A cost-benefit/cost-effectiveness analysis of an unsanctioned supervised smoking facility in the Downtown Eastside of Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Smoking crack involves the risk of transmitting diseases such as HIV and hepatitis C (HCV). The current study determines whether the formerly unsanctioned supervised smoking facility (SSF)-operated by the grassroot organization, Vancouver Area Network of Drug Users (VANDU) for the last few years-costs less than the costs incurred for health-care services as a direct consequence of not having such a program in Vancouver, Canada. METHODS: The data pertaining to the attendance at the SSF was gathered in 2012-2013 by VANDU. By relying on this data, a mathematical model was employed to estimate the number of HCV infections prevented by the former facility in Vancouver's Downtown Eastside (DTES). RESULTS: The DTES SSF's benefit-cost ratio was conservatively estimated at 12.1:1 due to its low operating cost. The study used 70% and 90% initial pipe-sharing rates for sensitivity analysis. At 80% sharing rate, the marginal HCV cases prevented were determined to be 55 cases. Moreover, at 80% sharing rate, the marginal cost-effectiveness ratio ranges from $1,705 to $97,203. The results from both the baseline and sensitivity analysis demonstrated that the establishment of the SSF by VANDU on average had annually saved CAD$1.8 million dollars in taxpayer's money. CONCLUSIONS: Funding SSFs in Vancouver is an efficient and effective use of financial resources in the public health domain; therefore, Vancouver Coastal Health should actively participate in their establishment in order to reduce HCV and other blood-borne infections such as HIV within the non-injecting drug users.
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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 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".