Assessing Trauma Center Accessibility for Healthcare Equity Using an Anti-Covering Approach

Abstract

Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient’s chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current placement of trauma centers and explore optimal placements based on the population distribution and spatial pattern of motor vehicle accidents in 2015 through 2019 in Tennessee. The ACLP models seek to offer a method of exploring feasible scenarios for locating trauma centers that intend to provide accessibility to patients in underserved areas who suffer trauma as a result of vehicle accidents. The proposed ACLP approach also seeks to adjust the locations of trauma centers to reduce areas with excessive service coverage while improving coverage for less accessible areas of demand. In this study, three models are prescribed for finding optimal locations for trauma centers: (a) TraCt: ACLP model with a geometric approach and weighted models of population, fatalities, and spatial fatality clusters of vehicle accidents; (b) TraCt-ESC: an extended ACLP model mitigating excessive service supply among trauma center candidates, while expanding services to less served areas for more beneficiaries using fewer facilities; and (c) TraCt-ESCr: another extended ACLP model exploring the optimal location of additional trauma centers.

Location Planning of Emergency Medical Facilities Using the p-Dispersed-Median Modeling Approach

“…It should be noted that the existing models using covering or median location problems are centered around enhancing the operational efficiency of the EMS rather than mitigating or resolving the spatial disparity regarding medical facility access [13,23], stressing that there is room to employ a different class of location problems to reflect different requirements and criteria by the roles of healthcare facilities as well as planning situations [13,16]. To overcome the problem, alternatively, Chea et al [4] employed an anti-covering location problem (ACLP) to address the optimal placement of trauma centers, the highest level of EMS; thus, the number of facilities is quite limited. They argued that the principle for locating trauma centers should prioritize maximizing the spatial equity of accessibility from clusters of trauma/accident-prone areas while avoiding overlapping coverage among trauma centers.…”

“…It is important to define a standard for the maximal service range of EMS facilities to evaluate the effectiveness of the EMS planning scenarios. However, there is little consensus in the literature regarding how to define the geographic coverage for primary EMS facilities [4,23]. In our study, the service coverage standard for LEMIs is defined based on the street-network-based travel time from a LEMI to demand areas, which is a 30 min street-network-based travel time distance as the threshold for emergency service coverage for a LEMI, which is generally accepted for the geographic scale of the study area in South Korea [43,44].…”

“…With the requirements of the emergency medical services (EMS), dispersing the emergency medical facilities in a region is a natural consideration to achieve spatial equity of medical facilities, serving the potential demand as much as possible within the service area [4]. This principle holds true, especially for time-sensitive critical services such as trauma centers, as EMS represents the primary tier of the emergency services, and travel time becomes a crucial factor in handling life-threatening conditions for cases in rural or underserved areas.…”

“…In the context of the EMS system, common approaches include the strategic distribution of the EMS facilities using median-or coverage-based models such as p-median problems and coverage location problems, respectively, which are formulated to meet specific criteria and requirements aiming at improving the efficiency of the system. In terms of spatial equity, a recent study conducted by Chea et al [4] proposed to determine the optimal placement of trauma centers in response to traffic accidents in Tennessee, United States using an anti-covering location modeling approach. Their research's fundamental concept involves integrating spatially informed clustering methods into anti-covering models, aiming to achieve an equitable distribution of service coverage for trauma centers, minimizing excessive overlap within accident hotspots.…”

Assessing trauma center accessibility in the Southeastern region of the U.S. to improve healthcare efficacy using an anti-covering approach

“…This research consists of three analyses: The first part compares the number and percentage of potential demands of each state covered by the current trauma system in the Southeastern region. The second analysis uses the anti-covering location problem for trauma centers (TraCt model) presented by Chea et al [5] to locate potential trauma centers with better coverage for vehicle crashes with fatalities. The TraCt model is applied to each state to find the optimal location of trauma centers.…”