Application of GIS Spatial Analysis and Scanning Statistics in the Gynecological Cancer Clustering Pattern and Risk Screening: A Case Study in Northern Jiangxi Province, China
Authors Wan Z, Wang Y, Deng C
Received 6 May 2020
Accepted for publication 28 July 2020
Published 10 August 2020 Volume 2020:13 Pages 1079—1093
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 6
Editor who approved publication: Professor Marco Carotenuto
Zhiwei Wan,1,* Yaqi Wang,2,* Chunhong Deng2
1School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, People’s Republic of China; 2Jiangxi Provincial Cancer Center, Jiangxi Provincial Cancer Hospital, Nanchang 330029, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Zhiwei Wan
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, People’s Republic of China
Jiangxi Provincial Cancer Center, Jiangxi Provincial Cancer Hospital, Nanchang 330029, People’s Republic of China
Objective: The incidence of gynecological cancer is high in China, and the effects of related treatments and preventive measures need to be improved.
Methods: This study uses GIS spatial analysis methods and a scanning statistical analysis to study the major gynecological cancers in northern Jiangxi Province from 2016 to 2018.
Results: The incidence and spatial pattern of cervical cancer, ovarian cancer, and uterine cancer had agglomeration characteristics and changes during the study period. The gynecological cancer had a spatial autocorrelation and agglomeration in its spatial pattern. The Moran’s Index of the overall gynecological cancer incidence rate was 0.289 (p = 0.005). Ripley’s L(d) function showed that the agglomeration radius was between 51.40 and 52.82 km. The results of the kernel density estimation showed that the cases of gynecological cancer were concentrated in the central and northeastern areas of the study area. The overall county-level incidence of gynecological cancer varied from 0.26 to 11.14 per 100,000. The results of the gravity center analysis showed that the spatial distribution of the gravity center point of gynecological cancer had moved toward the east during the past three years. The results of a hotspot analysis showed that there were five hotspot areas that had gynecological cancers. The most likely clusters of gynecological cancer at the county level in northern Jiangxi Province were distributed in the adjacent areas of Jiujiang, Yichun, and Nanchang, with a relative risk of 1.85.
Conclusion: The research shows that GIS can display the distribution of cancer cases and can use spatial analysis methods and scanning statistical techniques to obtain key areas of cancer incidence. These results can provide data and key areas for the formulation of regional public health policies and provide recommendations for cancer screening and the rational allocation of health resources.
Keywords: gynecological cancer, cervical cancer, ovarian cancer, uterine cancer, spatial pattern, spatiotemporal heterogeneity, risk scanning, GIS spatial analysis
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