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.

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The medical records of 3922 school children residing in the Greater Haifa Metropolitan Area in Northern Israel were analyzed. Individual exposure to ambient air pollution (SO(2) and PM(10)) for each child was estimated using Geographic Information Systems tools. Factors affecting childhood asthma risk were then investigated using logistic regression and the more recently developed Bayesian Model Averaging (BMA) tools. The analysis reveals that childhood asthma in the study area appears to be significantly associated with particulate matter of less than 10 μm in aerodynamic diameter (PM(10)) (Odds Ratio (OR) = .11; P<0.001). However, no significant association with asthma prevalence was found for SO(2) (P >0.2), when PM(10) and SO(2) were introduced into the models simultaneously. When considering a change in PM(10) between the least and the most polluted parts of the study area (9.4 μg/m(3)), the corresponding OR, calculated using the BMA analysis, is 2.58 (with 95% posterior probability limits of OR ranging from 1.52 to 4.41), controlled for gender, age, proximity to main roads, the town of a child's residence, and family's socio-economic status. Thus, it is concluded that exposure to airborne particular matter, even at relatively low concentrations (40-50 μg/m(3)), generally below international air pollution standards (55-70 μg/m(3)), appears to be a considerable risk factor for childhood asthma in urban areas. This should be a cause of concern for public health authorities and environmental decision-makers.

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