Potential impact of curative and preventive interventions toward hepatitis C elimination in people who inject drugs-A network modeling study.

Direct-acting antiviral treatment Harm reduction Hepatitis C People who inject drugs Simulation model Social network

Journal

The International journal on drug policy
ISSN: 1873-4758
Titre abrégé: Int J Drug Policy
Pays: Netherlands
ID NLM: 9014759

Informations de publication

Date de publication:
20 Jul 2024
Historique:
received: 20 04 2024
revised: 09 07 2024
accepted: 11 07 2024
medline: 22 7 2024
pubmed: 22 7 2024
entrez: 21 7 2024
Statut: aheadofprint

Résumé

Injection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear. We developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter 'degree-based'). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics. Combining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree. DAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

Sections du résumé

BACKGROUND BACKGROUND
Injection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear.
METHODS METHODS
We developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter 'degree-based'). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics.
RESULTS RESULTS
Combining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree.
CONCLUSION CONCLUSIONS
DAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

Identifiants

pubmed: 39033645
pii: S0955-3959(24)00223-8
doi: 10.1016/j.drugpo.2024.104539
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104539

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare there is no conflict of interests.

Auteurs

Lin Zhu (L)

Department of Global Health and Population, Harvard T. H. Chan School of Public, Boston, MA, USA; Department of Health Policy, School of Medicine, Stanford University, Stanford, CA, USA. Electronic address: linzhu1@stanford.edu.

William W Thompson (WW)

Prevention Branch, Division of Viral Hepatitis, Centers for Disease Control and Prevention, GA, USA.

Liesl Hagan (L)

Prevention Branch, Division of Viral Hepatitis, Centers for Disease Control and Prevention, GA, USA.

Liisa M Randall (LM)

Bureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, MA, USA.

Abby E Rudolph (AE)

Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA.

April M Young (AM)

Center on Drug and Alcohol Research, University of Kentucky, Lexington, KY, USA; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA.

Jennifer R Havens (JR)

Center on Drug and Alcohol Research, University of Kentucky, Lexington, KY, USA.

Joshua A Salomon (JA)

Department of Health Policy, School of Medicine, Stanford University, Stanford, CA, USA.

Benjamin P Linas (BP)

Section of Infectious Disease, Department of Medicine, Boston Medical Center, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.

Classifications MeSH