数据抑制对当地死亡率的影响：以疾控中心 Wonder 数据库为例。
The impact of data suppression on local mortality rates: the case of CDC WONDER.
机构信息
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.
出版信息
Am J Public Health. 2014 Aug;104(8):1386-8. doi: 10.2105/AJPH.2014.301900. Epub 2014 Jun 12.
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.
摘要
疾病预防控制中心 Wonder（疾病预防控制中心广泛在线数据用于流行病学研究）是美国主要的健康统计数据存储库。在 Wonder 数据向公众发布之前，病例数少于 10 的数据单元将被屏蔽。我们表明，从屏蔽数据生成的地图具有可预测的地理偏差，可以通过应用系统中的人口数据和使用区域比率来估计缺失数据的算法来消除。通过使用疾病预防控制中心 Wonder 心脏病死亡率数据，我们证明了屏蔽的影响可以在很大程度上被克服。
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