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.

Using Prior Toxicological Data to Support Dose–Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose–Response Models

“…Estimating the parameters of the distributions of a and b is to get the estimate of μ a , σ a , μ b , and σ b based on their posterior samples. A more detailed description of the Bayesian hierarchical model and its validity can be found in previous publications − p ( a , b , μ a , σ a , μ b , σ b | y , n , d ) ∝ p ( y | a , b , n , d ) p ( a , b | μ a , σ a , μ b , σ b ) p ( μ a , σ a , μ b , σ b ) …”

Size-fractionated Particulate Matter in Indoor and Outdoor Environments during the 2015 Haze in Singapore: Potential Human Health Risk Assessment

“…Therefore, five input exposure parameters (as defined in SI), namely, exposure frequency (EF), exposure duration (ED), average life time (AT), inhalation rate (IR) and body weight (BW) were considered variables. To estimate probabilistic health risk, these parameters were assumed to follow lognormal distribution and MC simulations were performed using Python language (Chiu and Slob, 2015;Shao et al, 2017). Results obtained from the output file were then analyzed to estimate 50 th and 95 th percentile values of potential health risk.…”

“…However, to the best of our knowledge, no scientific reports are available in the literature from SEA on the assessment of potential human health risk based on bioavailable concentrations of PM-bound TEs measured simultaneously both indoors and outdoors during transboundary haze episodes in SEA. Additionally, health risk evaluations are done based on a number of assumptions, which may introduce uncertainties in exposure parameters and limitations in the risk assessment (USEPA, 1992;Megido et al, 2017;Shao et al, 2017). Uncertainties exist in the estimation of exposure duration and individuals' characteristics as well as exposure concentrations and toxicity data for the potentially toxic elements in PM.…”

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

“…Bayesian hierarchical models have been widely adopted in probabilistic safety assessment to treat source-to-source variability, 29–36 as well as in many other applications for inference of population-level quantities from group-level evidence and vice versa. 37–42…”

“…Bayesian hierarchical models have been widely adopted in probabilistic safety assessment to treat source-tosource variability, [29][30][31][32][33][34][35][36] as well as in many other applications for inference of population-level quantities from group-level evidence and vice versa. [37][38][39][40][41][42] The paper is structured as follows. Section ''Concepts: Behavioral patterns from simulator data and variability modelling'' first introduces the concept of crew behavioral patterns to characterize behavioral aspects in nuclear power plant operations.…”