Document Type : Original Article(s)

Authors

• Seyed Mehdi Hosseini ¹
• Masoud Parvin ²
• Payam Shokri ³
• Milad Fadaie ⁴
• Bahman Ghaytasi ⁵
• Manoochehr Khondabi ¹
• Meysam Olfatifar ^{3, 1}
• Ebrahim Chavoshi ⁶

¹ Student Research Committee, Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran

² Student Research Committee, Department of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran

³ Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran

⁴ Department of Biotechnology, Hamadan University of Medical Sciences, Hamadan, Iran

⁵ Department of Public Health and Disease Prevention and Control Center, Health Deputy, Kurdistan University of Medical Sciences, Sanandaj, Iran

⁶ Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran

Abstract

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 2005-2014 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=0.0051) had the highest population attribution risk (%) of 3.8 and the primary space-time unadjusted cluster (P=0.0019) had the lowest population attribution risk (%) of 0.67 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 0.02 and 0.4).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.