title: Global, local and focused geographic clustering for case-control data with residential histories.
abstract: BACKGROUND
  This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.
  
  
  METHODS
  Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only.
  
  
  RESULTS
  Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study.
  
  
  CONCLUSIONS
  Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account.
authors.affiliation: BioMedware, Inc., 516 North State Street, Ann Arbor, MI, 48104-1236, USA. jacquez@biomedware.com
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Jacquez
authors.forename: Andy
authors.initials: A
authors.lastname: Kaufmann
authors.forename: Jaymie
authors.initials: J
authors.lastname: Meliker
authors.forename: Pierre
authors.initials: P
authors.lastname: Goovaerts
authors.forename: Gillian
authors.initials: G
authors.lastname: AvRuskin
authors.forename: Jerome
authors.initials: J
authors.lastname: Nriagu
mesh_terms: D016022:Case-Control Studies; D016000:Cluster Analysis; D004781:Environmental Exposure / Q000032:analysis / Q000706:statistics & numerical data*; D005843:Geography; D006801:Humans; D038622:Internationality; D008824:Michigan; D015233:Models, Statistical; D016012:Poisson Distribution; D011157:Population Dynamics; D011336:Probability; D001749:Urinary Bladder Neoplasms / Q000453:epidemiology / Q000209:etiology