Geospatial Visualization of Mobile Stroke Unit Dispatches: A Method to Optimize Service Performance.

Q1 Medicine Interventional Neurology Pub Date : 2018-10-01 Epub Date: 2018-07-24 DOI:10.1159/000490581
James P Rhudy, Anne W Alexandrov, Joseph Rike, Tomas Bryndziar, Ana Hossein Zadeh Maleki, Victoria Swatzell, Wendy Dusenbury, E Jeffrey Metter, Andrei V Alexandrov
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引用次数: 8

Abstract

Background: Timely treatment of acute ischemic stroke is crucial to optimize outcomes. Mobile stroke units (MSU) have demonstrated ultrafast treatment compared to standard emergency care. Geospatial analysis of the distribution of MSU cases to optimize service delivery has not been reported.

Methods: We aggregated all first-year MSU dispatch occurrences and all cases classified by clinical teams as true stroke by zip code and calculated dispatch and true stroke incidence rates. We mapped dispatch and stroke cases and symbolized incidence rates by standard deviation. We confirmed visual impressions of clusters from map inspection by local Moran's I, boxplot inspection, and t test. We calculated service areas using drive times to meet dispatch and true stroke need.

Results: A significant cluster of high dispatch incident rate was confirmed around our MSU base in urban Memphis within a 5-min driving area supporting the initial placement of the MSU based on 911 activation. A significant cluster of high true stroke rate was confirmed to the east of our MSU base in suburban Memphis within a 10-min driving area. Mean incident longitude of cases of true stroke versus disregarded status was significantly eastward (p = 0.001785).

Conclusion: Our findings will facilitate determination of socio-spatial antecedents of neighborhood overutilization of 911 and MSU services in our urban neighborhoods and service delivery optimization to reach neighborhoods with true stroke burden.

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移动行程单元调度的地理空间可视化:一种优化服务性能的方法。
背景:及时治疗是优化急性缺血性脑卒中预后的关键。与标准的紧急护理相比,移动卒中单元(MSU)已经证明了超快的治疗。对MSU病例分布进行地理空间分析以优化服务提供尚未见报道。方法:我们汇总所有一年级MSU调度发生率和所有临床小组按邮政编码分类为真卒中的病例,并计算调度和真卒中发生率。我们绘制了调度和中风病例图,并用标准差表示发病率。我们通过本地Moran’s I检验、箱线图检验和t检验确认了地图检查中集群的视觉印象。我们使用驱动时间来计算服务区域,以满足调度和真冲程需求。结果:在我们位于孟菲斯市区的MSU基地附近,在5分钟的车程范围内,确认了一个显著的高调度事故率集群,这支持了基于911激活的MSU的初始位置。在我们位于孟菲斯郊区的密歇根州立大学基地以东10分钟车程范围内,证实了一个显著的高真实中风率集群。真实卒中病例与未诊断卒中病例的平均事件经度显著向东(p = 0.001785)。结论:我们的研究结果将有助于确定城市社区911和MSU服务过度使用的社会空间前因,并优化服务提供以达到真正卒中负担的社区。
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Interventional Neurology
Interventional Neurology CLINICAL NEUROLOGY-
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