Commentary
Hepatitis C transmission and treatment as prevention – The role of the injecting network

https://doi.org/10.1016/j.drugpo.2015.05.006Get rights and content

Highlights

  • Injecting relationships are strongly correlated with HCV transmission clusters.

  • Relative to homogenous models, real-world injecting networks restrict HCV spread.

  • A “bring your friends” network method surpasses random HCV treatment of PWIDs.

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.

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)

There are more references available in the full text version of this article.

Cited by (52)

View all citing articles on Scopus
View full text