Longitudinal Associations Between Smoking Cessation Medications and Alcohol Consumption Among Smokers in the International Tobacco Control Four Country Survey
Bibliographic record
Abstract
BACKGROUND: Available evidence suggests that quitting smoking does not alter alcohol consumption. However, smoking cessation medications may have a direct impact on alcohol consumption independent of any effects on smoking cessation. Using an international longitudinal epidemiological sample of smokers, we evaluated whether smoking cessation medications altered alcohol consumption independent of quitting smoking. METHODS: Longitudinal data were analyzed from the International Tobacco Control Four Country (ITC-4) Survey between 2007 and 2008, a telephone survey of nationally representative samples of smokers from the United Kingdom, Australia, Canada, and the United States (n = 4,995). Quantity and frequency of alcohol consumption, use of smoking cessation medications (varenicline, nicotine replacement [NRT], and no medications), and smoking behavior were assessed across 2 yearly waves. Controlling for baseline drinking and changes in smoking status, we evaluated whether smoking cessation medications were associated with reduced alcohol consumption. RESULTS: Varenicline was associated with a reduced likelihood of any drinking compared with nicotine replacement (OR = 0.56; 95% CI = 0.34 to 0.94), and consuming alcohol once a month or more compared to nicotine replacement (OR = 0.43; 95% CI = 0.27 to 0.69) or no medication (OR = 0.63; 95% CI = 0.41 to 0.99). Nicotine replacement was associated with an increased likelihood of consuming alcohol once a month or more compared to no medication (OR = 1.14; 95% CI = 1.03 to 1.25). Smoking cessation medications were not associated with more frequent drinking (once a week or more) or typical quantity consumed per episode. Medication effects on drinking frequency were independent of smoking cessation. CONCLUSIONS: This epidemiological investigation demonstrated that varenicline was associated with a reduced frequency of alcohol consumption. Continued work should clarify under what conditions nicotine replacement therapies may increase or decrease patterns of alcohol consumption.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".