Predictors of Crystal Methamphetamine Use Initiation or Re-initiation among People receiving Opioid Agonist Therapy : A Prospective Cohort Study
Bibliographic record
Abstract
Background: In the context of the ongoing opioid crisis in the United States and Canada, opioid agonist therapy (OAT) is the first-line treatment for opioid use disorder. However, there is growing concern regarding the increasing methamphetamine use among those on OAT, as well as the impact of such use may have on OAT retention and outcomes. We sought to identify the predictors of crystal methamphetamine initiation or re-initiation among people on OAT, in order to facilitate the development of effective preventive strategies. Methods: We employed multivariable generalized estimate equations to identify the predictors of crystal methamphetamine use initiation or re-initiation among those who were on OAT within two prospective cohorts in Vancouver, Canada between 2005 and 2020. Results: Of the 1281 participants receiving OAT, the median age was 43 years, and 59.2% were male at baseline. During study follow-up, 564 (44.0%) initiated or re-initiated crystal methamphetamine use while receiving OAT. In a multivariable model, a higher crystal methamphetamine use initiation or re-initiation rate was positively associated with younger age, unstable housing, unprotected sex, history of crystal methamphetamine use, as well as recent cocaine, prescription opioid, and unregulated opioid use (all p < 0.05). Conclusions: We identified high and increasing rates of crystal methamphetamine use initiation or re-initiation among our sample of people on OAT. Intervention strategies including housing program referral, sexual risk reduction, and integrated treatment approaches targeting polysubstance use are urgently needed to reduce the risks associated with methamphetamine use as well as the co-use of methamphetamine and opioids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".