Geospatial mapping can be used to identify geographic areas and social factors associated with intentional injury as targets for prevention efforts distinct to a given community. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Lasecki, CH; Mujica, FC; Stutsman, S; Williams, AY; Ding, L; Simmons, JD; Brevard, SB
• Is Part Of: Journal of trauma and acute care surgery. Volume 84:Issue 1(2018)
• Publication Date: 2018-01
• Subjects: GIS -- intentional injury -- trauma -- prevention -- mapping; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000001720
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: Geographic information systems (GIS) have proven effective in studying intentional injury in various communities; however, GIS is not implemented widely for use by Level I trauma centers in understanding patient populations. Our study of intentional injury combines the capabilities of GIS with a Level I trauma center registry to determine the spatial distribution of victims and correlated socioeconomic factors. METHODS: One thousand ninety-nine of 3, 109 total incidents of intentional trauma in the trauma registry from 2005 to 2015 had sufficient street address information to be mapped in GIS. Comparison of these data, coupled with demographic data at the block group level, determined if any clustering or spatial patterns existed. Geographic information systems delivered these comparisons using several spatial statistics including kernel density, ordinary least squares test, and Moran's index. RESULTS: Kernel density analysis identified four major areas with significant clustering of incidents. The Moran's I value was 0.0318. Clustering exhibited a positive z -score and significant p value ( p < 0.01). Examination of socioeconomic factors by spatial correlation with the distribution of intentional injury incidents identified three significant factors: unemployment, single-parent households, and lack of a high school degree. Tested factors did not exhibit substantial redundancy (variance inflation factor < 7.5). Nonsignificant tested factors included race, proximity to liquor stores and bars, median household income, per capita income, rate with public assistance, and population density. CONCLUSION: Spatial representation of trauma registry data using GIS effectively identifies high-risk areas for intentional injury. Analysis of local socioeconomic data identifies factors unique to those high-risk areas in the observed community. Implications of this study may include the routine use of GIS by Level I trauma centers in assessing intentional injury in a given community, the use of that data to guide the development of trauma prevention, and the assessment of other mechanisms of trauma using GIS. LEVEL OF EVIDENCE: Epidemiological, level IV.