AJOG Global Reports (Feb 2024)
Examination of the Black-White racial disparity in severe maternal morbidity among Georgia deliveries, 2016 to 2020AJOG Global Reports at a Glance
Abstract
BACKGROUND: Studies find that delivery hospital explains a significant portion of the Black-White gap in severe maternal morbidity. No such studies have focused on the US Southeast, where racial disparities are widest, and few have examined the relative contribution of hospital, residential, and maternal factors. OBJECTIVE: This study aimed to estimate the portion of Georgia's Black-White gap in severe maternal morbidity during delivery through 42 days postpartum explained by hospital, residential, and maternal factors. STUDY DESIGN: Using linked Georgia hospital discharge, birth, and fetal death records for 2016 through 2020, we identified 413,124 deliveries to non-Hispanic White (229,357; 56%) or Black (183,767; 44%) individuals. We linked hospital data from the American Hospital Association and Center for Medicare and Medicaid Services, and area data from the Area Resource File and American Community Survey. We identified severe maternal morbidity indicator conditions during delivery or subsequent hospitalizations through 42 days postpartum. Using race-specific logistic models followed by a decomposition technique, we estimated the portion of the Black-White severe maternal morbidity gap explained by the following: (1) sociodemographic factors (age, education, marital status, and nativity), (2) medical conditions (diabetes mellitus, gestational diabetes, chronic hypertension, gestational hypertension or preeclampsia, and smoking), (3) obstetrical factors (singleton or multiple, and birth order); (4) access to care (no or third trimester care, and payer), (5) hospital factors that are time-varying (delivery volume, deliveries per full-time equivalent nurse, doctor communication, patient safety, and adverse event composite score) or measured time-invariant characteristics (ownership, profit status, religious affiliation, teaching status, and perinatal level), and (6) residential factors (county urban/rural classification, percent uninsured women of reproductive age, obstetrician-gynecologists per women of reproductive age, number of federally-qualified and community health centers, medically-underserved area [yes/no], and census tract neighborhood deprivation index). We estimated models with and without hospital fixed-effects, which account for unobserved time-invariant hospital characteristics such as within-hospital care processes or unmeasured hospital-specific factors. RESULTS: There was 1.8 times the rate of severe maternal morbidity per 100 discharges among non-Hispanic Black (3.15) than among White (1.73) individuals, with an explained proportion of 30.4% in models without and 49.8% in models with hospital fixed-effects. In the latter, hospital fixed-effects explained the largest portion of the Black-White severe maternal morbidity gap (15.1%) followed by access to care (14.9%) and sociodemographic factors (14.4%), with residential factors being protective for Black individuals (-7.5%). Smaller proportions were explained by medical (5.6%), obstetrical (4.0%), and time-varying hospital factors (3.2%). Within each category, the largest explanatory portion was payer type (13.3%) for access to care, marital status (10.3%) for sociodemographic, gestational hypertension (3.3%) for medical, birth order (3.6%) for obstetrical, and patient safety indicator (3.1%) for time-varying hospital factors. CONCLUSION: Models with hospital fixed-effects explain a greater proportion of Georgia's Black-White severe maternal morbidity gap than models without them, thereby supporting the point that differences in care processes or other unmeasured factors within the same hospital translate into racial differences in severe maternal morbidity during delivery through 42 days postpartum. Research is needed to discern and ameliorate sources of within-hospital differences in care. The substantial proportion of the gap attributable to racial differences in access to care and sociodemographic factors points to other needed policy interventions.
