Doing Good with Good OR: Supporting Cost‐Effective Hepatitis B Interventions

In an era of limited health‐care budgets, mathematical models can be useful tools to identify cost‐effective programs and support policy makers in making informed decisions. This paper reports the results of our work, which we carried out over several years with the Asian Liver Center at Stanford University. Hepatitis B is a vaccine‐preventable viral disease that, if untreated, can lead to death from cirrhosis and liver cancer. It is a major public health problem, particularly in Asian populations. We used new combinations of decision analysis and Markov models to analyze the cost effectiveness of several interventions to combat the disease in the United States and China. The results of our operations research (OR)‐based analyses have helped change US public health policy on hepatitis B screening for millions of people, and have helped encourage policy makers in China to enact legislation to provide free catch‐up vaccination for hundreds of millions of children. These policies are an important step in eliminating health disparities, reducing discrimination, and ensuring that millions of people can now receive the hepatitis B vaccination or life‐saving treatment that they need.