基于地理信息系统的克山病病情综合评估

Comprehensive assessment of Keshan disease based on a geographic information system

目的对2007年克山病15个监测省（直辖市、自治区）进行病情空间区划，为制订防控策略提供依据。方法从2007年全国克山病监测数据中选择5个指标对15个监测省（市、自治区）进行病情评估，再用主成分分析法计算反映克山病病情的综合指标得分，采用标准差分段法，将综合指标得分分成轻、中、重病区，并在地理信息系统（GIS）中进行克山病空间区划显示。结果提取的3个主成分从不同维度反映克山病病情，包含全体指标信息的88．128％。第一主成分与总检出率、慢型克山病检出率、潜在型克山病检出率之间的关系较密切，第二主成分与各省受威胁人数的关系较密切，第三主成分与新发病例检出率之间的关系较为密切。从综合指标得分看，克山病重病区分布在甘肃、吉林、黑龙江、河北、辽宁5省，中病区分布在内蒙古、山东、湖北、四川4省（自治区），轻病区分布在陕西、山西、云南、河南、贵州、重庆6省（市）。结论将GIS引入到克山病监测。为观察克山病病情空间分布提供了一种简便而直接的方法，为因地制宜地监控克山病流行的重点地区提供了依据。

Objective To explore the spatial distribution of Keshan disease（KD） in the 15 surveillance provinces （municipalities, autonomous regions） and to provide the basis for the development of prevention and control strategies. Methods Based on the KD surveillance data of the 15 provinces in 2007, five indicators were selected. Moreover, a comprehensive indicator score to assess KD of different areas was made through the method of principle components analysis, which was applied for regionalization of the KD areas by the subsection method of standard deviation in whole China. The KD areas were divided into mild, moderate and severe endemic areas. The spatial distribution feature of the comprehensive indicator score was displayed by using geographic information system （GIS）. Results The three principal components contained 88.123% information of all the selected indicators, the first principal component had a close relationship with total KD detection rate, chronic KD detection rate and latent KD detection rate; the second principal component had a close relationship with the threatened number in KD areas, and the third principal component had a close relationship with new KD detection rate; the comprehensive indicator indicates that Gansu, Jilin, I-Ieilongjiang, Hebei, and Liaoning provinces were serious prevalent KD areas; Inner Mongolia, Shandong, Hubei, Sichuang provinces（autonomous region） were moderate prevalent KD areas, and Shanxi, Shaanxi, Yunnan, Henan, Guizhou, Chongqing provinces（municipality） were mild prevalent KD areas. Conclusions The introduction of the GIS to Keshan disease monitoring, provides a convenient and direct method to observe the spatial distribution of the disease, and the results point out the key areas for further KD surveillance according to local conditions.