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From sources to biomarkers: A hierarchical Bayesian approach for human exposure modeling. (English) Zbl 1146.62104

Summary: 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).

MSC:

62P12 Applications of statistics to environmental and related topics
62F15 Bayesian inference
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

spBayes; BayesDA
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Full Text: DOI

References:

[1] ATSDR, 2005. Toxicological Profile for Arsenic. Draft Report of the Agency for Toxic Substances and Disease Registry, Public Health Services, U.S. Department of Health and Human Services, Washington, DC.; ATSDR, 2005. Toxicological Profile for Arsenic. Draft Report of the Agency for Toxic Substances and Disease Registry, Public Health Services, U.S. Department of Health and Human Services, Washington, DC.
[2] Banerjee, S.; Carlin, B. P.; Gelfand, A. E., Hierarchical Modeling and Analysis for Spatial Data (2004), Chapman & Hall/CRC: Chapman & Hall/CRC Boca Raton, FL · Zbl 1053.62105
[3] Berliner, L. M.; Wikle, C. K.; Cressie, N., Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling, J. Climate, 13, 3953-3968 (2000)
[4] Calder, C.A., Holloman, C.H., Bortnick, S.M., Strauss, W.J., Morara, M., 2007. Relating ambient particulate matter concentration levels to mortality using an exposure simulator. J. Amer. Statist. Assoc., forthcoming.; Calder, C.A., Holloman, C.H., Bortnick, S.M., Strauss, W.J., Morara, M., 2007. Relating ambient particulate matter concentration levels to mortality using an exposure simulator. J. Amer. Statist. Assoc., forthcoming. · Zbl 1469.62388
[5] Callahan, M. A.; Clickner, R. P.; Whitmore, G. K.; Sexton, K., Overview of important design issues for a national human exposure assessment survey, J. Exposure Anal. Environmental Epidemiology, 5, 257-282 (1995)
[6] Chen, M.-H.; Shao, Q.-M.; Ibrahim, J. G., Monte Carlo Methods in Bayesian Computation (2000), Springer: Springer New York, NY · Zbl 0949.65005
[7] Clayton, C. A.; Pellizzari, E. D.; Quackenboss, J. J., National human exposure assessment survey: analysis of exposure pathways and routes for arsenic and lead in EPA region 5, J. Exposure Anal. Environmental Epidemiology, 12, 29-43 (2002)
[8] Cressie, N., Statistics for Spatial Data (1993), Wiley: Wiley New York, NY
[9] Cressie, N.; Davidson, J. L., Image analysis with partially ordered Markov models, Comput. Statist. Data Anal., 29, 1-26 (1998) · Zbl 1042.62611
[10] Cressie, N.; Richardson, S.; Jaussent, I., Ecological bias: use of maximum-entropy approximations, Austral. New Zealand J. Statist., 46, 233-255 (2004) · Zbl 1061.62175
[11] De Oliveira, V., Bayesian inference and prediction of Gaussian random fields based on censored data, J. Comput. Graphical Statist., 14, 95-115 (2005)
[12] Environment Canada, 1993. Canadian Environmental Protection Act Priority Substance List Assessment Report: Arsenic and its Compounds. Canada Communication Group, Ottawa, Canada.; Environment Canada, 1993. Canadian Environmental Protection Act Priority Substance List Assessment Report: Arsenic and its Compounds. Canada Communication Group, Ottawa, Canada.
[13] Gelfand, A. E.; Smith, A. F.M., Sampling based approaches to calculating marginal densities, J. Amer. Statist. Assoc., 85, 398-409 (1990) · Zbl 0702.62020
[14] Gelman, A.; Carlin, J. B.; Stern, H. S.; Rubin, D. B., Bayesian Data Analysis (2004), Chapman & Hall/CRC: Chapman & Hall/CRC Boca Raton, FL · Zbl 1039.62018
[15] Geweke, J., Efficient simulation from the multivariate normal and student-\(t\) distributions subject to linear constraints, (Keramidas, E. M., Computing Science and Statistics: Proceedings of the Twenty-Third Symposium on the Interface (1991), Interface Foundation of North America: Interface Foundation of North America Fairfax, VA), 571-578
