Factors moderating the relative effectiveness of varenicline and nicotine replacement therapy in clients using smoking cessation services
Bibliographic record
Abstract
AIMS: To assess how far the greater effectiveness of varenicline over nicotine replacement therapy (NRT) is moderated by characteristics of the smokers or setting in clinical practice. DESIGN: We used observational data from 22 472 treatment episodes between 2013 and 2016 from smoking cessation services in England to assess whether differences between varenicline and NRT were moderated by a set of smoker and setting characteristics: these included level of social deprivation, age, gender, ethnic group, nicotine dependence and treatment context. From the above, 15 640 episodes were analysed in relation to 4-week quit and 14 273 episodes at 12 weeks. All two-way interactions involving pharmacotherapy were fitted in addition to the main effects and a parsimonious model identified using a backwards stepwise selection procedure. SETTING: England PARTICIPANTS: Clients of smoking cessation service (number of individuals in 4-week quit analysis = 15 640). MEASUREMENTS: Four-week carbon monoxide-validated (primary outcome) and 12-week self-reported (secondary outcome) quit success/failure. FINDINGS: At both follow-up points, varenicline was associated with higher success rates overall [P < 0.001 at both 4 and 12 weeks; adjusted odds ratio (OR) varenicline versus NRT = 1.82 (95% confidence interval (CI) = 1.61, 2.06) and 2.58 (95% CI = 2.26, 2.94) at 4 and 12 weeks, respectively]. At 12 weeks, the relative benefits of varenicline were found to be influenced by the setting in which advice was provided [P < 0.001 for interaction pharmacotherapy × setting; adjusted odds ratio for varenicline × pharmacy setting = 0.53 (95% CI = 0.42, 0.69) and for varenicline × general practice (GP) setting = 0.79 (95% CI = 0.64, 0.98) against a baseline of 1 for varenicline × community setting]. The same trends were evident at 4 weeks, but this did not translate to statistical significance. There was inconclusive evidence for moderating effects of other variables. CONCLUSIONS: Varenicline use was associated with higher smoking cessation rates than nicotine replacement therapy in routine clinical practice, irrespective of a wide range of smoker characteristics, but the difference was less in certain intervention settings, most notably pharmacy but also GP practice, compared with community setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".