SimSmoke simulation models by educational status: past and future US trends and the potential role of policy
Bibliographic record
Abstract
INTRODUCTION: Simulation models are helpful in anticipating future trends and developing effective tobacco control policies to reduce smoking-related inequities. However, few models have systematically analysed smoking trends by education group. METHODS: We developed four separate SimSmoke models by educational group: less than high school, high school, some college and college and above. Education status is based on the US Census estimates, and smoking prevalence is based on the Current Population Survey-Tobacco Use Supplement (CPS-TUS). Following a first-order Markov process, smoking prevalence evolves through yearly initiation, cessation and relapse, subject to tobacco control policies. The models begin in 2006 and incorporate the impact of tobacco control policies implemented through 2023. They are used to project trends in smoking prevalence and smoking-attributable deaths (SADs) and the impact of policies. The smoking prevalence estimates from the model have also been compared with the CPS-TUS in recent years. RESULTS: Adults with higher educational attainment had the lowest smoking prevalence with the greatest relative decline from 2006 to 2023. Per capita SADs were highest among adults with less education. Price increases, Tobacco 21 laws and smoke-free air laws were most effective in reducing long-term smoking prevalence. The models underestimate the reduction in smoking prevalence relative to CPS-TUS estimates in recent years, especially among youth. DISCUSSION: We found major differences in the initial levels and rates of decline in smoking prevalence by education, leading to widening health inequities. Further study is warranted on education-related policy impacts and the relationship of electronic nicotine delivery systems and other non-cigarette product use to cigarette 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 | 0.013 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".