Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures

TLDR

Human exposure to complex chemical mixtures necessitates accurate estimation of multi‑pollutant health effects, yet most studies focus on single agents due to limited statistical methods. The study introduces Bayesian kernel machine regression (BKMR) to model health outcomes as a flexible function of multi‑pollutant mixtures. BKMR models the outcome via a kernel‑specified flexible function and, in high‑dimensional settings, uses a hierarchical variable‑selection scheme to identify key components while accounting for correlation, with its features illustrated in epidemiologic and toxicologic case studies. Simulation studies show that BKMR accurately estimates exposure‑response functions and successfully identifies the mixture components driving health effects.

Abstract

Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.