Dr. Turgay Ayer
Assistant Professor of Predictive Health
Georgia Institute of Technology
Friday, April 8, 2016
JDT 410 2:30-3:30
The United States is in the midst of a hepatitis C virus (HCV) epidemic, with nearly 2% of the entire population (about 3-5 million people) currently infected. Untreated HCV infection is the leading cause of cirrhosis, liver cancer, and liver transplantation in the US. Indeed, as of 2006, deaths from HCV-related diseases has already exceeded deaths from HIV-related diseases, and by 2060, more than 1 million Americans are expected to die from HCV. In the past five years, there have been incredible advancements in hepatitis C treatment and cure rates have increased from about 50% to more than 90%. On the other hand, while these state-of-the-art HCV treatments are very effective, they are also outrageously expensive, with one single pill costing about $1000.
One of the venues where hepatitis C is most concentrated in the U.S. is the criminal justice system. About one out of every six prisoners is infected, making the prevalence of hepatitis C nearly ten times higher compared with the general society. Therefore, the criminal justice system is
considered as the best place to identify and treat the greatest number of HCV-infected people. However, state prison systems typically have very tight budgets and cannot bear the cost burden of HCV treatment. Therefore, in current practice, only a small portion of HCV-infected inmates are treated in state prison systems, and these treatment decisions are made based on prioritization of infected inmates.
In this study, we propose a restless bandit modeling framework to support hepatitis C treatment prioritization decisions in prison systems. We first prove indexability for our problem. We then show that under certain conditions that are satisfied by real state prison data, the well-known Whittle’s index of each patient coincides with the marginal benefit of treating him/her assuming, s/he will not be treated in future periods until release. Based on this observation, we propose a capacity-adjusted closed-form index policy (Whittle’s index is capacity independent). Using a validated agent-based simulation model, we numerically solve the HCV treatment prioritization problem, and show that our proposed policy can significantly improve the cost effectiveness of hepatitis C treatment decisions, compared with the current practice, myopic policy and the Whittle’s index policy. Our analytical and numerical results also lead to the following policy implications: 1) Patients with shorter remaining sentence lengths should be prioritized when the infection rate outside prison is high, the linkage to care level outside prison is low, and the treatment capacity within prison is high, 2) IDUs should be prioritized when IDUs’ infection rate is high and reinfection rate is low, and, 3) Increasing the budget level for hepatitis C treatment within the prison system can significantly benefit also the broader society, in addition to the prison population. We further quantify the health outcomes loss from taking a narrow prison perspective and ignoring the health outcomes for the overall society. This is a joint work with Anne Spaulding, MD from Emory University Public Health, Jagpreet Chattwal, Ph.D. from Harvard Medical School, as well as my Ph.D. students Anthony Bonifonte and Can Zhang.
Turgay Ayer is the George Family Foundation Assistant Professor of Predictive Health at Industrial and Systems Engineering at Georgia Institute of Technology. In addition to ISyE, Dr. Ayer is the research director for medical decision-making in the Center for Health & Humanitarian Systems at Georgia Tech.
Dr. Ayer conducts research on stochastic modeling and optimization, with applications in predictive health, medical decision making, healthcare operations, and health policy. He has been working on various projects related to different aspects of healthcare, including health outcomes research, disease prevention & control modeling, infectious disease modeling, healthcare delivery planning, and new payment models, and health information exchanges. His research has been published in top tier engineering, management, and medical journals.
Together with his students, Dr. Ayer has received several awards for his work, including an NSF CAREER award, first place in the 2011 & 2015 INFORMS Doing Good with Good OR Student Paper Competitions, finalist in the 2015 INFORMS George Nicholson Student Paper Competition, 2012 & 2014 Seth Bounder Foundation Research Award, and second place in the 2011 MSOM Student Paper Competition.
Ayer received a B.S. in industrial engineering from Sabanci University in Istanbul, Turkey, and his M.S. and Ph.D. degrees in industrial and systems engineering from the University of Wisconsin – Madison.
He is a member INFORMS and the Society for Medical Decision Making, and currently serves as the president of the INFORMS Health Application Society.