A model-based framework for chronic hepatitis C prevalence estimation

• Published in: PloS one Vol. 14; no. 11; p. e0225366
• Main Authors: Hamadeh, Abdullah, Feng, Zeny, Krahn, Murray, Wong, William W. L.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.11.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bayesian analysis; Biology and Life Sciences; Cohort analysis; Control; Decision making; Disease eradication; Eradication; Expected values; Hepacivirus; Hepatitis; Hepatitis C; Hepatitis C, Chronic - diagnosis; Hepatitis C, Chronic - epidemiology; Hepatitis C, Chronic - virology; Humans; Interferon; Liver cancer; Medicine and health sciences; Models, Theoretical; People and places; Prevalence; Research and Analysis Methods; Canada
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0225366

Chronic hepatitis C (CHC) continues to be a highly burdensome disease worldwide. The often-asymptomatic nature of early-stage CHC means that the disease often remains undiagnosed, leaving its prevalence highly uncertain. This generates significant uncertainty in the planning of hepatitis C eradication programs to meet WHO targets. The aim of this work is to establish a mathematical framework for the estimation of a geographic locale's CHC prevalence and the proportion of its CHC population that remains undiagnosed. A Bayesian MCMC approach is taken to infer these populations from the observed occurrence of CHC-related events using a recently published natural history model of the disease. Using the Canadian context as a specific example, this study estimates that in 2013, the CHC prevalence rate in Canada was 0.63% (95% CI: 0.53% - 0.72%), with 27.1% (95% CI: 19.3% - 36.1%) of the infected population undiagnosed.