Wakefield Jonathan
Department of Statistics and Biostatistics, University of Washington, Seattle, WA 98195, USA.
Annu Rev Public Health. 2008;29:75-90. doi: 10.1146/annurev.publhealth.29.020907.090821.
Ecologic studies use data aggregated over groups rather than data on individuals. Such studies are popular because they use existing databases and can offer large exposure variation if the data arise from broad geographical areas. Unfortunately, the aggregation of data that define ecologic studies results in an information loss that can lead to ecologic bias. Specifically, ecologic bias arises from the inability of ecologic data to characterize within-area variability in exposures and confounders. We describe in detail particular forms of ecologic bias so that their potential impact on any particular study may be assessed. The only way to overcome such bias, while avoiding uncheckable assumptions concerning the missing information, is to supplement the ecologic with individual-level information, and we outline a number of proposals that may achieve this aim.
生态学研究使用的是群体层面汇总的数据,而非个体数据。这类研究很受欢迎,因为它们利用现有的数据库,并且如果数据来自广阔的地理区域,就能呈现出较大的暴露差异。不幸的是,定义生态学研究的数据汇总导致了信息损失,进而可能引发生态学偏倚。具体而言,生态学偏倚源于生态学数据无法描述区域内暴露因素和混杂因素的变异性。我们详细描述了生态学偏倚的具体形式,以便评估其对任何特定研究可能产生的潜在影响。克服这种偏倚的唯一方法,同时又避免对缺失信息做出无法检验的假设,就是用个体层面的信息来补充生态学数据,并且我们概述了一些可能实现这一目标的建议。