A molecular transmission network of recent hepatitis C infection in people with and without HIV: Implications for targeted treatment strategies. Issue 5 (24th November 2016)

• Record Type: Journal Article
• Authors: Bartlett, S. R.; Wertheim, J. O.; Bull, R. A.; Matthews, G. V.; Lamoury, F. M. J.; Scheffler, K.; Hellard, M.; Maher, L.; Dore, G. J.; Lloyd, A. R.; Applegate, T. L.; Grebely, J.
• Is Part Of: Journal of viral hepatitis. Volume 24:Issue 5(2017)
• Page Start: 404
• Page End: 411
• Publication Date: 2016-11-24
• Subjects: hepatitis C virus -- human immunodeficiency virus -- modelling -- molecular epidemiology -- treatment as prevention; Hepatitis, Viral -- Periodicals; Hepatitis, Viral, Animal; Hepatitis, Viral, Human; 616.3623
• DOI: 10.1111/jvh.12652
• Languages: English
• ISSNs: 1352-0504

Abstract

Summary: Combining phylogenetic and network methodologies has the potential to better inform targeted interventions to prevent and treat infectious diseases. This study reconstructed a molecular transmission network for people with recent hepatitis C virus (HCV) infection and modelled the impact of targeting directly acting antiviral (DAA) treatment for HCV in the network. Participants were selected from three Australian studies of recent HCV from 2004 to 2014. HCV sequence data (Core‐E2) from participants at the time of recent HCV detection were analysed to infer a network by connecting pairs of sequences whose divergence was ≤.03 substitutions/site. Logistic regression was used to identify factors associated with connectivity. Impact of targeting HCV DAAs at both HIV co‐infected and random nodes was simulated (1 million replicates). Among 236 participants, 21% (n=49) were connected in the network. HCV/HIV co‐infected participants (47%) were more likely to be connected compared to HCV mono‐infected participants (16%) (OR 4.56; 95% CI; 2.13‐9.74). Simulations targeting DAA HCV treatment to HCV/HIV co‐infected individuals prevented 2.5 times more onward infections than providing DAAs to randomly selected individuals. Results demonstrate that genetic distance‐based network analyses can be used to identify characteristics associated with HCV transmission, informing targeted prevention and treatment strategies.