{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T03:41:34Z","timestamp":1789270894831,"version":"build-2803163510"},"reference-count":106,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>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\u2013related 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>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\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Among 2809 patients (median age 6 years [IQR 1\u201312 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 &lt; 0.001). In multivariable regression, higher vacant housing units (IRR 1.032, 95% CI 1.014\u20131.051; p &lt; 0.001), lower proportions of individuals working from home (IRR 0.941, 95% CI 0.921\u20130.963; p &lt; 0.001), lower private health insurance coverage (IRR 0.979, 95% CI 0.969\u20130.990; p &lt; 0.001), and higher poverty (IRR 1.073, 95% CI 1.047\u20131.110; p &lt; 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 &lt; 0.001) and fewer prolonged hospital LOS events (25.1% vs 32.0%, p &lt; 0.001), but no significant differences in discharge disposition or mortality (both p &gt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2025.4.peds2572","type":"journal-article","created":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T02:00:30Z","timestamp":1754704830000},"page":"630-640","source":"Crossref","is-referenced-by-count":1,"title":["Variation of demographic and socioeconomic factors associated with pediatric traumatic brain injury: a geospatial analysis"],"prefix":"10.3171","volume":"36","author":[{"given":"Foad","family":"Kazemi","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan R.","family":"Cohen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"2","key":"b1","series-title":"Neurol Med Chir (Tokyo)","first-page":"82","article-title":"Pediatric traumatic brain injury: characteristic features, diagnosis, and management","volume":"57","author":"Araki T","year":"2017","unstructured":"Araki T, Yokota H, Morita A. 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