Prevalence, Characteristics, and Management of Chronic Noncancer Pain Among People Who Use Drugs: A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Most studies on chronic noncancer pain (CNCP) in people who use drugs (PWUD) are restricted to people attending substance use disorder treatment programs. This study assessed the prevalence of CNCP in a community-based sample of PWUD, identified factors associated with pain, and documented strategies used for pain relief. METHODS: This was a cross-sectional study nested in an ongoing cohort of PWUD in Montreal, Canada. Questionnaires were administered to PWUD seen between February 2017 and January 2018. CNCP was defined as pain lasting three or more months and not associated with cancer. RESULTS: A total of 417 PWUD were included (mean age = 44.6 ± 10.6 years, 84% men). The prevalence of CNCP was 44.8%, and the median pain duration (interquartile range) was 12 (5-18) years. The presence of CNCP was associated with older age (>45 years old; odds ratio [OR] = 1.8, 95% CI = 1.2-2.7), male sex (OR = 2.3, 95% CI = 1.2-4.2), poor health condition (OR = 1.9, 95% CI = 1.3-3.0), moderate to severe psychological distress (OR = 2.9, 95% CI = 1.8-4.7), and less frequent cocaine use (OR = 0.5, 95% CI = 0.3-0.9). Among CNCP participants, 20.3% used pain medication from other people, whereas 22.5% used alcohol, cannabis, or illicit drugs to relieve pain. Among those who asked for pain medication (N = 24), 29.2% faced a refusal from the doctor. CONCLUSIONS: CNCP was common among PWUD, and a good proportion of them used substances other than prescribed pain medication to relieve pain. Close collaboration of pain and addiction specialists as well as better pain assessment and access to nonpharmacological treatments could improve pain management in PWUD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".