A Decision Support System for Including Equity in the Siting of Emergency Services

Abstract Background We propose a method to identify inequity in access to emergency services and present a model to address inequity by siting additional facilities, recommending both quantity and location to decision makers. Methods We classify emergencies by the median income bracket of their ZIP code tabulation areas. We determine whether the proportion of emergency responses classified by income bracket are within the average response, defined by time or distance, and quantify overall equity using mean absolute deviation. To address inequity, we iterate a bounded maximal covering location problem over coverage distance and additional facilities to provide a set of possible solutions. We evaluate the mean absolute deviation of each solution and identify the trade-off of adding facilities and minimizing deviation. Results We use 2019 data from the United States Internal Revenue Service and San Francisco Fire Department as an example case for our model. In this example, we find the distribution of emergency rooms leave the low income population of San Francisco significantly under-serviced and recommend to decision makers additional locations that reduce the average distance to an emergency room while minimizing mean absolute deviation—providing more equitable access to emergency rooms across income groups in San Francisco. Conclusions We offer a model to site additional locations to address any inequity found by minimizing mean absolute deviation informed by locations recommended as maximal covering location problem solutions--to maximize the number of emergencies within the overall average distance. We use these solutions to recommend additional locations to decision makers, and provide an example solution that significantly increases the access to emergency rooms for low income ZIP code tabulation areas while reducing overall average distance to an emergency room and the mean absolute deviation.