Background: Maternal mortality is a public health crisis in the U.S., with no improvement in decades and worsening disparities during COVID-19. Social determinants of health (SDoH) shape risk for morbidity and mortality but maternal structural and SDoH are under-researched using population health data. To expand knowledge of those at risk for or who have experienced maternal morbidity and inform clinical, policy, and legislative action, creative use of and leveraging existing population health datasets is logical and needed.
Methods: We review a sample of population health datasets and highlight recommended changes to the datasets or data collection to better inform existing gaps in maternal health research.
Results: Across each of the datasets we found insufficient representation of pregnant and postpartum individuals and provide recommendations to enhance these datasets to inform maternal health research.
Conclusions: Pregnant and postpartum individuals should be oversampled in population health data to facilitate rapid policy and program evaluation. Postpartum individuals should no longer be hidden within population health datasets. Individuals with pregnancies resulting in outcomes other than livebirth (e.g., abortion, stillbirth, miscarriage) should be included, or asked about these experiences.
We review population health datasets and provide recommendations that would enable maternal health researchers to unlock the full potential of these datasets by exploring the influence of structural factors and SDoH on maternal health among under-researched groups.
Keywords: Maternal health; Postpartum; Pregnancy; Social determinants of health; Structural determinants of health.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.