Temporal Changes in Non-Fatal Opioid Overdose Patterns among People who use Drugs in a Canadian Setting
Bibliographic record
Abstract
Background and Aims: Little is known about how the expansion of opioid agonist therapy (OAT) and emergence of fentanyl in the illicit drug supply in North America has influenced non-fatal opioid overdose (NFOD) risk. Therefore, we sought to identify patterns of substance use and addiction treatment engagement (i.e., OAT, other inpatient or outpatient treatment) prior to NFOD, as well as the trends and correlates of each pattern among people who use drugs (PWUD) in Vancouver, Canada. Methods: Data were derived from participants in three prospective cohorts of PWUD in Vancouver in 2009–2016. Observations from participants reporting opioid-related NFOD in the previous six months were included. A latent class analysis was used to identify classes based on substances used at the time of last NFOD and addiction treatment engagement in the month prior to the last NFOD. Multivariable generalized estimating equations estimated the correlates of each class membership. Results: In total, 889 observations from 570 participants were included. Four distinct classes were identified: (1) polysubstance use (PSU) and addiction treatment engagement; (2) PSU without treatment engagement; (3) exposure to unknown substances, mostly without treatment engagement; and (4) primary heroin users without treatment engagement. The class of exposure to unknown substances appeared in 2015 and became the dominant group (76.9%) in 2016. In multivariable analyses, the odds of membership in the class of primary heroin users decreased over time (adjusted odds ratio [AOR]: 0.74, 95% confidence interval [CI]: 0.68–0.81). Conclusions: Changing profiles of PWUD reporting opioid-related NFOD were seen over time. Notably, there was a sudden increase in reports of overdose following exposure to unknown substances since 2015, the majority of whom reported no recent addiction treatment engagement. Further study into patterns of substance use and strategies to improve addiction treatment engagement is needed to improve and focus overdose prevention efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".