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Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs

Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long tr...

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Autores principales: Rolls, David A., Sacks-Davis, Rachel, Jenkinson, Rebecca, McBryde, Emma, Pattison, Philippa, Robins, Garry, Hellard, Margaret
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3815209/
https://www.ncbi.nlm.nih.gov/pubmed/24223787
http://dx.doi.org/10.1371/journal.pone.0078286
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author Rolls, David A.
Sacks-Davis, Rachel
Jenkinson, Rebecca
McBryde, Emma
Pattison, Philippa
Robins, Garry
Hellard, Margaret
author_facet Rolls, David A.
Sacks-Davis, Rachel
Jenkinson, Rebecca
McBryde, Emma
Pattison, Philippa
Robins, Garry
Hellard, Margaret
author_sort Rolls, David A.
collection PubMed
description Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from “less-” to “more-frequent” injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.
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spelling pubmed-38152092013-11-09 Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs Rolls, David A. Sacks-Davis, Rachel Jenkinson, Rebecca McBryde, Emma Pattison, Philippa Robins, Garry Hellard, Margaret PLoS One Research Article Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from “less-” to “more-frequent” injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective. Public Library of Science 2013-11-01 /pmc/articles/PMC3815209/ /pubmed/24223787 http://dx.doi.org/10.1371/journal.pone.0078286 Text en © 2013 Rolls et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Rolls, David A.
Sacks-Davis, Rachel
Jenkinson, Rebecca
McBryde, Emma
Pattison, Philippa
Robins, Garry
Hellard, Margaret
Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title_full Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title_fullStr Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title_full_unstemmed Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title_short Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs
title_sort hepatitis c transmission and treatment in contact networks of people who inject drugs
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3815209/
https://www.ncbi.nlm.nih.gov/pubmed/24223787
http://dx.doi.org/10.1371/journal.pone.0078286
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