Prescription Opioid Use and Non-fatal Overdose in a Cohort of Injection Drug Users
Bibliographic record
Abstract
Background There is growing concern regarding rising rates of prescription drug-related deaths among the general North American population as well as increasing availability of illicitly obtained prescription opioids. Concurrently among people who inject drugs (IDU), illicit prescription opioid use has increased while non-fatal overdose remains a major source of morbidity. Objectives This study aimed to evaluate whether the use of POs was associated with non-fatal overdose among IDU in Vancouver, Canada. Methods Data was obtained from two open prospective cohorts of IDU between December 2005 and May 2013. We used generalized estimating equation (GEE) logistic regression to evaluate the association between prescription opioid use and non-fatal overdose, adjusting for various social, demographic, and behavioral factors. Results There were 1,614 IDU, including 541 (33.5%) women, who were recruited and included in this analysis. At baseline, 526 (32.6%) reported using POs and 118 (7.3%) reported experiencing an overdose in the previous six months. In a multivariable analysis, prescription opioid use remained independently associated with non-fatal overdose (adjusted odds ratio: 1.61, 95% confidence interval: 1.32–1.95), after adjusting for confounders. Conclusion We observed relatively high rates of prescription opioid use among IDU in this setting, and found an independent association between prescription opioid use and non-fatal overdose. Our data is likely representative of riskier substance use associated with those who use prescription opioids within our sample. Interventions to prevent and respond to overdoses should consider the higher risk profiles of IDU who use prescription opioids.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".