Hepatitis C virus treatment as prevention in an extended network of people who inject drugs in the USA: a modelling study

Abstract

Background: Chronic infections with hepatitis C virus (HCV) and HIV are highly prevalent in the USA and concentrated in people who inject drugs. Treatment as prevention with highly effective new direct-acting antivirals is a prospective HCV elimination strategy. We used network-based modelling to analyse the effect of this strategy in HCV-infected people who inject drugs in a US city.

Methods: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.

Findings: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.

Interpretation: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.

Funding: National Institute on Drug Abuse.

Figure 1. The Injection Network among People Who Inject Drugs in Hartford, CT (N=1574)

Legend: The depicted network is derived from respondent-driven sampling adapted to construct injection and social ties. Each node represents a person who injected drugs, and each edge – an injection partnership. Green Nodes were sampled directly through recruitment, and red nodes were not recruited into the sample through network referrals.

Figure 2

Network Model Simulation of HCV and HIV Transmission and Evaluation of Various Treatment as Prevention Strategies

Figure 3

Mean HCV Prevalence at the End of the 10-year estimation simulation window (10,000 replications)

Figure 4

Mean HCV Prevalence at the End of the 20-year simulation window (10,000 replications)