In an era of limited healthcare budgets, mathematical models can be useful tools to identify cost-effective programs and to support policymakers in informed decision making. This paper reports results of our work carried out over several years with the Asian Liver Center at Stanford University, a nonprofit outreach and advocacy organization that is an international leader in the fight against hepatitis B and liver cancer. Hepatitis B is a vaccine-preventable viral disease that, if untreated, can lead to death from cirrhosis and liver cancer. Infection with hepatitis B 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 hepatitis B in the United States and China. The results of our OR-based analyses have helped change United States public health policy on hepatitis B screening for millions of people and have helped encourage policymakers 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 who need it can now receive hepatitis B vaccination or lifesaving treatment.