Toxicology and prescribed medication histories among people experiencing fatal illicit drug overdose in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Since 2015, illicit drug overdose has been one of British Columbia’s most pressing public health issues. Our objective was to assess prescription history in the context of postmortem toxicology among people who had a fatal illicit drug overdose in BC. METHODS: Toxicology results from drug overdose deaths involving 1 or more illicit drugs, as identified by the BC Coroners Service in 2015–2017, were linked to the prescription drug histories of individuals as recorded in BC’s PharmaNet database for a descriptive analysis. Substances identified in toxicology were considered prescribed if the individual had an active dispensation for a matching medication within 60 days before overdose. RESULTS: There were 2872 deaths from illicit drug toxicity during the study period; 1789 (62.3%) were closed cases with toxicology results available. In 85.5% of cases, 1 or more opioids were found to be relevant to death. Prescribed opioids in the absence of nonprescribed opioids were detected in only 2.0% of cases, and 6.7% had a combination of prescribed and nonprescribed opioids. Among those with 1 or more nonprescribed opioids, 78.5% had fentanyl or fentanyl analogues detected. Medications used in opioid agonist therapy (methadone and buprenorphine) were found to be relevant to death in 7.4% of cases, with methadone (130 cases) much more common than buprenorphine (< 5 cases). Stimulants were detected in 70.6% of cases. INTERPRETATION: Our data show a high prevalence of nonprescribed fentanyl and stimulants, and a low prevalence of prescribed opioids detected on toxicology in people who died from illicit drug overdose. These results suggest that strategies to address the current overdose crisis in Canada must do much more than target deprescribing of opioids.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".