Chronic disease diagnoses and health service use among people who died of illicit drug toxicity in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Illicit drug toxicity (i.e., overdose) is the leading cause of death in British Columbia (BC) for people aged 10-59. Stimulants are increasingly detected among drug toxicity deaths. As stimulant use and detection in deaths rises, it is important to understand how people who die of stimulant toxicity differ from people who die of opioid toxicity. METHODS: BC Coroners Service records were retrieved for all people who died of unintentional illicit drug toxicity (accidental or undetermined) between January 1, 2015, and December 31, 2019, whose coroner investigation had concluded and who had an opioid and/or stimulant detected in post-mortem toxicology and identified by the coroner as relevant to the death (N = 3788). BC Chronic Disease Registry definitions were used to identify people with chronic disease. Multinomial regression models were used to examine the relationship between chronic disease diagnoses and drug toxicity death type. RESULTS: Of the 3788 deaths, 11.1% (N = 422) had stimulants but not opioids deemed relevant to the cause of death (stimulant group), 26.8% (N = 1014) had opioids but not stimulants deemed relevant (opioid group), and 62.1% (N = 2352) had both opioids and stimulants deemed relevant (opioid/stimulant group). People with ischemic heart disease (1.80 (1.14-2.85)) and people with heart failure (2.29 (1.25-4.20)) had approximately twice the odds of being in the stimulant group as compared to the opioid group. CONCLUSIONS: Findings suggest that people with heart disease who use illicit stimulants face an elevated risk of drug toxicity death. Future research should explore this association and should identify opportunities for targeted interventions to reduce drug toxicity deaths among people with medical comorbidities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".