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dc.contributor.authorRhudy, James P., Jr.
dc.contributor.authorAlexandrov, Anne W.
dc.contributor.authorRike, Joseph
dc.contributor.authorBryndziar, Tomas
dc.contributor.authorMaleki, Ana Hossein Zadeh
dc.contributor.authorSwatzell, Victoria
dc.contributor.authorDusenbury, Wendy L.
dc.contributor.authorMetter, E. Jeffrey
dc.contributor.authorAlexandrov, Andrei, V.
dc.date.accessioned2018-10-25T19:29:51Z
dc.date.available2018-10-25T19:29:51Z
dc.date.issued2018-10
dc.identifier.citationRhudy, James P., Jr.; Alexandrov, Anne W.; Rike, Joseph; Bryndziar, Tomas; Maleki, Ana Hossein Zadeh; Swatzell, Victoria; Dusenbury, Wendy L.; Metter, E. Jeffrey; Alexandrov, Andrei, V. 2018. Geospatial visualization of Mobile Stroke Unit dispatches: a method to optimize service performance. Intervent Neurol, vol. 7:no. 6:pp 464-470en_US
dc.identifier.issn1664-9737
dc.identifier.otherWOS:000447256900022
dc.identifier.urihttps://doi.org/10.1159/000490581
dc.identifier.urihttp://hdl.handle.net/10057/15620
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipPCORI subaward for Benefits of Stroke Treatment Delivered Using a Mobile Stroke Unit Compared to Standard Management by Emergency Medical Services (BEST-MSU) and Asissi Foundation grant for Respond, Evaluate, Cure, Heal: Mobile Stroke Unit (REACH-MOST).en_US
dc.language.isoen_USen_US
dc.publisherKarger Publishersen_US
dc.relation.ispartofseriesIntervent Neurol;v.7:no.6
dc.subjectAcute ischemic strokeen_US
dc.subjectEmergency medical servicesen_US
dc.subjectMobile stroke uniten_US
dc.subjectService area analysisen_US
dc.subjectSpatial analysisen_US
dc.titleGeospatial visualization of Mobile Stroke Unit dispatches: a method to optimize service performanceen_US
dc.typeArticleen_US
dc.rights.holder© 2018 S. Karger AG, Baselen_US


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