Impact of vaping introduction on cigarette smoking in six jurisdictions with varied regulatory approaches to vaping: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: We sought to quantify the impact of vaping introduction on cigarette smoking across settings with varied regulatory approaches to vaping. DESIGN: Interrupted time series analysis, adjusted for cigarette tax levels. SETTING: Four Canadian provinces, UK and Australia. PARTICIPANTS: Entire population of smokers in each country. INTERVENTIONS: The year that vaping was widely introduced in each country. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is cigarette consumption per adult, and the secondary outcome is smoking prevalence among young adults. RESULTS: Based on allowable nicotine levels, restrictions on e-cigarette advertising, sales and access, and taxation, the least to most restrictive jurisdictions were, in order, Alberta, Ontario, Quebec and British Columbia (all in Canada), UK and Australia. In most, but not all, settings where higher nicotine content was permitted in vaping products (66 mg/mL), vaping introduction led to a reduction in cigarette consumption per capita (Ontario: p=0.037, Quebec: p=0.007) or in smoking prevalence among young adults (Alberta men, p=0.027; Quebec men, p=0.008; Quebec women, p=0.008). In the UK, where the maximum permitted nicotine content in vaping products was 20 mg/mL, vaping introduction slowed the declining trend in cigarette smoking among men aged 16-24 years (p=0.031) and 25-34 years (p=0.002) but not in cigarette consumption per adult. In Australia, where nicotine was not permitted in e-cigarettes, e-cigarette introduction slowed the declining trend in cigarette consumption per capita and in smoking prevalence among men aged 18-24 years (cigarette consumption: p=0.015, prevalence: p=0.044). CONCLUSION: In environments that enable substitution of cigarettes with e-cigarettes, e-cigarette introduction reduces overall cigarette consumption. Thus, to reduce cigarette smoking, policies that encourage adults to substitute cigarette smoking with vaping should be considered.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".