Smoking Cessation Outcomes and Predictors Among Individuals With Co-occurring Substance Use and/or Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVE: Individuals with substance use and psychiatric disorders have a high prevalence of tobacco use disorders and are disproportionately affected by tobacco-related morbidity and mortality. However, it is unclear how having co-occurring disorders affects tobacco cessation. Our aim was to examine smoking cessation outcomes and relevant predictors of smoking cessation among smokers with substance use and/or psychiatric disorders. METHODS: Data from medical records of 674 participants in a tobacco treatment program within mental health and addictions services in Vancouver, Canada, were analyzed. The 26-week treatment program included an 8-week structured behavioral counseling group, an 18-week support group, and 26 weeks of no-cost pharmacotherapy. Information on demographics, tobacco use and history, type of pharmacotherapy received, nicotine dependence, importance of and confidence in quitting smoking, expired carbon monoxide level, substance use and psychiatric disorder history, and total program visits were gathered. RESULTS: Approximately 67% (n = 449) of participants had co-occurring substance use and psychiatric disorders, while 20% (n = 136) had substance use disorder only, 10% (n = 67) had psychiatric disorder only, and 3% (n = 22) had tobacco dependence only. Rates of tobacco cessation (i.e., 7-day point prevalence of abstinence verified by expired carbon monoxide of ≤8 ppm) by group in the 522 people who completed treatment were as follows: 38.2% for those with co-occurring disorders, 47.1% for those with tobacco dependence only, 47.1% for those with substance use disorder only, and 41.8% for those with psychiatric disorder only. Length of treatment was a significant predictor of smoking cessation for those with co-occurring disorders and substance use disorder only. In the final stratified multivariate analysis, for individuals with co-occurring disorders, having an opiate use disorder (as compared to an alcohol use disorder) and higher nicotine dependence scores at baseline were predictive of poor cessation outcomes, while greater length of treatment was predictive of successful smoking cessation. CONCLUSIONS: Tobacco cessation treatment for individuals with co-occurring substance use and psychiatric disorders is likely to be as effective as for smokers with either disorder alone. Treatment duration predicts success among these smokers so strategies to enhance engagement and retention are needed.
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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 | 0.001 |
| 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".