Birth Cohort‒Specific Smoking Patterns by Family Income in the U.S.
Bibliographic record
Abstract
INTRODUCTION: In the U.S., low-income individuals generally smoke more than high-income individuals. However, detailed information about how smoking patterns differ by income, especially differences by birth cohort, is lacking. METHODS: Using the National Health Interview Survey 1983-2018 data, individual family income was calculated as a ratio of the federal poverty level. Missing income data from 1983 to 1996 were imputed using sequential regression multivariate imputation. Age‒period‒cohort models with constrained natural splines were used to estimate annual probabilities of smoking initiation and cessation and smoking prevalence and intensity by gender and birth cohort (1900-2000) for 5 income groups: <100%, 100%-199%, 200%-299%, 300%-399%, and ≥400% of the federal poverty level. Analysis was conducted in 2020-2021. RESULTS: Across all income groups, smoking prevalence and initiation probabilities are decreasing by birth cohort, whereas cessation probabilities are increasing. However, relative differences between low- and high-income groups are increasing markedly, such that there were greater declines in prevalence among those in high-income groups in more recent cohorts. Smoking initiation probabilities are lowest in the ≥400% federal poverty level group for males across birth cohorts, whereas for females, this income group has the highest initiation probabilities in older cohorts but the lowest in recent cohorts. People living below the federal poverty level have the lowest cessation probabilities across cohorts. CONCLUSIONS: Smoking prevalence has been decreasing in all income groups; however, disparities in smoking by family income are widening in recent birth cohorts. Future studies evaluating smoking disparities should account for cohort differences. Intervention strategies should focus on reducing initiation and improving quit success among low-income groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".