Improving ecological inference using individual‐level data

Abstract

In typical small‐area studies of health and environment we wish to make inference on the relationship between individual‐level quantities using aggregate, or ecological, data. Such ecological inference is often subject to bias and imprecision, due to the lack of individual‐level information in the data. Conversely, individual‐level survey data often have insufficient power to study small‐area variations in health. Such problems can be reduced by supplementing the aggregate‐level data with small samples of data from individuals within the areas, which directly link exposures and outcomes. We outline a hierarchical model framework for estimating individual‐level associations using a combination of aggregate and individual data. We perform a comprehensive simulation study, under a variety of realistic conditions, to determine when aggregate data are sufficient for accurate inference, and when we also require individual‐level information. Finally, we illustrate the methods in a case study investigating the relationship between limiting long‐term illness, ethnicity and income in London. Copyright © 2005 John Wiley & Sons, Ltd.