[16] Gilks, W. R.; Richardson, S.; Spiegelhalter, D. J., Markov Chain Monte Carlo in Practice (1998), Chapman & Hall: Chapman & Hall London · Zbl 0832.00018
[17] Kibria, G.; Sun, L.; Zidek, J. V.; Le, N. D., Bayesian spatial prediction of random space-time fields with application to mapping \(PM_{2.5}\) exposure, J. Amer. Statist. Assoc., 97, 112-124 (2002) · Zbl 1073.62557
[18] Lebowitz, M. D.; O’Rourke, M. K.; Gordon, S.; Moschandreas, D. J.; Buckley, T.; Nishioka, M., Population-based exposure measurements in Arizona: a phase I field study in support of the national human exposure assessment survey, J. Exposure Anal. Environmental Epidemiology, 5, 297-325 (1995)
[19] Lockwood, J. R.; Schervish, M. J.; Gurian, P.; Small, M. J., Characterization of arsenic occurrence in source waters of U.S. community water systems, J. Amer. Statist. Assoc., 96, 1184-1193 (2001) · Zbl 1073.62594
[20] McMillan, N., Morara, M., Young, G., 2006. Hierarchical Bayesian modeling of human exposure pathways and routes. Proceedings of the Joint Statistical Meetings, Seattle, WA, August 60-10, 2006.; McMillan, N., Morara, M., Young, G., 2006. Hierarchical Bayesian modeling of human exposure pathways and routes. Proceedings of the Joint Statistical Meetings, Seattle, WA, August 60-10, 2006.
[21] Pellizzari, E.; Lioy, P.; Quackenboss, J.; Whitmore, R.; Clayton, A.; Freeman, N.; Waldman, J.; Thomas, K.; Rodes, C.; Wilcosky, T., Population-based exposure measurements in EPA Region 5: a phase I field study in support of the national human exposure assessment survey, J. Exposure Anal. Environmental Epidemiology, 5, 327-358 (1995)
[22] Robert, C. P.; Casella, G., Statistical Methods (2004), Springer: Springer New York, NY
[23] Royle, J. A.; Berliner, L. M.; Wikle, C. K.; Milliff, R. F., A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea, (Case Studies in Bayesian Statistics IV (1999), Springer: Springer New York, NY), 367-382 · Zbl 0921.62123
[24] Sexton, K.; Callahan, M. A.; Bryan, E. F.; Saint, C. G.; Wood, W. P., Informed decisions about protecting and promoting public health: rationale for a national human exposure assessment survey, J. Exposure Anal. Environmental Epidemiology, 5, 233-256 (1995)
[25] Tierney, L., Markov chains for exploring posterior distributions, The Ann. Statist., 22, 1701-1728 (1994) · Zbl 0829.62080
[26] US EPA, 1982. Exposure and Risk Assessment for Arsenic. Environmental Protection Agency, Office of Water Regulation and Standards, Washington, DC. EPA440485005, 1.1-4.68.; US EPA, 1982. Exposure and Risk Assessment for Arsenic. Environmental Protection Agency, Office of Water Regulation and Standards, Washington, DC. EPA440485005, 1.1-4.68.
[27] Vahter, M. E., Arsenic. In Biological Monitoring of Toxic Metals (1988), Plenum Press: Plenum Press New York
[28] WHO, 1981. Arsenic. World Health Organization, Geneva. Environmental Health Criteria, 18.; WHO, 1981. Arsenic. World Health Organization, Geneva. Environmental Health Criteria, 18.
[29] Wikle, C. K.; Berliner, L. M.; Cressie, N., Hierarchical Bayesian space-time models, Environmental Ecological Statist., 5, 117-154 (1998)
[30] Wikle, C. K.; Milliff, R. F.; Nychka, D.; Berliner, L. M., Spatio-temporal hierarchical Bayesian modeling: tropical ocean surface winds, J. Amer. Statist. Assoc., 96, 382-397 (2001) · Zbl 1022.62117
[31] Zidek, J.V., Meloche, J., Shaddick, G., Chatfield, C., White, R., 2003. A computational model for estimating personal exposure to air pollutants with application to London’s \(\mathit{PM}_{10} \); Zidek, J.V., Meloche, J., Shaddick, G., Chatfield, C., White, R., 2003. A computational model for estimating personal exposure to air pollutants with application to London’s \(\mathit{PM}_{10} \)
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