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.
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http://dx.doi.org/10.1186/s12889-020-10064-1 | DOI Listing |
J Med Internet Res
January 2025
College of Nursing Science, Kyung Hee University, Seoul, Republic of Korea.
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Nanomaterials Laboratory, Department of Polymers and Functional Materials, CSIR-Indian Institute of Chemical Technology, Uppal Road, Tarnaka, Hyderabad 500 007, India.
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Department of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
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Biosensors and Nanobiotechnology Laboratory, Chemical Sciences, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE 1410, Brunei Darussalam.
The field of healthcare diagnostics is navigating complex challenges driven by evolving patient demographics and the rapid advancement of new technologies worldwide. In response to these challenges, these biosensors offer distinctive advantages over traditional diagnostic methods, such as cost-effectiveness, enhanced specificity, and adaptability, making their integration with point-of-care (POC) platforms more feasible. In recent years, aptasensors have significantly evolved in diagnostic capabilities through the integration of emerging technologies such as microfluidics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems, wearable devices, and machine learning (ML), driving progress in precision medicine and global healthcare solutions.
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Global Emergency Medicine Innovation and Implementation (GEMINI) Research Center, Duke University, Durham, North Carolina, United States of America.
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