title: Ecologic studies revisited.
abstract: 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.
authors.affiliation: Department of Statistics and Biostatistics, University of Washington, Seattle, WA 98195, USA. jonno@u.washington.edu
authors.forename: Jonathan
authors.initials: J
authors.lastname: Wakefield
mesh_terms: D015982:Bias; D016000:Cluster Analysis; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000379:methods* / Q000592:standards; D004812:Epidemiologic Methods; D040362:Geographic Information Systems; D006801:Humans; D007223:Infant; D009657:North Carolina / Q000453:epidemiology; D013398:Sudden Infant Death / Q000453:epidemiology