Kevin Rix1, Nora J Demchur2, David F Zane3, Lawrence H Brown4. 1. Trauma Service/Injury Prevention, Dell-Seton Medical Center at the University of Texas, Austin, TX, USA. 2. Division of Emergency Medicine, Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas, Austin, TX, USA. 3. Epidemiology and Disease Surveillance Unit, Austin Public Health, Austin, TX, USA. 4. Division of Emergency Medicine, Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas, Austin, TX, USA; U.S. Acute Care Solutions, Canton, OH, USA. Electronic address: lhbrown@ascension.org.
Abstract
OBJECTIVE: This study determined the vehicle-miles-traveled (VMT)-based injury rate for stand-up, dockless electric rental scooters (e-scooters), and compare it with the VMT-based injury rate for motor vehicle travel. METHODS: In this secondary analysis of existing data, the e-scooter injury rate was calculated based on e-scooter injuries presenting to an emergency department or the emergency medical services system in Austin, TX between September and November 2018. Injuries were identified by Austin Public Health through a targeted e-scooter epidemiological injury investigation; e-scooter VMT data were reported by e-scooter vendors as a condition of their city licensing. Comparative injury rates for motor vehicle travel in Texas, and specifically in Travis County were calculated using annual motor vehicle crash (MVC) injury and VMT data reported by the Texas Department of Transportation. RESULTS: There were 160 confirmed e-scooter injuries identified by the e-scooter injury investigation, with 891,121 reported miles of e-scooter travel during the study period. This produces an injury rate estimate of 180 injuries/million VMT (MVMT). The injury rates for motor vehicle travel for Texas and for Travis County were 0.9 injuries/MVMT and 1.0 injuries/MVMT, respectively. CONCLUSION: The observed VMT-based e-scooter injury rate was approximately 175 to 200 times higher than statewide or county specific injury rates for motor vehicle travel. These findings raise concerns about the potential higher injury rate associated with e-scooters, and highlight the need for further injury surveillance, research and prevention activities addressing this emerging transportation technology.
OBJECTIVE: This study determined the vehicle-miles-traveled (VMT)-based injury rate for stand-up, dockless electric rental scooters (e-scooters), and compare it with the VMT-based injury rate for motor vehicle travel. METHODS: In this secondary analysis of existing data, the e-scooter injury rate was calculated based on e-scooter injuries presenting to an emergency department or the emergency medical services system in Austin, TX between September and November 2018. Injuries were identified by Austin Public Health through a targeted e-scooter epidemiological injury investigation; e-scooter VMT data were reported by e-scooter vendors as a condition of their city licensing. Comparative injury rates for motor vehicle travel in Texas, and specifically in Travis County were calculated using annual motor vehicle crash (MVC) injury and VMT data reported by the Texas Department of Transportation. RESULTS: There were 160 confirmed e-scooter injuries identified by the e-scooter injury investigation, with 891,121 reported miles of e-scooter travel during the study period. This produces an injury rate estimate of 180 injuries/million VMT (MVMT). The injury rates for motor vehicle travel for Texas and for Travis County were 0.9 injuries/MVMT and 1.0 injuries/MVMT, respectively. CONCLUSION: The observed VMT-based e-scooter injury rate was approximately 175 to 200 times higher than statewide or county specific injury rates for motor vehicle travel. These findings raise concerns about the potential higher injury rate associated with e-scooters, and highlight the need for further injury surveillance, research and prevention activities addressing this emerging transportation technology.
Authors: Bjorn Anderson; Jonathan D Rupp; Tim P Moran; Lauren A Hudak; Daniel T Wu Journal: Int J Environ Res Public Health Date: 2021-09-29 Impact factor: 3.390
Authors: Kimon L H Ioannides; Pin-Chieh Wang; Kamran Kowsari; Vu Vu; Noah Kojima; Dayna Clayton; Charles Liu; Tarak K Trivedi; David L Schriger; Joann G Elmore Journal: PLoS One Date: 2022-04-06 Impact factor: 3.240