Opioid Agonist Therapy Engagement and Crystal Methamphetamine Use : The Impact of Unregulated Opioid Use in Vancouver, Canada
Bibliographic record
Abstract
Background: Crystal methamphetamine use has substantially increased among people who use opioids in recent years, yet the impact of opioid agonist therapy (OAT) on crystal methamphetamine use remains poorly characterized. Therefore, we sought to examine the relationship between OAT engagement and crystal methamphetamine use and to assess if this relationship differs according to the ongoing use of unregulated opioids. Methods: Data was collected from two harmonized ongoing prospective cohorts of people who use drugs in Vancouver, Canada, between December 2005 and March 2020. We employed multivariable generalized estimating equations to study the relationship between OAT engagement and crystal meth use stratified by ongoing unregulated opioid use. Results: Of 1742 participants who reported frequent opioid use at baseline, the median age was 42 years, and 61.3% were male. Multivariable analyses showed that compared to those who had not received OAT for at least one year: in the absence of ongoing unregulated opioid use, individuals who recently discontinued (adjusted Odds Ratio [aOR] = 0.47, 95% CI = 0.27-0.79), newly initiated (aOR = 0.52, 95% CI = 0.31-0.89), or were retained on OAT (aOR = 0.48, 95% CI = 0.31-0.72) reported a lower frequency of crystal methamphetamine use; in the presence of ongoing unregulated opioid use, individuals who newly initiated OAT reported a greater crystal methamphetamine use frequency (aOR = 1.24, 95% CI = 1.02-1.51). Conclusions: We demonstrated a differential relationship between OAT engagement and crystal methamphetamine use that was conditional on the ongoing use of unregulated opioids. Our findings highlight the complexity of OAT implementation and suggest that polysubstance use patterns should be an important consideration for care providers when devising comprehensive treatment strategies and prognosticating treatment effects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".