Spatial Distribution Analysis of Hypertension Patients: A Case Study

Introduction: Hypertension is a major global public health challenge, closely linked to serious complications such as stroke and heart attack. Spatial analysis, as a modern tool in health geography, provides insights into disease distribution and its relationship with environmental and demographic factors. This study aimed to investigate the spatial distribution of hypertension patients in the Abotaleb neighborhood of Ardabil city and to offer spatially informed recommendations for improving access to healthcare services . Method: This descriptive -analytical study utilized secondary data from medical records of 600 patients diagnosed with hypertension, aged 36 to 87, between 2018 and 2022. Patients’ addresses were geocoded using Google Maps and OpenStreetMap and analyzed in ArcGIS. Analytical techniques included Kernel Density Estimation, Hot Spot Analysis, Shortest Distance to Facilities, Average Nearest Neighbor Index, Global and Local Moran’s I, Buffer Analysis, Service Area Network, and the Operational Radius Method. To reduce geolocation errors, ambiguous or incomplete records were reviewed and removed. A consistent coordinate system was maintained throughout the analysis . Results: The findings revealed that the spatial pattern of hypertension was clustered. Initially, clusters were concentrated in the central and northern parts of the neighborhood but later spread to the southern and western areas. Higher densities were linked to proximity to healthcare centers and population concentration. The disease was more prevalent among women (62.7%), with most cases occurring in the 56 –65 age group. Additionally, 68% of patients had at least one comorbid condition, such as diabetes or obesity. The highest number of cases was recorded in 2021, possibly due to enhanced screening and lifestyle changes during the COVID -19 pandemic. These findings align with global reports, including those from the World Health Organization . Conclusion: This study highlights the need for optimized healthcare facility locations, targeted screening for high -risk groups, and integrated interventions in vulnerable areas. Despite limitations related to clinical data and geographic scope, the results provide a valuable foundation for localized health planning and reducing inequalities in healthcare access .