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Activity Number: 364
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #316715 View Presentation
Title: Quantifying Human Variability for Health Risk Assessment via Bayesian Hierarchical Model
Author(s): Kan Shao*
Companies:
Keywords: human variability ; dose-response ; Bayesian hierarchical model ; probabilistic risk assessment
Abstract:

Quantitative human health risk assessment (HHRA) characterizes the risk of an adverse health outcome caused by exposure to an environmental hazard. One critical step in regulatory HHRA is to extrapolate risk estimated from selected population to the general population by considering variability among individuals. Traditionally, the extrapolation is achieved by dividing an estimated point of departure by an uncertainty factor. As moving towards probabilistic RA, new approaches are needed to quantify such variability probabilistically. We propose a data-driven method to quantify variability by modeling a set of epidemiological dose-response (DR) data obtained from different populations via Bayesian hierarchical analysis. Given that each parameter in DR models has different biological meaning, fully or partially building hierarchical structure over the parameters allows us to compare the magnitude of variability contributed by different factors. We apply this method to multiple datasets using various DR models. Preliminary results show that the proposed method can adequately quantify the variability suggested by the selected studies with recognition of major sources of variability.


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