Severe Maternal Morbidity: An Investigation of Joint Impacts of Risk Factors and Cumulative Risks Across Successive Pregnancies

Stanford University, Stanford CA

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Abstract

Severe maternal morbidity (SMM), which encompasses conditions that put pregnant women 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. This Renewal proposal builds on our prior work to address maternal outcomes. Via the Parent Grant, our team enhanced current understanding of the contribution of social context and maternal pre-pregnancy health to SMM risk, using a unique data resource we built of California (CA) births. This Renewal addresses 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 14 million births in four states from 1997-2022. The dataset longitudinally links vital records (live birth and fetal death certificates) with hospital discharge data for mother and baby; includes residential census tracts; 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). Aim 1 will examine joint impacts of multiple risk factors on risk of SMM, its subtypes (i.e., hypertension-, hemorrhage-, and sepsis-related SMM), and its precursors (i.e., HDP, anemia, mode of birth). Risk factors include education, health care payer, and census tract-level markers of social disadvantage (e.g., unemployment, rurality). Using a reproductive life-course framework, Aim 2 will determine the cumulative impact of 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., persistently high census tract unemployment), morbidity (e.g., persistent hypertension), 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. 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 b) community-engaged prioritization methods to synthesize our findings and prioritize next steps. By understanding risks across multiple risk factors and successive pregnancies, and guided by rigorous analytics and patient input, our work will contribute to advancing the next generation of actionable population-level SMM research.