Concurrent use of opioids and stimulants and risk of fatal overdose: A cohort study
Bibliographic record
Abstract
BACKGROUND: Stimulant use has been rising among people with opioid use disorder in recent years in North America, alongside a parallel rise in illicit drug toxicity (overdose) deaths. This study aimed to examine the association between stimulant use and overdose mortality. METHODS: Data from a universal health insurance client roster were used to identify a 20% random general population sample (aged ≥12) in British Columbia, Canada between January 1 2015 and December 31 2018 (N = 1,089,682). Provincial health records were used to identify people who used opioids and/or stimulants. Fatal overdose observed during follow-up (January 12,015- December 312,018) was retrieved from Vital Statistics Death Registry and BC Coroners Service Data. Potential confounders including age, sex, health region, comorbidities and prescribed medications were retrieved from the provincial client roster and health records. RESULTS: We identified 7460 people who used stimulants and or opioids. During follow-up there were 272 fatal overdose events. People who used both opioids and stimulants had more than twice the hazard of fatal overdose (HR: 2.02, 95% CI: 1.47-2.78, p < 0.001) compared to people who used opioids only. The hazard of death increased over time among people who used both opioids and stimulants. CONCLUSIONS: There is an urgent need to prioritize the service needs of people who use stimulants to reduce overdose mortality in British Columbia. Findings have relevance more broadly in other North American settings, where similar trends in opioid and stimulant polysubstance use have been observed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".