Timing of Testing and Treatment of Hepatitis C and other Diseases

Many papers in the medical literature analyze the cost-effectiveness/savings of screening by simulating the disease and a limited number of a priori testing policies. However, this may be insufficient to determine the best timing of the tests or incorporate changes over time. In this paper, we develop a Markov Decision Process (MDP) model for diseases where our goal is to determine the best timing for testing (and treatment) decisions when the presence of the disease is not known in advance. We develop a model that allows for the awareness of a disease to change behavior and analyze the model for structural results and find that under certain assumptions, the immediate costs and rewards determine a condition that is sufficient to establish that testing (and treatment) is beneficial, and we discuss the insights for healthcare practice. Using the model and medical data, we solve for the optimal timing of a limited number of tests and treatments for the case of Hepatitis C, and we also apply the model results to screening for Chlamydia. ∗Research supported in part by the AT&T Labs Fellowship Program, NASA Harriet G. Jenkins Predoctoral Fellowship, and DMI-0348532. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the National Science Foundation or other sponsors.