Seyed Mehdi Hosseini - Student Research Committee, Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran
Masoud Parvin - Student Research Committee, Department of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran
Payam Shokri - Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
Milad Fadaie - Department of Biotechnology, Hamadan University of Medical Sciences, Hamadan, Iran
Bahman Ghaytasi - Department of Public Health and Disease Prevention and Control Center, Health Deputy, Kurdistan University of Medical Sciences, Sanandaj, Iran
Manoochehr Khondabi - Student Research Committee, Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran
Meysam Olfatifar - Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran
Ebrahim Chavoshi - Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran

Background: Spatial analysis is one of the required tools of epidemiology and public health sciences. This study intends to detect significant clusters of breast cancer cases in Kurdistan Province, Iran.Methods: We obtained data that pertained to breast cancer cases during ۲۰۰۵-۲۰۱۴ from the Health Deputy at Kurdistan University of Medical Sciences. After application of spatial scan statistics to detect the purely spatial (aggregation of cases in particular locations of space) and space-time (diseases clusters in space that depend on the time period) clusters, we calculated the population attribution risk (%) values to better distinguish the detected clusters.Results: We observed that the second secondary purely spatial cluster (P=۰.۰۰۵۱) had the highest population attribution risk (%) of ۳.۸ and the primary space-time unadjusted cluster (P=۰.۰۰۱۹) had the lowest population attribution risk (%) of ۰.۶۷ of all the detected clusters. Before we applied the adjustment, both the space-time and purely spatial clusters had similar locations. However, after adjustment for age, the space-time clusters location shifted and population attribution risk (%) values changed (between ۰.۰۲ and ۰.۴).Conclusion: Population attribution risk (%) value differences and clusters’ temporal and spatial variations before and after adjustments can represent disease interventions impact. Additional studies should be conducted to strengthen the registering and reporting system to determine other influencing factors.