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

Journal Article


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). © 2007 Elsevier B.V. All rights reserved.

Authors


  •   Cressie, Noel A.
  •   Buxton, B (external author)
  •   Calder, Catherine (external author)
  •   Craigmile, Peter (external author)
  •   Dong, C (external author)
  •   McMillan, Nigel (external author)
  •   Morara, M (external author)
  •   Santner, T (external author)
  •   Wang, Ke (external author)
  •   Young, G (external author)
  •   Zhang, Jian (external author)

Publication Date


  • 2007

Citation


  • Cressie, N. A., Buxton, B. E., Calder, C. A., Craigmile, P. F., Dong, C., McMillan, N. J., Morara, M., Santner, T. J., Wang, K., Young, G. & Zhang, J. (2007). From sources to biomarkers: a hierarchical Bayesian approach for human exposure modeling. Journal of Statistical Planning and Inference, 137 (11), 3361-3379.

Scopus Eid


  • 2-s2.0-34447505852

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2348

Number Of Pages


  • 18

Start Page


  • 3361

End Page


  • 3379

Volume


  • 137

Issue


  • 11

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). © 2007 Elsevier B.V. All rights reserved.

Authors


  •   Cressie, Noel A.
  •   Buxton, B (external author)
  •   Calder, Catherine (external author)
  •   Craigmile, Peter (external author)
  •   Dong, C (external author)
  •   McMillan, Nigel (external author)
  •   Morara, M (external author)
  •   Santner, T (external author)
  •   Wang, Ke (external author)
  •   Young, G (external author)
  •   Zhang, Jian (external author)

Publication Date


  • 2007

Citation


  • Cressie, N. A., Buxton, B. E., Calder, C. A., Craigmile, P. F., Dong, C., McMillan, N. J., Morara, M., Santner, T. J., Wang, K., Young, G. & Zhang, J. (2007). From sources to biomarkers: a hierarchical Bayesian approach for human exposure modeling. Journal of Statistical Planning and Inference, 137 (11), 3361-3379.

Scopus Eid


  • 2-s2.0-34447505852

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2348

Number Of Pages


  • 18

Start Page


  • 3361

End Page


  • 3379

Volume


  • 137

Issue


  • 11