GEOSPATIAL ANALYSIS OF TERRITORIAL ASSESIBILITY OF THE AMBULANCE EMERGENCY STATION COVERAGE OF THE WITH ACUTE CORONARY SYNDROME INCIDENTS IN PAVLODAR (KAZAKHSTAN)
Background: Diseases of the cardiovascular system (CVD) remain the leading cause of death in the world today. Nowadays heart disease accounts for 16 per cent of all deaths in the world. This indicator in the Republic of Kazakhstan annually takes a leading position. Among CVDs, ischemic heart disease (IHD) remains the leading cause of death. Half of the patients die at the pre-hospital stage, without waiting for medical help, and many survivors become disabled. The time factor plays a very important role in the treatment of acute myocardial infarction (AMI).
The purpose of the study is to conduct a spatial analysis in order to determine the territorial availability of emergency cardiac care in Pavlodar city, taking into account the time, and using a geographic information system.
Materials and methods: Based on the number of the calls received at Pavlodar Emergency station for the period from 1st August 2017 to 30th July 2018, a retrospective analysis of 2053 cases of Acute Coronary Syndrome with and without ST segment elevation was carried out.
Spatial analysis and Network Analyst were conducted on QGIS 3.16 (Hannover) to determine the density of calls with acute coronary syndrome (e.g. to find 10, 15, and 20-minute areas of accessibility). Tools such as the Hot Spot Analysis and heat map were also used to identify a square kilometer congestion of calls and Kernel Density. That tool calculated a magnitude-per-unit area from point or polyline features using a kernel function to fit a smoothly tapered surface to each point or polyline. New Service Area tool creates a region that encompasses all accessible streets (e.g. streets within specified impedance). Statistical significance was set at the 95% confidence level.
Results: We found clusters in the largest cluster of calls, marked with red and orange colors, which, like the heat map analysis, corresponded to densely built-up areas. Thus, using Kernel density analysis, we identified 6 separate clusters with the call density of more than 42 calls per km2: four clusters located in the northwest, north, and northeast of the city, and two clusters located in the southwest and southeast of the city. From the rest of the city, represented by multi-storey houses, there were received between 18.8 and 32.8 calls per km2. About 18 calls per square kilometer were received from the outskirts of the city and from areas that were mostly of private sector.
To the areas densely built up with multi-storey buildings, as well as cluster plots, the ambulance can arrive within 5 minutes from the moment the call is received. The areas with low call density, an emergency medical services reaches within 10 minutes. Outskirts of the city and the suburbs can be served within 15 minutes.
Conclusion: Based on the data presented above, it is possible to assume that additional research is needed using geographic information systems.
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Abiltaev A., Myssayev A., Abiltaeva A., Prilutskaya M., Zhagiparova Zh., Shaltynov A., Konabekov B., Jamedinova U., Zhussupov S. Geospatial Analysis of Ambulance Station Coverage of the Acute Coronary Syndrome Incidents in Pavlodar (Kazakhstan) // Nauka i Zdravookhranenie [Science & Healthcare]. 2022, (Vol.24) 1, pp. 30-38. doi:10.34689/SH.2022.24.1.004Похожие публикации:
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