文章基本信息

标题：The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER
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作者：Chetan Tiwari ; Kirsten Beyer ; Gerard Rushton 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2014
卷号：104
期号：8
页码：1386-1388
DOI：10.2105/AJPH.2014.301900
语种：English
出版社：American Public Health Association
摘要：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. CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) provides county-level data on directly age-adjusted mortality rates, and age- and gender-stratified mortality and population counts. 1 To protect against the potential disclosure of personal health information, WONDER suppresses any statistic (counts or rates) calculated using fewer than 10 observations. 2 However, such suppression restricts the utility of WONDER data to compute and map reliable rates for areas with small populations, for short time periods, or for rare diseases. 3,4 Furthermore, rates that are indirectly adjusted for age, which are currently not provided by WONDER, can only be calculated for those counties where count data are not suppressed. 5,6 Using an example of heart disease mortality, we showed that rates computed from suppressed mortality count data provided by WONDER are biased in predictable ways and that our algorithm can be used to remove these known biases.