Joint disease mapping of breast, uterine, and ovarian cancers in cities of Isfahan Province from 2005 to 2010 using spatial shared component model

• Main Authors: Marzieh Nasr, Behzad Mahaki, Mehdi Kargar, Pejman Aghdak
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2021-01-01
• Series: International Journal of Preventive Medicine
• Subjects: breast neoplasms; mapping; ovarian neoplasms; uterine neoplasms
• Online Access: http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2021;volume=12;issue=1;spage=65;epage=65;aulast=Nasr

Background: Breast, uterine, and ovarian cancers are the most prevalent types of cancers among women. The aim of this study was to estimate the relative risk of these cancers and recognizing spatial patterns of their shared and specific risk factors in cities of Isfahan province, one of the most populated provinces of Iran, using spatial shared component model. Methods: In this ecological study, the population consisted of all the registered patients having breast, ovarian, and uterine cancers in the cities of Isfahan from 2005 to 2010. In order to simultaneously analyze these diseases and clarify common and specific patterns of disease, spatial Shared component model was applied. Model fitting was done using Bayesian inference in OpenBUGS software. Results: The highest relative risk of breast cancer was seen in Isfahan (4.96), Shahreza (2.37), Dehaghan (5.01), Lenjan (2.33), and Najafabad (2.68), respectively. For ovarian cancer, Isfahan (4.29), Shahreza (2.51), Dehaghan (5.02), Lenjan (2.06), Najafabad (2.00), and Borkhar (2.39) had the highest relative risk, respectively. However, no significant difference was seen among the cities for uterine cancer (P > 0.05). Conclusions: Since ovarian and uterine cancers are the less prevalent disease in comparison with breast cancer, the preciseness of these estimates were improved remarkably over simple mapping models. Based on this model, the estimates were done according to the correlation between the diseases. After recognizing the spatial patterns of the shared and specific risk factors and reviewing of previous studies, regardless of risk factors data, environmental pollution arises as a potential risk factor.