A cost-benefit/cost-effectiveness analysis of proposed supervised injection facilities in Ottawa, Canada
Bibliographic record
Abstract
BACKGROUND: Supervised injection facilities (SIFs) are venues where people who inject drugs (PWID) have access to a clean and medically supervised environment in which they can safely inject their own illicit drugs. There is currently only one legal SIF in North America: Insite in Vancouver, British Columbia, Canada. The responses and feedback generated by the evaluations of Insite in Vancouver have been overwhelmingly positive. This study assesses whether the above mentioned facility in the Downtown Eastside of Vancouver needs to be expanded to other locations, more specifically that of Canada's capital city, Ottawa. METHODS: The current study is aimed at contributing to the existing literature on health policy by conducting cost-benefit and cost-effective analyses for the opening of SIFs in Ottawa, Ontario. In particular, the costs of operating numerous SIFs in Ottawa was compared to the savings incurred; this was done after accounting for the prevention of new HIV and Hepatitis C (HCV) infections. To ensure accuracy, two distinct mathematical models and a sensitivity analysis were employed. RESULTS: The sensitivity analyses conducted with the models reveals the potential for SIFs in Ottawa to be a fiscally responsible harm reduction strategy for the prevention of HCV cases--when considered independently. With a baseline sharing rate of 19%, the cumulative annual cost model supported the establishment of two SIFs and the marginal annual cost model supported the establishment of a single SIF. More often, the prevention of HIV or HCV alone were not sufficient to justify the establishment cost-effectiveness; rather, only when both HIV and HCV are considered does sufficient economic support became apparent. CONCLUSIONS: Funded supervised injection facilities in Ottawa appear to be an efficient and effective use of financial resources in the public health domain.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".