Skip to Content

Arnold School of Public Health

Big Data Health Science Center wins grant to assess impacts of COVID-19 pandemic on maternal health disparities

September 27, 2021 | Erin Bluvas, bluvase@sc.edu

Researchers from the UofSC Big Data Health Science Center have been awarded $886K from the  National Institute of Allergy and Infectious Diseases to better understand how the COVID-19 pandemic has affected severe maternal morbidity (i.e., unexpected complications of labor and delivery) and mortality. Specifically, they will examine how pre-existing maternal health disparities and disproportionate rates of COVID-19 have combined to exacerbate racial disparities in maternal morbidity and mortality.

Supported by a team of co-investigators*, the one-year project will be led by Xiaoming Li (principal investigator) and Jihong Liu (co-principal investigator). Li is a professor in the Department of Health Promotion, Education, and Behavior, the South Carolina SmartState Endowed Chair for Clinical Translational Research of the SmartState Center for Healthcare Quality and co-principal investigator for the UofSC Big Data Health Science Center. Liu is a professor in the Department of Epidemiology and Biostatistics, director of the Arnold School’s Maternal and Child Health (MCH) Public Health Catalyst Program and MCH LEAP Program, and co-lead for the Health in Pregnancy and Postpartum study.   

“Although the COVID-19 pandemic has led to unprecedented societal disruptions to individuals, communities, healthcare institutions and society as a whole, it has hit communities of color the hardest,” Li says. “Pregnant non-Hispanic Black and Hispanic women appear to have disproportionate SARS-CoV-2 infection and death rates, and this study will investigate how the COVID-19 pandemic, structural racism and racial discrimination have contributed to these racial disparities in maternal morbidity and mortality.”

According to the Centers for Disease Control and Prevention, nearly 60,000 women experience severe maternal morbidity and mortality each year in the United States, with significant racial disparities present. Every 12 hours, a mother dies as a result of pregnancy/delivery complications.

Ranked 42nd in overall health, 41st in maternal mortality and 39th in child well-being and infant mortality, South Carolina has the added challenge of widespread medically underserved areas. Further, Black women give birth to 30 percent of all infants born in South Carolina and experience a two- to three-fold higher risk of severe maternal morbidity and mortality.

Since the onset of the COVID-19 pandemic, factors such as unemployment, income instability and financial stress (with Black and Hispanic families hit the hardest) have worsened longstanding inequities (e.g., access to quality healthcare, psychosocial stress, unhealthy lifestyles) across the United States. South Carolina has had some of the worst infection rates and lowest vaccination rates in the country.

“These inequities often stem from structural racism and discrimination, such as residential segregation, poverty, inadequate education, unemployment and lack of home ownership, all of which increase the risk for severe maternal morbidity and mortality,” Liu says. “The underlying causes of adverse maternal health outcomes are complex and multifaceted, so our goal is to untangle the numerous layers of social contexts to understand how these factors impact maternal health, particularly during the COVID-19 pandemic.”

With this study, the researchers will examine the distributions of COVID-19 cases in conjunction with the multilevel determinants of maternal health in South Carolina and in the United States. Building on their existing SC COVID-19 Cohort, the team will add health data (COVID-19 and birth-related) from a pregnancy cohort and nationwide social context/COVID-19 data to their database. Using big data techniques (e.g., machine learning models) and additional methods (e.g., qualitative interviews with postpartum women and maternal care providers), they will leverage current (e.g., COVID-19) and historical (e.g., systemic racism) contextual factors to identify their impacts on maternal health and inform recommendations for addressing these inequities.

*Co-investigators include Jiajia Zhang (Epidemiology and Biostatistics), Banky Olatosi (Health Services Policy and Management), Shan Qiao (Health Promotion, Education, and Behavior), Peiyin Hung (Health Services Policy and Management), Chen Liang (Health Services Policy and Management), Myriam Torres (Epidemiology and Biostatistics), Neset Hikmet (College of Computer and Engineering) and Berry Campbell (School of Medicine-Columbia).


Related:

UofSC Big Data Health Science Center wins NIH award to launch big data fellowship program

New training program recruits underrepresented undergraduate students to join maternal and child health field

The coronavirus meets big data

Maternal and Child Health Catalyst Program recognizes undergraduate, graduate student winners of iPoster and abstract competitions

Arnold School’s Maternal and Child Health Public Health Catalyst Program selects inaugural Maternal and Child Health Graduate Scholars

Jihong Liu wins grant to advance Arnold School training and curriculum in maternal and child health

Doctoral students Kelsey Day and Qian Huang selected as inaugural Health GIS Scholars by Big Data Health Science Center

UofSC Big Data Health Science Center investigators receive $1.25 million NIH grant to develop data-driven strategies in fighting COVID-19

Twitter could help predict future outbreaks thanks to UofSC research


Challenge the conventional. Create the exceptional. No Limits.

©