Prioritising Hepatitis C treatment in people with multiple injecting partners maximises prevention: A real-world network study

Abstract

Objective: To describe an injecting network of PWID living in an isolated community on the Isle of Wight (UK) and the results of a agent-based simulation, testing the effect of Hepatitis C (HCV) treatment on transmission. Method: People who inject drugs (PWID) were identified via respondent driven sampling and recruited to a network and bio-behavioural survey. The injecting network they described formed the baseline population and potential transmission pathways in an agent-based simulation of HCV transmission and the effects of treatment over 12 months. Results: On average each PWID had 2.6 injecting partners (range 0-14) and 137 were connected into a single component. HCV in the network was associated with a higher proportion of positive injecting partners ( p = 0.003) and increasing age ( p = 0.011). The treatment of well-connected PWID led to significantly fewer new infections of HCV than treating at random (10 vs. 7, p < 0.001). In all scenarios less than one individual was re-infected. Conclusion: In our model the preferential treatment of well-connected PWID maximised treatment as prevention. In the real-world setting, targeting treatment to actively injecting PWID, with multiple injecting partners may therefore represent the most efficient elimination strategy for HCV.

Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs

“…However, a third modelling study from the United Kingdom (UK) has shown the opposite 7 . In this study degree‐based treatment partners reduced transmission and the overall prevalence of HCV at one year.…”

“…We independently altered baseline prevalence, treatment coverage rate and the prevalence of receptive needle sharing (RNS) to test the effect on the primary outcome. Treatment coverage and baseline prevalence are known to have an important impact on TasP effects, 9 and change in RNS rate was found to be the greatest determinant of the relative effectiveness of different treatment strategies in a prior study 7 …”

“…By including all named injecting partners in the network, Zelenev et al are likely to have missed friends of friend ties in the network – decreasing the clustering coefficient. However, Buchanan et al and Zelenev et al have contrasting clustering coefficients but very similar sampling techniques 5,7 . This variation may be accounted for by the study setting.…”

“…We altered the degree sequence and clustering coefficient to produce three different network structures. The values we used were based on network data from Melbourne (Australia), Hartford (Connecticut, USA), and the Isle of Wight (UK) 5‐7,12 . We henceforth refer to the three network structures as AUS‐net, UK‐net, and USA‐net, to denote the network structure derived from the corresponding prior study.…”

“…Data on the frequency of injecting and the probability of sharing equipment were taken from a recent study describing PWID behaviours in a population geographically close to Southampton (Table 1). 7 Parameters relating to the natural history of HCV infection and response to treatment were taken from the available published literature (Table 1). We assumed that the rate of sustained virological response (SVR) was the same for all treated nodes; this is consistent with recent studies that have shown PWID have comparable rates treatment completion 17 and SVR 18 to non‐PWID.…”