Smoking Disparities by Level of Educational Attainment and Birth Cohort in the U.S.
Bibliographic record
Abstract
INTRODUCTION: Little is known about how U.S. smoking patterns of initiation, cessation, and intensity vary by birth cohort across education levels or how these patterns may be driven by other demographic characteristics. METHODS: Smoking data for adults aged ≥25 years was obtained from the National Health Interview Surveys 1966-2018. Age-period-cohort models were developed to estimate the probabilities of smoking initiation, cessation, intensity, and prevalence by age, cohort, calendar year, and gender for education levels: ≤8th grade, 9th-11th grade, high school graduate or GED, some college, and college degree or above. Further analyses were conducted to identify the demographic factors (race/ethnicity and birthplace) that may explain the smoking patterns by education. Analyses were conducted in 2020-2021. RESULTS: Smoking disparities by education have increased by birth cohort. In recent cohorts, initiation probabilities were highest among individuals with 9th-11th-grade education and lowest among individuals with at least a college degree. Cessation probabilities were higher among those with higher education. Current smoking prevalence decreased over time across all education groups, with important differences by gender. However, it decreased more rapidly among individuals with ≤8th grade education, resulting in this group having the second lowest prevalence in recent cohorts. This may be driven by the increasing proportion of non-U.S. born Hispanics in this group. CONCLUSIONS: Although smoking is decreasing by cohort across all education groups, disparities in smoking behaviors by education have widened in recent cohorts. Demographic changes for the ≤8th-grade education group need special consideration in analyses of tobacco use by education.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".