Sheryl’s primary research focus is understanding the relative contribution of social factors and environmental exposures on chronic disease. She has worked extensively in the elementary school setting on developing surveillance methods and educational programs for childhood asthma, understanding the role of lead exposure in educational outcomes, and analyzing the role of social culture and indoor environmental quality and the health and performance of students and teachers.
Application of the Total Environmental Exposures Framework: The Case of Milwaukee
Over the past three decades, the risk factor paradigm guiding modern epidemiologic research has shifted to reflect the importance of social and environmental contexts as fundamental causes of disease. The operational framework that currently motivates epidemiologic research is the contextualization of individual risk factors: understanding how people come to be exposed to proximate causes of diseases as a function of their social and physical environments. This change in focus not only serves to elucidate the biological mechanisms of disease but informs effective interventions that promote population health, characterized as consequential epidemiology. One of the major challenges to the development of appropriate interventions to promote community well-being is the coincident nature of chemical, physical, and social stressors in communities throughout the United States. Current research on environmental justice has focused on the differential exposure to environmental risk factors by socioeconomic status. The high correlation among these modifiable exposures, potential synergies within and between modifiable and non-modifiable exposures, and the variety of pathways by which these exposures may operate creates statistical and practical complications for identification of causal relationships. In this presentation, we will demonstrate three applications of the Total Environmental Exposures Framework through research on environmental mixtures, multidomain exposures and social and chemical factors associated with childhood lead exposures in Milwaukee. We combine novel statistical and machine learning methods with diverse environmental and social exposures to describe a pathway for how interventions to improve the health and well-being of communities may develop.