Margaret Hellard1, Emma McBryde2, Rachel Sacks Davis3, David A Rolls4, Peter Higgs5, Campbell Aitken3, Alex Thompson6, Joe Doyle7, Pip Pattison4, Garry Robins4. 1. Centre for Population Health, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia; Infectious Diseases Unit, The Alfred Hospital, 55 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Epidemiology and Preventative Medicine, Monash University, 89 Commercial Road, Melbourne, Victoria 3004, Australia; Centre for Research Excellence in Injecting Drug Use, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia. Electronic address: hellard@burnet.edu.au. 2. Centre for Population Health, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia; Victorian Infectious Disease Service, Royal Melbourne Hospital, Royal Parade, Parkville, Victoria 3050, Australia; Department of Medicine, University of Melbourne, Victoria 3010, Australia. 3. Centre for Population Health, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Epidemiology and Preventative Medicine, Monash University, 89 Commercial Road, Melbourne, Victoria 3004, Australia; Centre for Research Excellence in Injecting Drug Use, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia. 4. Melbourne School of Psychological Sciences, University of Melbourne, Victoria 3010, Australia. 5. Centre for Population Health, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia; National Drug Research Institute, Faculty of Health Sciences, Curtin University (Melbourne Office), 19-35 Gertrude St, Fitzroy, Victoria 3065, Australia; Department of Medicine, University of Melbourne, Victoria 3010, Australia. 6. Department of Gastroenterology, St Vincent's Hospital, Melbourne, Victoria 3065, Australia. 7. Centre for Population Health, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia; Infectious Diseases Unit, The Alfred Hospital, 55 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Epidemiology and Preventative Medicine, Monash University, 89 Commercial Road, Melbourne, Victoria 3004, Australia; Centre for Research Excellence in Injecting Drug Use, Burnet Institute, 85 Commercial Road, Melbourne, Victoria 3004, Australia.
Abstract
BACKGROUND: The hepatitis C virus (HCV) epidemic is a major health issue; in most developed countries it is driven by people who inject drugs (PWID). Injecting networks powerfully influence HCV transmission. In this paper we provide an overview of 10 years of research into injecting networks and HCV, culminating in a network-based approach to provision of direct-acting antiviral therapy. METHODS: Between 2005 and 2010 we followed a cohort of 413 PWID, measuring HCV incidence, prevalence and injecting risk, including network-related factors. We developed an individual-based HCV transmission model, using it to simulate the spread of HCV through the empirical social network of PWID. In addition, we created an empirically grounded network model of injecting relationships using exponential random graph models (ERGMs), allowing simulation of realistic networks for investigating HCV treatment and intervention strategies. Our empirical work and modelling underpins the TAP Study, which is examining the feasibility of community-based treatment of PWID with DAAs. RESULTS: We observed incidence rates of HCV primary infection and reinfection of 12.8 per 100 person-years (PY) (95%CI: 7.7-20.0) and 28.8 per 100 PY (95%CI: 15.0-55.4), respectively, and determined that HCV transmission clusters correlated with reported injecting relationships. Transmission modelling showed that the empirical network provided some protective effect, slowing HCV transmission compared to a fully connected, homogenous PWID population. Our ERGMs revealed that treating PWID and all their contacts was the most effective strategy and targeting treatment to infected PWID with the most contacts the least effective. CONCLUSION: Networks-based approaches greatly increase understanding of HCV transmission and will inform the implementation of treatment as prevention using DAAs.
BACKGROUND: The hepatitis C virus (HCV) epidemic is a major health issue; in most developed countries it is driven by people who inject drugs (PWID). Injecting networks powerfully influence HCV transmission. In this paper we provide an overview of 10 years of research into injecting networks and HCV, culminating in a network-based approach to provision of direct-acting antiviral therapy. METHODS: Between 2005 and 2010 we followed a cohort of 413 PWID, measuring HCV incidence, prevalence and injecting risk, including network-related factors. We developed an individual-based HCV transmission model, using it to simulate the spread of HCV through the empirical social network of PWID. In addition, we created an empirically grounded network model of injecting relationships using exponential random graph models (ERGMs), allowing simulation of realistic networks for investigating HCV treatment and intervention strategies. Our empirical work and modelling underpins the TAP Study, which is examining the feasibility of community-based treatment of PWID with DAAs. RESULTS: We observed incidence rates of HCV primary infection and reinfection of 12.8 per 100 person-years (PY) (95%CI: 7.7-20.0) and 28.8 per 100 PY (95%CI: 15.0-55.4), respectively, and determined that HCV transmission clusters correlated with reported injecting relationships. Transmission modelling showed that the empirical network provided some protective effect, slowing HCV transmission compared to a fully connected, homogenous PWID population. Our ERGMs revealed that treating PWID and all their contacts was the most effective strategy and targeting treatment to infected PWID with the most contacts the least effective. CONCLUSION: Networks-based approaches greatly increase understanding of HCV transmission and will inform the implementation of treatment as prevention using DAAs.
Authors: Sofia R Bartlett; Brendan Jacka; Rowena A Bull; Fabio Luciani; Gail V Matthews; Francois M J Lamoury; Margaret E Hellard; Behzad Hajarizadeh; Suzy Teutsch; Bethany White; Lisa Maher; Gregory J Dore; Andrew R Lloyd; Jason Grebely; Tanya L Applegate Journal: Infect Genet Evol Date: 2015-11-26 Impact factor: 3.342
Authors: Margaret Hellard; Sophia E Schroeder; Alisa Pedrana; Joseph Doyle; Campbell Aitken Journal: Cold Spring Harb Perspect Med Date: 2020-04-01 Impact factor: 6.915