Document Type : Article extracted from dissertations
Authors
1
M.A student of geography and urban planning, Department of Geography, Faculty of Social Science, University of Mohaghegh Ardabili, Ardabil, Iran.
2
Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil Iran
3
Department of Geography, Faculty of Social Science, University of Mohaghegh Ardabili, Ardabil, Iran.
4
Department of Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
Abstract
Abstract
Background and Objective: Diabetes is a major chronic non-communicable disease with growing global and national prevalence, imposing substantial burdens on healthcare systems and negatively affecting patients' quality of life. As a modern analytical approach, spatial analysis offers valuable insights into the geographic distribution of diseases and supports the development of targeted interventions. This study aims to fill a gap in neighborhood-scale research by employing advanced spatial tools to explore the spatial distribution of diabetes, emphasizing equity in access to healthcare services.
Methodology: This applied, descriptive-analytical research examined the spatial and temporal distribution of diabetic cases in the Abotaleb neighborhood of Ardabil between 2018 and 2022. Data from 235 patients recorded at the local health center were analyzed using Google Maps and ArcGIS. The study utilized techniques such as Kernel Density Estimation, Hot Spot Analysis (Getis-Ord Gi*), Average Nearest Neighbor (ANN), and proximity analysis.
Findings and Conclusion: The results indicated that the spatial distribution of diabetic patients followed a clustered pattern, with the highest concentration observed in the central and southern parts of the neighborhood, comprising approximately 30% of all patients. In the later years of the study, the clusters expanded toward the northern and western areas. The hot spot analysis showed that about 5% of patients were located in zones with 99% confidence, 10% in 95% confidence zones, and 15% in 90% confidence zones. Additionally, nearly 25% of patients were found in cold spots, indicating areas of lower patient density. Proximity and spatial neighborhood analyses revealed that some areas faced significant limitations in access to healthcare services. Age was also identified as a key variable contributing to the formation of spatial clusters. These findings underscore the need for revisiting the location of healthcare facilities and incorporating spatial analysis into health policy planning.
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