A0233
Title: A Bayesian approach for chronic hepatitis C prevalence estimation to improve the accuracy of economic evaluation
Authors: William WL Wong - University of Waterloo (Canada) [presenting]
Zeny Feng - University of Guelph (Canada)
Abstract: The majority of those infected with chronic hepatitis C (CHC) have a clinical silent disease. The asymptomatic nature means the disease often remains undiagnosed, leaving its prevalence highly uncertain. This generates significant uncertainty for the associated economic evaluations. The purpose is to establish a mathematical framework for estimating CHC prevalence and undiagnosed proportion. A state-transition model describing infection, disease progression and treatment response was mathematically formulated and developed. Model parameters were obtained from the published literature. The historical prevalence of CHC is estimated through a calibration process based on a Bayesian MCMC algorithm. The algorithm constructed posterior distributions of the historical prevalence of CHC by comparing the model-generated predictions of the annual numbers of health events related to CHC against the observed calibration targets. The prevalence of CHC in Ontario, Canada, in 2018 was estimated to be 0.89\%, and the percentage of undiagnosed among the total infected was 33.6\%. The results are in line with a recently conducted seroprevalence survey. Prevalence estimates impact economic evaluation results on interventions concerning CHC screening and treatment. Considering the rapid development of treatments for CHC, updated prevalence estimates will become necessary. A platform is provided for estimating this information in a robust and efficient way.
