多元正态空间扫描统计量模型在探测地方病最强聚集性中的应用

Application of Multivariate Normal Scan Statistic to Most Severe Cluster of Local Diseases

研究了在医疗资源有限的条件下,如何在多组人群中探测某疾病最强地理聚集性的问题.为了有效地探测疾病爆发,只需特别监测和评估某些特定人群即可,这类人群称为具有最强聚集性的人群(most severe cluster,MSC).由于各组人群会相互影响,因而提出了一种多元正态空间扫描统计量(multivariate normal scan statistic,MNSS)模型,并以美国纽约州肺癌患者为例验证了该模型的适用性.

This paper focuses on the problem of detecting ageographical cluster with the most severe status in multiple groups of populations with the consideration of limited reed- ical resources. In an early stage of a disease, an outbreak may only be present in some specific population group. Therefore, to efficiently detect the outbreak, specific groups need to be particularly monitored and evaluated. The objective of detection as the most severe cluster （MSC） is defined. Considering interaction among population groups, a multivariate normal scan statistic is proposed. The method is applied to an example of lung cancer in New York State to detect the MSC with a high mortality rate at the aggregate level.