Smoking Histories by State in the U.S.
Bibliographic record
Abstract
INTRODUCTION: Smoking rates across U.S. states have declined at different rates over time because some states have progressive tobacco control policies, whereas others have yet to adopt them. Therefore, each state has its own unique historical experience of smoking initiation, cessation, and prevalence. This study characterizes smoking histories for each U.S. state by birth cohort. METHODS: Using 1965-2018 National Health Interview Survey and 1992-2019 Tobacco Use Supplement to the Current Population Survey data, statistical methods applied an age‒period‒cohort modeling framework to reconstruct population-level smoking histories for each state. Smoking initiation, cessation, and intensity by age, gender, and cohort were estimated for each state. These were used to construct state-specific trends in the prevalence of current, former, and never smoking as well as the mean smoking duration and pack years. Analysis was conducted from 2017 to 2022. RESULTS: California and Kentucky, respectively, are exemplar states of more and less aggressive tobacco control. Initiation probabilities were consistently lower in California than in Kentucky, and cessation probabilities were higher. Hence, the smoking prevalence derived from these parameters is higher in Kentucky. The intensity of cigarette smoking was higher in Kentucky than in California, yielding considerably higher estimated pack years when used with the other parameters. Summaries of smoking trends are given for all states. CONCLUSIONS: Smoking initiation, cessation, and intensity trends vary substantially across states, resulting in major differences in estimated smoking prevalence, duration, and pack years. Some states show improvements in smoking metrics over time with more recent birth cohorts, but others have shown very little.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".