Using Sexual Network Transmission Models to Explain HIV Disparities Between Black

描述（由申请人提供）：在美国，黑人和白色男性同性恋者（MSM）之间的艾滋病毒差异很大，无法解释。这些差异的个人行为原因通常被认为是可能的解释。网络的解释已被证明是有用的，在了解其他人群中的STI负担的持续差异，并已假设解释种族/民族的差异在美国男男性接触者。然而，由于两个原因，这些假设尚未使用建模方法进行测试。第一，历史上一直没有足够的数据参数化模型的关键网络特征，如并发性，传递性，和关键特征的混合。第二，模型结构尚未开发，占MSM性网络的独特方面。我们建议通过分析来自亚特兰大调查人员进行的多项研究的关于MSM性网络的新数据来发展这种理解，亚特兰大是一个大都市地区，黑人和白色MSM人口众多，艾滋病毒存在显著的种族差异。我们将召回当前和最近研究的参与者， 必要的数据。使用这些新的网络参数，我们将扩展现有的艾滋病毒在男男性接触者传播的随机模拟模型，以反映这些网络属性。这种方法将使用STERGMs（可分离的时间指数随机图模型），它可以处理动态网络数据的横截面网络结构的形式结合相关的持续时间或溶解模式的信息。我们将使用扩展的、新参数化的模型用于两个目的。首先，我们将使用该模型试图描述网络参数在美国男男性接触者艾滋病发病率的黑人/白色差异中的作用。其次，我们将使用该模型来测试哪些可修改的网络参数可能最有可能降低艾滋病毒的发病率，如果修改的MSM人群。这些活动的完成，预计将导致在测试的假设有关的原因，黑人/白色的差异，在美国男男性接触者，建模工具，将是有用的，以进一步评估男男性接触者网络，和具体的，提出的目标，为新的预防干预措施，这个最受影响的社区在美国艾滋病毒流行。我们的具体目标是：（1）开发一套综合的个体、二元和三元网络参数，考虑传播潜力，用于亚特兰大大都市的黑人和白色MSM的基于代理的HIV传播模型;（2）扩展现有的针对MSM的随机的、基于代理的HIV传播模型，以包括用于对黑人和白色MSM的网络特征建模的代码;（3）利用该增强的模型和参数来（a）确定是否可以使用现有的关于其来源的假设结合我们测量的网络现象来解释在亚特兰大地区MSM中观察到的HIV的种族差异;（B）识别用于解决美国MSM中HIV的种族/民族差异的最高影响的网络属性。

DESCRIPTION (provided by applicant): In the United States, disparities in HIV between black and white men who have sex with men (MSM) are large and unexplained. Individual behavioral reasons for these disparities have been generally dismissed as possible explanations. Network explanations have proven useful in understanding persistent disparities in STI burdens in other populations, and have been hypothesized to explain racial/ethnic disparities in US MSM. However, these hypotheses have not been tested using modeling approaches for two reasons. First, there have historically been inadequate data to parameterize models with respect to critical network characteristics, such as concurrency, transitivity, and assortative mixing by key characteristics. Second, model structures have not been developed that account for the unique aspects of MSM sexual networks. We propose to develop this understanding by analyzing new data on MSM sexual networks from multiple studies conducted by the investigators in Atlanta, a metropolitan area with large populations of both black and white MSM and significant racial disparities in HIV. We will recall participants in current and recent studies to collect additional data as necessary. Using these new network parameters, we will extend an existing stochastic simulation model of HIV transmission in MSM to reflect these network attributes. This approach will use STERGMs (separable temporal exponential random graph models), which can handle dynamic network data in the form of cross-sectional network structure combined with information on relational duration or dissolution patterns. We will use the expanded, newly parameterized model for two purposes. First, we will use the model to attempt to describe the role of network parameters in black/white disparities in HIV incidence among US MSM. Second, we will use the model to test which modifiable network parameters might have the most potential in reducing HIV incidence, if modified among MSM populations. Completion of these activities is expected to result in the testing of hypotheses about reasons for black/white disparities in HIV among US MSM, modeling tools that will be useful to further evaluation of MSM networks, and specific, proposed targets for new prevention interventions for this most highly impacted community in the US HIV epidemic. Our Specific Aims are: (1) to develop a comprehensive set of individual, dyadic, and triadic network parameters, accounting for transmission potential, for use in an agent-based HIV transmission model for black and white MSM in metropolitan Atlanta; (2) to expand an existing stochastic, agent-based HIV transmission model for MSM to include code for modeling network features for black and white MSM; (3) to utilize this enhanced model and parameters to (a) determine whether racial disparities for HIV observed among Atlanta-area MSM can be explained using existing hypotheses for their sources in combination with our measured network phenomena; (b) identify the highest- impact network properties for addressing racial/ethnic disparities in HIV among US MSM.