Youth violence prevention can be enhanced by geospatial analysis of trauma registry data

Abstract

BACKGROUND: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 aged 14 years to 29 years with census tract data would identify geospatial and structural determinants of youth violence. METHODS:Admissions to a Level I trauma center from 2010 to 2019 were retrospectively reviewed to identify assaults in those 14 years to 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 1,608 admissions, 1,517 (92.4%) had complete addresses and were included in the analysis. Mean age was 23 ± 3.8 years, 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 (Z score = 17.4, p < 0.001). Linear regression of American Communities Survey demographics showed predictors of assault were unemployment (odds ratio, 4.5; 95% confidence interval, 2.7-6.4; p < 0.001), Spanish spoken at home (odds ratio, 6.6; 95% confidence interval, 3.4-9.8; p < 0.001) and poverty level (odds ratio, 1.9; 95% confidence interval, 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.

Trends in disparities research on trauma and acute care surgery outcomes: A 10-year systematic review of articles published in The Journal of Trauma and Acute Care Surgery

“…Of these publications, 4,104 were excluded and 74 articles were included (Supplemental Digital Content, Supplementary Figure 1, http://links.lww.com/TA/D174). 2–76 A bimodal distribution shows that location and access disparities represented the majority of articles, followed by age and race (Fig. 1).…”

“…Deeb et al 46 2020 Under triage rate, mortality Under triage is more common in rural than urban settings and is associated with increased mortality Byrne et al 49 2021 Mortality Nearby pediatric trauma centers are associated with lower rates of MVC death Maish III et al 47 2021 Under triage rate Under triage may occur in areas where trauma centers are near large competing non-trauma centers Phelos et al 48 2021 Fatality rates Increasing social vulnerability is associated with higher fatality rates Ali et al 53 2022 Firearm injury Food insecurity was an independent predictor of assault in victims of firearm injuries, and food insecurity was associated with an increase in the overall gun-related trauma. Doucet et al 50 2022 Predictors of assault Geographic analysis of trauma registry data can identify high-risk areas for youth violence and related social/structural determinants. Neiman et al 3 2022 Mortality Neighborhoods with higher rates of social vulnerability experience high mortality rates after trauma.…”

Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation

“…These models have also been utilized to anticipate blood product transfusion needs in pediatric patients undergoing craniofacial surgery [26]. Furthermore, machine learning has been applied to identify geospatial and structural factors influencing youth violence [27] and predict the risk of prolonged mechanical ventilation for patients with traumatic brain injury [28]. Finally, machine learning has been applied to surgical imaging for diagnosing and treating spine disorders [29].…”