The potential impact of removing a ban on electronic nicotine delivery systems using the Mexico smoking and vaping model (SAVM)
Bibliographic record
Abstract
Abstract Objective To develop the Mexico Smoking and Vaping Model (Mexico SAVM) to estimate cigarette and electronic nicotine delivery systems (ENDS) prevalence and the public health impact of legalizing ENDS use. Methods SAVM, a cohort-based discrete-time simulation model, compares two scenarios. The ENDS-Restricted Scenario estimates smoking prevalence and associated mortality outcomes under the current policy of an ENDS ban, using Mexico-specific population projections, death rates, life expectancy, and smoking and e-cigarette prevalence. The ENDS-Unrestricted Scenario projects smoking and vaping prevalence under a hypothetical scenario where ENDS use is allowed. The impact of legalizing ENDS use is estimated as the difference in smoking- and vaping-attributable deaths (SVADs) and life-years lost (LYLs) between the ENDS-Restricted and Unrestricted scenarios. Results Compared to a national ENDS ban, The Mexico SAVM projects that legalizing ENDS use could decrease smoking prevalence by 40.1% in males and 30.9% in females by 2049 compared to continuing the national ENDS ban. This reduction in prevalence would save 2.9 (2.5 males and 0.4 females) million life-years and avert almost 106 (91.0 males and 15.5 females) thousand deaths between 2025 and 2049. Public health gains decline by 43% to 59,748 SVADs averted when the switching rate is reduced by half and by 24.3% (92,806 SVADs averted) with a 25% ENDS risk level from that of cigarettes but increased by 24.3% (121,375 SVADs averted) with the 5% ENDS risk. Conclusions Mexico SAVM suggests that greater access to ENDS and a more permissive ENDS regulation, simultaneous with strong cigarette policies, would reduce smoking prevalence and decrease smoking-related mortality. The unanticipated effects of an ENDS ban merit closer scrutiny, with further consideration of how specific ENDS restrictions may maximize public health benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".