A simulation study of three methods for detecting disease clusters.
A simulation study of three methods for detecting disease clusters.
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DOI：
10.1186/1476-072x-5-15
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发表时间：
2006-04-12
影响因子：
4.9
通讯作者：
Skrondal, Anders
中科院分区：
文献类型：
--
作者：
Aamodt, Geir;Samuelsen, Sven O;Skrondal, Anders
背景：聚类检测是空间流行病学的重要组成部分，因为它可以帮助识别与疾病相关的环境因素，从而指导疾病的病因调查。本文研究了三种适用于局部空间聚类检测的方法：(1)空间扫描统计（SaTScan），(2)广义加性模型（GAM）和(3)贝叶斯疾病制图（BYM）。我们进行了模拟研究来比较这两种方法。初步选择了7个不同形状的地理集群作为高危区。与正常风险区相比，考虑了这些地区相对风险程度的不同情况。对于每种情况，根据每个簇的灵敏度、特异性和正确分类的百分比来评估方法的性能。结果：性能取决于相对风险，但所有方法一般适用于识别相对风险大于1.5的聚类。然而，很难发现相对风险较低的群集。GAM方法具有最高的灵敏度，但相对较低的特异性导致对簇面积的高估。BYM和SaTScan方法都能很好地工作。不规则形状的星团比圆形的星团更难检测。结论：基于我们的模拟，我们得出结论，这些方法在检测空间集群的能力上存在差异。在选择合适的方法时，应考虑不同的方面，如假设的空间簇的大小和形状以及灵敏度和特异性的相对重要性。一般来说，BYM方法似乎更适合相对风险较高的局部聚类检测，而SaTScan方法似乎更适合相对风险较低的局部聚类检测。需要对GAM方法进行调优（使用交叉验证）以获得满意的结果。
BACKGROUND: Cluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized additive models (GAM) and (3) Bayesian disease mapping (BYM). We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster.RESULTS: The performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters.CONCLUSION: Based on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation) to get satisfactory results.