The smoking and vaping model, A user-friendly model for examining the country-specific impact of nicotine VAPING product use: application to Germany
Bibliographic record
Abstract
BACKGROUND: Simulation models play an increasingly important role in tobacco control. Models examining the impact of nicotine vaping products (NVPs) and smoking tend to be highly specialized and inaccessible. We present the Smoking and Vaping Model (SAVM),a user-friendly cohort-based simulation model, adaptable to any country, that projects the public health impact of smokers switching to NVPs. METHODS: SAVM compares two scenarios. The No-NVP scenario projects smoking rates in the absence of NVPs using population projections, deaths rates, life expectancy, and smoking prevalence. The NVP scenario models vaping prevalence and its impact on smoking once NVPs became popular. NVP use impact is estimated as the difference in smoking- and vaping-attributable deaths (SVADs) and life-years lost (LYLs) between the No-NVP and NVP scenarios. We illustrate SAVM's adaptation to the German adult ages 18+ population, the Germany-SAVM by adjusting the model using population, mortality, smoking and NVP use data. RESULTS: Assuming that the excess NVP mortality risk is 5% that of smoking, Germany-SAVM projected 4.7 million LYLs and almost 300,000 SVADs averted associated with NVP use from 2012 to 2060. Increasing the excess NVP mortality risk to 40% with other rates constant resulted in averted 2.8 million LYLs and 200,000 SVADs during the same period. CONCLUSIONS: SAVM enables non-modelers, policymakers, and other stakeholders to analyze the potential population health effects of NVP use and public health interventions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".