[Study on the quantified indices to describe the distributional status of diseases in the spatial point pattern analysis].
OBJECTIVE
To study the quantified indices for describing the distributional status of diseases in the spatial point pattern analysis, and provide the a statistic in disease prevention and control.


METHODS
G function, F function, J function and K function were summarized based on the inter-case distances from the view of spatial point pattern analysis. Through the introduction of the basic principles, these were used to analyze the data of acute schistosomiasis in the Guichi District, Chizhou City, Anhui province, with the study distances being from 0 to 3000 meters with 50-meter intervals. The findings were also validated by means of spatial moving scan window performed in SaTScan software.


RESULTS
A total of 83 cases of acute schistosomiasis identified in Guichi District, and the point map showed that these cases were mainly distributed around the Yangtze and Qiupu rivers. The computational methods and characteristics of the four quantified indices were obtained. These acute schistosomiasis cases were also explored by using these indices, and the results showed that C and K functions were above 95% confidence interval. While, F and J functions were below 95% confidence interval. Ml these four indices showed that spatial clustering existed in the acute cases, which was consistent with the results of spatial moving scan window method. The latter method also found a most likely cluster, the coordinate of the circle center is (30.65 N, 117.44 E), radius is 2.69 km, and relative risk is 12.78 (DIR = 32. 80, P = 0. 0001).


CONCLUSIONS
The quantified indices to describe the distributional status of diseases have not only solved the obstacle that spatial point pattern map which could only be analyzed qualitatively, but also supplied a theoretical foundation to deepen spatial clustering analysis.


OBJECTIVE
To study the quantified indices for describing the distributional status of
Zhang
Zhi-jie
ZJ
Health, Fudan University, the Key Laboratory on Public Health Safety, Ministry of Education, Shanghai, China.
Peng
Wen-xiang
WX
Zhou
Yi-biao
YB
Chen
Geng-xin
GX
Jiang
Qing-wu
QW
D015991:Epidemiologic Measurements; D015233:Models, Statistical; D013029:Space-Time Clustering