A model-based framework for chronic hepatitis C prevalence estimation
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 eradicati...
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| Published in | PloS one Vol. 14; no. 11; p. e0225366 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
21.11.2019
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0225366 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Feng, Zeny Hamadeh, Abdullah Wong, William W. L. Krahn, Murray |
| AuthorAffiliation | 1 School of Pharmacy, University of Waterloo, Kitchener, ON, Canada 4 Toronto General Research Institute, Toronto, ON, Canada Centre de Recherche en Cancerologie de Lyon, FRANCE 3 Toronto Health Economics and Technology Assessment Collaborative, University Health Network, Toronto General Hospital, Toronto, ON, Canada 2 Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada |
| AuthorAffiliation_xml | – name: 3 Toronto Health Economics and Technology Assessment Collaborative, University Health Network, Toronto General Hospital, Toronto, ON, Canada – name: Centre de Recherche en Cancerologie de Lyon, FRANCE – name: 4 Toronto General Research Institute, Toronto, ON, Canada – name: 1 School of Pharmacy, University of Waterloo, Kitchener, ON, Canada – name: 2 Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada |
| Author_xml | – sequence: 1 givenname: Abdullah orcidid: 0000-0002-9419-1797 surname: Hamadeh fullname: Hamadeh, Abdullah – sequence: 2 givenname: Zeny surname: Feng fullname: Feng, Zeny – sequence: 3 givenname: Murray surname: Krahn fullname: Krahn, Murray – sequence: 4 givenname: William W. L. surname: Wong fullname: Wong, William W. L. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31751393$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.9778/cmajo.20170048 10.1503/cmaj.140711 10.1001/jama.2012.144878 10.1111/j.1365-2893.2005.00651.x 10.1177/0272989X13505910 10.3350/cmh.2015.21.2.105 10.1016/j.jhep.2017.06.025 10.1002/hep.22375 10.1111/j.1524-4733.2008.00389.x 10.1503/cmaj.161521 10.1186/1477-7517-4-1 10.14745/ccdr.v40i19a02 10.1503/cmaj.170453 |
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| Copyright | COPYRIGHT 2019 Public Library of Science 2019 Hamadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 Hamadeh et al 2019 Hamadeh et al |
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| SubjectTerms | Algorithms Analysis Bayesian analysis Biology and Life Sciences Cohort analysis Control Decision making Disease eradication Eradication Expected values Hepacivirus Hepatitis Hepatitis C Hepatitis C, Chronic - diagnosis Hepatitis C, Chronic - epidemiology Hepatitis C, Chronic - virology Humans Interferon Liver cancer Medicine and health sciences Models, Theoretical People and places Prevalence Research and Analysis Methods |
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| Title | A model-based framework for chronic hepatitis C prevalence estimation |
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