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

用于评估艾滋病毒和药物滥用的社会结构干预措施的计算模型

• 批准号: 10789121
• 负责人: Anna Hotton
• 金额: $ 77.57万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10789121
• 关键词: AIDS prevention; Address; Affect; Behavior; Behavioral; Black race; Caring; Collection; Communities; Complex; Computer Models; Computing Methodologies; Continuity of Patient Care; Data; Data Sources; Decision Making; Dependence; Disease; Disease Outcome; Disparity; Distal; Employment; Epidemic; Epidemiologic Methods; Epidemiology; Ethics; Evaluation; Face; Feedback; Future; Geographic Locations; Goals; Grant; HIV; HIV diagnosis; HIV/AIDS; Housing; Illinois; Imprisonment; Individual; Inequity; Intervention; Link; Measures; Mental Health; Methamphetamine; Methods; Modeling; Outcome; Overdose; Parameter Estimation; Pattern; Persons; Policy Maker; Prevention; Prevention Research; Process; Public Health; Research; Resources; Risk; Science; Series; Sexual and Gender Minorities; Social Environment; Specific qualifier value; Statistical Methods; Structure; Substance Use Disorder; System; Testing; Uncertainty; Unemployment; Work; care outcomes; experimental study; future implementation; health inequalities; housing instability; implementation barriers; improved; insight; member; methamphetamine use; minority communities; model building; opioid epidemic; opioid use; polysubstance use; preventive intervention; programs; reduced substance use; social health determinants; social influence; social structure; stressor; structural determinants; substance use; substance use prevention; success; therapy development; transmission process; trend; uptake; virtual; vulnerable community

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.

抽象的 背景：黑人性和性别少数民族（SGM）受艾滋病毒和现有的影响不成比例 随着阿片类药物的变化趋势，增加的物质使用障碍可能会加剧差异 流行病伴随着甲基苯丙胺的增加和黑色的多苯甲酸的使用 SGM。有证据表明，诸如住房不稳定性，监禁和失业等因素可能会带来 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相关。因为这样的干预措施是资源密集的，并且在逻辑上是 具有挑战性，特别是对于易受伤害的社区，他们通常是高度流动且不太可能参与的弱势社区 在传统环境中的研究，需要在干预开发阶段进行指导以确定 最有影响力，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开发。因为他们 允许进行反事实实验，ABM还可以促进识别效果 难以使用传统的统计方法来识别，并可以提供有价值的见解以了解 引起复杂系统的因果机制。目的：在现有的ABM平台上构建，这是 提案将利用多个现有数据源来表征社会结构之间的关系 黑人的压力源，药物使用，心理健康以及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和强大决策（RDM）的方法，以了解 结构干预措施对减少药物使用，过量和HIV传播的潜在影响。 方法：我们将应用统计和计算方法，以更好地了解社会结构 因素，药物使用以及心理健康影响预防和护理连续体的参与。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吸收 生物医学干预措施并在不同的干预措施组合中比较结果 使用RDM。意义：更好地理解何处以及如何重点干预工作提供 改善黑人SGM的药物使用和艾滋病毒预防和护理结果的潜力。一旦发展， 我们的方法和模型可以适应其他地理区域，以反映当地的预防优先级和 可以作为流行病学，ABM和RDM方法的示例应用程序，以推动艾滋病毒和物质 使用预防科学。