Author/Authors :
Olfatifar Meysam نويسنده Student Research Committee,Hamadan University of Medical Sciences,Hamadan,Iran , Parvin Masoud نويسنده Student Research Committee,Hamadan University of Medical Sciences,Hamadan,Iran , Hosseini Seyed Mehdi نويسنده Student Research Committee, Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran , Shokri Payam نويسنده Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran , Fadaie Milad نويسنده Department of Biotechnology, Hamadan University of Medical Sciences, Hamadan, Iran , Ghaytasi Bahman نويسنده Department of Public Health and Disease Prevention and Control Center, Health Deputy, Kurdistan University of Medical Sciences, Sanandaj, Iran , Khondabi Manoochehr نويسنده Student Research Committee, Department of Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran , Chavoshi Ebrahim نويسنده 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.
