Initiation of Prescription Opioids for Non-Cancer Pain and Risk of Treated Opioid Use Disorder: a Population-Based Study
Bibliographic record
Abstract
Background: When measured in milligrams of morphine equivalents (MME) dispensed, Canada has the second highest rate of opioid prescribing in the world. Prescription opioids have been associated with adverse health outcomes, including opioid use disorder (OUD). This study will help to understand how patterns of initial prescription opioid use affect the risk of being treated for OUD. Objectives: To examine population-level patterns of opioid prescribing among previously opioid naïve individuals who initiated prescription opioid treatment for non-cancer pain, and to determine whether a dose-response relationship exists between average daily dose (in MME) at initiation and time to treated OUD. Methods: We conducted a population-based cohort study among opioid naïve individuals dispensed opioids for non-cancer pain between 2013 and 2016 in Ontario, Canada. The primary exposure was average daily dose (MME) at initiation of opioid therapy. Secondary exposures included opioid formulation type and total days’ supply. The outcome of interest was treated OUD. Individuals were followed for a maximum of 57 months (to March 31, 2018). We performed Kaplan-Meier survival and multivariable Cox regression to examine factors associated with risk of treated OUD. Results: 1,607,659 opioid-naïve individuals initiated a prescription opioid within the study period. The median age of individuals at initiation was 47 years (IQR 30-61), 52% were female and 88% lived in an urban community setting. The incidence rate of treated OUD within the study period was 86 cases per 100,000 person-years. Compared to an average daily dose of <20 MME at initiation, higher average daily doses were associated with greater hazard of treated OUD, 20-50 MME (HR 1.11, 95% CI: 1.02, 1.21), >50-90 MME (HR 1.29, 95% CI: 1.16, 1.44), >90-150 MME (HR 1.29, 95% CI: 1.06, 1.56), >150-200 MME (HR 2.49, 95% CI: 1.54, 4.03) and >200 MME (HR 4.15, 95% CI: 2.89, 5.97). Conclusions: Certain demographic, clinical and opioid prescription characteristics at initiation are associated with shorter times to treated OUD. Results suggest a dose-response relationship between average daily dose and time to treated OUD. This study highlights the importance of adhering to clinical practice guidelines when weighing the risks and benefits of prescription opioid initiation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".