From sources to biomarkers: a hierarchical Bayesian approach for human exposure modeling

Cressie, N. et al. (2007) From sources to biomarkers: a hierarchical Bayesian approach for human exposure modeling. Journal of Statistical Planning and Inference, 137(11), pp. 3361-3379.

Abstract

This paper investigates, from sources to biomarkers, the pathways of human exposure to arsenic. We use a multi-scale (individual level, county level) hierarchical Bayesian model (HBM) that has explicit stages for pollutant sources, global and local environmental levels, personal exposures, and biomarkers. By analyzing these stages simultaneously, we provide an analysis of exposure pathways from the sources of toxic substances in the environment to biomarker levels observed in individuals. The complexity of our approach, in terms of levels of hierarchy, variety of (misaligned) data sources, and computational requirements, illustrates what is possible using hierarchical Bayesian modeling. Our HBM draws on individual-specific measurements from the National Human Exposure Assessment Survey (NHEXAS) Phase I, supplemented by arsenic-concentration measurements in topsoil and stream sediments. We focus on arsenic and its air, soil, water, and food pathways of exposure for individuals in the US Environmental Protection Agency's Region 5 (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin).

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Craigmile, Dr Peter
• Authors: Cressie, N., Buxton, B., Calder, C.A., Craigmile, P.F., Dong, C., McMillan, N., Morara, M., Santner, T., Wang, K., Young, G., and Zhang, J.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of Statistical Planning and Inference
• ISSN: 0378-3758