DRIVERs: Data systems Research to Identify driVers of Ethnic & Racial Inequities in Maternal Mortality - Summary and Abstract
There is a critical gap in understanding hospital- and structural-level causes leading to maternal
mortality and severe maternal morbidity (SMM), particularly with regard to preventable deaths.
Understanding and addressing these root causes of mortality and SMM is particularly urgent for Black,
Indigenous and other birthing people of color. Black pregnant and postpartum people are 3-4 times more likely
to die from pregnancy-related causes and have a two-fold higher risk of SMM compared to their White
counterparts. Race is not a biological construct, but a social one with real health consequences. Both
structual racism (which creates differential distribution of opportunities for health by race), and experiences of
interpersonal racism (explicit and implicit bias) play a role in health consequences for Black, Indigenous, and
other birthing people.
Our central hypothesis is that social determinants of health and hospital factors significantly impact
maternal and pregnancy outcomes, and are more prognostic for BIPOC (Black, Indigenous, People of Color)
populations. Aim 1 will link data from the large patient catchment areas of two university hospital systems
(Universities of Missouri and Utah) with geocoding, social security death files, obituary files, and PCORnet to
elucidate the impact of structural and social determinants of health (SSDoH), on rates of maternal mortality and
SMM, and to assess the extent to which these factors explain or predict inequity in these outcomes among
Black birthing people. We hypothesize that downstream consequences of structural racism will have an
significant association with mortality and SMM, and will help explain inequity in mortality and SMM rates
between Black and White birthing people. Aim 2 will interrogate de-identified healthcare records from the
Cerner Corporation's multi-institutional Real-World DataTM system to identify hospital-level factors associated
with maternal mortality and SMM, and to assess the extent to which these factors explain or predict inequity in
these outcomes among Black birthing people. We hypothesize that hospital-level factors such as medical
services segregation, maternal levels of care, urban/rural status, and patient demographics and comorbidities
have significant impact.
The proposed research is innovative, as it will (a) use data to develop and validate a prognostic scoring
tool for maternal mortality/SMM; (b) assess hospital-level factors that may contribute to maternal mortality,
using data from 128 separate health systems in Cerner's database; (c) integrate geocoding and linked death
data to enable better estimates of structural and social determinants of health (SSDoH) factors that contribute
to maternal mortality/SMM; and (d) facilitate a rapid “scale-up” to a national level, given the multi-site nature of
the Greater Plains Collaborative (GPC)/PCORnet data system.
