CommentaryHepatitis C transmission and treatment as prevention – The role of the injecting network
Introduction
The hepatitis C virus (HCV) is a major health issue leading to significant morbidity and mortality, affecting an estimated 184 million people globally (Mohd Hanafiah, Groeger, Flaxman, & Wiersma, 2013). In more developed countries like Australia the epidemic is driven by people who inject drugs (PWID) (Mohd Hanafiah et al., 2013, Shepard et al., 2005). To reduce transmission of HCV it is important to understand the specific risk factors that drive it; some are individual behaviours like sharing needles and syringes, and others are related to the context in which injecting occurs (Morris et al., 2014). The injecting network – the pattern of relationships between people who inject drugs – is a contextual factor that powerfully influences HCV transmission.
For the past 10 years our group (comprising epidemiologists, immune-virologists, mathematical and network modellers and a team of field researchers experienced in working with PWID) has examined the role of the injecting network in influencing HCV transmission and explored network-related strategies to reduce disease transmission. This paper provides an overview of that research and briefly describes the Hepatitis C Treatment and Prevention (TAP) Study, which evaluates the effect of a network-based approach to provision of direct-acting antiviral (DAA) therapy on HCV prevalence and incidence.
Section snippets
Tracing HCV in networks of PWID
Between 2005 and 2010 we followed a cohort of PWID, measuring HCV incidence, prevalence and injecting risk, including network-related factors (Aitken et al., 2008, Miller et al., 2009, Sacks-Davis et al., 2012). Four hundred and thirteen PWID were recruited to a longitudinal study of risk factors associated with the transmission of HCV, hepatitis B virus (HBV) and HIV. Participants completed detailed questionnaires on their drug use and risk behaviours, provided blood samples for serology
Modelling network influence on HCV transmission
In addition to establishing empirically that the social-injecting network was related to the HCV transmission network using phylogenetic analysis, we wanted to understand how the social network affected HCV transmission. We used mathematical modelling to explore this phenomenon in detail. First, we developed an individual-based HCV transmission model that could be applied to a social network of PWID (Rolls et al., 2012). We then used this model to simulate the transmission of HCV through the
Simulation of HCV treatment
A further objective was to learn how HCV is transmitted in social networks of injectors and identify strategies that could be used to reduce the frequency of HCV transmissions. To this end, after the development of the initial network-based transmission model, we developed an empirically grounded network model of who might inject with whom (injecting occurs at the same time and space) using exponential random graph models (ERGMs). ERGMs are based on theories of social network formation, and can
Application of models to clinical trials
Informed by the results of our models, between 2014 and 2016 we are undertaking the Hepatitis C Treatment and Prevention (TAP) Study. The TAP Study is designed to examine the feasibility of treating PWID in a community-based setting with a 12-week course of oral therapy that combines the DAAs sofosbuvir and ledipasvir (SOF + LDP) for participants infected with genotype 1 and SOF + LDP and ribavirin (Rib) for participants infected with all other genotypes. Another key aim of the study is to measure
Conclusion
Our work highlights the importance of using a networks-based approach to increase our understanding of HCV transmission and its role in informing the roll out of treatment as prevention. Nevertheless, we are yet to determine how differences in injecting networks, such as variation in the network structure, injecting risk behaviour and HCV prevalence, alter the influence of the injecting network. Further research is required to understand the broader impact of the injecting network on HCV
Conflicts of interest
MH and JD: Research/grant support from Gilead Sciences to the Burnet Institute.
AT: Research/grant support–Merck, Roche, Gilead; Consulting/advisory capacity–Merck, Roche, Janssen-Cilag (Johnson and Johnson), Gilead, Novartis; Speaker's fee–Merck, Roche, Bristol-Myers Squibb, Bayer, Janssen, Gilead. Co-inventor of a patent related to the IL28B-HCV discovery.
MH, AT and JD have received funding from Gilead Sciences to support an investigator initiated research study on treatment of PWID.
Acknowledgements
MH, EMc, RSD and JD acknowledge fellowship support from the National Health and Medical Research Council. PH is supported by a Curtin University Fellowship. The authors acknowledge the contribution to this work of support from the NHMRC (App–331312 and App–1001144), the ARC (App DP0987730) and the Victorian Operational Infrastructure Support Program (Department of Health, Victoria, Australia) to the Burnet Institute.
References (15)
- et al.
Markers and risk factors for HCV, HBV and HIV in a network of injecting drug users in Melbourne, Australia
Journal of Infection
(2009) - et al.
Modelling hepatitis C transmission over a social network of injecting drug users
Journal of Theoretical Biology
(2012) - et al.
Modelling a disease-relevant contact network of people who inject drugs
Social Networks
(2013) - et al.
Global epidemiology of hepatitis C virus infection
The Lancet Infectious Diseases
(2005) - et al.
High incidence of hepatitis C virus reinfection in a cohort of injecting drug users
Hepatology
(2008) - et al.
Stochastic epidemic models and their statistical analysis
(2000) - et al.
Mathematical epidemiology of infectious diseases: Model building, analysis and interpretation
(2000)
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