Imputation of missing values in a large job exposure matrix using hierarchical information.

ABSTRACT: Job exposure matrices (JEMs) represent a useful and efficient approach for estimating occupational exposures. This study uses a large dataset of full-shift measurements and employs imputation strategies to develop noise exposure estimates for almost all broad level standard occupational classification (SOC) groups in the US. The JEM was constructed using 753,702 measurements from the government, private industry, and the published literature. Parametric Bayes imputation was used to take advantage of the hierarchical structure of the SOCs and the mean occupational noise exposures were estimated for all broad level SOCs, except those in major group 23-0000, for which no data were available. The estimated posterior mean for all broad SOCs was found to be 82.1 dBA with within- and between-majo

SUBMITTER: Roberts B 

PROVIDER: S-EPMC9929916 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature