Increasing Cannabis Use Is Associated With Poorer Cigarette Smoking Cessation Outcomes: Findings From the ITC Four Country Smoking and Vaping Surveys, 2016–2018
Bibliographic record
Abstract
INTRODUCTION: Concurrent use of tobacco and cannabis may impede successful cigarette smoking cessation. This study examined whether changes in cannabis use frequency were associated with smoking cessation. AIMS AND METHODS: Nationally representative samples of adult cigarette smokers from Canada (n = 1455), the United States (n = 892), England (n = 1416), and Australia (n = 717) were surveyed in 2016 and 2018. In each year, smokers reported how often they used cannabis in the previous 12 months. Reports were compared to determine whether cannabis use increased, remained unchanged, or decreased. Smoking cessation outcomes, measured in 2018, were (1) any attempt to quit in the previous year, (2) currently quit, and (3) currently quit for at least 6 months. Weighted multivariable logistic regression estimated the association between changes in cannabis use and cessation outcomes. RESULTS: Cigarette smokers who increased their frequency of cannabis use were significantly less likely to be currently quit than noncannabis-using smokers (adjusted odds ratio (aOR) = 0.52, 95% CI = 0.31% to 0.86%); they were also less likely to have quit for at least 6 months (aOR = 0.30; 95% CI = 0.15% to 0.62%). CONCLUSIONS: Smokers who increase their frequency of cannabis use have poorer smoking cessation outcomes compared to noncannabis-using smokers. It will be important to monitor the impact of cannabis legalization on patterns of cannabis use, and whether this influences cigarette smoking cessation rates. IMPLICATIONS: Cigarette smokers who start using cannabis may be less likely to quit cigarettes compared with smokers who do not use cannabis at all. If smokers who also use cannabis are more likely to continue smoking, it is important to monitor these trends and understand the impact, if any, on smoking cessation in jurisdictions that have legalized cannabis for nonmedical use.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".