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The impact of data suppression on local mortality rates: the case of CDC WONDER. Am J Public Health 2014 Aug;104(8):1386-8

Date

06/13/2014

Pubmed ID

24922161

Pubmed Central ID

PMC4103252

DOI

10.2105/AJPH.2014.301900

Scopus ID

2-s2.0-84904328906 (requires institutional sign-in at Scopus site)   27 Citations

Abstract

CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nation's primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps produced from suppressed data have predictable geographic biases that can be removed by applying population data in the system and an algorithm that uses regional rates to estimate missing data. By using CDC WONDER heart disease mortality data, we demonstrated that effects of suppression could be largely overcome.

Author List

Tiwari C, Beyer K, Rushton G

Author

Kirsten M. Beyer PhD, MPH Professor in the Institute for Health and Equity department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Algorithms
Data Interpretation, Statistical
Databases, Factual
Heart Diseases
Humans
Mortality
United States