Differences in heroin overdose risk associated with the unregulated drug market: Insights from a supervised injecting facility in Melbourne, Australia
Bibliographic record
Abstract
AIMS: To determine the contribution that variation in the unregulated drug market has on the risk of heroin overdose across individuals with different levels of personal overdose risk. DESIGN: A retrospective cohort study of heroin injecting episodes and overdose cases were examined over a 12-month period between 30 June 2022 and 30 June 2023. SETTING: The Medically Supervised Injecting Room in Melbourne, Australia. CASES: 1474 witnessed heroin overdose cases were examined amongst a cohort of 337 individuals who were predominantly male (n = 276, 81.7%) with a median age of 43.5 years (interquartile range 37.25-49.00 years, range 20-75 years). MEASUREMENTS: The daily overdose rate was used to differentiate High and Low daily overdose risk categories. The number of overdose events that an individual experienced during the study period was used to differentiate people into Standard, Moderate and High personal overdose risk categories. Each overdose case was differentiated by the personal overdose risk of the individual who experienced the overdose, as well as the overdose risk of the day that overdose occurred. A stratified overdose risk profile was then derived across the nine different daily overdose risk and personal overdose risk categories. FINDINGS: The rate of overdose approximately doubled on High overdose risk days compared with Standard overdose risk days, increasing by a factor of 2.11, 2.41 and 2.03 times for individuals in the Standard, Moderate and High personal overdose risk groups. Conversely, the rate of overdose was also substantially reduced on Low overdose risk days to a factor of 0.17, 0.28 and 0.20, respectively. CONCLUSION: Among heroin users in Melbourne, Australia, there is an approximately 10-times difference in the risk of overdose on some days compared with others, which appears to be attributable to the effects of the unregulated drug market and not the effects of variation in personal overdose risk of individuals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".