Correlates of concurrent use of stimulants and opioids among people who access harm reduction services in British Columbia, Canada: Findings from the 2019 Harm Reduction Client Survey
Bibliographic record
Abstract
BACKGROUND: During a public health overdose emergency driven by fentanyl in British Columbia (BC), Canada, a parallel increase in concurrent use of methamphetamines may be contributing to the risk of overdose. Concurrent use refers to the use of stimulants and opioids one after the other, or together. Describing substance use practices and elucidating correlates of concurrent use can inform harm reduction service provision and prevent overdose events. METHODS: This cross-sectional study analyzed the 2019 Harm Reduction Client Survey administered at 22 harm reduction sites across BC and sampled individuals aged 19 and older. Bivariable and multivariable logistic regression models identified correlates of concurrent use of stimulants and opioids in the last three days. RESULTS: The study sample was composed of 574 individuals who used stimulants and/or opioids, among whom 307 (53.5%) reported concurrent use. Compared with individuals who used stimulants and/or opioids separately, the odds of concurrent use were significantly increased (OR=2.74, 95%CI 1.71-4.51) while the adjusted odds were increased (aOR=1.79, 95%CI 0.98-3.34) among individuals who experienced an opioid overdose. Further, the adjusted odds of concurrent use decreased with every one-year increase in age (aOR=0.97, 95%CI 0.95-0.99), among individuals with paid work (aOR=0.58, 95%CI 0.33-0.99), and stable housing (aOR=0.61, 95%CI 0.37-1.02). The adjusted odds of concurrent use increased among individuals who used drugs daily (aOR=3.78, 95%CI 2.28-6.40), used tobacco (aOR=2.03, 95%CI 1.09-3.87), used benzodiazepines (aOR=3.72, 95%CI 1.76-8.41), owned a naloxone kit (aOR=1.94, 95%CI 1.15-3.31), used observed consumption sites (aOR=2.51, 95%CI 1.57-4.07), and were prescribed opioid agonist therapy (aOR=2.92, 95%CI 1.81-4.77). CONCLUSIONS: Individuals who used stimulants and opioids concurrently tend to be younger, without paid work or housing. They were engaged in harm reduction and treatment services, yet used illicit drugs with unknown concentration daily. Improving access to age-appropriate services and expanding the availability of both legal and regulated stimulants and opioids may further reduce harms of concurrent use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".