Division of Biostatistics and Epidemiology, Medical University of South Carolina, 135 Cannon St, Suite 303, Charleston, SC 29425, USA.
Stat Med. 2011 Nov 20;30(26):3095-116. doi: 10.1002/sim.4340. Epub 2011 Sep 5.
This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina.
本文探讨了及时检测时空疾病集群的统计方法学的发展。随着有关事件时间和地点的数据日益增多，构建了多变量监测技术，这可能提高相对于监测整个研究区域疾病总病例数而言检测局部疾病集群的能力。我们引入监测条件预测有序变量作为一种基于贝叶斯模型的通用监测技术，当有空间数据时，该技术可用于检测发病率增加的小区域。为了解决多次比较的问题，我们将当疾病风险模式没有变化时每个小区域发出警报的共同概率纳入到分析中。我们使用模拟研究在贝叶斯层次泊松模型框架内研究了所提出的监测技术的性能。最后，我们提出了南卡罗来纳州沙门氏菌病的案例研究。
