Analysis of Human Co-exposure to Lead and Cadmium Using Human Biomonitoring (HBM) Data in a Bayesian Copula-Based Regression Framework

The identification of human co-exposure to industrial chemicals or environmental substances is of high interest in human health risk assessment. Due to their ubiquity and persistence in the environment, heavy metals such as cadmium (Cd) and lead (Pb) are of particular concern. Approaches to adequately investigating combinations of these and other often highly correlated variables are lacking. This study proposes a modeling approach to investigate the co-exposure to Cd and Pb, and better understanding the variations of blood Cd and Pb (CdB and PbB, respectively) together with potentially determinant factors. A copula-based regression model was built, using Bayesian inference and Markov Chain Monte Carlo simulation, to relate CdB and PbB of 3- to 14-year-old children participating in the German Environmental Survey for Children (GerES IV) with socio-demographic and ancillary exposure-relevant information. A minor to negligible dependence between CdB and PbB was observed, suggesting that Cd and Pb are subject to differing exposure sources/pathways or kinetics within human body. Despite the resulting low association between CdB and PbB, the developed approach provides methodological bases for enhancing the assessment of the cumulative exposure to multiple substances and for deepening the understanding of the determinants of these exposures. [GRAPHICS] .