Geoinformatics, spatial epidemiology, and public health

Tsatsaris, Andreas; Kalogeropoulos, Kleomenis; Stathopoulos, Nikolaos

doi:10.1016/b978-0-323-98983-1.00002-8

Cited by 1 publication

(1 citation statement)

References 55 publications

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“…Spatial analysis is a particularly important component of geographical information systems (GIS), because the method includes all transformations and manipulations that can be applied to geographical data to reveal spatial patterns and anomalies that are not readily apparent, thus rendering the dataset potentially more valuable in supporting decision-making. This approach includes, spatial autocorrelation analysis, spatial regression models, cluster detection techniques, spatial interpolation, spatial join and overlay analysis as mentioned by Tsatsaris et al (2023).…”

Section: Geospatial Epidemiology Of Coronary Artery Disease Treated W...mentioning

confidence: 99%

Geospatial epidemiology of coronary artery disease treated with percutaneous coronary intervention in Crete, Greece

Melidoniotis,

Kalogeropoulos,

Tsatsaris

et al. 2024

Geospat Health

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Coronary artery disease (CAD) constitutes a leading cause of morbidity and mortality worldwide. Percutaneous coronary intervention (PCI) is indicated in a significant proportion of CAD patients, either to improve prognosis or to relieve symptoms not responding to optimal medical therapy. Thus the annual number of patients undergoing PCI in a given geographical area could serve as a surrogate marker of the total CAD burden there. The aim of this study was to analyze the potential, spatial patterns of PCItreated CAD patients in Crete. We evaluated data from all patients subjected to PCI at the island’s sole reference centre for cardiac catheterization within a 4-year study period (2013-2016). The analysis focused on regional variations of yearly PCI rates, as well as on the effect of several clinical parameters on the severity of the coronary artery stenosis treated with PCI across Crete. A spatial database within the ArcGIS environment was created and an analysis carried out based on global and local regression using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results revealed significant inter-municipality variation in PCI rates and thus potentially CAD burden, while the degree and direction of correlation between key clinical factors to coronary stenosis severity demonstrated specific geographical patterns. These preliminary results could set the basis for future research, with the ultimate aim to facilitate efficient healthcare strategies planning.

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Section: Geospatial Epidemiology Of Coronary Artery Disease Treated W...mentioning

confidence: 99%