Hepatitis C treatment strategies in prisons: A cost-effectiveness analysis
Bibliographic record
Abstract
In Australian prisons approximately 20% of inmates are chronically infected with hepatitis C virus (HCV), providing an important population for targeted treatment and prevention. A dynamic mathematical model of HCV transmission was used to assess the impact of increasing direct-acting antiviral (DAA) treatment uptake on HCV incidence and prevalence in the prisons in New South Wales, Australia, and to assess the cost-effectiveness of alternate treatment strategies. We developed four separate models reflecting different average prison lengths of stay (LOS) of 2, 6, 24, and 36 months. Each model considered four DAA treatment coverage scenarios of 10% (status-quo), 25%, 50%, and 90% over 2016-2045. For each model and scenario, we estimated the lifetime burden of disease, costs and changes in quality-adjusted life years (QALYs) in prison and in the community during 2016-2075. Costs and QALYs were discounted 3.5% annually and adjusted to 2015 Australian dollars. Compared to treating 10% of infected prisoners, increasing DAA coverage to 25%, 50%, and 90% reduced HCV incidence in prisons by 9-33% (2-months LOS), 26-65% (6-months LOS), 37-70% (24-months LOS), and 35-65% (36-months LOS). DAA treatment was highly cost-effective among all LOS models at conservative willingness-to-pay thresholds. DAA therapy became increasingly cost-effective with increasing coverage. Compared to 10% treatment coverage, the incremental cost per QALY ranged from $497-$569 (2-months LOS), -$280-$323 (6-months LOS), -$432-$426 (24-months LOS), and -$245-$477 (36-months LOS). Treating more than 25% of HCV-infected prisoners with DAA therapy is highly cost-effective. This study shows that treating HCV-infected prisoners is highly cost-effective and should be a government priority for the global HCV elimination effort.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".