At Risk: Modeling HIV/Hepatitis C Coinfection and Interventions in Urban Populations A Case Study of Newark, NJ

Abstract

Background HIV-Hepatitis C (HCV) coinfection is an increasingly problematic health concern worldwide, particularly for low-resource populations without sufficient access to health care or risk groups who participate in activities that perpetuate the bloodborne pathogens’ spread, such as high risk sexual activity and injection drug use. Mathematical models are powerful tools that can be used to capture the dynamics of this disease interaction and project the effects of various treatment options to help select the optimal interventions to target for scale-up. While these models have been used on both HIV and HCV monoinfection, HIV/HCV coinfection has not been explored to the same degree, especially for interventions. Methods This thesis constructs a layered disease model by adapting a previously published model of HIV infection to emulate the dynamics of HIV, HCV, and their risk groups based on gender, sexual activity, and injection drug use. Through this model, combinations of key interventions – methadone maintenance treatment (MMT) for IDU, antiretroviral therapy (ART) for HIV, and treatments drugs for HCV – will be applied to determine which combination and level of intervention is most effective at controlling HIV/HCV infections over time. Results A scale-up of a combination of MMT, ART, and HCV treatment was found to be the most effective at reducing HIV incidence (almost 20% over 25 years at optimal intervention levels). A scale-up of a combination of MMT and HCV treatment was found to be the most effective at reducing HCV incidence (almost 14% over 25 years at optimal intervention level). Treating early levels of HCV had a profound impact on reducing HCV incidence versus only treating late-stage HCV (30% vs 0.7% reduction at moderate intervention levels). HCV incidences were also slightly reduced by treating IDU the same degree as non-IDU (5.5% vs 5% reduction at moderate intervention levels). Discussion The outcomes of this model could inform future HIV, HCV, and IDU treatment policy. Future directions could include modeling risk groups such as those in extreme poverty who are at risk of homelessness and sex workers, examining recent interventions through the model, such as pre-exposure prophylaxis (PrEP) for HIV, and studying the treatment of all genotypes of HCV.