A model-based framework for chronic hepatitis C prevalence estimation


Autoři: Abdullah Hamadeh aff001;  Zeny Feng aff002;  Murray Krahn aff003;  William W. L. Wong aff001
Působiště autorů: School of Pharmacy, University of Waterloo, Kitchener, ON, Canada aff001;  Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada aff002;  Toronto Health Economics and Technology Assessment Collaborative, University Health Network, Toronto General Hospital, Toronto, ON, Canada aff003;  Toronto General Research Institute, Toronto, ON, Canada aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225366

Souhrn

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.

Klíčová slova:

Canada – Cohort studies – Fibrosis – Hepatitis C – Hepatitis C virus – Hepatocellular carcinoma – Liver diseases – Natural history of disease


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Článek vyšel v časopise

PLOS One


2019 Číslo 11