Injecting network structure determines the most efficient strategy to achieve Hepatitis C elimination in people who inject drugs. Issue 9 (16th June 2021) - Brown, Chloe; Siegele, Martin; Wright, Mark; Cook, Charlotte; Parkes, Julie; I Khakoo, Salim; Sacks‐Davis, Rachel; Buchanan, Ryan M
Abstract: Transmission of Hepatitis C (HCV) continues via sharing of injection equipment between people who inject drugs (PWID). Network‐based modelling studies have produced conflicting results about whether random treatment is preferable to targeting treatment at PWID with multiple partners. We hypothesise that differences in the modelled injecting network structure produce this heterogeneity. The study aimed to test how changing network structure affects HCV transmission and treatment effects. We created three dynamic injecting network structures connecting 689 PWID (UK‐net, AUS‐net and USA‐net) based on published empirical data. We modelled HCV in the networks and at 5 years compared prevalence of HCV 1) with no treatment, 2) with randomly targeted treatment and 3) with treatment targeted at PWID with the most injecting partnerships (degree‐based treatment). HCV prevalence at 5 years without treatment differed significantly between the three networks (UK‐net (42.8%) vs. AUS‐net (38.2%), p < 0.0001 and vs. USA‐net (54.0%), p < 0.0001). In the treatment scenarios UK‐net and AUS‐net showed a benefit of degree‐based treatment with a 5‐year prevalence of 1.0% vs. 9.6% p < 0.0001 and 0.15% vs. 0.44%, p < 0.0001. USA‐net showed no significant difference (29.3% vs. 29.2%, p = 0.0681). Degree‐based treatment was optimised with low prevalence, moderate treatment coverage conditions whereas random treatment was optimised in low treatment coverage, high prevalence conditions. In conclusion, injecting network structure determines the transmission rate of HCV and the most efficient treatment strategy. In real‐world injecting network structures, the benefit of targeting HCV treatment at individuals with multiple injecting partnerships may have been underestimated.