Open AccessThe Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER
Author(s) -
Chetan Tiwari,
Kirsten Beyer,
Gérard Rushton
Publication year - 2014
Publication title -
american journal of public health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.284
H-Index - 264
eISSN - 1541-0048
pISSN - 0090-0036
DOI - 10.2105/ajph.2014.301900
Subject(s) - wonder , disease control , public health , data collection , population , demography , missing data , environmental health , geography , medicine , statistics , psychology , sociology , mathematics , social psychology , pathology
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.