Complex Systems Approaches to Advance Maternal Health Disparities Research and Prevention: Developing a System Dynamics Simulation Model of Severe Maternal Morbidity among Black Women

项目摘要 非西班牙裔黑人妇女（NHBW）患SMM的可能性是NH白色妇女的两倍， 增加产妇死亡的风险。虽然SMM的临床原因是多因素的，但差异是由 受到结构性种族主义的影响。尽管知识的广度和现有的预防战略，长期的SMM dis- 均等表明，单靠目前的努力不足以实现公平。因此，替代方法是 需要改变对SMM差异原因的理解，并在预防和应对方面取得突破， 是的。我们认为，SMM差异最好概念化为动态复杂系统，包括 紧密耦合的元素，随着时间的推移不断发展。因此，为了解决NHBW之间的SMM差异， 我们将应用复杂系统方法--即系统动力学（SD）建模和系统动力学 小组模型建设（SD GMB）-回答复杂的研究问题，并使更有效的， 积极的预防策略。 该项目的总体目标是应用复杂系统的方法，以产生一个整体的下- 在NHBW中，作为SMM结果基础的动态复杂系统的地位，然后 将这种理解转化为一种新的、基于证据的模拟模型，用于评估 干预措施的有效性，可以遏制SMM结果之间NHBW。我们的具体目标是 如下：1）使用SD GMB来创建动态复杂系统的定性SD模型， NHBW中的SMM结果; 2）将定性SD模型转化为定量SD模拟 模型; 3）识别，评估和提高现有的和可行的干预措施的有效性， 减少NHBW之间不同的SMM结果;以及4）将定量SD模型作为模拟模型传播， 为利益相关者和研究人员提供基于信息的仪表板，旨在减少NHBW中的SMM结果。 该项目的成功完成预计将改变对发展的理解， （a）利用系统科学方法确定有效的解决方案， 减少健康方面的种族差距; B）向不同的利益攸关方传播见解，以指导更广泛的社区- 在存在动态复杂性的情况下，水平医疗保健和社会政策决策;以及c）提供 孕产妇健康差异的初始可持续发展模型，可以刺激在其他情况下复制。这个项目 还将通过以下方式更广泛地影响健康差异研究：a）展示召开二- 孕产妇健康，复杂系统科学和参与性研究方法的诗句观点 健康差异研究; B）提供复杂系统方法效用的变革性证据 在孕产妇健康差距研究和预防;和c）生成第一个可持续发展模型的SMM 可以扩展或适应其他情况的联系（例如作为国家模式），具有预期和可行的 对更广泛的公众和人口健康领域及相关学科产生可能的影响。

PROJECT SUMMARY Non-Hispanic Black women (NHBW) are twice as likely as NH White women to suffer SMM, which in- creases risk for maternal mortality. Although the clinical cause of SMM is multifactorial, the disparity is driven by structural racism. Despite the breadth of knowledge and existing preventive strategies, prolonged SMM dis- parities suggest that current efforts alone are insufficient to achieve equity. Thus, alternate approaches are needed to transform understanding of the causes of SMM disparities and to generate breakthroughs in preven- tion. We argue that SMM disparities are best conceptualized as dynamically complex systems, consisting of tightly coupled elements that continually evolve over time. Thus, to address SMM disparities among NHBW, we will apply complex systems approaches – namely, system dynamics (SD) modeling and system dynamics group model building (SD GMB) – to answer complexity-informed research questions and enable more effica- cious prevention strategies. The overall aim of this project is to apply complex systems approaches to generate a holistic under- standing of the dynamically complex systems underlying SMM outcomes among NHBW, and then translate this understanding into a novel, evidence-based simulation model to be used for evaluating the effectiveness of interventions that can curb SMM outcomes among NHBW. Our specific aims are as follows: 1) to use SD GMB to create a qualitative SD model of the dynamically complex systems that generate SMM outcomes among NHBW; 2) to translate the qualitative SD model into a quantitative SD simulation model; 3) to identify, evaluate, and enhance the effectiveness of existing and plausible interventions that can reduce disparate SMM outcomes among NHBW; and 4) to disseminate the quantitative SD model as a simula- tion-based dashboard for stakeholders and researchers aiming to reduce SMM outcomes among NHBW. The successful completion of this project is expected to transform understanding of the development and perpetuation of SMM disparities by: a) using systems science methods to identify effective solutions to re- duce racial gaps in health; b) disseminating insights to diverse stakeholders to guide broader community- level healthcare and social policy decision-making in the presence of dynamic complexity; and c) providing an initial SD model of maternal health disparities that can spur replication across other contexts. This project will also impact health disparities research more broadly by: a) demonstrating the potential of convening di- verse perspectives in maternal health, complex systems science, and participatory research methods in health disparities research; b) providing transformative evidence of the utility of complex systems approaches in maternal health disparities research and prevention; and c) generating the first SD model of SMM dispari- ties that can be expanded or adapted to other contexts (e.g. as a national model), with anticipated and plau- sible impacts for the broader public and population health fields and related disciplines.