Canada smoking and vaping model (SAVM): the public health impact of NVP availability in Canada
Bibliographic record
Abstract
Introduction The 2018 Tobacco and Vaping Products Act (TVPA) legalised the sale of nicotine vaping products (NVPs) to adults in Canada, prior to which NVP sales were banned, although widely available. Little is known about the impact of NVPs on cigarette smoking prevalence once legal and regulated. We adapted a simulation model to the Canadian context to project the public health impact of the TVPA after NVPs were legalised. Methods Using an adapted state-transition smoking and vaping model (SAVM), we assessed public health impacts of the TVPA measured by the difference in smoking and vaping-attributable deaths (SVADs) and life years lost (LYLs) between the pre-TVPA and post-TVPA scenarios. Prevalence data for smoking are from the Canadian Community Health Survey (CCHS), and for NVPs are from the CCHS and Canadian Tobacco and Nicotine Survey. Cigarette and NVP use are projected under a pre-TVPA scenario once NVPs were available but before NVP sales were legal and under a post-TVPA scenario after NVP legalisation. Results In the post-TVPA scenario, Canada SAVM predicts that adult male (female) smoking prevalence declines from 23.5% (16.5%) in 2012 to 19.0% (12.9%) in 2018 and 14.2% (10.6%) in 2021. Compared with the pre-TVPA scenario, Canada SAVM predicts relative reductions in smoking prevalence of 16.7% (7.3%) by 2021. By 2035, post-TVPA cigarette prevalence declines to 5.6% (5.1%) and NVP use increases to 5.9% (3.7%). From 2010 to 2060, 125 300 SVADs (5.2% reduction) and 2.6 million LYLs (10.9% reduction) are averted in the post-TVPA compared with pre-TVPA scenario. The impact on averted SVADs/LYLs is sensitive to the switching rate from cigarette to NVP use. Conclusions Canada SAVM suggests substantial public health gains since legalising NVP sales in Canada. However, cigarette and NVP use and associated health risks should be continually monitored, and further study is needed on the impact of relaxing NVP regulations in other countries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".