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

摘要 背景：黑人、性少数群体和性别少数群体(SGM)不成比例地受到艾滋病毒的影响， 随着阿片类药物趋势的转变，物质使用障碍的增加可能会加剧差异 在流行的同时，黑人中甲基苯丙胺和多种物质的使用也有所增加 SGM。有证据表明，住房不稳定、监禁和失业等因素可能会构成 参与黑人SGM艾滋病毒预防和护理的重大障碍，这些因素也是 与冰毒的使用有关。因为这样的干预是资源密集型和后勤密集型的 具有挑战性，特别是对于流动性高、不太可能参与的弱势社区 在传统的研究环境中，需要在干预开发阶段进行指导，以确定 最有效、最有效的干预策略。基于代理的模型(ABM)可用于虚拟 评估候选干预措施，以促进更有效和及时的干预措施开发。因为他们 考虑到进行反事实实验，ABM还可以促进识别将 很难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 产生复杂系统的因果机制。目标：建立在现有的ABM平台上， 提案将利用多个现有数据来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结果 SGM。我们将结合流行病学、ABM和稳健决策(RDM)的方法来理解 结构性干预措施对减少药物使用、过量用药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来更好地了解社会结构 影响参与艾滋病毒预防和护理的因素、物质使用和精神健康。我们会 然后进行一系列实验，评估社会结构因素如何影响对现有知识的吸收 生物医学干预并比较具有不同干预组合的情景下的结果 使用RDM。意义：更好地理解在哪里以及如何集中干预努力所提供的 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， 我们的方法和模型可以适用于其他地理区域，以反映当地的预防优先事项和 可作为应用流行病学、ABM和RDM方法的范例，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.