SimSmoke simulation models distinguished by race/ethnicity: past and future trends and the potential role of policy
Bibliographic record
Abstract
INTRODUCTION: Policy interventions to reduce racial/ethnic cigarette smoking and related health disparities are needed to improve health equity. Simulation models can be useful in gauging the impact of tobacco control policies on trends in smoking-related outcomes, but few have systematically analyzed the impact of tobacco control policies across racial/ethnic groups. METHODS: We developed 3 separate SimSmoke models for the non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic populations. Following a first-order Markov process, population projections evolve through net immigration and death rates, and smoking prevalence evolves through initiation, cessation, and relapse. The models incorporate policies implemented from 2011 to 2023 and are used to consider trends in NHW, NHB, and Hispanic smoking prevalence and smoking-attributable death and the impact of policies on those trends. RESULTS: The models indicate major differences in smoking trends and smoking-attributable deaths (SADs) among NHW, NHB, and Hispanic adults, with NHB males experiencing the smallest smoking decline through 2023 and having the highest 2023 smoking prevalence. The models predict major differences in the impact of tobacco control policies, especially the greater effect of cigarette taxes on NHB and Hispanic adults than NHW adults and the reduced impact of T21 laws on NHB compared to NHW and Hispanic adults. DISCUSSION: The models predict large differences in levels and rates of decline in NHW, NHB, and Hispanic smoking prevalence, leading to widening health disparities between racial/ethnic groups. Further study is needed on differential race/ethnicity impacts of tobacco control policies and the role of cigars, e-cigarettes, and other product use.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".