A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound

Epidemiology. 2016 May;27(3):347-55. doi: 10.1097/EDE.0000000000000450.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Bayes Theorem
  • Brain Neoplasms / epidemiology*
  • Breast Neoplasms / epidemiology*
  • Child
  • Child, Preschool
  • Cluster Analysis
  • Colorectal Neoplasms / epidemiology
  • Female
  • Humans
  • Infant
  • Infant, Newborn
  • Lung Neoplasms / epidemiology
  • Male
  • Middle Aged
  • Prostatic Neoplasms / epidemiology
  • Washington / epidemiology
  • Young Adult