A Retrospective Analysis of the Outcomes of Smoking Cessation Pharmacotherapy Among Persons With Mental Health and Substance Use Disorders
Bibliographic record
Abstract
Objectives: It is common practice to individualize smoking cessation pharmacotherapy based on clinical judgment and patient response. However, little has been documented about the use and outcomes of smoking cessation pharmacotherapy in real-world settings. This study examines factors associated with using smoking cessation pharmacotherapy and related outcomes among smokers with psychiatric and/or substance use disorders who completed an intensive tobacco treatment program within mental health and addiction services settings in Vancouver, Canada. Methods: A retrospective analysis was used to examine combined program participation data (N = 889) from two tobacco treatment programs (i.e., the Tobacco Dependence Clinic and the Butt Out group) between September 2007 and July 2013. Changes in smoking cessation pharmacotherapy from the initial to final treatment and seven-day point prevalence of smoking abstinence (verified by expired carbon monoxide) were assessed at the end of treatment. Results: During treatment, 60% of participants remained on the initial pharmacotherapy plan, 30% received adjunctive treatment, and 10% had treatment plans that were switched. Those whose pharmacotherapy was switched had higher cigarette consumption and nicotine dependence at baseline and were less likely to have a psychiatric disorder history. When comparing between pharmacotherapy groups, individuals who switched medications were less likely to achieve abstinence at the end of treatment as compared to those whose medication treatment plans remained the same or who received adjunctive treatment (unchanged = 36.8%, adjunctive = 38.1% vs. switched = 20.9%, χ2 = 9.59, df = 2, p = .008). In multivariate regression analysis, switching pharmacotherapy was associated with lower smoking cessation (OR = .33, 95% CI [.17, .63]) and significantly mediated the effectiveness of pharmacotherapy. As there were differences in medication switching rates at the clinical level, there were limitations in assessing the impact of mental illness or substance use disorder variables. Conclusions: At least 40% of individuals may have their smoking cessation pharmacotherapy plan changed during treatment. Switching pharmacotherapy may indicate a subgroup of smokers characterized by greater challenges in smoking cessation. Our findings may enhance algorithms for using smoking cessation pharmacotherapy in clinical practice and provide directions for future research in treating tobacco use disorder among individuals with mental health and substance use disorders.
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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 | 0.000 | 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".