Estimating the Impact of Structural Factorson HIV Transmission: A Multi-agent Spatial Simulation Modeling Study

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PROJECT SUMMARY/ABSTRACT The disparity in HIV incidence rates between African Americans/Blacks and whites continues to grow. Interventions aimed towards reducing HIV incidence by changing individual-level behaviors have been implemented among African Americans/Blacks. Unfortunately, there has been little to no success in closing the gap between the two racial groups. Many studies have determined neighborhood-level factors (e.g., neighborhood disadvantage, poverty, and incarceration) significantly influence HIV-related risk behaviors, engagement in testing and treatment, and adherence to HIV care and mortality among African Americans/Blacks, especially those who identify as heterosexual. In this study, we will develop a multi-agent spatial simulation model that will estimate the effect of neighborhood-level factors such as neighborhood disadvantage and gentrification on HIV incidence among heterosexual African American/Black adults to develop interventions that can reduce transmission among this population. We will first implement spatial dynamics into an agent-based model to create the multi-agent spatial simulation model. We will then estimate the effect of neighborhood disadvantage on HIV incidence among heterosexual African American/Black adults. Next, we will evaluate the effect of migration and gentrification on HIV incidence within this population by integrating migration into the model. Using innovative methods such as multi-agent spatial simulation modeling, we will simulate the complex interactions between individual-level behaviors, networks, and neighborhoods for estimating the structural influence of HIV on a population level and test structural-level interventions. We will be the first to develop a spatially dynamic model for studying HIV among heterosexual African American/Black adults in an urban setting through the development of the model. This research will also be used as a framework for estimating structural influences on populations in different jurisdictions within the United States to reduce HIV-related disparities and the structures that allow it to prosper.