摘要：Several methodological issues have been identified in analysis of epidemiological data to better assess the distributional effects of exposures and hypotheses about effect modification. We discuss the hierarchical mixed model and some more complex methods. Methods of capturing inequality are a second dimension of risk assessment, and simulation studies are important because plausible choices for air pollution effects and effect modifiers could result in extremely high risks in a small subset of the population. Future epidemiological studies should explore contextual and individual-level factors that might modify these relationships. The Environmental Protection Agency should make this a standard part of their risk assessments whenever the necessary information is available. In our first article in this supplement, 1 we identify several critical concepts that need to be incorporated into risk assessment to adequately address differential vulnerability and susceptibility to environmental hazards. In our second article, 2 we illustrate these concepts, drawing examples primarily from the literature on lead exposure and air pollution. Here, we discuss methodological issues arising from our recommendations in those articles. Several issues are not addressed here, such as problems of measurement; a rich literature on measurement issues in lead research is available. 3 – 7 We focus on issues related to the study of differential vulnerability and susceptibility. This research faces 3 core methodological challenges, but existing, new, and emerging methods can address them. These challenges are (1) complex interactions and synergies, (2) nested data at multiple spatial scales, and (3) methods to quantify risk inequality to identify hidden pockets of vulnerability.
