Severe Maternal Morbidity: An Investigation of Racial-Ethnic Disparities, Social Disadvantage & Maternal Weight

摘要 严重孕产妇发病率(SMM)，包括将孕妇置于最大风险的情况 死亡人数(如出血、败血症、器官衰竭)在过去20年中翻了一番。最常见的 SMM的前驱疾病--贫血、妊娠高血压疾病(HDP)和剖宫产--也是 越来越多。有色人种，特别是黑人和美洲原住民，对所有人来说都面临更高的风险 这些结果。这项续订提案建立在我们以前解决孕产妇不平等问题的工作的基础上 结果。通过家长助学金，我们的团队加深了目前对社会责任贡献的理解 背景(关注邻里社会劣势)和孕产妇孕前健康(关注身体 质量指数)，使用我们建立的加州(CA)出生的独特数据资源。此续订使用 以理论为基础的方法来解决我们对孕产妇健康理解中的几个剩余差距 在美国，被父母格兰特照亮的。我们将建立一个独特的1600万出生人口资源 1997年至2021年的三个州。该数据集纵向链接重要记录(活产和胎儿死亡证明) 母亲和婴儿的出院数据；包括住址；链接数据以供重复 随着时间的推移，怀孕到同一个人，从而提供了高质量的大规模数据类型 关于孕产妇健康和社会背景的信息，外地需要这些信息来推动人口层面的研究 产妇健康。研究的所有阶段都将由社区咨询委员会(CAB)指导。使用 交叉框架，目标1将审查多种形式的边缘化对SMM的联合影响，其 亚型(即高血压、出血和败血症相关的SMM)及其前体(即HDP、贫血、 出生方式)。边缘化的指标包括种族、教育、医疗保健支付者、出生地和 人口普查地区一级的社会劣势和结构性不平等(例如贫穷、隔离)的标志。使用 生殖生命过程框架，目标2将确定社会和医疗风险的累积影响 连续怀孕对产妇健康的影响因素(即SMM、SMM亚型、SMM前体)。我们 将审查与社会背景(例如，持续的人口普查区域贫困)、发病率(例如， 持续性HDP)和分娩方式(初次剖腹产)会影响随后的发生和复发 研究结果。目标3将使用目标1和目标2的调查结果来确定改进的策略并确定其优先顺序 孕产妇健康和公平。我们将使用a)因果推理方法(中介和g-计算)来 了解机制并比较选定的假设性干预对研究的潜在影响 结果和差异，以及b)社区参与的优先排序方法，以综合我们的调查结果和 确定下一步的优先顺序。通过了解多种形式的边缘化和连续 在严格的分析和社区基础知识的指导下，我们的工作将有助于 推进下一代可操作的人口层面SMM股权研究。

ABSTRACT Severe maternal morbidity (SMM), which encompasses conditions that put pregnant people most at risk of dying (e.g., hemorrhage, sepsis, organ failure), doubled in the last two decades. The most common precursors to SMM – anemia, hypertensive disorders of pregnancy (HDP), and cesarean birth – are also increasing. People of color, especially Black and Native American people, are at increased risk for all these outcomes. This Renewal proposal builds on our prior work to address inequities in maternal outcomes. Via the Parent Grant, our team enhanced current understanding of the contribution of social context (focusing on neighborhood social disadvantage) and maternal pre-pregnancy health (focusing on body mass index) to SMM risk, using a unique data resource we built of California (CA) births. This Renewal uses theoretically grounded approaches to address several remaining gaps in our understanding of maternal health in the U.S. that were illuminated by the Parent Grant. We will build a unique resource of 16 million births in three states from 1997-2021. The dataset longitudinally links vital records (live birth and fetal death certificates) with hospital discharge data for mother and baby; includes residential address; and links data for repeat pregnancies to the same person over time, thus providing the type of large-scale data with high-quality information on maternal health and social context that the field needs to advance population-level research on maternal health. All phases of the research will be guided by a community advisory board (CAB). Using an intersectionality framework, Aim 1 will examine joint impacts of multiple forms of marginalization on SMM, its subtypes (i.e., hypertension-, hemorrhage-, and sepsis-related SMM), and its precursors (i.e., HDP, anemia, mode of birth). Indicators of marginalization include race-ethnicity, education, health care payer, nativity, and census tract-level markers of social disadvantage and structural inequality (e.g., poverty, segregation). Using a reproductive life-course framework, Aim 2 will determine the cumulative impact of social and medical risk factors across successive pregnancies on maternal health (i.e., SMM, SMM subtypes, SMM precursors). We will examine how factors related to social context (e.g., persistent census tract poverty), morbidity (e.g., persistent HDP), and mode of birth (primary cesarean birth) affect subsequent occurrence and recurrence of the study outcomes. Aim 3 will use findings from Aims 1 and 2 to identify and prioritize strategies to improve maternal health and equity. We will use a) causal inference methods (mediation and g-computation) to understand mechanisms and compare the potential impact of selected hypothetical interventions on study outcomes and disparities, and b) community-engaged prioritization methods to synthesize our findings and prioritize next steps. By understanding risks across multiple forms of marginalization and successive pregnancies, and guided by rigorous analytics and community-grounded knowledge, our work will contribute to advancing the next generation of actionable population-level SMM equity research.