Bayesian Kernel Machine Regression for Estimating the Health Effects of Air Pollution Mixtures

• Activity Number: 34
• Type: Contributed
• Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jennifer Bobb*+ and Brent A. Coull
• Companies: Harvard School of Public Health and Harvard School of Public Health
• Keywords: air pollution ; mixtures ; health

Abstract

Studying the health effects of mixtures of environmental stressors is an important problem with applications ranging from estimating how simultaneous exposure to multiple air pollutants impacts mortality, to investigating how metal mixtures jointly affect cognitive function. However, despite the widespread occurrence of mixtures, most epidemiological studies tend to either focus on single agents at a time or to consider simple interaction models while recognizing that such models likely oversimplify the causal pathway. In this talk we introduce Bayesian kernel machine regression (BKMR) as a new approach for studying the health effects of mixtures. This approach simultaneously estimates the health effects of exposure to the mixture in a way that accounts for potentially complex nonlinear and interactive effects and identifies which components (e.g., elements) are most harmful. We evaluate performance of BKMR through simulation studies, and we apply the approach to investigate the relationship between elemental pollution components and blood pressure in a panel study of 70 subjects followed over five visits.