Methadone Maintenance Treatment Discontinuation Among Young People who use Opioids in Vancouver, Canada
Bibliographic record
Abstract
OBJECTIVE: Retaining adolescents and young adults (AYA) in medications for opioid use disorder (MOUD), like methadone maintenance treatment (MMT), is critical to reducing toxic drug fatalities. This analysis sought to identify factors associated with MMT discontinuation among AYA. METHOD: Data were derived from the At-Risk Youth Study, a prospective cohort study of street-involved AYA in Vancouver, Canada, between December 2005 and June 2018. Multivariable extended Cox regression identified factors associated with time to MMT discontinuation among AYA who recently initiated MMT. In subanalysis, multivariable extended Cox regression analysis identified factors associated with time to "actionable" MMT discontinuation, which could be addressed through policy changes. RESULTS: = 11, 3.6%). Of the remaining 160 participants who initiated MMT over the study period, 102 (63.8%) discontinued MMT accounting for 119 unique discontinuation events. In multivariable extended Cox regression, MMT discontinuation was positively associated with recent weekly crystal methamphetamine use (adjusted hazard ratio [AHR] = 1.67, 95% confidence interval [CI]: 1.19 to 2.35), but negatively associated with age of first "hard" drug use (per year older) (AHR = 0.95, 95% CI: 0.90 to 1.00) and female sex (AHR = 0.66, 95% CI: 0.44 to 0.99). In subanalysis, recent weekly crystal methamphetamine use (AHR = 4.61, 95% CI: 1.78 to 11.9) and weekly heroin or fentanyl use (AHR = 3.37, 95% CI: 1.21 to 9.38) were positively associated with "actionable" MMT discontinuation, while older age (AHR = 0.87, 95% CI: 0.76 to 0.99) was negatively associated. CONCLUSIONS: Efforts to revise MMT programming; provide access to a range of MOUD, harm reduction, and treatments; and explore coprescribing stimulants to AYA with concurrent stimulant use may improve treatment retention and reduce toxic drug fatalities.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".