Evidence map›Paper›PMID 40154796›Full record

ArticleVirus research2025

Estimating the impact of direct acting antiviral therapy on the prevalence of hepatitis C virus infection using phylogenetics.

Hossain M S Sazzad, Hui Li, Behzad Hajarizadeh, Bethany A Horsburgh, Jason Grebely, Gregory J Dore, Rowena A Bull, Andrew R Lloyd, Chaturaka Rodrigo

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Article in Virus research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hossain M S SazzadKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Hui LiKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Behzad HajarizadehKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Bethany A HorsburghKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Jason GrebelyKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Gregory J DoreKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Rowena A BullKirby Institute, UNSW, 2052 Sydney, NSW, Australia; School of Biomedical Sciences, UNSW 2052, NSW, Australia.
Andrew R LloydKirby Institute, UNSW, 2052 Sydney, NSW, Australia.
Chaturaka RodrigoKirby Institute, UNSW, 2052 Sydney, NSW, Australia; School of Biomedical Sciences, UNSW 2052, NSW, Australia. Electronic address: c.rodrigo@unsw.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAustralia has provided unrestricted subsidized access to direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection since 2016. Epidemiological surveillance estimates suggest prevalence of chronic HCV infection has declined since 2016, but these estimates are not separated by genotype and may not capture 'hidden' infected populations, notably the most marginalized groups affected, including people who inject drugs and people in prison. This study used phylogenetics to assess whether epidemiological estimates of declining HCV prevalence in the prisons of New South Wales, Australia due to DAA scale up could be reproduced.

methodNear-full-length 280 HCV consensus sequences (GT1a: n = 140, GT3a: n = 140) sampled between 2006 - 2019 from two prison-based cohort studies in NSW were used for phylogenetic estimates. These included 110 acute infection sequences (GT1a: n = 48, GT3a: n = 62) which were considered in a separate sensitivity analysis given the differences in virus mutation rates in acute and chronic infection. Changes in the effective population size of infected people for each genotype were explored with BEAST software suite (v1.10) using a coalescent Bayesian skyline approach.

resultsBoth the main and sensitivity analyses for GT3a showed a reduction in the effective population size with the latter showing a 36 % decline between 2011-2019 which is more concordant with the decline estimated from non-phylogenetic methods. A decline of similar magnitude was not demonstrated for GT1a. Overall, the analyses using acute infection sequences only were closer to the trends of independent epidemiological estimates.

conclusionsAn adequately powered Bayesian evolutionary analysis using acute stage infection sequences may reproduce the decline in HCV infections observed by traditional epidemiological methods during DAA scale up.

Indexed as

Antiviral AgentsHepacivirusHepatitis CHepatitis C, ChronicPhylogenyBayes TheoremGenotypeHumansMaleNew South WalesPrevalenceAntiviral AgentsAustraliaBayesian evolutionary analysisHepatitis C virusPeople who inject drugsPrisons

Identifiers

PMID40154796
PMCPMC11999633

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.