Department of Trauma Surgery, Leiden University Medical Center, Leiden, The Netherlands.
Division of Trauma, Burns, Acute and Critical Care, Department of Surgery, Weill Cornell Medicine, New York, NY, USA.
Value Health. 2020 Aug;23(8):1020-1026. doi: 10.1016/j.jval.2020.05.005. Epub 2020 Jul 4.
There is no generally accepted methodology to assess trauma system access. The goal of this study is to determine the influence of the number and geographical distribution of trauma centers (TCs) on transport times (TT) using geographic information system (GIS)-technology.
Using ArcGIS-PRO, we calculated differences in TT and population coverage in 7 scenarios with 1, 2, or 3 TCs during rush (R) and low-traffic (L) hours in a densely populated region with 3 TCs in the Netherlands.
In all 7 scenarios, the population that could reach the nearest TC within <45 minutes varied between 96% and 99%. In the 3-TC scenario, roughly 57% of the population could reach the nearest TC <15 minutes both during R and L. The hypothetical geographically well-spread 2-TC scenario showed similar results as the 3-TC scenario. In the 1-TC scenarios, the population reaching the nearest TC <15 minutes decreased to between 19% and 32% in R and L. In the 3-TC scenario, the average TT increased by about 1.5 minutes to almost 21 minutes during R and 19 minutes during L. Similar results were seen in the scenarios with 2 geographically well-spread TCs. In the 1-TC scenarios and the less well-spread 2-TC scenario, the average TT increased by 5 to 8 minutes (L) and 7 to 9 minutes (R) compared to the 3-TC scenario.
This study shows that a GIS-based model offers a quantifiable and objective method to evaluate trauma system access under different potential trauma system configurations. Transport time from accident to TC would remain acceptable, around 20 minutes, if the current 3-TC situation would be changed to a geographically well-spread 2-center scenario.
目前尚无被广泛认可的创伤救治体系评估方法。本研究旨在利用地理信息系统(GIS)技术,确定创伤中心(TC)的数量和地理分布对转运时间(TT)的影响。
使用 ArcGIS-PRO,我们在荷兰一个人口密集地区的 3 个 TC 基础上,计算了 7 种不同场景(R 和 L 高峰时段各有 1、2 或 3 个 TC)下 TT 和人口覆盖的差异。
在所有 7 种场景中,在<45 分钟内能够到达最近 TC 的人群比例在 96%至 99%之间。在 3-TC 场景中,高峰时段约有 57%的人群能够在<15 分钟内到达最近的 TC,而低峰时段则为约 45%。假设地理分布广泛的 2-TC 场景与 3-TC 场景结果相似。在 1-TC 场景中,高峰时段和低峰时段能够在<15 分钟内到达最近 TC 的人群比例分别下降至 19%至 32%。在 3-TC 场景中,平均 TT 在高峰时段增加约 1.5 分钟,至近 21 分钟,而在低峰时段则增加约 1.9 分钟,至近 19 分钟。在有 2 个地理分布广泛的 TC 的场景中,也出现了类似的结果。在 1-TC 场景和分布较差的 2-TC 场景中,与 3-TC 场景相比,平均 TT 在低峰时段增加了 5 至 8 分钟,在高峰时段增加了 7 至 9 分钟。
本研究表明,基于 GIS 的模型为在不同潜在创伤救治体系配置下评估创伤救治体系提供了一种量化且客观的方法。如果当前的 3-TC 模式改为地理分布广泛的 2 中心模式,从事故现场到 TC 的转运时间(TT)仍将保持在可接受的 20 分钟左右。