Forecasting the Morbidity and Mortality Associated with Prevalent Cases of Pre-Cirrhotic Chronic Hepatitis C Infections in the United States
The Centers for Disease Control and Prevention’s (CDC’s) Division of Viral Hepatitis has contracted with RTI International to develop a multicohort simulation model of individuals with chronic hepatitis C infection. The model, which incorporated the full range of potential hepatitis C disease states, from asymptomatic disease to liver disease, transplants and death, was used to forecast the morbidity and mortality associated with prevalent cases of hepatitis C in 2005. This document is designed to facilitate the review and understanding of the model and its input parameters.
The model divided the population ages 20 and older into mutually exclusive subcategories and simulated progression of hepatitis C among each subgroup. Each mutually exclusive sub-category was described in terms of four primary characteristics:
? age group
? past injecting drug use (IDU) behavior
Based on these characteristics, the model estimates a patient’s hepatitis C virus (HCV) infection status based on data from the National Health and Nutrition Examination Survey (NHANES) and from published sources. The model categorizes patients with HCV infection based on three characteristics that are relevant to infected patients only:
? duration of infection
? heavy drinking status
? HIV/AIDS infection status
From the combination of primary characteristics assigned to all patients and secondary characteristics assigned only to patients with HCV, each unique patient group is assigned a fibrosis rate. The disease stage of each unique patient group is then calculated based on the fibrosis rate for that group multiplied by their expected duration of infection.
Health outcomes are collected from each unique cohort in each year, discounted into their net present value.
This document presents parameter data that drive the model. Each table is prefaced with the name of the variable, its purpose in the model, and any additional adjustments made to the parameters.