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INTRODUCTION: Domestic violence (DV) is a major health issue on both a national and global scale. In Richmond County, Georgia, there are 18.1 calls per 1000 women for DV, exceeding the national average of 5.9 and Georgia average of 15.3 calls per 1000 women. The goal of the study is to map epidemiologic and spatial trends in communities of DV survivors thereby assessing the feasibility of a prospective intervention initiative for high-risk areas. METHODS: In partnership with "SafeHomes", a local women's shelter, a retrospective review of physical addresses was compiled from first-time residential clients 18 and older from January 1, 2019 to December 31, 2019. Hot zones were overlaid with Census tracts to assess sociodemographic correlates including race, ethnicity, age, poverty, education, and employment status. RESULTS: From all records (n = 85), 5 hot zones were identified. Analysis of census tract data revealed minority predominance in all hot zones, in addition to an unemployment rate above the state average (23%, 12%, 8%, 5%, and 19%, respectively, vs 3.4%). CONCLUSION: DV affects both the inner city and suburban areas of Richmond County. Hot zone frequency was disproportionately increased in the urban center where community demographics showed minority predominance, especially of African-Americans. These hot zones also have more individuals living below the federal poverty line and experience unemployment rates greater than the state and national average of 3.4 and 3.5, respectively. This study demonstrated that mapping domestic violence epidemiologic and spatial trends is possible, allowing for targeted support and intervention to reduce the rate of domestic violence.
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INTRODUCTION: Needs Based Assessment of Trauma Systems 2 (NBATS-2) attempts to predict the impact on patient volume and travel time for patients when a new trauma center (TC) is added to the system. The purpose of this study was to examine NBATS-2 predictive accuracy regarding expected volume and travel times of trauma patients at a newly designated TC and nearby legacy TCs when compared with actual data. METHODS: Needs Based Assessment of Trauma Systems predictive model for volume of trauma patients at the new TC was run based on 25th, 50th, and 75th percentiles of both state and National Trauma Data Bank (NTDB) patients per 100 TC beds. This was compared with the actual number of trauma patients from the State Discharge Data set before (2011-2012) and after (2016-2017) designation of the TC. Analysis was then augmented using the geographic information system (ArcGIS) spatial modeling to characterize median travel times for actual trauma patients, before and after designation of the TC. RESULTS: Both state and NTDB 25th, 50th, and 75th percentiles resulted in significant overestimation of volume at the new TC in 2016. After another year of TC maturation (2017), overestimation decreased but was still present. The 25th percentile from state and NTDB data sets provided the most accurate predictions. For the legacy TCs, the model switched from under to overestimation as the state and NTDB percentiles increased. The geographic information system accurately showed patients traveling <40 minutes to a TC nearly doubled. CONCLUSION: Needs Based Assessment of Trauma Systems 2 provides an excellent template for state strategic planning; however, it overestimates new TC volume and under/overestimates volumes for legacy TCs depending on the state and NTDB percentiles used. This study shows that population density of the county in which the new or legacy TC is located should be considered when choosing the appropriate state or NTDB percentile. The geographic information system appropriately showed a decrease in trauma patient travel times after TC designation. LEVEL OF EVIDENCE: Care Management, level V.