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    The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs

    202646_202646.pdf (922.6Kb)
    Access Status
    Open access
    Authors
    Hellard, M.
    Rolls, D.
    Sacks-Davis, R.
    Robins, G.
    Pattison, P.
    Higgs, Peter
    Aitken, C.
    McBryde, E.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Hellard, M. and Rolls, D. and Sacks-Davis, R. and Robins, G. and Pattison, P. and Higgs, P. and Aitken, C. et al. 2014. The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs. Hepatology. 60 (6): pp. 1861-1870.
    Source Title
    Hepatology
    DOI
    10.1002/hep.27403
    ISSN
    0270-9139
    School
    National Drug Research Institute (Research Institute)
    Remarks

    This is the accepted version of the following article: Hellard, M. and Rolls, D. and Sacks-Davis, R. and Robins, G. and Pattison, P. and Higgs, P. and Aitken, C. et al. 2014. The impact of injecting networks on hepatitis C transmission and treatment in people who inject drugs. Hepatology. 60 (6): pp. 1861-1870, which has been published in final form at http://doi.org/10.1002/hep.27403

    URI
    http://hdl.handle.net/20.500.11937/12592
    Collection
    • Curtin Research Publications
    Abstract

    With the development of new highly efficacious direct acting antiviral treatments (DAAs) for hepatitis C (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 paper explores how using a networks based approach to treat people who inject drugs (PWID) with DAAs affects HCV prevalence. Method: 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 neighbours are treated. Results: As treatment coverage increases, HCV prevalence at 10 years reduces for both the high efficacy 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 1000 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% CI 5.1 – 8.1%). Discussion: “Treat your friends” is a feasible means of utilising 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 the community more broadly by reducing the prevalence of HCV amongst PWID.

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