Abstract
Chronic hepatitis C (HCV) is a significant public health problem affecting 2.7-3.9 million Americans. Quantifying mortality rates of HCV-infected individuals permits more accurate estimates of the potential benefits of HCV screening and treatment. With 5% of older Americans infected with HCV, cost-effectiveness analyses of expanded HCV screening and treatment require methods to appropriately quantify differential mortality risks. No single study contains data needed to estimate subgroup-specific prevalence of HCV, risk factor status, and mortality risks. We developed a combined modeling approach to infer risk-group-specific mortality rates for chronically HCV-infected U.S. adults. We incorporated estimates from public health data into a Markov model to infer the age-, sex-, race-, risk-, and HCV infection status-specific mortality rates that best fit the overall age-specific population mortality rates.
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© 2014 Springer International Publishing Switzerland
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Liu, S., Cipriano, L.E., Goldhaber-Fiebert, J.D. (2014). Extended Abstract: Combining Statistical Analysis and Markov Models with Public Health Data to Infer Age-Specific Background Mortality Rates for Hepatitis C Infection in the U.S.. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds) Smart Health. ICSH 2014. Lecture Notes in Computer Science, vol 8549. Springer, Cham. https://doi.org/10.1007/978-3-319-08416-9_15
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DOI: https://doi.org/10.1007/978-3-319-08416-9_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08415-2
Online ISBN: 978-3-319-08416-9
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