Potential cost‐effectiveness of supervised injection facilities in Toronto and Ottawa, Canada
Bibliographic record
Abstract
BACKGROUND AND AIMS: Supervised injection facilities (legally sanctioned spaces for supervised consumption of illicitly obtained drugs) are controversial public health interventions. We determined the optimal number of facilities in two Canadian cities using health economic methods. DESIGN: Dynamic compartmental model of HIV and hepatitis C transmission through sexual contact and sharing of drug use equipment. SETTING: Toronto and Ottawa, Canada. PARTICIPANTS: Simulated population of each city. INTERVENTIONS: Zero to five supervised injection facilities. MEASUREMENTS: Direct health-care costs and quality-adjusted life-years (QALYs) over 20 years, discounted at 5% per year; incremental cost-effectiveness ratios. FINDINGS: In Toronto, one facility cost $4.1 million and resulted in a gain of 385 QALYs over 20 years, for an incremental cost-effectiveness ratio (ICER) of $10,763 per QALY [95% credible interval (95CrI): cost-saving to $278,311]. Establishing one facility in Ottawa had an ICER of $6127 per QALY (95CrI: cost-saving to $179,272). At a $50,000 per QALY threshold, three facilities would be cost-effective in Toronto and two in Ottawa. The probability that establishing three, four, or five facilities in Toronto was cost-effective was 17, 21, and 41%, respectively. Establishing one, two, or three facilities in Ottawa was cost-effective with 13, 35, and 41% probability, respectively. Establishing no facility was unlikely to be the most cost-effective option (14% in Toronto and 10% in Ottawa). In both cities, results were robust if the reduction in needle-sharing among clients of the facilities was at least 50% and fixed operating costs were less than $2.0 million. CONCLUSIONS: Using a $50,000 per quality-adjusted life-years threshold for cost-effectiveness, it is likely to be cost-effective to establish at least three legally sanctioned spaces for supervised injection of illicitly obtained drugs in Toronto, Canada and two in Ottawa, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".