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. 2022 Jan 27;19(3):1459.
doi: 10.3390/ijerph19031459.

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

Heewon Chea  1 , Hyun Kim  1 , Shih-Lung Shaw  1 , Yongwan Chun  2
Affiliations

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

Heewon Chea et al. Int J Environ Res Public Health. .

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.

Keywords: ACLP; Getis–Ord G; accessibility; anti-covering location problem; trauma center; vehicle accident.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Computations of global G-statistic for TDCs with different intensity of fatalities. (a) G = 0.0076; (b) G = 0.0111.
Figure 2
Figure 2
Difference of spatial arrangement of facility coverages among (a) PMP, (b) LSCP, (c) MCLP, and (d) ACLP.
Figure 3
Figure 3
Study area of the state of Tennessee with the location of trauma centers, and data processing of hexagonal tessellation. (a) Locations of trauma centers and census tract in Tennessee, 2019; (b) Tessellation by hexagonal grids and vehicle accidents distribution.
Figure 4
Figure 4
The 60 min TDCs from existing trauma centers and locations of vehicle accidents.
Figure 5
Figure 5
Structure of the modeling approach by different types of weight and constraints.
Figure 6
Figure 6
Potential demands covered by trauma centers in Tennessee, 2019. (a) Percentage of covered demands; (b) Total demands under CSAs.
Figure 7
Figure 7
ACLP solutions from different weighted objectives by potential demands. (a) Percent of covered population; (b) Percent of covered fatality; (c) Percent of covered accident.
Figure 8
Figure 8
Potential demands under CSAs according to different objectives. (a) Percent of covered population; (b) Percent of covered fatality; (c) Percent of covered accident.
Figure 9
Figure 9
Number of facilities available from each demand by different TraCt models. (a) Geometric model; (b) Population weighted model; (c) Fatality weighted model; (d) G statistic-fatality weighted model.
Figure 10
Figure 10
The number of facilities accessible from each demand location by the models and γmax for the TraCt-ESC model. (a) γmax = ∞; (b) γmax = 2; (c) γmax = 3; (d) γmax = 4.
Figure 11
Figure 11
Locations of additional trauma centers according to the number of facilities available. (a) r = 1; (b) r = 5; (c) r = 7; (d) r = 10.
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