Exploring neighborhood inequality in female breast cancer incidence in Tehran using Bayesian spatial models and a spatial scan statistic
- Author:
Erfan AYUBI
1
;
Mohammad Ali MANSOURNIA
;
Ali Ghanbari MOTLAGH
;
Alireza MOSAVI-JARRAHI
;
Ali HOSSEINI
;
Kamran YAZDANI
Author Information
- Publication Type:Original Article
- Keywords: Breast neoplasms; Spatial analysis; Health status disparities; Iran
- MeSH: Bayes Theorem; Breast Neoplasms; Breast; Female; Health Status Disparities; Humans; Incidence; Iran; Residence Characteristics; Resource Allocation; Socioeconomic Factors; Spatial Analysis
- From:Epidemiology and Health 2017;39(1):2017021-
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: The aim of this study was to explore the spatial pattern of female breast cancer (BC) incidence at the neighborhood level in Tehran, Iran.METHODS: The present study included all registered incident cases of female BC from March 2008 to March 2011. The raw standardized incidence ratio (SIR) of BC for each neighborhood was estimated by comparing observed cases relative to expected cases. The estimated raw SIRs were smoothed by a Besag, York, and Mollie spatial model and the spatial empirical Bayesian method. The purely spatial scan statistic was used to identify spatial clusters.RESULTS: There were 4,175 incident BC cases in the study area from 2008 to 2011, of which 3,080 were successfully geocoded to the neighborhood level. Higher than expected rates of BC were found in neighborhoods located in northern and central Tehran, whereas lower rates appeared in southern areas. The most likely cluster of higher than expected BC incidence involved neighborhoods in districts 3 and 6, with an observed-to-expected ratio of 3.92 (p < 0.001), whereas the most likely cluster of lower than expected rates involved neighborhoods in districts 17, 18, and 19, with an observed-to-expected ratio of 0.05 (p < 0.001).CONCLUSIONS: Neighborhood-level inequality in the incidence of BC exists in Tehran. These findings can serve as a basis for resource allocation and preventive strategies in at-risk areas.
