = .001), or 18%, less likely to believe that regular e-cigarette use poses no harm and 6.2 percentage points (95% CI, 1.1-11.4; P = .02), or 16%, more likely to self-report greater difficulty in obtaining e-cigarettes. Among youths who reported using e-cigarettes, the likelihood of obtaining e-cigarettes from social sources was 17.3 percentage points (95% CI, 5.2 -29.4; P = .01), or 29%, higher in provinces with a ban. These findings were robust to several sensitivity analyses. Conclusions and Relevance: Banning e-cigarette sales to minors was associated with a significant reduction in the rate of increase in e-cigarette use by youths, but this policy alone could not reverse the overall increase in e-cigarette use. The findings from this study suggest that this policy should be supplemented with other measures that can reduce young people's desire to obtain e-cigarettes through social sources, such as a ban on e-cigarettes with flavors that appeal to youths and children.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".