Improving ecological inference using individual-level data

Stat Med. 2006 Jun 30;25(12):2136-59. doi: 10.1002/sim.2370.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Interpretation, Statistical*
  • Environment*
  • Humans
  • London
  • Models, Statistical*
  • Multivariate Analysis
  • Social Change*
  • Socioeconomic Factors