Promoting Emergency Medical Service Infrastructure Equality to Reduce Road Crash Fatalities

Soyoung Jung () and Xiao Qin
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Soyoung Jung: School of Smart Safety System, Dongyang University, 2741 Pyeonghwaro, Dongducheon 11307, Gyeonggido, Republic of Korea
Xiao Qin: Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, NWQ 4414, P.O. Box 784, Milwaukee, WI 53201, USA

Sustainability, 2024, vol. 16, issue 3, 1-20

Abstract: Timely emergency medical service (EMS) is critical to mitigate outcomes of severe traffic crashes, especially in rural areas. The urban–rural inequality of the EMS infrastructure has been one of the factors contributing to a higher death rate for patients in rural road crashes. To address the spatial imbalance of EMS infrastructure, this study aimed to provide a methodological framework for evaluating the existing EMS infrastructure by taking South Korea’s EMS infrastructure expansion plans as the case study. Specifically, this study developed a road crash data-driven methodology to promote spatial balance and economical expansion of EMS infrastructure of all types, including EMS stations, hospitals, the helicopter fleet, and landing spots. Geographically weighted binary logit regression and spatial analysis identified strategic locations for prioritizing the EMS infrastructure expansion using crash victim data and road networks to close the gap between urban and rural areas. The analysis of access to existing EMS infrastructure showed that the 16 to 20 min EMS response time, including on-scene time and transport time ranging from 11 to 15 min, are significantly associated with higher crash fatalities. The results also suggested that EMS stations and heliports are inadequate to meet the EMS time thresholds in the central province of Korea. The findings of this research could inform policymakers as they are working toward expanding the EMS infrastructure and creating a more equitable EMS response when it comes to transporting rural road crash patients.

Keywords: emergency medical service; crash; infrastructure; geographically weighted binary logit regression; inequality; accessibilities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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