Ecological bias in studies of the short-term effects of air pollution on health

There has been a great deal of research into the short–term effects of air pollution on health with a large number of studies modelling the association between aggregate disease counts and environmental exposures measured at point locations, for example via air pollution monitors. In such cases, the standard approach is to average the observed measurements from the individual monitors and use this in a log-linear health model. Hence such studies are ecological in nature being based on spatially aggregated health and exposure data. Here we investigate the potential for bias in the estimates of the effects on health when estimating the short–term effects of air pollution on health. Such ecological bias may occur if a simple summary measure, such as a daily mean, is not a suitable summary of a spatially variable pollution surface. We assess the performance of commonly used models when confronted with such issues using simulation studies and compare their performance with a model specifically designed to acknowledge the effects of exposure aggregation. In addition to simulation studies, we apply the models to a case study of the short–term effects of particulate matter on respiratory mortality using data from Greater London for the period 2002-2005. We found a significant increased risk of 3% (95% CI 1-5%) associated with the average of the previous three days exposure to particulate matter (per 10 μgm−3 PM10).