Use of stop‐smoking medications in the <scp>U</scp> nited <scp>S</scp> tates before and after the introduction of varenicline
Bibliographic record
Abstract
AIMS: To evaluate trends in use of stop-smoking medications (SSMs) before and after varenicline (Chantix™) was introduced to the market-place in the United States, and to determine whether varenicline reached segments of the population unlikely to use other SSMs. DESIGN: Cohort survey. SETTING: United States. PARTICIPANTS: A nationally representative sample of adult smokers in the United States interviewed as part of the International Tobacco Control Four Country Survey between 2004 and 2011. Primary analyses used cross-sectional data from 1737 smokers who attempted to quit (∼450 per wave). MEASUREMENTS: Reporting an attempt to quit smoking; use of each of the following types of SSMs for the purpose of quitting smoking: nicotine gum, nicotine patch, other nicotine replacement therapy, bupropion and varenicline. FINDINGS: There was a significant increase in the rate of use of any SSM among quit attempters across the study period [odds ratio (OR) = 1.15, 95% confidence interval (CI) = 1.10-1.21 per year]. This increase was largest after varenicline was introduced (OR = 1.16, 95% CI = 1.07-1.26 per year); however, there was a decline in nicotine patch use during this time (OR = 0.87, 95% CI = 0.76-0.99 per year). Varenicline users were generally similar to users of other SSMs but differed from those who did not use any SSMs, in that they tended to be older (OR = 5.46, P = 0.024), to be white (OR = 2.33, P = 0.002), to have high incomes (OR = 1.85, P = 0.005), to have high nicotine dependence prior to quitting (OR = 2.40, P = 0.001) and to have used medication in the past (OR = 3.29, P < 0.001). CONCLUSIONS: The introduction of varenicline in the United States coincided with a net increase in attempts to quit smoking and, among these, a net increase in use of stop-smoking medications. The demographic profile of varenicline users is similar to the profile of those who use other stop-smoking medications and different from the profile of those who attempt to quit without any medication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".