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A model-based framework for chronic hepatitis C prevalence estimation

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  • Abdullah Hamadeh
  • Zeny Feng
  • Murray Krahn
  • William W L Wong

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.

Suggested Citation

  • Abdullah Hamadeh & Zeny Feng & Murray Krahn & William W L Wong, 2019. "A model-based framework for chronic hepatitis C prevalence estimation," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-20, November.
    • Handle: RePEc:plo:pone00:0225366
    DOI: 10.1371/journal.pone.0225366
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