Prioritizing Hepatitis C Treatment in U.S. Prisons

66 Pages Posted: 15 Nov 2016 Last revised: 23 Nov 2018

See all articles by Turgay Ayer

Turgay Ayer

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Can Zhang

Duke University - Fuqua School of Business

Anthony Bonifonte

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Anne Spaulding

Emory University - Rollins School of Public Health

Jagpreet Chhatwal

Harvard University - Medical School

Date Written: November 14, 2016

Abstract

Hepatitis C virus (HCV) prevalence in prison systems is ten times higher than in the general population, and hence 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, which precludes universal HCV treatment in prison systems. As such, current practice recommends prioritizing treatment based on clinical and incarceration-related factors, including disease staging, remaining sentence length, and injection drug use (IDU) status. However, there is controversy about how these factors should be incorporated because of the complicated trade-offs. In this study, we propose a restless bandit modeling framework to support hepatitis C treatment prioritization decisions in U.S. prisons. We first prove indexability for our problem and derive several structural properties of the well-known Whittle's index, based on which, we derive a closed-form expression of the Whittle's index for patients with advanced liver disease. From the interpretation of this closed-form expression, we anticipate that the performance of the Whittle's index would degrade as the treatment capacity increases; and to address this limitation, we propose a capacity-adjusted closed-form index policy. We parameterize and validate our model using real-world data and published studies, and test the performance of our proposed policy against several benchmark policies using a realistic agent-based simulation model. Our results shed light on several controversial health policy issues in hepatitis C treatment prioritization: 1) prioritization based on only liver health status, a commonly practiced policy, is suboptimal compared with many other policies. Further, considering remaining sentence length of inmates and IDU status in addition to liver health status in prioritization decisions can lead to a significant performance improvement; 2) while usually prioritizing patients with longer sentence lengths is the right choice, prioritizing patients with shorter remaining sentence lengths may be preferable if the treatment capacity inside the prison system is not very tight and linkage-to-care level outside prison system is low; and 3) among patients with advanced liver disease, IDUs should not be prioritized unless their reinfection is very well controlled.

Keywords: public health, hepatitis C, resource allocation, treatment prioritization, multi-armed bandits, agent-based simulation

Suggested Citation

Ayer, Turgay and Zhang, Can and Bonifonte, Anthony and Spaulding, Anne and Chhatwal, Jagpreet, Prioritizing Hepatitis C Treatment in U.S. Prisons (November 14, 2016). Available at SSRN: https://ssrn.com/abstract=2869158 or http://dx.doi.org/10.2139/ssrn.2869158

Turgay Ayer (Contact Author)

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

Can Zhang

Duke University - Fuqua School of Business ( email )

100 Fuqua Dr
Durham, NC North Carolina 27708
United States

Anthony Bonifonte

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

765 Ferst Drive
Atlanta, GA 30332-0205
United States

Anne Spaulding

Emory University - Rollins School of Public Health

Atlanta, GA 30322
United States

Jagpreet Chhatwal

Harvard University - Medical School ( email )

25 Shattuck St
Boston, MA 02115
United States

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