Abstract:

Ecological bias may result from interactions between variables that are characterized by different spatial and temporal scales. Such an ecological bias, also known as aggregation bias or cross-level-bias, may occur as a result of using coarse environmental information about stressors together with fine (i.e., individual) information on health outcomes. This study examines the assumption that distinct within-area variability of spatial patterns of the risk metrics and confounders may result from artifacts of the aggregation of the underlying data layers, and that this may affect the statistical relationships between them. In particular, we demonstrate the importance of carefully linking information layers with distinct spatial resolutions and show that environmental epidemiology studies are prone to exposure misclassification as a result of statistically linking distinctly averaged spatial data (e.g., exposure metrics, confounders, health indices). Since area-level confounders and expo

Website