Opioid analgesic prescribing for opioid‐naïve individuals prior to identification of opioid use disorder in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND AND AIMS: Prescription opioid analgesics have contributed to the development of opioid use disorder (OUD) in many individuals. We aimed to characterize non-cancer opioid prescribing for opioid-naive individuals prior to OUD identification. DESIGN: Population-based retrospective cohort study using six linked health administrative databases. SETTING: British Columbia (BC), Canada. PARTICIPANTS: People with OUD between 1 January 2001 and 30 September 2018 who initiated opioid analgesic therapy for non-cancer pain prior to OUD identification. MEASUREMENTS: Dose (morphine milligram equivalent per day), days prescribed and clinical guideline non-concordance for initial opioid prescriptions (dose ≥ 90 morphine milligram equivalent per day; ≥ 7 days prescribed; concomitant sedative prescription). We estimated the probability of non-concordant initial prescriptions by source (inpatient post-discharge, non-inpatient acute, non-acute) using logistic regression, adjusting for individual characteristics and comorbidities. FINDINGS: Among 66 372 individuals identified with OUD from 2001 to 2018, 21 331 (32.1%) received opioid analgesics prior to OUD identification. This proportion increased from 3.0% in 2001 to 41.0% in 2011, before decreasing to 34.2% in 2017. Roughly half of opioid prescriptions were attributed to non-acute care visits, peaking at 56.8% in 2007, while the proportion from inpatient visits increased from 19.7% in 2001 to 28.5% in 2017. The predicted probability of receiving non-guideline concordant prescriptions declined over time-periods across all three measures for inpatient and non-inpatient acute care, while remaining stable for non-acute care. In particular, the predicted probability of receiving ≥ 7-day prescriptions following inpatient visits decreased from 53.3% [95% confidence interval (CI) = 50.9, 55.8%] in 2001-06 to 37.2% (95% CI = 33.9, 40.5%) in 2013-18. CONCLUSIONS: Among the 66 372 individuals in British Columbia, Canada diagnosed with opioid use disorder between 2001 and 2018, more than 32% were earlier prescribed non-cancer opioid analgesics. The proportion who had received an opioid analgesic prescription prior to OUD identification peaked at more than 40% in 2011, before stabilizing between 2011 and 2016 and declining thereafter. Guideline concordance improved over time for high-dose and concomitant sedative prescribing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".