Spatial Epidemiological Analysis of Keshan Disease in China. Issue 1 (12th September 2022)

• Record Type: Journal Article
• Authors: Jia, Yuehui; Han, Shan; Hou, Jie; Wang, Ruixiang; Li, Guijin; Su, Shengqi; Qi, Lei; Wang, Yuanyuan; Du, Linlin; Sun, Huixin; Hao, Shuxiu; Feng, Chen; Wang, Yanan; Liu, Xu; Zou, Yuanjie; Zhang, Yiyi; Li, Dandan; Wang, Tong
• Is Part Of: Annals of global health. Volume 88:Issue 1(2022)
• Publication Date: 2022-09-12
• Subjects: Keshan disease -- spatial epidemiology -- precision prevention and control -- spatial autocorrelation -- spatial regression; 362.1
• DOI: 10.5334/aogh.3836
• Languages: English
• ISSNs: 2214-9996

Abstract

Objectives: Few researchers have studied the national prevalence of Keshan disease (KD) in China using spatial epidemiological methods. This study aimed to provide geographically precise and visualized evidence for the strategies for KD prevention and control. Methods: We surveyed and analyzed 237, 000 people in 280 out of 328 KD-endemic counties (85.4%) in mainland China using a design of key investigation based on case-searching in 2015–2016. ArcGIS version 9.0 was used for spatial autocorrelation analysis, spatial interpolation analysis and spatial regression analysis. Results: Global autocorrelation analysis showed that global clustering of latent Keshan disease (LKD) prevalence was noted (Moran's I = 0.22, Z = 7.06, and P < 0.0001), no global clustering of chronic Keshan disease (CKD) prevalence (Moran's I = 0.03, Z = 1.10, and P = 0.27) was observed. Spatial regression analysis showed that LKD prevalence was negatively correlated with per capita disposable income ( t = –4.36, P < 0.0001). Local autocorrelation analysis at the county level effectively identified the cluster areas of LKD prevalence in the provinces of Shaanxi, Gansu, Shanxi, Inner Mongolia, and Jilin. The high-high cluster areas should be given priority for precision prevention and control of Keshan disease. Conclusions: This spatial epidemiological study revealed that LKD prevention and control should be strengthened in areas with high values of clustering. Our findings provided spatially, geographically precise and visualized evidence for prioritizing KD prevention and control.