Modelling Potential Health Gains and Health System Savings Associated with Vaporised Nicotine Products in Canada
Bibliographic record
Abstract
OBJECTIVES: To model population-wide health and cost impacts of vaporised nicotine products (VNPs) use among Canadian adults 20 years and older from 2015-2095. METHODS: A multi-state lifetable model was used to project potential changes in life expectancy and health-system costs, overall and by province/territory. The simulated population was divided into 68 cohorts by sex, ethnicity, and 5-year age groups. Each year, individuals could either remain in their current state, or transition to one of six smoking/vaping states. Input parameters were extracted or estimated using data from Statistics Canada and literature. Three scenarios were modelled to reflect a range of uncertainty: Status Quo (“SQ”, VNPs commercialised as they are currently in Canada); No-Vaping (“NV”, assuming VNPs never entered the Canadian market); and a Pro-Switching Policy (“PSP”, assuming increased VNP prevalence). RESULTS: Compared to NV, SQ projected to increase life-years by 922,547, while PSP increased them further (+718,137). SQ projected a C$39.0 billion reduction in cumulative lifetime costs compared to NV; PSP would further reduce them by C$30.4 billion. Statistical variability was assessed using sensitivity analyses on input parameters, and Monte-Carlo simulations. CONCLUSIONS: Accessibility to VNPs in Canada was projected to generate net public-health gains and health-system cost savings. These projected health and economic consequences are sensitive to assumptions about accessibility and use by adult smokers and may vary by type of policy environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".