title: The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs.
abstract: With the development of new highly efficacious direct-acting antiviral (DAA) treatments for hepatitis C virus (HCV), the concept of treatment as prevention is gaining credence. To date, the majority of mathematical models assume perfect mixing, with injectors having equal contact with all other injectors. This article explores how using a networks-based approach to treat people who inject drugs (PWID) with DAAs affects HCV prevalence. Using observational data, we parameterized an exponential random graph model containing 524 nodes. We simulated transmission of HCV through this network using a discrete time, stochastic transmission model. The effect of five treatment strategies on the prevalence of HCV was investigated; two of these strategies were (1) treat randomly selected nodes and (2) "treat your friends," where an individual is chosen at random for treatment and all their infected neighbors are treated. As treatment coverage increases, HCV prevalence at 10 years reduces for both the high- and low-efficacy treatment. Within each set of parameters, the treat your friends strategy performed better than the random strategy being most marked for higher-efficacy treatment. For example, over 10 years of treating 25 per 1,000 PWID, the prevalence drops from 50% to 40% for the random strategy and to 33% for the treat your friends strategy (6.5% difference; 95% confidence interval: 5.1-8.1).
  
  
  CONCLUSIONS
  Treat your friends is a feasible means of utilizing network strategies to improve treatment efficiency. In an era of highly efficacious and highly tolerable treatment, such an approach will benefit not just the individual, but also the community more broadly by reducing the prevalence of HCV among PWID.
authors.affiliation: Center for Population Health, Burnet Institute, Melbourne, Victoria, Australia; Infectious Diseases Unit, The Alfred Hospital, Melbourne, Victoria, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Center for Research Excellence in Injecting Drug Use, Burnet Institute, Melbourne, Victoria, Australia.
authors.forename: Margaret
authors.initials: M
authors.lastname: Hellard
authors.forename: David A
authors.initials: DA
authors.lastname: Rolls
authors.forename: Rachel
authors.initials: R
authors.lastname: Sacks-Davis
authors.forename: Garry
authors.initials: G
authors.lastname: Robins
authors.forename: Philippa
authors.initials: P
authors.lastname: Pattison
authors.forename: Peter
authors.initials: P
authors.lastname: Higgs
authors.forename: Campbell
authors.initials: C
authors.lastname: Aitken
authors.forename: Emma
authors.initials: E
authors.lastname: McBryde
mesh_terms: D000328:Adult; D003198:Computer Simulation; D055030:Drug Users / Q000706:statistics & numerical data*; D005260:Female; D006526:Hepatitis C / Q000453:epidemiology / Q000635:transmission*; D006801:Humans; D007267:Injections / Q000009:adverse effects; D008297:Male; D008962:Models, Theoretical; D015995:Prevalence; D060756:Social Networking; D014739:Victoria / Q000453:epidemiology; D055815:Young Adult