Strategies to Achieve HIV and HCV Infection Incidence Targets Among People Who Inject Drugs: A Stochastic Network-Based Multi-Disease Transmission Modeling Study
The United States aims to reduce the incidence of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections by 90% by 2030. The objective of this study is to identify strategies for achieving incidence reduction goals by scaling interventions that address the syndemic of substance use disorder, HIV infection, and HCV infection among people who inject drugs (PWID). We developed a stochastic agent-based multiplex network model representing people who inject drugs living in urban areas. The model is parameterized with data from the 2018 National HIV Behavioral Surveillance system among PWID. Using the model, we simulated scenarios scaling prevention, cessation, and treatment interventions from current baseline to moderate and high values. High coverage across all three intervention strategies resulted in an 86% (95% Uncertainty Interval (UI): 72-96%) decrease in new HIV infections, a 90% (95% UI: 87-94%) decrease in new HCV infections, and an increase of 1.8 (95% UI: 1.6-2.0) discounted quality-adjusted life-years among PWID. Moderate coverage across all three strategies yielded 62% (95% UI: 39-81%) and 68% (95% UI: 61-74%) decreases in new HIV and HCV infections among PWID, respectively. Increasing cessation of injection consistently produced the largest gains in quality-adjusted life-years. Increases in survival and health-related quality of life for PWID can be achieved by scaling syndemic-focused intervention strategies.
This repository contains code for the model and analysis. R/run_pipeline.R documents the analytic pipeline, which was run on a high-performance computing cluster. For support, please reach out to Marissa Reitsma (mreitsma@stanford.edu).