Socioeconomic Disparities and Geographic Variability in Pediatric Traumatic Brain Injury Rates: A Geospatial Analysis

Introduction: Traumatic brain injury (TBI) in children is a critical public health issue, leading to numerous emergency department (ED) visits and impacting nearly half a million children annually in the U.S. Pediatric TBI rates vary geographically, influenced by neighborhood-level structural factors. However, limited studies apply geospatial analysis to examine these differences. This cross-sectional study addresses this gap by analyzing geospatial disparities in pediatric TBI rates across Zip Code Tabulation Areas (ZCTAs).

Methods: The authors retrospectively reviewed 7 years (2016–2023) of ED admissions at a Level 1 trauma center, collecting data on demographics, patient zip codes, injury characteristics (including Injury Severity Score [ISS]), and outcomes. Demographic and socioeconomic ZCTA-level data were sourced from American Community Survey 5-year estimates. Pediatric TBI incidence was calculated via spatial Bayesian smoothing. Global Moran’s I tested spatial autocorrelation, while Local Indicators of Spatial Association (LISA) identified TBI hotspots and coldspots. A zero-inflated negative binomial regression model, adjusted for population density, urbanicity, and spatial lag in the zero component, estimated incidence rate ratios (IRRs). Interactive choropleth maps of pediatric TBI incidence were generated using the tmap package in R.

Results: Our cohort included 2809 patients with an average age of 7.03 years. We identified 47 ZCTAs as hotspots and 143 as coldspots. ZCTA factors associated with the risk of pediatric TBI included a higher percentage of Black residents(IRR,1.008;95%CI,1.001-1.016;P=0.025), lower median household income(IRR,0.297;95%CI,0.206-0.428;P <.001), vacant housing units(IRR,1.032;95%CI,1.014-1.051;P <.001), public health coverage(IRR, 1.030;95%CI,1.018-1.042;P <.001), and poverty(IRR,1.050;95%CI,1.032-1.069;P <.001). Comparing patients residing in hotspots to the rest of the cohort, we found that these patients had a lower ISS (Median[IQR]:5[2-10]vs.6[4-12];P <.001). Patients dwelling in hotspots had a higher rate of direct discharge from the ED (57.0%vs.47.1%;P < .001), a lower ED transfer rate to the ICU (16.5%vs.22.5%;P <.001), and a shorter hospital length of stay (25.1%vs.32.0%;P <.001).

Conclusion : Our findings reveal sociodemographic disparities across ZCTAs impacting pediatric TBI rates. Policy makers should focus on these disparities to implement targeted neighborhood-level interventions for effective prevention.