Exploratory disease mapping: kriging the spatial risk function from regional count data.
Exploratory disease mapping: kriging the spatial risk function from regional count data.
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DOI：
10.1186/1476-072x-3-18
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发表时间：
2004-08-26
影响因子：
4.9
通讯作者：
Berke, Olaf
中科院分区：
文献类型：
--
作者：
Berke, Olaf
背景：关于疾病测绘的文献有相当大的兴趣，以将疾病发生或疾病风险的估计从区域数据库插入到连续的表面上。除了许多可用的内插技术外，克里格法的地质统计学方法也被使用过，但也受到了批评。结果：为了绕过这些批评，人们可以使用克里金法以及已经平滑的区域估计，其中平滑是基于经验贝叶斯估计，也称为收缩估计。经验贝叶斯步骤的优势是将不稳定的、往往是极端的估计缩小到全球或局部平均水平，并通过借入实力对方差产生稳定作用。通过选择适当的克里格法，可以防止负内插。所提出的映射方法被应用于北卡罗来纳州小岛屿发展中国家的数据实例以及来自兽医流行病学的实例数据集。小岛屿发展中国家的数据没有建立空间趋势模型。空间内插是基于普通克里格法的。第二个例子演示了当所研究的现象表现出空间趋势并且内插基于泛克里格法时的方法。结论：区域估计的内插克服了面积偏差问题，所得到的等值线图比脉络图更容易阅读。平滑的经验贝叶斯估计与流行病学的内部标准化有关。因此，提出的概念很容易传达给地图用户。
BACKGROUND: There is considerable interest in the literature on disease mapping to interpolate estimates of disease occurrence or risk of disease from a regional database onto a continuous surface. In addition to many interpolation techniques available the geostatistical method of kriging has been used but also criticised. RESULTS: To circumvent these critics one may use kriging along with already smoothed regional estimates, where smoothing is based on empirical Bayes estimates, also known as shrinkage estimates. The empirical Bayes step has the advantage of shrinking the unstable and often extreme estimates to the global or local mean, and also has a stabilising effect on variance by borrowing strength, as well. Negative interpolates are prevented by choice of the appropriate kriging method. The proposed mapping method is applied to the North Carolina SIDS data example as well as to an example data set from veterinary epidemiology. The SIDS data are modelled without spatial trend. And spatial interpolation is based on ordinary kriging. The second example is included to demonstrate the method when the phenomenon under study exhibits a spatial trend and interpolation is based on universal kriging. CONCLUSION: Interpolation of the regional estimates overcomes the areal bias problem and the resulting isopleth maps are easier to read than choropleth maps. The empirical Bayesian estimate for smoothing is related to internal standardization in epidemiology. Therefore, the proposed concept is easily communicable to map users.