Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the fastest-growing cause of cancer-related deaths in the United States. Most HCC cases are attributed to chronic hepatitis C virus infection, which affects nearly 3 million Americans and 100 million people globally. Although surveillance for HCC in hepatitis C patients can improve survival, the optimal surveillance policies remain unknown. In this study, we develop a mixed-integer programming (MIP)-based framework to systematically analyze a rich set of policies and determine the optimal HCC surveillance policies that maximize societal net benefit. Our MIP-based framework addresses two problem features that make dynamic programming-based formulation computationally intractable. In particular, our proposed framework allows the formulation of (1) M-switch policies that are practical for implementation and (2) tailored surveillance policies with screening intervals stratified by the precursor disease states. We theoretically analyze the HCC surveillance problem, characterize when the surveillance policies should be adapted to populations with different disease progression rates, and quantify the trade-off between decreasing HCC incidence and increasing treatment outcomes. We parameterize our model using clinical trial data, a previously validated simulation model, and published clinical studies. Our numerical analyses lead to three main results with important policy implications. First, we find that, in addition to cirrhotic patients, expanding surveillance to patients in the earlier stages of hepatitis C infection improves the cost-effectiveness of HCC surveillance. Second, compared with the "one size fits all" routine policies, we find that it is cost-effective to stratify surveillance strategies based on the stage of hepatitis C infection with less frequent cancer surveillance in the earlier stages of infection. Finally, we find that a little flexibility in the policy structure as captured by M-switch policies is sufficient to capture almost as much benefit as from complex fully dynamic policies.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Management Science and Operations Research