Doing Good with Good OR: Supporting Cost-effective Hepatitis B Interventions

Abstract

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.

Figure 1. Schematic of decision model*

* This figure shows the decision model for our analysis of hepatitis B screening, vaccination, and treatment in the United States. The circles with “M” in them denote our Markov model of disease progression in treated and untreated individuals (Figure 2). For our analysis of catch-up vaccination in China, the decision tree consists only of Screen with Vaccinate or No Vaccinate and No Screen with Vaccinate or No Vaccinate (along with the Markov model of infection).

Figure 2. Schematic of Markov model of hepatitis B infection and progression*

* ALT = alanine aminotransferase, a blood enzyme measuring the health of the liver. Circles represent health states. Lines represent transitions between those states. Dotted lines represent death. When exposed to hepatitis B, individuals develop acute infection. Acute infection causes individuals to either become immune (if their immune system is able to resolve the infection) or to develop chronic HBV infection. The first sign of progression to liver disease is an elevated ALT level (“Elevated ALT”), at which point individuals are eligible for treatment. Treatment may be successful in suppressing the virus (“Treatment Response”) or liver disease may progress to cirrhosis or liver cancer. Patients with cirrhosis or liver cancer are eligible for a liver transplant. Individuals in all states can die, at a rate equal to the background mortality based on the age of the individual plus the incremental mortality related to being in that particular health state.

Figure 3. Screenshots from the Excel model

a: Input parameters screen shot b: Policy results screen shot

Figure 3. Screenshots from the Excel model

a: Input parameters screen shot b: Policy results screen shot