Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice
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Published:2021-01-04
Issue:1
Volume:21
Page:
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ISSN:1471-2458
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Container-title:BMC Public Health
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language:en
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Short-container-title:BMC Public Health
Author:
Azimi Ali, Bagheri Nasser, Mostafavi Sayyed Mostafa, Furst Mary Anne, Hashtarkhani Soheil, Amin Fateme Hashemi, Eslami Saeid, Kiani Fatemeh, VafaeiNezhad Reza, Akbari Toktam, Golabpour Amin, Kiani BehzadORCID
Abstract
Abstract
Background
Response time to cardiovascular emergency medical requests is an important indicator in reducing cardiovascular disease (CVD) -related mortality. This study aimed to visualize the spatial-time distribution of response time, scene time, and call-to-hospital time of these emergency requests. We also identified patterns of clusters of CVD-related calls.
Methods
This cross-sectional study was conducted in Mashhad, north-eastern Iran, between August 2017 and December 2019. The response time to every CVD-related emergency medical request call was computed using spatial and classical statistical analyses. The Anselin Local Moran’s I was performed to identify potential clusters in the patterns of CVD-related calls, response time, call-to-hospital arrival time, and scene-to-hospital arrival time at small area level (neighborhood level) in Mashhad, Iran.
Results
There were 84,239 CVD-related emergency request calls, 61.64% of which resulted in the transport of patients to clinical centers by EMS, while 2.62% of callers (a total of 2218 persons) died before EMS arrival. The number of CVD-related emergency calls increased by almost 7% between 2017 and 2018, and by 19% between 2017 and 2019. The peak time for calls was between 9 p.m. and 1 a.m., and the lowest number of calls were recorded between 3 a.m. and 9 a.m. Saturday was the busiest day of the week in terms of call volume. There were statistically significant clusters in the pattern of CVD-related calls in the south-eastern region of Mashhad. Further, we found a large spatial variation in scene-to-hospital arrival time and call-to-hospital arrival time in the area under study.
Conclusion
The use of geographical information systems and spatial analyses in modelling and quantifying EMS response time provides a new vein of knowledge for decision makers in emergency services management. Spatial as well as temporal clustering of EMS calls were present in the study area. The reasons for clustering of unfavorable time indices for EMS response requires further exploration. This approach enables policymakers to design tailored interventions to improve response time and reduce CVD-related mortality.
Funder
Mashhad University of Medical Sciences
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
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