Modeling HCV/HIV transmission and Treatment as Prevention in U.S. Networks of People Who Inject Drugs



 DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) and HIV, respectively, are the two most prevalent chronic viral infections. Among the 1.2 million people living with HIV, 25-30% are HCV/HIV coinfected, but HCV prevalence is highest (80.8%) among HIV+ people who inject drugs (PWIDs). Currently, most PWIDs are excluded from HCV treatment despite national guidelines recommending otherwise. With the availability of more tolerable and better HCV treatments, however, a Treatment as Prevention (TasP) approach could drastically tip the balance towards increased and potentially targeted HCV treatment approaches. Though eradicating HCV will require a strategic combination of prevention AND treatment, HCV treatment costs may become staggering and require strategic targeting of PWIDs - the only ones who can transmit virus. Treating PWIDs most effectively, however, requires a better understanding of injecting networks and understanding such structures may help guide informed treatment strategies. With the expectation that HCV treatment will be markedly easier by early 2015, it is timely to examine the influence of injection-network structures and model treatment strategies for HCV TasP in the U.S. that have the highest likelihood of success while doing so without excessive costs. The specific aims are to: 1) simulate injection networks using a measurement-calibrated network model of injection partnerships based on secondary data recently completed from a large, U.S. respondent-driven sample (RDS) of PWIDs; and 2) develop a HCV dynamic transmission model that incorporates injection network structures and agent-based simulations to achieve two goals to: a) analyze the effect of injection network structure on HCV incidence and prevalence; and b) evaluate the effectiveness of various HCV TasP strategies within US PWID injection networks. We will first develop an injection network model that we will "calibrate" using secondary data from the US PWID network. Calibration will involve "fitting" several graph models to the real injection network data, extracting a set of parameters that describe the network properties, and using the parameters to generate synthetic networks that are statistically similar to the original injection partnerships observed i US PWIDs. We will then simulate a dynamic HCV transmission model using a combination of the selected network model and agent-based micro-interactions. Last, we will evaluate the effectiveness of 4 potential TasP strategies on chronic HCV prevalence and incidence over 10 years. Findings should help guide future HCV TasP strategies focusing on active PWIDs who are now excluded from treatment by most HCV clinical experts and help to allocate scarce resources more effectively and produce better treatment and prevention outcomes. This research is significant and innovative in that it focuses on the two most prevalent and costly chronic viral infections. It comes at a time of rapid advances in HCV treatment and improves upon existing HCV transmission modeling strategies. It also provides guidance for how HCV TasP strategies should (or could) be deployed. Last, it takes advantage of a comprehensive injection network of PWIDs in the U.S. to inform domestic models and brings together researchers in the fields of mathematical modeling, networks analysis and HCV clinical care management.