Mark H Eckman1, John W Ward2, Kenneth E Sherman3. 1. Division of General Internal Medicine and the Center for Clinical Effectiveness, University of Cincinnati, Cincinnati, Ohio. Electronic address: mark.eckman@uc.edu. 2. Task Force for Global Health and Centers for Disease Control and Prevention, Atlanta, Georgia. 3. Division of Digestive Diseases, University of Cincinnati, Cincinnati, Ohio.
Abstract
BACKGROUND & AIMS: Most persons infected with hepatitis C virus (HCV) in the United States were born from 1945 through 1965; testing is recommended for this cohort. However, HCV incidence is increasing among younger persons in many parts of the country and treatment is recommended for all adults with HCV infection. We aimed to estimate the cost effectiveness of universal 1-time screening for HCV infection in all adults living in the United States and to determine the prevalence of HCV antibody above which HCV testing is cost effective. METHODS: We developed a Markov state transition model to estimate the effects of universal 1-time screening of adults 18 years or older in the United States, compared with the current guideline-based strategy of screening adults born from 1945 through 1965. We compared potential outcomes of 1-time universal screening of adults or birth cohort screening followed by antiviral treatment of those with HCV infection vs no screening. We measured effectiveness with quality-adjusted life-years (QALY), and costs with 2017 US dollars. RESULTS: Based on our model, universal 1-time screening of US residents with a general population prevalence of HCV antibody greater than 0.07% cost less than $50,000/QALY compared with a strategy of no screening. Compared with 1-time birth cohort screening, universal 1-time screening and treatment cost $11,378/QALY gained. Universal screening was cost effective compared with birth cohort screening when the prevalence of HCV antibody positivity was greater than 0.07% among adults not in the cohort born from 1945 through 1965. CONCLUSIONS: Using a Markov state transition model, we found a strategy of universal 1-time screening for chronic HCV infection to be cost effective compared with either no screening or birth cohort-based screening alone.
BACKGROUND & AIMS: Most persons infected with hepatitis C virus (HCV) in the United States were born from 1945 through 1965; testing is recommended for this cohort. However, HCV incidence is increasing among younger persons in many parts of the country and treatment is recommended for all adults with HCV infection. We aimed to estimate the cost effectiveness of universal 1-time screening for HCV infection in all adults living in the United States and to determine the prevalence of HCV antibody above which HCV testing is cost effective. METHODS: We developed a Markov state transition model to estimate the effects of universal 1-time screening of adults 18 years or older in the United States, compared with the current guideline-based strategy of screening adults born from 1945 through 1965. We compared potential outcomes of 1-time universal screening of adults or birth cohort screening followed by antiviral treatment of those with HCV infection vs no screening. We measured effectiveness with quality-adjusted life-years (QALY), and costs with 2017 US dollars. RESULTS: Based on our model, universal 1-time screening of US residents with a general population prevalence of HCV antibody greater than 0.07% cost less than $50,000/QALY compared with a strategy of no screening. Compared with 1-time birth cohort screening, universal 1-time screening and treatment cost $11,378/QALY gained. Universal screening was cost effective compared with birth cohort screening when the prevalence of HCV antibody positivity was greater than 0.07% among adults not in the cohort born from 1945 through 1965. CONCLUSIONS: Using a Markov state transition model, we found a strategy of universal 1-time screening for chronic HCV infection to be cost effective compared with either no screening or birth cohort-based screening alone.
Authors: Paul G Carty; Christopher G Fawsitt; Paddy Gillespie; Patricia Harrington; Michelle O'Neill; Susan M Smith; Conor Teljeur; Mairin Ryan Journal: Appl Health Econ Health Policy Date: 2021-12-06 Impact factor: 3.686