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Maternal Morbidity Predicted by an Intersectional Social Determinants of Health Phenotype: A Secondary Analysis of the NuMoM2b Dataset

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Abstract

Maternal race, ethnicity and socio-economic position are known to be associated with increased risk for a range of poor pregnancy outcomes, including maternal morbidity and mortality. Previously, researchers seeking to identify the contributing factors focused on maternal behaviors and pregnancy complications. Less understood is the contribution of the social determinants of health (SDoH) in observed differences by race/ethnicity in these key outcomes. In this secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset, latent mixture modeling was used to construct groups of healthy, nulliparous participants with a non-anomalous fetus in a cephalic presentation having a trial of labor (N = 5763) based on SDoH variables. The primary outcome was a composite score of postpartum maternal morbidity. A postpartum maternal morbidity event was experienced by 350 individuals (6.1%). Latent class analysis using SDoH variables revealed six groups of participants, with postpartum maternal morbidity rates ranging from 8.7% to 4.5% across groups (p < 0.001). Two SDoH groups had the highest odds for maternal morbidity. These higher-risk groups were comprised of participants with the lowest income and highest stress and those who had lived in the USA for the shortest periods of time. SDoH phenotype predicted MM outcomes and identified two important, yet distinct groups of pregnant people who were the most likely have a maternal morbidity event.

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Availability of data and material

Data is available via the DASH repository at the National Institutes of Health National Institute for Child Health and Human Development with approvals.

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Acknowledgments

The authors acknowledge NICHD Data and Specimen Hub (DASH) for providing the nUMoM2b data that was used for this research.

Funding

The NuMoM2b Study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be) was supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): U10 HD063036; U10 HD063072; U10 HD063047; U10 HD063037; U10 HD063041; U10 HD063020; U10 HD063046; U10 HD063048; and U10 HD063053. In addition, support was provided by Clinical and Translational Science Institutes: UL1TR001108 and UL1TR000153.

Dr. Nicole Carlson was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R01NR019254 during research contained in this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Dr. Elise Erickson was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number 1K99NR019596-01 during research contained in this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work also was awarded innovation and health disparities prizes in NICHD’s Decoding Maternal Morbidity Data Challenge.

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  1. Oregon Health & Sciences University School of Nursing, 3455 SW US Veterans Hospital Rd, Portland, OR, 97239, USA

    Elise N Erickson

  2. Emory University Nell Hodgson Woodruff School of Nursing, Atlanta, GA, USA

    Nicole S Carlson

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E.E. and N.C. contributed equally to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

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Erickson, E.N., Carlson, N.S. Maternal Morbidity Predicted by an Intersectional Social Determinants of Health Phenotype: A Secondary Analysis of the NuMoM2b Dataset. Reprod. Sci. 29, 2013–2029 (2022). https://doi.org/10.1007/s43032-022-00913-2

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