Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Details

• Alternative Title:
  Risk Anal
• Personal Author:
  Shao, Kan ; Allen, Bruce C. ; 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.
• Keywords:
  Algorithms Arsenic Bayesian Hierarchical Model Bayes Theorem Cardiovascular Disease Cardiovascular Diseases Dose-Response Relationship, Drug Exposure Response Genetic Variation Humans Human Variability Markov Chains Probability Risk Assessment Uncertainty

  More +
• Source:
  Risk Anal. 37(10):1865-1878
• Pubmed ID:
  28032899
• Pubmed Central ID:
  PMC6151353
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHSUnited States/
• Volume:
  37
• Issue:
  10
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:ff8646d6c70873d7bcf224c8dc650b03ee0e74df8d9537757f186874826666bf
• Download URL:
  https://stacks.cdc.gov/view/cdc/59293/cdc_59293_DS1.pdf
• File Type:
  [PDF - 1.14 MB ]