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

项目摘要/摘要 非裔美国人/黑人和白人之间的艾滋病毒发病率差距继续扩大 成长。旨在通过改变个人行为来减少艾滋病毒发病率的干预措施 已经在非裔美国人/黑人中实施。不幸的是，几乎没有什么可以 在缩小这两个种族群体之间的差距方面没有成功。许多研究已经确定 邻里层面的因素(例如，邻里不利、贫困和监禁) 显著影响艾滋病毒相关的危险行为，参与检测和治疗，以及 非裔美国人/黑人坚持艾滋病毒护理和死亡率，特别是那些 认定自己是异性恋者。在这项研究中，我们将开发一个多智能体空间模拟模型 将评估邻里层面因素的影响，如邻里劣势和 非裔美国人/黑人异性恋者中HIV发病率的绅士化发展 能够减少这一人群中传播的干预措施。我们将首先实现空间 将动力学转化为基于智能体的模型，创建多智能体空间仿真模型。我们会 然后估计邻里劣势对异性恋者HIV发病率的影响 非裔美国人/黑人成年人。接下来，我们将评估移民和士绅化对 通过将移民纳入模型，在这一人群中发现艾滋病毒发病率。使用创新的 采用多智能体空间仿真建模等方法，对复杂系统进行仿真 个体级别的行为、网络和邻居之间的交互作用，用于估计 艾滋病毒对人口水平的结构性影响，并测试结构性干预措施。我们会的 首次开发空间动态模型来研究非洲异性恋人群中的艾滋病毒 美国/黑人成年人在城市背景下通过模式的发展。这项研究 也将被用作评估不同地区人口结构影响的框架 美国境内的司法管辖区，以减少与艾滋病毒相关的差异，以及允许 它将繁荣兴旺。

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.