Cost–benefit analysis of Canada’s Prison Needle Exchange Program for the prevention of hepatitis C and injection-related infections
Bibliographic record
Abstract
BACKGROUND: Needle exchange programs are effective public health interventions that reduce blood-borne infections, including hepatitis C, and injection-related infections. We sought to assess the return on investment of existing Prison Needle Exchange Programs (PNEPs) in Canadian federal prisons and their expansion to all 43 institutions. METHODS: We developed a stochastic compartmental model that estimated hepatitis C and injection-related infections under different PNEP scenarios in Canadian federal prisons. Scenarios projected for 2018-2030 were no PNEP, status quo (actual PNEP implementation 2018-2022, with coverage maintained to 2030), and PNEP scale-up (coverage among people who inject drugs in prison increasing over 2025-2030 to reach 50% by 2030). We calculated the benefit-cost ratio as benefits from health care savings, divided by PNEP costs. RESULTS: By 2019, PNEPs were implemented in 9 of 43 federal prisons, with uptake reaching 10% of people who injected drugs in prison in 2022. Compared with no PNEP, this was estimated to cost Can$0.45 (uncertainty interval [UI] $0.32 to $0.98) million and avert 37 (UI 25 to 52) hepatitis C and 8 (UI -1 to 16) injection-related infections over 2018-2030, with a benefit-cost ratio of 1.9 (UI 0.56-3.0). Compared with the status quo, the PNEP scale-up scenario cost an additional $2.7 (UI $1.8 to $7.0) million and prevented 224 (UI 218 to 231) hepatitis C and 77 (UI 74 to 80) injection-related infections, with a benefit-cost ratio of 2.0 (UI 0.57 to 3.3). INTERPRETATION: Every dollar invested in the current PNEP or its expansion is estimated to save $2 in hepatitis C and injection-related infection treatment costs. This return on investment strongly supports ongoing maintenance and scale-up of the PNEP in Canada from an economic perspective.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".