Computational modeling to evaluate socio-structural interventions for HIV and substance use

摘要

ABSTRACT Background: Black sexual and gender minorities (SGM) are disproportionately affected by HIV and existing disparities could be exacerbated by increases in substance use disorders, as shifting trends in the opioid epidemic have been accompanied by increases in methamphetamine and polysubstance use among Black SGM. Evidence suggests that factors such as housing instability, incarceration, and unemployment may pose significant barriers to engagement in HIV prevention and care for Black SGM, and these factors are also associated with methamphetamine use. Because such interventions are resource intensive and logistically challenging, particularly for vulnerable communities who are often highly mobile and less likely to engage in research in traditional settings, guidance is needed at the intervention development stage to determine the most impactful and efficient intervention strategies. Agent-based models (ABMs) can be used to virtually evaluate candidate interventions to facilitate more efficient and timely intervention development. Because they allow for the conduct of counterfactual experiments, ABMs can also facilitate identification of effects that would be difficult to identify using traditional statistical approaches and can provide valuable insights to understand causal mechanisms that give rise to complex systems. Objective: Building on an existing ABM platform, this proposal will utilize multiple existing data sources to characterize relationships among socio-structural stressors, substance use, mental health, and HIV prevention and care continuum outcomes among Black SGM. We will combine methods from epidemiology, ABM, and robust decision making (RDM) to understand the potential impact of structural interventions for reducing substance use, overdose, and HIV transmission. Methods: We will apply statistical and computational methods to better understand how socio-structural factors, substance use, and mental health impact engagement in HIV prevention and care continuums. We will then conduct a series of experiments to evaluate how socio-structural factors impact the uptake of existing biomedical interventions and compare outcomes under scenarios with different combinations of interventions using RDM. Significance: A better understanding of where and how to focus intervention efforts offers potential to improve substance use and HIV prevention and care outcomes for Black SGM. Once developed, our methods and models can be adapted to other geographic areas to reflect local prevention priorities and can serve as an example application of epidemiology, ABM, and RDM methods to advance HIV and substance use prevention science.