Enhancing trauma registries by integrating traffic records and geospatial analysis to improve bicyclist safety. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Doucet, Jay J.; Godat, Laura N.; Kobayashi, Leslie; Berndtson, Allison E.; Liepert, Amy E.; Raschke, Eric; Denny, John W.; Weaver, Jessica; Smith, Alan; Costantini, Todd
• Is Part Of: Journal of trauma and acute care surgery. Volume 90:Issue 4(2021)
• Publication Date: 2021-04
• Subjects: Spatial data analysis -- GIS -- trauma registry -- bicycle -- helmet; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000003075
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: Trauma registries are used to identify modifiable injury risk factors for trauma prevention efforts. However, these may miss factors useful for prevention of bicycle-automobile collisions, such as vehicle speeds, driver intoxication, street conditions, and neighborhood characteristics. We hypothesize that (GIS) analysis of trauma registry data matched with a traffic accident database could identify risk factors for bicycle-automobile injuries and better inform injury prevention efforts. METHODS: The trauma registry of a US Level I trauma center was used retrospectively to identify bicycle-motor vehicle collision admissions from January 1, 2010, to December 31, 2018. Data collected included demographics, vitals, injury severity scores, toxicology, helmet use, and mortality. Matching with the Statewide Integrated Traffic Records System was done to provide collision, victim and GIS information. The GIS mapping of collisions was done with census tract data including poverty level scoring. Incident hot spot analysis to identify statistically significant incident clusters was done using the Getis Ord Gi* statistic. RESULTS: Of 25, 535 registry admissions, 531 (2.1%) were bicyclists struck by automobiles, 425 (80.0%) were matched to Statewide Integrated Traffic Records System. Younger age (odds ratio [OR], 1.026; 95% confidence interval [CI], 1.013–1.040, p < 0.001), higher census tract poverty level percentage (OR, 0.976; 95% CI, 0.959–0.993, p = 0.007), and high school or less education (OR, 0.60; 95 CI, 0.381–0.968; p = 0.036) were predictive of not wearing a helmet. Higher census tract poverty level percentage (OR, 1.019; 95% CI, 1.004–1.034; p = 0.012) but not educational level was predictive of toxicology positive—bicyclists in automobile collisions. Geographic information systems analysis identified hot spots in the catchment area for toxicology-positive bicyclists and lack of helmet use. CONCLUSION: Combining trauma registry data and matched traffic accident records data with GIS analysis identifies additional risk factors for bicyclist injury. Trauma centers should champion efforts to prospectively link public traffic accident data to their trauma registries. LEVEL OF EVIDENCE: Prognostic and Epidemiological, level III.