title: Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments.
abstract: 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.
authors.affiliation: Department of Environmental Health, Indiana University, Bloomington, IN, USA.
authors.forename: Kan
authors.initials: K
authors.lastname: Shao
authors.affiliation: Bruce Allen Consulting, Chapel Hill, NC, USA.
authors.forename: Bruce C
authors.initials: BC
authors.lastname: Allen
authors.affiliation: National Institute for Occupational Safety and Health, Cincinnati, OH, USA.
authors.forename: Matthew W
authors.initials: MW
authors.lastname: Wheeler
mesh_terms: D000465:Algorithms; D001151:Arsenic / Q000633:toxicity*; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000139:chemically induced*; D004305:Dose-Response Relationship, Drug; D014644:Genetic Variation; D006801:Humans; D008390:Markov Chains; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D035501:Uncertainty
chemical_list: D001151:Arsenic
keywords: Arsenic; Bayesian hierarchical model; cardiovascular disease; exposure response; human variability; risk assessment