INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH

• Published in: The annals of applied statistics Vol. 14; no. 4; p. 1945
• Main Authors: Reich, Brian J, Guan, Yawen, Fourches, Denis, Warren, Joshua L, Sarnat, Stefanie E, Chang, Howard H
• Format: Journal Article
• Language: English
• Published: United States 01.12.2020
• Subjects: factor analysis; Cheminformatics; principal components; stochastic search; variable selection; collinearity
• ISSN: 1932-6157
• DOI: 10.1214/20-AOAS1364

Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.