Patterns of Birth Cohort‒Specific Smoking Histories by Race and Ethnicity in the U.S.
Bibliographic record
Abstract
INTRODUCTION: U.S. smoking prevalence varies greatly by race/ethnicity. However, little is known about how smoking initiation, cessation, and intensity vary by birth cohort and race/ethnicity. METHODS: Adult smoking data were obtained from the 1978-2018 National Health Interview Surveys. Age‒period‒cohort models with constrained natural splines were developed to estimate historical smoking patterns among non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian and Pacific Islander, and non-Hispanic American Indian and Alaskan Native individuals. Annual smoking prevalence and probabilities of smoking initiation, cessation, and intensity by age, year, gender, and race/ethnicity were estimated for the 1900 to 2000 birth cohorts. Analysis was conducted in 2020-2021. RESULTS: Smoking initiation probabilities were highest for the American Indian and Alaskan Native population, second highest among the non-Hispanic White population, and lowest among Asian and Pacific Islander and Hispanic populations across birth cohorts. Historically, initiation probabilities among non-Hispanic Black populations were comparable with those among non-Hispanic White populations but have decreased since the 1970 birth cohort. Cessation probabilities were lowest among American Indian and Alaskan Native and non-Hispanic Black populations and highest among non-Hispanic White and Asian and Pacific Islander populations across cohorts and ages. Initiation and cessation probabilities produce observed patterns of smoking where prevalence among American Indian and Alaskan Native populations is highest across all ages and cohorts. Across cohorts, smoking prevalence among non-Hispanic Black populations, particularly males, is lower than among non-Hispanic White populations at younger ages but higher at older ages. CONCLUSIONS: There are important and persistent racial/ethnic differences in smoking prevalence, initiation, cessation, and intensity across U.S. birth cohorts. Targeted interventions should address widening smoking disparities by race/ethnicity, particularly for American Indian and Alaskan Native and non-Hispanic Black populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".