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Citation
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Doucet JJ, Godat LN, Berndtson AE, Liepert AE, Weaver JL, Smith A, Kobayashi L, Biffl WL, Costantini T. J. Trauma Acute Care Surg. 2022; ePub(ePub): ePub. |
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Abstract
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INTRODUCTION: Geographic information systems (GIS) have been used to understand relationships between trauma mechanisms, locations and social determinants for injury prevention. We hypothesized that GIS analysis of trauma center registry data for assault patients ages 14-29 with census tract data would identify geospatial and structural determinants of youth violence.
METHODS: Admissions to a Level 1 trauma center from 2010 to 2019 were retrospectively reviewed to identify assaults in those 14-29 years. Prisoners were excluded. Home and injury scene addresses were geocoded. Cluster analysis was performed with the Moran I test for spatial autocorrelation. Census tract comparisons were done using American Communities Survey (ACS) data by t-test and linear regression.
RESULTS: There were 1608 admissions, 1517 (92.4%) had complete addresses and were included in the analysis. Mean age was 23 yrs ± 3.8, mean ISS was 7.5 ± 6.2, there were 11 (0.7%) in-hospital deaths. Clusters in six areas of the trauma catchment were identified with a Moran I value of 0.24 (Fig 1, Z score = 17.4, p < 0.001). Linear regression of ACS demographics showed predictors of assault were unemployment (OR 4.5, 95% CI: 2.7- 6.4, p < 0.001), Spanish spoken at home (OR 6.6, 95% CI: 3.4-9.8, p < 0.001) and poverty level (OR 1.9, 95% CI: 1.1-2.7, p < 0.001). Education level of less than high school diploma, single parent households and race were not significant predictors.
CONCLUSION: GIS analysis of registry data can identify high risk areas for youth violence and correlated social and structural determinants. Violence prevention efforts can be better targeted geographically and socioeconomically with better understanding of these risk factors. LEVEL OF EVIDENCE: Epidemiological, level III.
Language: en |