Abstract
Hepatitis C virus (HCV) prevalence in prison systems is about 10 times higher than in the community. As such, prison systems offer a unique opportunity to control the HCV epidemic. New HCV-treatment drugs are very effective, but providing treatment to all inmates is prohibitively expensive unless prices fall. Current practice is to prioritize treatment based on disease severity and puts less emphasis on other factors such as the remaining sentence length and injection drug use behavior. In “Prioritizing Hepatitis C Treatment in U.S. Prisons,” T. Ayer, C. Zhang, A. Bonifonte, A. Spaulding, and J. Chhatwal analyze optimal approaches for treatment prioritization under resource constraints by developing a restless bandit modeling framework. They present an easy-to-implement closed-form index policy to support hepatitis C treatment prioritization decisions in U.S. prisons. They also test their proposed policy using a detailed, realistic agent-based simulation model and shed light on several controversial health policy decisions related to hepatitis C treatment prioritization.
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