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

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…

• UE
  U.S. Environmental Protection Agency

  Awards: 83479801, ES000002
• HE
  Health Effects Institute
• NI
  National Institutes of Health

  Awards: ES022585, ES017437, ES007142, ES015533, ES013744, ES000002, ES016454, ES014930