Strengthening perinatal healthcare utilization and quality of care for Indigenous and low socioeconomic status women through systems change: integrating person, provider, and policy perspectives.

项目总结 该项目的目标是确定高效和可行的策略，以提高围产期保健的利用率 和围产期保健质量(围产期保健利用和质量；PCUQ)和降低孕产妇发病率(MM) 美国印第安人(AI)和社会经济地位较低的妇女在北部平原所经历的差距 区域。多发性硬化症的高发病率和边缘化群体经历的不成比例的多发性硬化症负担导致了广泛的 改善产妇健康的一系列建议。许多建议侧重于减少壁垒和 加强促进者(B/F)，以提高围产期护理的利用率或改善围产期护理的质量 如果是这样的话。然而，对于哪种B/F对PCUQ的影响最大，因此是 在人/患者、提供者/卫生系统和政策范围内改善PCUQ的最有效目标 级别。这一理解对于减少MM的行动至关重要，因为PCUQ和MM之间的关联仍然是 不清楚。建议也很少考虑作为促进者的文化优势和作为推动者的文化优势的独特背景 系统性/结构性种族主义是人工智能社区遇到的障碍，或影响可行性和成功的背景 实施具体的变革。为了解决这些问题，这个项目涉及建立一个基于社区的系统 动态模拟模型，将详细说明个人/患者、提供者/医疗系统和政策层面的PCUQ B/F 使人工智能和社会经济地位低的妇女的PCUQ比率和差距较低(目标1)。将开发模型 与拥有个人和专业知识的利益相关者协作，使用基于社区的参与式方法 在PCUQ的多个组成部分方面有经验。这一模型将被模拟，社区知情的假设将 进行测试，以确定应将哪个B/F作为改善PCUQ和缩小差距的最佳战略。 模拟模型将部分使用380名生活在目标范围内的女性(65%的人工智能)的调查数据来创建 并且最近有过怀孕和/或分娩的经历。此数据还将用于估计路径 将PCUQ B/F评估为对MM的间接影响的模型(目标2)。最后，我们将发展质量社区- 基于系统动力学模型，详细说明了驱动决策和实施的机制的过程 改变围产期保健做法和政策的行动以及研究在这些过程中的作用(目标3)。 将与卫生系统和政策决策者和利益攸关方的样本合作开发模型， 并以人工智能健康和健康公平的草根倡导者为样本，进一步评估 决策制定和变革过程。变革和公平的关键机制将通过基于网络的 定性模型的分析。本项目中详细介绍的三项研究将累积关键信息 改善高需求地区人工智能和低社会经济地位妇女的产妇保健和保健公平，以及 应如何利用这些信息来实施行动计划的明确途径，该行动计划将使用 与社区利益相关者和合作者合作的项目成果。

PROJECT SUMMARY The goal of this project is to identify high-efficacy and actionable strategies to improve perinatal healthcare utilization and perinatal healthcare quality (perinatal care utilization and quality; PCUQ) and reduce maternal morbidity (MM) rates and disparities experienced by for American Indian (AI) and low socioeconomic status women within a Northern Plains area. High rates of MM and disproportionate burden of MM experienced by marginalized groups has resulted in a wide range of recommendations to improve maternal health. Many recommendations focus on reducing barriers and enhancing facilitators (B/F) that will increase utilization of perinatal care or will improve the quality of perinatal care provided. However, there is little understanding of which B/F have the strongest impact on PCUQ and are therefore the most effective targets to improve PCUQ across the spectrum of person/patient, provider/health system and policy levels. This understanding is critical for actions that will reduce MM, as the association between PCUQ and MM is still unclear. Recommendations also rarely account the unique contexts of both cultural strengths as facilitators and systemic/structural racism as barriers experienced by AI communities, or contexts that impact the feasibility and success of implementing specific changes. To address these issues, this project involves building a community-based system dynamics simulation model that will detail the PCUQ B/F on person/patient, provider/health system and policy levels driving low PCUQ rates and disparities for AI and low socioeconomic status women (Aim 1). Models will be developed using a community-based participatory approach in collaboration with stakeholders who have personal and professional experience in the multiple components of PCUQ. This model will be simulated, and community-informed hypotheses will be tested to identify which B/F should be targeted as the best strategies for improving PCUQ and reducing disparity. Simulation models will be created in part with survey data from 380 women (65% AI) who live within the target communities and have had recent pregnancy and/or childbirth experience. This data will also be used to estimate path models that will evaluate PCUQ B/F as indirect effects on MM (Aim 2). Finally, we will develop qualitative community- based system dynamics models detailing the process of mechanisms that drive decision making and implementing actions for changes to perinatal healthcare practice and policy and the role of research during these processes (Aim 3). Models will be developed in collaboration with a sample of health system and policy decisionmakers and stakeholders, and with a sample of grassroots advocates for AI health and health equity to further evaluate equity or inequity within decision making and change processes. Key mechanisms for change and equity will be identified through network-based analysis of the qualitative model. The three studies detailed within this project will cumulatively critical information to improve maternal health and health equity for AI and low socioeconomic status women in a high need region, as well as a clear pathway for how this information should be utilized to implement the action plan that will be developed using project findings in collaboration with community stakeholders and collaborators.