Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice
Ali Azimi1, Nasser Bagheri2, Sayyed Mostafa Mostafavi1
1Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Insights
Emergency response times for cardiovascular disease (CVD) calls show spatial and temporal clusters. Understanding these patterns can help improve emergency medical services (EMS) and reduce CVD mortality.
Area of Science:
- Public Health
- Emergency Medicine
- Geographic Information Systems (GIS)
Background:
- Rapid response to cardiovascular disease (CVD) emergencies is critical for reducing mortality.
- Spatial-temporal analysis of emergency medical service (EMS) response times can identify areas for improvement.
Purpose of the Study:
- To visualize the spatial-temporal distribution of response, scene, and call-to-hospital times for CVD emergencies.
- To identify clusters of CVD-related emergency calls and associated time metrics.
Main Methods:
- Cross-sectional study in Mashhad, Iran (August 2017 - December 2019).
- Spatial and classical statistical analyses computed EMS response times.
- Anselin Local Moran's I identified clusters in call patterns and time metrics at the neighborhood level.
Main Results:
- Over 84,000 CVD calls; 61.64% led to transport, 2.62% died before arrival.
- Call volume increased significantly from 2017-2019, peaking 9 PM - 1 AM and on Saturdays.
- Significant spatial clusters of CVD calls found in southeastern Mashhad, with notable variations in scene-to-hospital times.
Conclusions:
- GIS and spatial analysis offer valuable insights for EMS management.
- Spatial and temporal clustering of EMS calls and response times were observed.
- Further research into the causes of unfavorable time index clusters is needed to inform targeted interventions for reducing CVD mortality.
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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