title: The impact of data suppression on local mortality rates: the case of CDC WONDER.
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.
authors.affiliation: Chetan Tiwari is with the Department of Geography, University of North Texas, Denton. Kirsten Beyer is with the Division of Epidemiology, Institute for Health and Society, Medical College of Wisconsin, Milwaukee. Gerard Rushton is with the Department of Geography, The University of Iowa, Iowa City.
authors.forename: Chetan
authors.initials: C
authors.lastname: Tiwari
authors.forename: Kirsten
authors.initials: K
authors.lastname: Beyer
authors.forename: Gerard
authors.initials: G
authors.lastname: Rushton
mesh_terms: D000465:Algorithms; D002487:Centers for Disease Control and Prevention, U.S. / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006331:Heart Diseases / Q000401:mortality; D006801:Humans; D009026:Mortality; D014481:United States / Q000453:epidemiology