Bayesian hierarchical structure for quantifying population variability to inform probabilistic health risk assessments

Details

• Personal Author:
  Allen BC ; Shao K ; Wheeler, Matthew W.
• Description:
  Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations. [Description provided by NIOSH]
• Subjects:
  Arsenic Chemistry Environmental Exposure Hazardous Substances Inorganic Chemicals Occupational Health Preventive Medicine Risk Assessment Risk Factors Safety
• Keywords:
  Author Keywords: Arsenic Bayesian Hierarchical Model Cardiovascular Disease Chemical Composition Chemical Hazards Chemical Properties Exposure Levels Exposure Response Health Effects Health Hazards Human Variability Models Preventive Medicine Risk Assessment Risk Factors

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• ISSN:
  0272-4332
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Indiana ; North Carolina ; Ohio ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  EID - Education and Information Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  37
• Issue:
  10
• NIOSHTIC Number:
  nn:20049106
• Citation:
  Risk Anal 2017 Oct; 37(10):1865-1878
• Contact Point Address:
  Kan Shao, PhD, Assistant Professor, Department of Environmental Health, Indiana University School of Public Health, 1025 E. Seventh Street, Bloomington, IN 47405
• Email:
  kshao@indiana.edu
• Federal Fiscal Year:
  2018
• Peer Reviewed:
  True
• Source Full Name:
  Risk Analysis
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:edf201fe772d688f11d490cfa94f3543a38afa4d3dd758b53b26777f78589e4b2bf44a963eca5fd6059c9fc88356c0b788aee0c1d63b2066abe77f1e0c7d859c
• Download URL:
  https://stacks.cdc.gov/view/cdc/203676/cdc_203676_DS1.pdf
• File Type:
  [PDF - 659.04 KB ]