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• W1913786670 abstract "Abstract Background: Hepatitis C virus (HCV) infection is associated with chronic progressiveliver disease. Its global epidemiology is still not well ascertained and its impact will beconfrontedwithahigher burden inthenextdecade. Aim: The goalofthisstudywastodevelop a tool that can be used to predict the future prevalence of the disease indifferent countries and, more importantly, to understand the cause and effect relation-ship between the key assumptions and future trends. Methods: A system approach wasused to build a simulation model where each population was modeled with theappropriate inflows and outflows. Sensitivity analysis was used to identify the keydrivers of future prevalence. Results: The total HCV-infected population in the USwasestimated to decline 24% from 3.15 million in 2005 to 2.47 million in 2021, whilediseaseburdenwillincreaseastheremaininginfectedpopulationages.Duringthesameperiod, the mortality rate was forecasted to increase from 2.1 to 3.1%. The diagnosedpopulation was 50% of the total infections, while less than 2% of the total infectionswere treated. Conclusion: We have created a framework to evaluate the HCV-infectedpopulations in countries around the world. This model may help assess the impact ofpoliciestomeetthechallengespredictedbytheevolutionofHCVinfectionanddisease.This prediction tool may help to target new public health strategies.Hepatitis C virus (HCV) infection is associated with chronic,progressive liverdisease. Chronic hepatitis C is a leading cause ofcirrhosis and hepatocellular carcinoma (HCC) (1, 2), and thelatter two are a major indication for liver transplantation (3). Abetterunderstandingof HCVinfectionprevalence anditsmodel-ling can help medical communities and government agenciesmanage the disease burden and develop treatment strategies inlight of the emergence of several potentanti-HCV therapies.Considerable work has been undertaken to forecast theepidemiology of hepatitis C and numerous authors have devel-oped Markov models to estimate the disease burden (4–16).However, these models have limitations resulting from theassumption that the studied cohorts remain homogeneous andtraverse through different statesat a fixed rate over time when, inreality, populations are very heterogeneous (17). Other investiga-tors have developed multicohortnaturalhistory models (17), butallforecastsarecomplicatedbyavailabilityanduncertaintyofkeyinputs, especially outside a few well-studied countries.We addressed this problem by using a system approach tobuildasimulationmodel.Inthismethod,eachpopulationgroup" @default.
• W1913786670 created "2016-06-24" @default.
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• W1913786670 date "2011-06-08" @default.
• W1913786670 modified "2023-10-12" @default.
• W1913786670 title "The global health burden of hepatitis C virus infection" @default.
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• W1913786670 doi "https://doi.org/10.1111/j.1478-3231.2011.02537.x" @default.
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