Increasing Preference for Fentanyl among a Cohort of People who use Opioids in Vancouver, Canada, 2017-2018
Bibliographic record
Abstract
Background: Despite increasing prevalence of illicit fentanyl use in the US and Canada, preference for fentanyl over other illicit opioids has not been fully characterized. Therefore, we sought to describe changes in illicit opioid preferences over time among people who inject drugs (PWID). Methods: Data were obtained from two prospective cohort studies between 2017 and 2018. Trends in opioid preference over time were examined using bivariable generalized estimating equation (GEE) analysis. Multivariable models were used to identify factors associated with fentanyl preference. Results: Among 732 eligible participants, including 425 (58%) males, the prevalence of preference for fentanyl increased from 4.4% in 2017 to 6.6% in 2018 (Odds Ratio [OR] = 1.27, 95% Confidence Interval [CI]: 1.05–1.52). In a multivariable analysis, younger age (Adjusted Odds Ratio [AOR] = 0.94, 95% CI: 0.92–0.96) and daily crystal methamphetamine injection (AOR = 1.68, 95% CI: 1.01–2.78) were independently associated with preference for fentanyl. The most common reasons for preferring fentanyl included “better high than other opioids” (45%), and “lasts longer than heroin” (27%). Conclusions: The current study has demonstrated that preference for fentanyl has been increasing over time among our sample of PWID who use opioids. Further work is needed to clarify risk factors surrounding transitions to illicit fentanyl.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".