Doctor shopping among chronic noncancer pain patients treated with opioids in the province of Quebec (Canada): incidence, risk factors, and association with the occurrence of opioid overdoses
Bibliographic record
Abstract
INTRODUCTION: Prescription opioids continue to be involved in the opioid crisis, and a better understanding of factors associated with problematic opioid use is needed. OBJECTIVES: The aim of this study was to assess the incidence of opioid doctor shopping, a proxy for problematic opioid use, to identify associated risk factors, and to assess its association with the occurrence of opioid overdoses. METHODS: This was a retrospective cohort study of people living with chronic noncancer pain (CNCP) and treated with opioids for at least 6 months between 2006 and 2017 in the province of Quebec (Canada). Data were drawn from the Quebec health administrative databases. Doctor shopping was defined as overlapping prescriptions written by ≥ 2 prescribers and filled in ≥3 pharmacies. RESULTS: A total of 8,398 persons with CNCP were included. The median age was 68.0 (Q1: 54; Q3: 82) years, and 37.1% were male. The 1-year incidence of opioid doctor shopping was 7.8%, 95% confidence interval (CI): 7.2-8.5. Doctor shopping was associated with younger age (hazard ratio [HR] 18-44 vs ≥65 years: 2.22, 95% CI: 1.77-2.79; HR 45-64 vs ≥65 years: 1.34, 95% CI: 1.11-1.63), male sex (HR = 1.20, 95% CI: 1.01-1.43), history of substance use disorder (HR = 1.32, 95% CI: 1.01-1.72), and anxiety (HR = 1.41, 95% CI: 1.13-1.77). People who exhibited doctor shopping were 5 times more likely to experience opioid overdoses (HR = 5.25, 95% CI: 1.44-19.13). CONCLUSION: Opioid doctor shopping is a marginal phenomenon among people with CNCP, but which is associated with the occurrence of opioid overdoses. Better monitoring of persons at high risk to develop doctor shopping could help prevent opioid overdoses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".