A Cost-Benefit Analysis of a Potential Supervised Injection Facility in San Francisco, California, USA
Bibliographic record
Abstract
Supervised injection facilities (SIFs) have been shown to reduce infection, prevent overdose deaths, and increase treatment uptake. The United States is in the midst of an opioid epidemic, yet no sanctioned SIF currently operates in the United States. We estimate the economic costs and benefits of establishing a potential SIF in San Francisco using mathematical models that combine local public health data with previous research on the effects of existing SIFs. We consider potential savings from five outcomes: averted HIV and hepatitis C virus (HCV) infections, reduced skin and soft tissue infection (SSTI), averted overdose deaths, and increased medication-assisted treatment (MAT) uptake. We find that each dollar spent on a SIF would generate US$2.33 in savings, for total annual net savings of US$3.5 million for a single 13-booth SIF. Our analysis suggests that a SIF in San Francisco would not only be a cost-effective intervention but also a significant boost to the public health system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".