Reducing Racial Disparities in Antepartum Anemia: An Interdisciplinary Quality Improvement Initiative at NYU Grossman Long Island [ID 1765]

INTRODUCTION: Anemia is linked to increased severe maternal morbidity in Black women. This initiative aimed to reduce maternal anemia rates at delivery while addressing racial disparities. With an anemia rate of 30% at NYU Grossman Long Island (NYUG-LI) (fiscal year 2023), exceeding the national benchmark of 19.9%, the goal was a 20% reduction in anemia rates while reducing disparities in screening and treatment among racial and ethnic groups. METHODS: This single-institution initiative targeted antepartum anemia screening and iron treatment for eligible women with iron deficiency anemia (IDA) between 24 and 32 weeks of gestation at two NYUG-LI sites. Interventions included provider and patient education on standardized treatment protocols. Rates were stratified by race/ethnicity (self-identified) and tracked using Epic reporting tools, focusing on anemia/ferritin screening, hematology referrals, and iron treatment orders, comparing pre-intervention (January to April 2024) and post-intervention (May to August 2024) groups using monthly run charts. RESULTS: Among 574 patients with hemoglobin less than 10 at delivery, anemia rates declined significantly across all ethnic/racial groups. In Black/African American women, the rate decreased from 21.1% to 3.7% (82% reduction). Ferritin screening for Black/African American women increased from 37.5% to 100%, and intravenous iron treatment rose from 25% to 100%. Other groups experienced minimal change, with Asian/Pacific Islander rates dropping from 7.1% to 3.3%, Hispanic/Latino from 7.9% to 5.9%, and White/Caucasian from 2.7% to 2.4%. CONCLUSIONS/IMPLICATIONS: Standardizing screening and treatment protocols reduced racial and ethnic disparities in antepartum iron deficiency anemia, promoting equitable care. Next steps include evaluating delivery outcomes and blood product utilization by ethnicity, tracking long-term trends, and using heat maps for resource allocation.