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Population Health Metrics
Published in: Population Health Metrics 1/2011

Open Access 01-12-2011 | Research

Assessing community variation and randomness in public health indicators

Authors: Stephan Arndt, Laura Acion, Kristin Caspers, Ousmane Diallo

Published in: Population Health Metrics | Issue 1/2011

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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.
Appendix
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Metadata
Title
Assessing community variation and randomness in public health indicators
Authors
Stephan Arndt
Laura Acion
Kristin Caspers
Ousmane Diallo
Publication date
01-12-2011
Publisher
BioMed Central
Published in
Population Health Metrics / Issue 1/2011
Electronic ISSN: 1478-7954
DOI
https://doi.org/10.1186/1478-7954-9-3

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