Using Sexual Network Transmission Models to Explain HIV Disparities Between Black

描述(申请人提供)：在美国，男男性行为者(MSM)黑人和白人之间的艾滋病毒差异很大，而且无法解释。造成这些差异的个人行为原因通常被认为是可能的解释。事实证明，网络解释有助于理解其他人群中STI负担的持续差异，并被假设为解释美国MSM中的种族/民族差异。然而，由于两个原因，这些假设尚未使用建模方法进行检验。首先，关于关键网络特征，例如并发性、传递性和按关键特征分类混合，历来没有足够的数据来对模型进行参数化。其次，还没有开发出模型结构来解释MSM性网络的独特方面。我们建议通过分析调查人员在亚特兰大进行的多项研究中关于MSM性网络的新数据来发展这种理解。亚特兰大是一个拥有大量黑人和白人MSM人口的大都市地区，在艾滋病毒方面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，以收集更多 根据需要提供数据。使用这些新的网络参数，我们将扩展现有的MSM中HIV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(可分离时间指数随机图模型)，它可以处理横截面网络结构形式的动态网络数据，并结合关系持续时间或解散模式的信息。我们将使用扩展的、新参数化的模型来实现两个目的。首先，我们将使用该模型来尝试描述网络参数在美国MSM中HIV发病率的黑人/白人差异中的作用。其次，我们将使用该模型来测试哪些可修改的网络参数如果在MSM人群中进行修改，可能在降低艾滋病毒发病率方面具有最大的潜力。这些活动的完成预计将导致对美国男男性接触者中艾滋病毒黑人/白人差异的原因的假设进行测试，将有助于进一步评估MSM网络的建模工具，以及为这个在美国艾滋病毒流行中受影响最大的社区提供新的预防干预措施的具体建议目标。我们的具体目标是：(1)开发一套全面的个人、二元和三元网络参数，用于亚特兰大大都市黑人和白人MSM基于代理的HIV传播模型；(2)扩展现有的基于代理的MSM随机、基于代理的HIV传播模型，包括为黑人和白人MSM建模网络特征的代码；(3)利用这个增强的模型和参数来确定是否可以使用现有的来源假设和我们测量的网络现象来解释亚特兰大地区MSM中观察到的HIV的种族差异；(B)确定在解决美国男男性接触者中艾滋病毒方面的种族/民族差异方面影响最大的网络属性。

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.