Financial Strain and Smoking Cessation and Relapse Among U.S. Adults Who Smoke: A Longitudinal Cohort Study
Bibliographic record
Abstract
INTRODUCTION: This study examines the prospective association between financial strain and smoking cessation and smoking relapse among U.S. adults with established smoking. METHODS: Discrete-time survival models were fit to nationally representative data in Waves 1-5 (2013-2019) of the U.S. Population Assessment of Tobacco and Health Study for smoking cessation (n=6,972) and smoking relapse (n=1,195). Models were adjusted for demographics (age, sex, race, and ethnicity), socioeconomic positioning (education, income, health insurance status), and tobacco-related confounders (quit attempts, coupon receipt, and nicotine dependence). Data were collected between 2013 and 2019, and the analysis was conducted in 2023-2024. RESULTS: Among adults with established cigarette smoking, financial strain was associated with a reduced likelihood of cigarette smoking cessation (HR: 0.81, 95% CI: 0.72, 0.92) and an increased likelihood of cigarette smoking relapse (HR: 1.56, 95% CI: 1.24, 1.96) in multivariable models. Results were robust to sensitivity analyses varying confounder control, sample restrictions, and survey weights used. CONCLUSIONS: The results from this study suggest that financial strain is a barrier to cigarette smoking without relapse, which may be due to stress and coping processes. Smoking cessation interventions would benefit from considering the role that financial strain plays in inhibiting smoking cessation without relapse.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".