title: Assessing community variation and randomness in public health indicators.
abstract: BACKGROUND
  Evidence-based health indicators are vital to needs-based programming and epidemiological planning. Agencies frequently make programming funds available to local jurisdictions based on need. The use of objective indicators to determine need is attractive but assumes that selection of communities with the highest indicators reflects something other than random variability from sampling error.
  
  
  METHODS
  The authors compare the statistical performance of two heterogeneity measures applied to community differences that provide tests for randomness and measures of the percentage of true community variation, as well as estimates of the true variation. One measure comes from the meta-analysis literature and the other from the simple Pearson chi-square statistic. Simulations of populations and an example using real data are provided.
  
  
  RESULTS
  The measure based on the simple chi-square statistic seems superior, offering better protection against Type I errors and providing more accurate estimates of the true community variance.
  
  
  CONCLUSIONS
  The heterogeneity measure based on Pearson's χ2 should be used to assess indices. Methods for improving poor indices are discussed.
authors.affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242 USA. stephan-arndt@uiowa.edu.
authors.forename: Stephan
authors.initials: S
authors.lastname: Arndt
authors.forename: Laura
authors.initials: L
authors.lastname: Acion
authors.forename: Kristin
authors.initials: K
authors.lastname: Caspers
authors.forename: Ousmane
authors.initials: O
authors.lastname: Diallo