Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
Department of Mathematics, National Chung Cheng University, Zhunan, Taiwan.
Biometrics. 2020 Jun;76(2):403-413. doi: 10.1111/biom.13145. Epub 2019 Nov 6.
Mapping of disease incidence has long been of importance to epidemiology and public health. In this paper, we consider identification of clusters of spatial units with elevated disease rates and develop a new approach that estimates the relative disease risk in association with potential risk factors and simultaneously identifies clusters corresponding to elevated risks. A heterogeneity measure is proposed to enable the comparison of a candidate cluster and its complement under a pair of complementary models. A quasi-likelihood procedure is developed for estimating the model parameters and identifying the clusters. An advantage of our approach over traditional spatial clustering methods is the identification of clusters that can have arbitrary shapes due to abrupt or noncontiguous changes while accounting for risk factors and spatial correlation. Asymptotic properties of the proposed methodology are established and a simulation study shows empirically sound finite-sample properties. The mapping and clustering of enterovirus 71 infections in Taiwan are carried out for illustration.
疾病发病率的制图长期以来一直对流行病学和公共卫生具有重要意义。在本文中，我们考虑识别具有升高的疾病率的空间单元的聚类，并开发一种新方法，该方法估计与潜在风险因素相关的相对疾病风险，同时识别对应于升高风险的聚类。提出了一种异质性度量，以能够在一对互补模型下比较候选聚类及其补集。开发了拟似然程序来估计模型参数并识别聚类。与传统的空间聚类方法相比，我们的方法的一个优点是能够识别由于风险因素和空间相关性而导致的突然或不连续变化而具有任意形状的聚类。建立了所提出方法的渐近性质，并且模拟研究表明具有经验上合理的有限样本性质。为了说明，对台湾肠病毒 71 感染进行了制图和聚类。
