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

摘要 背景：黑人性和性别少数群体（SGM）受艾滋病毒影响不成比例， 物质使用障碍的增加可能会加剧这种差异，因为阿片类药物使用的趋势不断变化， 流行病伴随着黑人中甲基苯丙胺和多种物质使用的增加 SGM有证据表明，住房不稳定、监禁和失业等因素可能会造成 黑人SGM参与艾滋病毒预防和护理的重大障碍，这些因素也是 与甲基苯丙胺的使用有关因为这种干预措施需要大量资源和后勤支持， 具有挑战性，特别是对弱势群体来说，他们往往移动的很多， 在传统环境下的研究，在干预发展阶段需要指导，以确定 最具影响力和效率的干预战略。基于代理的模型（ABM）可以用来虚拟地 评估候选干预措施，以促进更有效和及时地制定干预措施。因为他们 考虑到反事实实验的进行，反弹道导弹还可以促进识别影响， 难以使用传统的统计方法进行识别，并且可以提供有价值的见解来理解 导致复杂系统的因果机制。目标：建立在现有的ABM平台上， 该提案将利用多种现有数据源来描述社会结构之间的关系， 黑人中的压力源、物质使用、心理健康和艾滋病毒预防和护理连续性结果 SGM我们将结合流行病学、ABM和稳健决策（RDM）的联合收割机方法来了解 结构性干预对减少药物使用、过量和艾滋病毒传播的潜在影响。 方法：我们将应用统计和计算方法，以更好地了解如何社会结构 因素，物质使用和心理健康影响参与艾滋病毒预防和护理的连续性。我们将 然后进行一系列实验，以评估社会结构因素如何影响现有的 生物医学干预措施，并比较不同干预措施组合情景下的结果 使用RDM。意义：更好地了解干预工作的重点和方式， 改善黑人SGM的物质使用和艾滋病毒预防和护理结果的潜力。一旦开发出来， 我们的方法和模式可以适用于其他地理区域，以反映当地的预防优先事项， 可以作为流行病学，ABM和RDM方法的应用实例，以促进艾滋病毒和物质 使用预防科学。

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.