On ecological fallacy, assessment errors stemming from misguided variable selection, and the effect of aggregation on the outcome of epidemiological study

J Expo Sci Environ Epidemiol. 2007 Jan;17(1):106-21. doi: 10.1038/sj.jes.7500533. Epub 2006 Oct 11.


In social and environmental sciences, ecological fallacy is an incorrect assumption about an individual based on aggregate data for a group. In the present study, the validity of this assumption was tested using both individual estimates of exposure to air pollution and aggregate data for 1,492 schoolchildren living in the in vicinity of a major coal-fired power station in the Hadera region of Israel. In 1996 and 1999, the children underwent subsequent pulmonary function tests (PFT), and their parents completed a detailed questionnaire on their health status and housing conditions. The association between children's PFT results and their exposure to air pollution was investigated in two phases. During the first phase, PFT averages were compared with average levels of air pollution detected in townships, and small census areas in which the children reside. During the second phase, individual pollution estimates were compared with individual PFT results, and pattern detection techniques (Getis-Ord statistic) were used to investigate the spatial data structure. While different levels of areal data aggregation changed the results only marginally, the choice of indices measuring the children's PFT performance had a significant influence on the outcome of the analysis. As argued, differences between individual-level and group-level effects of exposure (i.e., ecological or cross-level bias) are not necessary outcomes of data aggregation, and that seemingly unexpected results may often stem from a misguided selection of variables chosen to measure health effects. The implications of the results of the analysis for epidemiological studies are discussed, and recommendations for public health policy are formulated.

MeSH terms

  • Child
  • Ecology*
  • Epidemiologic Studies
  • Geographic Information Systems
  • Health Status Indicators
  • Humans
  • Israel / epidemiology
  • Respiratory Function Tests