JOURNAL ARTICLE

Long-Term Outcomes of a Decentralized, Nurse-Led, Statewide Model of Care for Hepatitis C Among People in Prison in Victoria, Australia.

  • Published In: Clinical Infectious Diseases, 2025, v. 80, n. 4. P. 826 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: MacIsaac, Michael B; Papaluca, Timothy; McDonald, Lucy; Craigie, Anne; Edwards, Amy; Layton, Chloe; Gibson, Annabelle; Winter, Rebecca J; Iyer, Kiran; Sim, Abigail; Evans, Sophia; Kumaragama, Kavindu; Howell, Jessica; Desmond, Paul; Iser, David; Scott, Nick; Hellard, Margaret; Stoové, Mark; Wilson, David; Pedrana, Alisa 3 of 3

Abstract

This article focuses on the evaluation of a decentralized, nurse-led hepatitis C treatment program implemented across all adult prisons in Victoria, Australia, from 2015 to 2021. The program provided direct-acting antiviral (DAA) therapy to 2,768 incarcerated individuals, achieving a high sustained virologic response rate (SVR12) of 93% among those with complete follow-up, regardless of hepatitis C virus genotype or cirrhosis status. The prison-based program accounted for an increasing proportion of all DAA prescriptions in Victoria, rising from 6% in 2016 to 23% in 2020, and facilitated treatment for many who had not previously engaged in hepatitis C care. The study highlights the effectiveness of nurse-led prison programs in scaling up hepatitis C treatment and their critical role in public health efforts toward hepatitis C elimination, while also noting challenges such as reinfection risk and loss to follow-up after release.

Additional Information

  • Source:Clinical Infectious Diseases. 2025/04, Vol. 80, Issue 4, p826
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1058-4838
  • DOI:10.1093/cid/ciae471
  • Accession Number:184861991
  • Copyright Statement:Copyright of Clinical Infectious Diseases is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.