Bayesian Hierarchical Modelling of Historical Data of the South African Coal Mining Industry for Compliance Testing.

ABSTRACT: Bayesian hierarchical framework for exposure data compliance testing is highly recommended in occupational hygiene. However, it has not been used for coal dust exposure compliance testing in South Africa (SA). The Bayesian analysis incorporates prior information, which increases solid decision making regarding risk management. This study compared the posterior 95th percentile (P95) of the Bayesian non-informative and informative prior from historical data relative to the occupational exposure limit (OEL) and exposure categories, and the South African Mining Industry Code of Practice (SAMI CoP) approach. A total of nine homogenous exposure groups (HEGs) with a combined 243 coal mine workers' coal dust exposure data were included in this study. Bayesian framework with Markov chain Monte Carl

SUBMITTER: Made F 

PROVIDER: S-EPMC9032634 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature