Initiation and/or re-initiation of drug use among people who use drugs in Vancouver, Canada from 2021 to 2022: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Widespread health service disruptions resulting from the COVID-19 pandemic coincided with a dramatic increase in overdose deaths among people who use drugs (PWUD) in Vancouver, Canada. Those with a history of injection drug use are known to be at heightened risk of substance-associated harms. Drug use patterns and associated sociodemographic and health care utilization trends have been understudied in this population since the pandemic onset. We sought to understand patterns of drug use initiation and/or re-initiation among people with a history of injection drug use (IVDU). METHODS: Data were obtained from three harmonized prospective cohort studies of PWUD in Vancouver. Participants with a lifetime history of IVDU who responded to a survey between June 2021 and May 2022 were included. The primary outcome variable was a composite of substance use initiation and re-initiation over the study period, labelled as drug (re)-initiation. A multivariable generalized linear mixed-effects model was used to examine factors associated with self-reported (re)-initiation of substance use over the past six months. RESULTS: Among 1061 participants, the median age was 47 years at baseline and 589 (55.5%) identified as men. In total, 183 (17.2%) participants reported initiating and/or re-initiating a drug, with 44 (4.1%) reporting new drug initiation and 148 (14.0%) reporting drug re-initiation (9 participants responded 'yes' to both). Overall, unregulated stimulants (e.g., crystal methamphetamine and cocaine) were the most common drug class (re-)initiated (n = 101; 55.2%), followed by opioids (n = 74; 40.4%) and psychedelics (n = 36; 19.7%). In the multivariable analysis, (re-)initiation of drug use was independently associated with recent IVDU (adjusted odds ratio [AOR] 2.62, 95% confidence interval [CI] 1.02, 6.76), incarceration (AOR 3.36, CI 1.12, 10.14) and inability to access addiction treatment (AOR 4.91, 95% CI 1.22, 19.75). CONCLUSIONS: In an era impacted by the intersecting effects of the COVID-19 pandemic and the overdose crisis, nearly one in five PWUD with a history of IVDU began using a new drug and/or re-started use of a previous drug. Those who reported drug (re-)initiation exhibited riskier substance use behaviours and reported difficulty accessing treatment services. Our findings underscore the need to provide additional resources to support this high-risk population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".