Are health conditions and concerns about health effects of smoking predictive of quitting? Findings from the ITC 4CV Survey (2016–2018)
Bibliographic record
Abstract
INTRODUCTION: Limited research has investigated the relationship between multiple health conditions and subsequent quitting activities at the population level. This study examines whether nine health conditions and concerns related to smoking are predictive of quit attempts and success among those who tried. METHODS: Data came from the International Tobacco Control Four Country Smoking and Vaping Survey conducted in Australia, Canada, England and the US. A total of 3998 daily smokers were surveyed in 2016 and recontacted in 2018. Respondents were asked in 2016 whether they had a medical diagnosis for depression, anxiety, alcohol problems, obesity, chronic pain, diabetes, heart disease, cancer, and chronic lung disease, and whether they had concerns about past/future health effects of smoking. Outcomes were quit attempts and success (having been abstinent for at least one month between surveys). RESULTS: Across all four countries, 44.4% of smokers tried to quit between the two survey years, and of these 36.8% were successful. Concerns about past (adjusted odds ratio, AOR=1.66, 95% CI: 1.32-2.08, p<0.001) and future effects of smoking (AOR=2.17, 95% CI: 1.62-2.91, p<0.001) and most health conditions predicted quit attempts, but were mostly unrelated to quit success, with concerns about future effects (AOR=0.59, 95% CI: 0.35-0.99, p<0.05), chronic lung conditions (AOR=0.56, 95% CI: 0.37-0.86, p<0.01) and chronic pain (with a trend) being associated with lower success. CONCLUSIONS: Having a major chronic health condition does, generally, motivate making quit attempts, but in some cases it is associated with failure among those who try. More effective cessation support is required for these high priority groups.
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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".