High Prevalence of Self-Reported Exposure to Adulterated Drugs Among People Who Experienced an Opioid Overdose in Canada: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: In North America, rates of overdoses are increasing largely due to the adulteration of illicit drugs by illicit synthetic opioids. OBJECTIVES: We sought to examine the prevalence and correlates of self-reported exposure to adulterated drugs among people who experienced a non-fatal opioid overdose. METHODS: Data were derived from three prospective cohort studies of people who use drugs in Vancouver, Canada between June and November 2016. Multivariable logistic regression analyses were used to examine the prevalence and correlates of self-reported exposure to adulterated drugs. RESULTS: Among 117 participants who reported symptoms consistent with a non-fatal opioid overdose, 78 (66.7%) reported believing the drug was adulterated during their last overdose. Of those, 42 (53.8%) had not perceived adulteration prior to overdose. In the multivariable analysis, engagement in opioid agonist therapy (Adjusted Odds Ratio [AOR] = 2.79, 95% Confidence Interval [CI]: 1.10, 7.45) was independently associated with having not perceived adulteration prior to overdose. Daily heroin use (AOR = 5.28; 95% CI: 1.92, 15.97) and reporting supervised injection site staff were present at most recent overdose (AOR = 6.16; 95% CI: 1.25, 47.27) were independently associated with having perceived adulteration prior to overdose. Conclusions/Importance: We found a high prevalence of believing adulterated drugs were present for the most recent overdose. Further, the high prevalence of unperceived adulteration prior to overdose supports the need to lower the risk of overdose by providing individuals with options to consume drugs in a safer manner, including supervised consumption sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".