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
prolekare.web.journal.doi_sk:
https://doi.org/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:
Cohort studies – Hepatitis C virus – Liver diseases – Fibrosis – Hepatocellular carcinoma – Canada – Hepatitis C – Natural history of disease
Zdroje
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