WIP 78 - Impact of redlining and social deprivation index on survivors of moderate to severe Hypoxic ischemic encephalopathy

Publication Number: WIP 78.7467

khawar nawaz, University of Texas Southwestern Medical School, Dallas, TX, United States; Rachel L.. Leon, University of Texas Southwestern Medical School, Dallas, TX, United States; srinivas kota, University of Texas Southwestern Medical School, Dallas, TX, United States; Lynn Bitar, University of Texas Southwestern Medical School, Dallas, TX, United States; Lina Chalak, UTSW, DALLAS, TX, United States

Background: Despite the U.S. expenditure on healthcare, disparities persist and begin long before neonatal intensive care (NICU) admission. These disparities result from an intricate interaction among genetic, environmental, and social factors, including the continued institutional disinvestment by the 1930s Home Owners' Loan Corporation (HOLC), which created color-coded maps of over 200 U.S. cities based on racial composition, systematically marginalizing communities of color. These factors continued to contribute to adverse birth circumstances and NICU complications, resulting in brain injury risk and affecting Neurodevelopmental outcomes. There is limited literature on the effect of sociodemographic factors like redlining and Social Deprivation Index (SDI) on survivors of moderate to severe hypoxic-ischemic encephalopathy (HIE) and how these factors affect neurodevelopmental outcomes.

Objective: To describe historical redlining, as implemented via HOLC color-coded maps, is associated with the risk of moderate to severe HIE in infants who received therapeutic hypothermia. 
To evaluate how the SDI influences the Bayley scores < 70 severe disability or death at two years of age of neonates with moderate to severe HIE.

Design/Methods: Using Street Map Pro on ArcGIS, we will georeference the Parkland neonatal data for neonates with moderate to severe HIE from 2009 to 2023. we will digitally color code areas using HOLC's A, B, C, and D grading, with an additional ungraded area for the expanded city since 1930, utilizing ArcGIS spatial join features we will connect our data to the map according to their geographic locations. A regression analysis will assess the association between historical redlining and neonates born with moderate to severe HIE. and analyze current census tract social characteristics for the current SDI. Subsequently, to assess SDI influence on Bayley scores < 70 severe disability or death, we will overlay the SDI based on census data on the map of HIE cases and analyze the relationship between SDI and these neonates using multivariate regression.