Applying a system approach to forecast the total hepatitis C virus-infected population size

model validation using US data

David Kershenobich, Homie A. Razavi, Curtis L. Cooper, Alfredo Alberti, Geoffrey M. Dusheiko, Stanislas Pol, Eli Zuckerman, Kazuhiko Koike, KwangHyub Han, Carolyn M. Wallace, Stefan Zeuzem, Francesco Negro

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Background: Hepatitis C virus (HCV) infection is associated with chronic progressive liver disease. Its global epidemiology is still not well ascertained and its impact will be confronted with a higher burden in the next decade. Aim: The goal of this study was to develop a tool that can be used to predict the future prevalence of the disease in different countries and, more importantly, to understand the cause and effect relationship between the key assumptions and future trends. Methods: A system approach was used to build a simulation model where each population was modeled with the appropriate inflows and outflows. Sensitivity analysis was used to identify the key drivers of future prevalence. Results: The total HCV-infected population in the US was estimated to decline 24% from 3.15 million in 2005 to 2.47 million in 2021, while disease burden will increase as the remaining infected population ages. During the same period, the mortality rate was forecasted to increase from 2.1 to 3.1%. The diagnosed population was 50% of the total infections, while less than 2% of the total infections were treated. Conclusion: We have created a framework to evaluate the HCV-infected populations in countries around the world. This model may help assess the impact of policies to meet the challenges predicted by the evolution of HCV infection and disease. This prediction tool may help to target new public health strategies.

Original languageEnglish
Pages (from-to)4-17
Number of pages14
JournalLiver International
Volume31
Issue numberSUPPL. 2
DOIs
Publication statusPublished - 2011 Jul 1

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Population Density
Hepacivirus
Virus Diseases
Population
Infection
Liver Diseases
Epidemiology
Public Health
Mortality

All Science Journal Classification (ASJC) codes

  • Hepatology

Cite this

Kershenobich, D., Razavi, H. A., Cooper, C. L., Alberti, A., Dusheiko, G. M., Pol, S., ... Negro, F. (2011). Applying a system approach to forecast the total hepatitis C virus-infected population size: model validation using US data. Liver International, 31(SUPPL. 2), 4-17. https://doi.org/10.1111/j.1478-3231.2011.02535.x
Kershenobich, David ; Razavi, Homie A. ; Cooper, Curtis L. ; Alberti, Alfredo ; Dusheiko, Geoffrey M. ; Pol, Stanislas ; Zuckerman, Eli ; Koike, Kazuhiko ; Han, KwangHyub ; Wallace, Carolyn M. ; Zeuzem, Stefan ; Negro, Francesco. / Applying a system approach to forecast the total hepatitis C virus-infected population size : model validation using US data. In: Liver International. 2011 ; Vol. 31, No. SUPPL. 2. pp. 4-17.
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Kershenobich, D, Razavi, HA, Cooper, CL, Alberti, A, Dusheiko, GM, Pol, S, Zuckerman, E, Koike, K, Han, K, Wallace, CM, Zeuzem, S & Negro, F 2011, 'Applying a system approach to forecast the total hepatitis C virus-infected population size: model validation using US data', Liver International, vol. 31, no. SUPPL. 2, pp. 4-17. https://doi.org/10.1111/j.1478-3231.2011.02535.x

Applying a system approach to forecast the total hepatitis C virus-infected population size : model validation using US data. / Kershenobich, David; Razavi, Homie A.; Cooper, Curtis L.; Alberti, Alfredo; Dusheiko, Geoffrey M.; Pol, Stanislas; Zuckerman, Eli; Koike, Kazuhiko; Han, KwangHyub; Wallace, Carolyn M.; Zeuzem, Stefan; Negro, Francesco.

In: Liver International, Vol. 31, No. SUPPL. 2, 01.07.2011, p. 4-17.

Research output: Contribution to journalArticle

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T1 - Applying a system approach to forecast the total hepatitis C virus-infected population size

T2 - model validation using US data

AU - Kershenobich, David

AU - Razavi, Homie A.

AU - Cooper, Curtis L.

AU - Alberti, Alfredo

AU - Dusheiko, Geoffrey M.

AU - Pol, Stanislas

AU - Zuckerman, Eli

AU - Koike, Kazuhiko

AU - Han, KwangHyub

AU - Wallace, Carolyn M.

AU - Zeuzem, Stefan

AU - Negro, Francesco

PY - 2011/7/1

Y1 - 2011/7/1

N2 - Background: Hepatitis C virus (HCV) infection is associated with chronic progressive liver disease. Its global epidemiology is still not well ascertained and its impact will be confronted with a higher burden in the next decade. Aim: The goal of this study was to develop a tool that can be used to predict the future prevalence of the disease in different countries and, more importantly, to understand the cause and effect relationship between the key assumptions and future trends. Methods: A system approach was used to build a simulation model where each population was modeled with the appropriate inflows and outflows. Sensitivity analysis was used to identify the key drivers of future prevalence. Results: The total HCV-infected population in the US was estimated to decline 24% from 3.15 million in 2005 to 2.47 million in 2021, while disease burden will increase as the remaining infected population ages. During the same period, the mortality rate was forecasted to increase from 2.1 to 3.1%. The diagnosed population was 50% of the total infections, while less than 2% of the total infections were treated. Conclusion: We have created a framework to evaluate the HCV-infected populations in countries around the world. This model may help assess the impact of policies to meet the challenges predicted by the evolution of HCV infection and disease. This prediction tool may help to target new public health strategies.

AB - Background: Hepatitis C virus (HCV) infection is associated with chronic progressive liver disease. Its global epidemiology is still not well ascertained and its impact will be confronted with a higher burden in the next decade. Aim: The goal of this study was to develop a tool that can be used to predict the future prevalence of the disease in different countries and, more importantly, to understand the cause and effect relationship between the key assumptions and future trends. Methods: A system approach was used to build a simulation model where each population was modeled with the appropriate inflows and outflows. Sensitivity analysis was used to identify the key drivers of future prevalence. Results: The total HCV-infected population in the US was estimated to decline 24% from 3.15 million in 2005 to 2.47 million in 2021, while disease burden will increase as the remaining infected population ages. During the same period, the mortality rate was forecasted to increase from 2.1 to 3.1%. The diagnosed population was 50% of the total infections, while less than 2% of the total infections were treated. Conclusion: We have created a framework to evaluate the HCV-infected populations in countries around the world. This model may help assess the impact of policies to meet the challenges predicted by the evolution of HCV infection and disease. This prediction tool may help to target new public health strategies.

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