Variation of demographic and socioeconomic factors associated with pediatric traumatic brain injury: a geospatial analysis
Insights
Pediatric traumatic brain injury (TBI) rates are higher in socioeconomically disadvantaged neighborhoods. This highlights the need for targeted prevention strategies addressing social determinants to reduce childhood TBI.
Area of Science:
- Pediatric Traumatic Brain Injury Research
- Geospatial Epidemiology
- Social Determinants of Health
Background:
- Traumatic brain injury (TBI) is a significant cause of childhood mortality and morbidity in the US, with nearly 500,000 emergency visits annually.
- Geospatial disparities in pediatric TBI incidence and outcomes may be linked to neighborhood socioeconomic factors.
Purpose of the Study:
- To investigate geospatial disparities in pediatric TBI across ZIP Code Tabulation Areas (ZCTAs).
- To assess the association between neighborhood sociodemographic factors and pediatric TBI incidence rates and outcomes.
Main Methods:
- Retrospective cross-sectional study of pediatric TBI patients (June 2016-June 2023) at a level I pediatric trauma center.
- Linked electronic medical records with ZCTA-level socioeconomic data (American Community Survey). Assessed social disadvantage using the Social Deprivation Index (SDI).
- Employed spatial Bayesian smoothing for incidence rates, Global Moran's I for spatial autocorrelation, and local indicators of spatial association (LISA) for hot/cold spots. Zero-inflated negative binomial regression analyzed incidence rate ratios (IRRs).
Main Results:
- Identified 47 TBI hot spots and 143 cold spots across ZCTAs.
- Hot spot ZCTAs exhibited higher child population density, more renter-occupied housing, lower median incomes, shorter commute times, higher public insurance rates, and higher SDI scores (p < 0.001).
- Higher vacant housing, lower remote work rates, lower private insurance, and higher poverty were associated with increased TBI incidence (p < 0.001). Patients from hot spots had lower Injury Severity Scores (ISS) and fewer prolonged hospital stays (p < 0.001), with no significant differences in discharge disposition or mortality.
Conclusions:
- Pediatric TBI incidence is disproportionately concentrated in socioeconomically disadvantaged neighborhoods.
- Findings emphasize the necessity of targeted, neighborhood-level prevention strategies.
- Addressing social determinants of health is crucial to mitigate the burden of pediatric TBI.
Objective:
Traumatic brain injury (TBI) is a leading cause of mortality and morbidity among children in the United States, with nearly a half million pediatric TBI-related emergency visits annually. The authors aimed to investigate geospatial disparities in pediatric TBI across ZIP Code Tabulation Areas (ZCTAs) and to assess the association of neighborhood sociodemographic factors with pediatric TBI incidence rate and outcomes.
Methods:
A retrospective cross-sectional study was conducted to examine the electronic medical records of pediatric patients treated at a level I pediatric trauma center between June 2016 and June 2023. Data were linked with ZCTA-level socioeconomic indicators from the American Community Survey 5-year estimates. Neighborhood-level social disadvantage, including the Social Deprivation Index (SDI), median household income, housing characteristics, and health coverage patterns, was assessed. Pediatric TBI incidence rates were calculated using spatial Bayesian smoothing techniques. Global Moran's I test was used to assess spatial autocorrelation, while the local indicators of spatial association test was used to identify TBI hot spots and cold spots. Incidence rate ratios (IRRs) were derived using zero-inflated negative binomial regression. Injury severity (via the Injury Severity Score [ISS]), hospital length of stay (LOS), discharge disposition, and mortality were examined.
Results:
Among 2809 patients (median age 6 years [IQR 1-12 years], 36.4% female), 47 ZCTAs were identified as hot spots and 143 as cold spots. Compared with cold spots, hot spot ZCTAs had a higher child population density, greater proportions of renter-occupied housing units, lower median household incomes, shorter mean travel times to work, higher rates of public health insurance coverage, and higher SDI scores (all p < 0.001). In multivariable regression, higher vacant housing units (IRR 1.032, 95% CI 1.014-1.051; p < 0.001), lower proportions of individuals working from home (IRR 0.941, 95% CI 0.921-0.963; p < 0.001), lower private health insurance coverage (IRR 0.979, 95% CI 0.969-0.990; p < 0.001), and higher poverty (IRR 1.073, 95% CI 1.047-1.110; p < 0.001) were associated with increased TBI incidence rates. Compared with other areas, patients from hot spots had a lower median ISS (5 vs 6, p < 0.001) and fewer prolonged hospital LOS events (25.1% vs 32.0%, p < 0.001), but no significant differences in discharge disposition or mortality (both p > 0.05).
Conclusions:
In this cross-sectional study, pediatric TBI rates clustered disproportionately in socioeconomically disadvantaged areas. These findings underscore the need for targeted, neighborhood-level prevention strategies and policies addressing social determinants to mitigate the rising burden of pediatric TBI.
