Does alcohol consumption elevate smoking relapse risk of people who used to smoke? Differences by duration of smoking abstinence
Bibliographic record
Abstract
BACKGROUND: Past research indicates dual users of tobacco and alcohol find it harder to quit smoking and may be more likely to relapse. This study investigated whether post-quit alcohol use predicted smoking relapse among ex-smokers, and whether this relationship varied by length of smoking abstinence. METHOD: The study included 1064 ex-smokers (18+ years) from Canada (n = 340), US (n = 314), England (n = 261), and Australia (n = 149) who participated in the 2018 and 2020 International Tobacco Control Four Country Smoking and Vaping Survey, and we conducted analyses using multivariable logistic regression. We assessed alcohol consumption in 2018 using AUDIT-C and coded as never/low, moderate or heavy level and used alcohol consumption to predict smoking status in 2020. RESULTS: Overall 26 % and 21 % of ex-smokers consumed alcohol at a moderate and heavy level, respectively. Compared to never/low alcohol consumption, risk of smoking relapse among those who consumed alcohol at a moderate level was significantly lower within the first year of abstinence (OR = 0.34, 95 % CI = 0.14-0.81, p = 0.015) but higher thereafter (OR = 2.44, 95 % CI = 1.13-5.23, p = 0.023). The pattern of results was similar for those who consumed alcohol at a heavy level. CONCLUSIONS: Overall, baseline alcohol consumption of ex-smokers did not predict their smoking relapse risk. As expected, risk differed by smoking abstinence duration. However, the pattern was unexpected among the short-term quitters as the subgroup who drank moderately/heavily had lower relapse risk than their counterparts who never drink or at low level, underscoring the need to replicate this unexpected finding.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".