Investigating opioid preference to inform safe supply services: A cross sectional study
Bibliographic record
Abstract
BACKGROUND: The drug toxicity crisis continues to be a significant cause of death. Over 24,600 people died from opioid toxicity in Canada over the last 5 years. Safe supply programs are required now more than ever to address the high rate of drug toxicity overdose deaths caused by illicit fentanyl and its analogues. This study aims to identify opioid preferences and associated variables to inform further phases of safe supply program implementation. METHODS: The Harm Reduction Client Survey, an annual cross-sectional survey of people who use drugs (PWUD), was administered at harm reduction supply distribution sites in BC in October-December 2019. The survey collects information on substance use patterns, associated harms, stigma, and utilization of harm reduction services. Eligibility criteria for survey participation included aged 19 years or older; self-reported substance use of any illicit substance in the past six months, and ability to provide verbal informed consent. We conducted multivariate logistic regression to investigate associations with opioid preference. We used the dichotomized preference for either heroin or fentanyl as an outcome variable. Explanatory variables of interest included: geographic region, urbanicity, gender, age category, Indigenous identity, housing, employment, witnessing or experiencing an overdose, using drugs alone, using drugs at an observed consumption site, injection as preferred mode of use, injecting any drug, frequency of use, and drugs used in last 3 days. RESULTS: Of the 621 survey participants, 405 reported a preferred opioid; of these 57.8% preferred heroin, 32.8% preferred fentanyl and 9.4% preferred prescription opioids. The proportion of participants who preferred heroin over fentanyl significantly increased with age. The adjusted odds of a participant 50 or older preferring heroin was 6.76 (95% CI: 2.78-16.41, p-value: < 0.01) times the odds of an individual 29 or under. The adjusted odds of an Indigenous participant reporting a preference for heroin compared to fentanyl was 1.75 (95% CI: 1.03-2.98, p-value: 0.04) the odds of a non-Indigenous participant reporting the same. Adjusted odds of heroin preference also differed between geographic regions within British Columbia, Canada. CONCLUSION: Opioid preference differs by age, geographic area, and Indigenous identity. To create effective safe supply programs, we need to engage PWUD about their drugs of choice.
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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 | 0.005 | 0.009 |
| 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) | 0.003 | 0.001 |
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".