A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound. Issue 3 (May 2016)

• Record Type: Journal Article
• Authors: Kim, Albert Y.; Wakefield, Jon
• Is Part Of: Epidemiology. Volume 27:Issue 3(2016:May)
• Publication Date: 2016-05
• Subjects: Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000450
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : Cluster detection is an important public health endeavor, and in this article, we describe and apply a recently developed Bayesian method. Commonly used approaches are based on so-called scan statistics and suffer from a number of difficulties, which include how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas that are either "null" or "non-null, " the latter corresponding to clusters (excess risk) or anticlusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate, and colorectal cancer, in the Puget Sound region of Washington State. Abstract : Supplemental Digital Content is available in the text.