Does the Regulatory Environment for E-Cigarettes Influence the Effectiveness of E-Cigarettes for Smoking Cessation?: Longitudinal Findings From the ITC Four Country Survey
Bibliographic record
Abstract
INTRODUCTION: To date, no studies have explored how different regulatory environments may influence the effectiveness of electronic cigarettes (ECs) as a smoking cessation aid. OBJECTIVE: This study compares the real-world effectiveness of adult smokers using ECs for quitting compared with quitting unassisted or quitting with nicotine replacement therapy (NRT) and/or prescription medications in two countries with restrictive policies towards ECs (ie, Canada and Australia) versus two countries with less restrictive policies (ie, United States and United Kingdom). METHODS: Data were drawn from the International Tobacco Control Four Country surveys, from the United States and Canada (2 waves, n = 318 and 380, respectively), the United Kingdom (3 waves, n = 439) and Australia (4 waves, n = 662), collected 2010-2014. Smokers at baseline wave who reported making a quit attempt at follow-up were included. The primary outcome was self-reported abstinence for at least 30 days regardless of smoking status at follow-up assessment. Data across waves were combined and analyzed using generalized estimating equations. RESULTS: Compared to unassisted quitting (ie, no medications or ECs), smokers who used ECs for quitting from countries with less restrictive EC policy environments were more likely (OR = 1.95, 95%CI = 1.19-3.20, p < .01), whereas smokers who used ECs for quitting from countries with more restrictive EC policies were less likely (OR = 0.36, 95%CI = 0.18-0.72, p < .01), to report sustained abstinence for at least 30 days. CONCLUSIONS: Use of ECs in the real world during a quit attempt appears only effective for sustaining smoking abstinence in a less restrictive EC environment suggesting that the benefits of ECs for smoking cessation are likely highly dependent on the regulatory environment. IMPLICATIONS: What this study adds: This is the first study to examine the impact of regulatory environment for ECs on their real-world effectiveness for smoking cessation. This study shows that in a less restrictive EC regulatory environment, use of ECs during a quit attempt facilitates, but in a more restrictive environment, it inhibits, short-term sustained abstinence. The findings underscore the need for careful consideration on how best to regulate this emerging product so that EC benefits for smoking cessation are maximized and its risks to public health are minimized.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".