The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER

被引：31
|
作者：
Tiwari, Chetan [1 ]
Beyer, Kirsten [2 ]
Rushton, Gerard [3 ]
机构：
[1] Univ N Texas, Dept Geog, Denton, TX 76203 USA
[2] Med Coll Wisconsin, Div Epidemiol, Inst Hlth & Soc, Milwaukee, WI 53226 USA
[3] Univ Iowa, Dept Geog, Iowa City, IA 52242 USA
来源：
AMERICAN JOURNAL OF PUBLIC HEALTH | 2014年 / 104卷 / 08期
关键词：
MAPS;
D O I：
10.2105/AJPH.2014.301900
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
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.
引用
收藏
页码：1386 / 1388
页数：4
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