2019 International Conference of Geospatial Information and Public Health (GIPH 2019)

121 / 2019-11-04 16:52:09

Analysis of the spatial characteristics of Keshan disease in China

Keshan disease, Spatial autocorrelation, Precision prevention and control, Inverse distance weighted, Spatial regression analysis

Objective: The spatial characteristics of KD prevalence in China were analyzed to provide precise and visualized evidence for KD prevention and control.
Methods: We investigated the KD prevalence in China by combining case-searching and key investigation, and conducted spatial autocorrelation analysis, interpolation analysis and spatial regression analysis of the KD prevalence.
Results: There was no spatial aggregation of the prevalence of CKD (Moran's I=0.03, Z=10.96, P＞0.05), and there was spatial aggregation of the prevalence of LKD (Moran's I=0.22, Z=7.12, P＜0.01). Cluster analysis showed that the high-high clustering areas of the LKD were located in Gansu, Shaanxi, Shanxi, and Hebei provinces, the high-low clustering area was located in Yunnan province, and the low-high clustering areas were located in Gansu and Shanxi provinces. The hot spots were mainly located in Shanxi, Gansu and Shaanxi provinces. The results of inverse distance weighted analysis showed that the high prevalence of LKD was mainly located in some of the KD endemic counties in Shaanxi, Gansu, Shanxi, Hebei, Jilin and Yunnan provinces, while the prevalence of LKD was low in other KD endemic counties. The results of spatial regression analysis showed that there was a negative spatial correlation between the prevalence of LKD and the per capita disposable income of rural people (t =﹣5.187, P< 0.05).
Conclusion: There was global spatial clustering for LKD, but not for CKD in China. The aggregation and hot spots areas of LKD were mainly located in endemic counties of Shaanxi, Gansu, Shanxi and Hebei provinces with low per capita disposable income of rural people.