Locating emergency medical services to reduce urban-rural inequalities

Emergency Medical Service (EMS) systems provide fundamental services in relation to public health and safety. The spatial configuration of EMS stations is crucial to the efficiency and equality of service provision. While urban-rural inequalities in EMS have been widely acknowledged, how to optimize EMS station locations to reduce such inequalities remains challenging. This research proposes a multi-objective optimization model to reduce urban-rural inequalities in EMS accessibility and coverage, in addition to maximizing the total covered population. The proposed model is applied in an empirical study in Wuhan, China, to seek locations for new EMS stations in order to improve local EMS capacity in the pandemic period. The results indicate that the total covered population, particularly in urban area, decreases when urban-rural equality in service accessibility increases, but it has a U-shaped relationship with urban-rural inequality in service coverage. Pareto-optimal solutions suggest that all new stations should be located in rural areas if lower urban-rural inequality in EMS is to be obtained, but one new station is needed in the urban area if higher coverage of total population is more desirable. The work presented in this paper can aid the planning practice of public services like EMS systems where reducing urban-rural inequalities is an essential concern.

Automated summary

Emergency Medical Service (EMS) systems are crucial for public health and safety. Optimizing EMS station locations to reduce urban-rural inequalities remains challenging. This research proposes a multi-objective optimization model to reduce EMS inequalities in accessibility and coverage and maximize the total covered population. The study in Wuhan, China suggests that to reduce urban-rural inequalities, all new EMS stations should be located in rural areas, but an additional station is needed in urban areas to increase overall coverage. This work has important implications for the planning of public services like EMS.