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-major SOC variabilities of 22.1 and 13.8, respectively. Of the 443 broad SOCs, 85 were found to have an estimated mean exposure >85 dBA while 10 were >90 dBA. By taking advantage of the size and structure of the dataset, we were able to employ imputation techniques to estimate mean levels of noise exposure for nearly all SOCs in the US. Possible sources of errors in the estimates include misclassification of job titles due to limited data, temporal variations that were not accounted for, and variation in exposures within the same SOC. Our efforts have resulted in an almost completely populated noise JEM that provides a valuable tool for the assessment of occupational exposures to noise. Imputation techniques can lead to maximal use of available information that may be incomplete.
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Acknowledgements
This work was supported by the National Institute of Occupational Safety and Health. Grant # R21OH0 10482: Development of a US/Canadian Job Exposure Matrix (JEM) for Noise.
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Appendices
Appendix 1
The imputation procedure
The unknown quantities in our system include the broad SOC means \(\theta _{ij}^{{\rm obs}},\,i = 1, \ldots ,I;j = 1, \ldots ,n_i^{{\rm obs}},\,\)\(\theta _{ik}^{{\rm mis}},\,i = 1, \ldots ,I;k = 1, \ldots ,n_i^{{\rm mis}}\), the major SOC means \(\theta _i,\,i = 1, \ldots ,I\), the population mean \(\mu \), the within major SOC sampling variance \(\sigma ^2\), and the between major SOC sampling variance \(\tau ^2\). Joint posterior inference for these parameters can be made by constructing a Gibbs sampler which approximates the posterior distribution \(p\left({\theta_{11}^{\rm{obs}},}\,{\ldots,}\right.\)\( \left. \theta_{In_I}^{\rm{obs}},\theta _{11}^{{\rm mis}}, \ldots ,\theta _{In_I}^{{\rm mis}},\theta_1, \ldots ,\theta_I,\mu ,\tau^2,\sigma^2|{\rm observed}\,{\rm data}\right)\):
Collecting the terms that depend on \(\theta _{ij}^{{\rm obs}}\) shows that the full conditional distribution of \(\theta _{ij}^{{\rm obs}}\) must be proportional to
After some calculations, we find that conditional on \(\sigma ^2\) and \(\theta _i\), \(\theta _{ij}^{{\rm obs}}\) must be conditionally independent of other \(\theta _{ij}^{{\rm obs}}\) as well as independent of the data from broad SOCs other than ij:
where \(\mu _{ij}^{{\rm obs}} = \frac{{Y_{ij}^{{\rm obs}}\sigma ^2 + \theta _i\frac{{\left( {s_{ij}^{{\rm obs}}} \right)^2}}{{n_{ij}^{{\rm obs}}}}}}{{\sigma ^2 + \frac{{\left( {s_{ij}^{{\rm obs}}} \right)^2}}{{n_{ij}^{{\rm obs}}}}}}\) and \(\left( {\sigma _{ij}^{{\rm obs}}} \right)^2 = \frac{{\frac{{\left( {s_{ij}^{{\rm obs}}} \right)^2}}{{n_{ij}^{{\rm obs}}}}\sigma ^2}}{{\left( {\sigma ^2 + \frac{{\left( {s_{ij}^{{\rm obs}}} \right)^2}}{{n_{ij}^{{\rm obs}}}}} \right)}}.\)
The conditional distribution of \(\theta _{ik}^{{\rm mis}}\) will be normal distribution
The conditional distribution of \(\theta _i\) is also normal distribution
where \(\mu _i = \frac{{\mu \sigma ^2 + \mathop {\sum}\nolimits_{j = 1}^{n_i^{{\rm obs}}} \theta _{ij}^{{\rm obs}}\tau ^2 + \mathop {\sum}\nolimits_{k = 1}^{n_i^{{\rm mis}}} \theta _{ik}^{{\rm mis}}\tau ^2}}{{n_i^{{\rm obs}}\tau ^2 + n_i^{{\rm mis}}\tau ^2 + \sigma ^2}}\) and \(\tau _i^2 = \frac{{\sigma ^2\tau ^2}}{{n_i^{{\rm obs}}\tau ^2 + n_i^{{\rm mis}}\tau ^2 + \sigma ^2}}.\)
The conditional distribution of \(\mu \) is normal distribution
The conditional distribution of \(\tau ^2\) will be inverse gamma distribution
The conditional distribution of \(\sigma ^2\) will be inverse gamma distribution
Posterior approximation proceeds by iterative sampling of each unknown quantity from its full conditional distribution. First we choose the number of iterations S to be 10,000 and decide starting values for each of these parameters. Given a current state of the unknowns \(\left\{{\theta _{11}^{{\rm obs}(s)}, \ldots ,\theta _{In_I}^{{\rm obs}\left( s \right)},\theta _{11}^{{\rm mis}\left( s \right)}, \ldots ,\theta _{In_I}^{{\rm mis}(s)},\theta _i^{(s)},\mu ^{(s)},\tau ^{2(s)},\sigma ^{2(s)}} \right\}\), a new state is generated as follows:
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1.
Posterior step: sample \(\theta _i^{(s + 1)},i = 1, \ldots ,I\) from \(\theta _i|\mu ^{\left( s \right)},\theta _{i1}^{{\rm obs}\left( s \right)}, \ldots ,\theta _{in_i}^{{\rm obs}\left( s \right)},\theta _{i1}^{{\rm mis}\left( s \right)}, \ldots ,\theta _{in_i}^{{\rm mis}(s)},\tau ^{2(s)},\sigma ^{2(s)}\) based on its full conditional distribution
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2.
Posterior step: sample \(\mu ^{(s + 1)}\) from \(\mu |\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{\left( {s + 1} \right)},\tau ^{2(s)}\)
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3.
Posterior step: sample \(\tau ^{2(s + 1)}\) from \(\tau ^2|\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{\left( {s + 1} \right)},\,\mu ^{(s + 1)}\)
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4.
Posterior step: sample \(\sigma ^{2(s + 1)}\) from \(\sigma ^2|\theta _{11}^{{\rm obs}\left( s \right)}, \ldots ,\)\(\theta _{In_I}^{{\rm obs}\left( s \right)},\theta _{11}^{{\rm mis}\left( s \right)}, \ldots ,\theta _{In_I}^{{\rm mis}\left( s \right)},\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{(s + 1)}\)
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5.
Posterior step: sample \(\theta _{ij}^{{\rm obs}(s + 1)},i = 1, \ldots ,I,j = 1, \ldots ,n_i^{{\rm obs}}\) from \(\theta _{ij}^{{\rm obs}}|\theta _i^{(s + 1)},\sigma ^{2\left( {s + 1} \right)}\)
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6.
Imputation step: sample \(\theta _{ij}^{{\rm mis}(s + 1)},i = 1, \ldots ,I,j = 1, \ldots ,n_i^{{\rm mis}}\) from \(\theta _{ij}^{{\rm mis}}|\theta _i^{(s + 1)},\sigma ^{2\left( {s + 1} \right)}.\)
Appendix 2
Table 6
Appendix 3 – Full Time Trend Results
Table 7
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Roberts, B., Cheng, W., Mukherjee, B. et al. Imputation of missing values in a large job exposure matrix using hierarchical information. J Expo Sci Environ Epidemiol 28, 615–648 (2018). https://doi.org/10.1038/s41370-018-0037-x
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DOI: https://doi.org/10.1038/s41370-018-0037-x
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