Detection of clusters of a rare disease over a large territory: performance of cluster detection methods.
Detection of clusters of a rare disease over a large territory: performance of cluster detection methods.
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DOI：
10.1186/1476-072x-10-53
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发表时间：
2011-10-04
影响因子：
4.9
通讯作者：
Clavel J
中科院分区：
文献类型：
--
作者：
Goujon-Bellec S;Demoury C;Guyot-Goubin A;Hémon D;Clavel J
多年来，星团的检测一直是公众健康的重大问题。已经开发了几种检测方法，其中最著名的是圆形扫描法。本研究是在一种广泛分布的罕见疾病的背景下进行的(17年登记了7675例，分布在1895个单位)，目的是评估几种方法在现实热点集群情况下的表现。所有考虑的方法都旨在识别一组候选聚类中最可能的聚类区域，即最大化似然比函数的区域。发展了圆形和椭圆形扫描方法来检测规则形状的星团。还考虑了其他四种聚焦于不规则形状集群的方法(灵活扫描法、遗传算法方法以及双连通和最大链接空间扫描法)。通过蒙特卡罗模拟在27个备选方案下评估了方法的能力，这些方案对应于三个集群总体规模(20、45和115个预期案例)、三个集群形状(线性、U形和紧凑型)和三个相对风险值(1.5、2.0和3.0)。从这项权力研究中得出了三种情况。所有方法都未能检测到相对风险低于3.0的最小聚类。对于所有方法，检测相对风险为1.5的最大星团的能力明显更好，但最多只能捕获真正星团的一半。对于其他较大或相对风险最高的星团，标准椭圆扫描方法似乎是检测线状星团的最佳方法，而灵活扫描方法比其他方法更准确地定位U形星团。所有方法都能很好地检测出大而致密的星团，其中圆形和椭圆扫描法的检测效果更好。椭圆扫描法和灵活扫描法似乎最能在大范围内检测到一种罕见疾病的集群。然而，在所有测试的方法中，检测到相对风险低于3.0的小集群的概率仍然很低。
For many years, the detection of clusters has been of great public health interest. Several detection methods have been developed, the most famous of which is the circular scan method. The present study, which was conducted in the context of a rare disease distributed over a large territory (7675 cases registered over 17 years and located in 1895 units), aimed to evaluate the performance of several of the methods in realistic hot-spot cluster situations. All the methods considered aim to identify the most likely cluster area, i.e. the zone that maximizes the likelihood ratio function, among a set of cluster candidates. The circular and elliptic scan methods were developed to detect regularly shaped clusters. Four other methods that focus on irregularly shaped clusters were also considered (the flexible scan method, the genetic algorithm method, and the double connected and maximum linkage spatial scan methods). The power of the methods was evaluated via Monte Carlo simulations under 27 alternative scenarios that corresponded to three cluster population sizes (20, 45 and 115 expected cases), three cluster shapes (linear, U-shaped and compact) and three relative risk values (1.5, 2.0 and 3.0). Three situations emerged from this power study. All the methods failed to detect the smallest clusters with a relative risk lower than 3.0. The power to detect the largest cluster with relative risk of 1.5 was markedly better for all methods, but, at most, half of the true cluster was captured. For other clusters, either large or with the highest relative risk, the standard elliptic scan method appeared to be the best method to detect linear clusters, while the flexible scan method localized the U-shaped clusters more precisely than other methods. Large compact clusters were detected well by all methods, with better results for the circular and elliptic scan methods. The elliptic scan method and flexible scan method seemed the most able to detect clusters of a rare disease in a large territory. However, the probability of detecting small clusters with relative risk lower than 3.0 remained low with all the methods tested.