PS2-25 ESTIMATING THE PREVALENCE OF HEPATITIS C IN PENNSYLVANIA MEDICAID USING A MICROSIMULATION MODEL

Purpose: New highly effective therapies have changed the treatment paradigm for hepatitis C virus (HCV), with very high cure rates. However, the unknown true prevalence of HCV-infected individuals and their distribution of disease stages, along with high cost of treatment, present challenges for healthcare payers like state Medicaid programs with limited budgets for HCV treatment. Our objective was to estimate the prevalence of HCV in the Pennsylvania (PA) Medicaid.

Method: We used PA Medicaid claims data from 2007­–2012 to identify individuals diagnosed with HCV, individuals who received HCV therapies, and those who developed advanced liver disease due to HCV infection. To estimate the current HCV prevalence, we used an innovative approach of combining the results of claims data with a validated microsimulation model that accurately predicted national HCV prevalence in the United States. In this process, we accounted for trends in Medicaid enrollment, and adjusted the rates of treatment contraindications, such as substance abuse, that are often higher in Medicaid populations. We calibrated our model such that it simulated the observed number of patients diagnosed with HCV, and “hard outcomes” (liver transplants, hepatocellular carcinoma, decompensated cirrhosis) from 2007­–2012 claims data. Our model included historic as well as current HCV screening and treatment recommendations. From the calibrated PA model, we estimated the number of patients who will need treatment in 2015 and beyond by disease stage (represented by fibrosis scores F0–F4) and by HCV genotype.

Result: Our calibrated model matched the number of individuals with HCV diagnoses based on PA Medicaid claims data at 26,400 in 2012. The model estimated 22 liver transplants in 2012, closely matching the true incidence found in claims data. Our model estimated that 46,400 beneficiaries were infected with HCV in 2015, of whom 65% were aware of their disease, and 72% were treatment naïve. In the following 10 years, 8,500 new patients would be added to PA Medicaid either because of HCV screening or new enrollments.

Conclusion: We provide a novel approach to estimate the prevalence of HCV by using a combination of claims data and simulation modeling. Our results can assist state Medicaid programs in effective allocation of their resources to manage HCV patients in a rapidly changing clinical and policy environment.