Spatial Cluster Analysis of Stomach Neoplasms in the Center of Iran Based on a Population-Based Study, 2009-2014: Application of the Poisson-Based Probability Model

Overview

Journal Int J Prev Med

Date 2022 Apr 8

PMID 35392311

Authors

Rahmatollah Moradzadeh

Mohsen Shamsi

Saeed Heidari

Affiliations

Soon will be listed here.

Abstract

Background: A better understanding of the spatial pattern of stomach cancer can be helpful in the implementation of preventive strategies. This study is one of the first studies to establish a study unit based on the smallest possible size for cluster analysis. This study includes a novel evaluation of spatial differences in different geographical areas of Arak. The present study seeks to discover the spatial clusters of stomach cancer from 2009 to 2014 in Arak.

Methods: All of the addresses of the stomach cancer cases were geocoded. After that, the number of stomach cancer cases in each census block was calculated to be entered in the SaTScan software. A discrete Poisson-based probability model was used to analyze this cluster.

Results: In Arak, there are 5502 census blocks with a population of about 526,182. The number of identified and registered stomach cancer cases with an acceptable residence address was 392. Totally, 11 stomach cancer clusters were established in the area of Arak, from these; two clusters were detected statistically significant. Relative risks of the first and second clusters were 1.75 ( = 0.01) and 17.60 ( = 0.04) and those are located within the radius of 1.73 and 0.085 km, respectively.

Conclusions: Our results have confirmed that two areas are at a higher risk than others. However, based on the results of this study, community-based interventions in certain geographical areas can be designed to reduce and control the incidence of stomach cancer.

Citing Articles

Trends of colorectal cancer in a central area of Iran during 2009-2014. An application of joinpoint regression.

Moradzadeh R, Mirgaloybayat S Contemp Oncol (Pozn). 2022; 26(2):150-154.

PMID: 35903203 PMC: 9319183. DOI: 10.5114/wo.2022.118213.

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