The Role of Chronic Pain in Smoking Cessation: Results From a Large Smoking Cessation Program in Primary Care
Bibliographic record
Abstract
INTRODUCTION: Chronic pain often co-occurs with tobacco dependence. Nicotine's acute analgesic effects may increase the reward value of cigarettes and patients report smoking to cope with pain. These factors may hinder smoking cessation outcomes for individuals experiencing chronic pain; however, research on smoking cessation outcomes in this population has been limited. This study examined whether self-reported chronic pain diagnosis was associated with smoking cessation outcome among primary care patients seeking treatment to quit smoking. AIMS AND METHODS: A secondary analysis was conducted using data from 48 573 patients who enrolled in a primary care-based smoking cessation program in Ontario between 2016 and 2019. We compared baseline and treatment characteristics of patients with and without a self-reported lifetime chronic pain diagnosis and used logistic generalized estimating equations to assess the association between chronic pain diagnosis at enrolment and 30-day point prevalence smoking abstinence at 6 months. RESULTS: Approximately one-third of the sample (34.6%, n = 16 793) reported having a chronic pain diagnosis, of whom 72.2% (n = 11 369) were currently using medication for this condition. Those who reported a chronic pain diagnosis had a lower probability of past 30-day smoking abstinence at 6-month follow-up: unadjusted, 20.1% (19.4%-20.8%) vs. 24.7% (24.1%-25.3%), OR = 0.77, 95% CI = 0.73 to 0.82, p < .001; adjusted, 20.7% (19.8%-21.6%) vs. 22.4% (21.6%-23.2%), AOR = 0.90, 95% CI = 0.85 to 0.96, p = .001. CONCLUSIONS: Self-reported lifetime chronic pain diagnosis was associated with a modest decrease in response to treatment with nicotine replacement therapy combined with behavioral support. Further research is needed to clarify how and for whom chronic pain impacts cessation outcomes. IMPLICATIONS: The findings of this study suggest that primary care patients with a self-reported chronic pain diagnosis experience significantly worse smoking cessation outcomes following treatment with nicotine replacement therapy, and that opioid use is also independently associated with poorer quit outcomes. These associations were still significant after controlling for important potential confounding factors such as cannabis and alcohol use, heaviness of smoking, psychiatric comorbidity, and confidence in quitting. Further work is needed to establish whether addressing smoking cessation and pain management together may improve smoking cessation outcomes and reduce the burden of smoking-related health issues in this population.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".