Building a causal pathway framework to identify interventions to eliminate racial/ethnic disparities in severe maternal morbidity

摘要 美国正处于孕产妇健康危机之中。美国的孕产妇死亡率（MM）比任何 这是其他高收入国家中唯一一个增长率上升的国家。严重的孕产妇发病率 (SMM)包括使孕妇最有可能死亡的疾病， 二十年有色人种的女性，尤其是黑人和美洲土著女性， 这些结果的风险增加。该提案侧重于SMM，作为导致MM的前哨结局， 但却比普通人多100倍。大多数先前关于SMM的研究都集中在近端临床因素上 （主要与合并症和产科管理有关）;仅这些因素不足以 解释SMM或其种族/民族差异。本建议的目标是：1）制定一个因果关系 路径框架，以了解社会决定因素和更近端的健康相关因素 共同导致SMM风险和差异，以及2）使用此框架来确定可行的战略 扭转目前的趋势，消除不平等。我们将创建一个独特的数据集， 来自6个州的440万例分娩和66，000名患有SMM的妇女的多年数据。这些国家集体 包括四分之一美国出生和足够多样化的社会环境，以解开复杂的多层次驱动因素 产妇健康不平等的问题。我们的重点是来自4个特定领域的社会决定因素：社会经济 弱势，结构性不平等，社区资源和医疗保健的获得，其特点是 县、社区（人口普查区或邮政编码）和个人级别。卫生中介机构 包括出生医院护理质量、分娩方式和产妇发病率（如高血压、贫血）。 选择这些领域和中介是因为它们与种族/民族差异的相关性， 产妇保健。此外，我们还将评估3个质量改进（QI）合作伙伴对 SMM差异，旨在改善医院护理质量的特定方面， 由全州围产期质量协作机构（PQC）在本报告所列的6个州中的3个州实施。 提议我们的具体目标是：1）评估多层次社会的相对和联合贡献 SMM和SMM差异的决定因素; 2）确定潜在的健康相关机制， 通过因果路径分析，多层次社会因素影响SMM和SMM差距; 和3）评估旨在减少SMM的3个QI协作对SMM中种族/民族差异的影响 (3a)，并将目标1和目标2中提出的因果框架应用于这些具体国家的情况，以确定 解决SMM差异的各州具体战略（3b）。我们目前对 社会决定因素如何促成SMM或其差异。这项研究将填补这一重要的 知识差距，这是实现可持续改善孕产妇健康的一个重要组成部分， 消除种族/民族差异。

ABSTRACT The US is in the midst of a maternal health crisis. The US has worse maternal mortality (MM) than any other high-income country and is the only one for which the rate is increasing. Severe maternal morbidity (SMM), which encompasses conditions that put pregnant women most at risk of dying, doubled in the last two decades. Women of color, especially Black and Native American women, are at 2-3-fold or greater increased risk of these outcomes. This proposal focuses on SMM, as a sentinel outcome leading to MM, yet 100 times more common. Most prior research on SMM has focused on proximal clinical factors (primarily related to co-morbidities and obstetric management); these factors alone are insufficient for explaining SMM or its racial/ethnic disparities. The objectives of this proposal are: 1) develop a causal pathway framework to understand how social determinants and more proximal health-related factors together contribute to SMM risk and disparities, and 2) use this framework to identify actionable strategies to reverse current trends and eliminate inequities. We will create a unique dataset that harmonizes 4 years of data from 6 states on 4.4 million births and 66,000 women with SMM. These states collectively include 1 in 4 US births and sufficiently diverse social environments to disentangle complex multi-level drivers of maternal health inequity. Our focus is on social determinants from 4 specific domains: socioeconomic disadvantage, structural inequality, community resources, and health care access, characterized at the county, neighborhood (census tract or ZIP code), and individual level. Health-related intermediaries include birth hospital quality of care, mode of birth, and maternal morbidities (eg, hypertension, anemia). These domains and intermediaries were selected for their relevance to racial/ethnic disparities in maternal health. In addition, we will evaluate impacts of 3 Quality Improvement (QI) collaboratives on SMM disparities, which were designed to improve specific aspects of hospital quality of care and implemented by state-wide perinatal quality collaboratives (PQCs) in 3 of the 6 states included in this proposal. Our Specific Aims are: 1) Assess the relative and joint contributions of multi-level social determinants to SMM and SMM disparities; 2) Identify potential health-related mechanisms by which multi-level social determinants affect SMM and SMM disparities by conducting causal pathway analyses; and 3) Evaluate the impact of 3 QI collaboratives designed to reduce SMM, on racial/ethnic disparities in SMM (3a), and apply the causal framework developed in Aims 1 and 2 to these state-specific contexts, to identify state-specific strategies for addressing SMM disparities (3b). We currently have very limited understanding of how social determinants contribute to SMM or its disparities. The proposed research will fill this important knowledge gap, which is an essential part of realizing sustainable improvement of maternal health and elimination of racial/ethnic disparities.