A model-based framework for chronic hepatitis C prevalence estimation

PLoS One. 2019 Nov 21;14(11):e0225366. doi: 10.1371/journal.pone.0225366. eCollection 2019.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Hepacivirus
  • Hepatitis C, Chronic / diagnosis
  • Hepatitis C, Chronic / epidemiology*
  • Hepatitis C, Chronic / virology
  • Humans
  • Models, Theoretical
  • Prevalence

Grant support