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

Journal of Exposure Science & Environmental Epidemiology volume 28, pages 615–648 (2018)Cite this article

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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Authors and Affiliations

  1. Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Benjamin Roberts & Richard L Neitzel

  2. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Wenting Cheng & Bhramar Mukherjee

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  1. Benjamin Roberts
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  2. Wenting Cheng
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  3. Bhramar Mukherjee
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  4. Richard L Neitzel
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Correspondence to Richard L Neitzel.

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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 \(p\left({\theta_{11}^{\rm{obs}},}\,{\ldots,}\right.\)\( \left. \theta_{In_I}^{\rm{obs}},\theta _{11}^{{\rm mis}}, \ldots ,\theta _{In_I}^{{\rm mis}},\theta_1, \ldots ,\theta_I,\mu ,\tau^2,\sigma^2|{\rm observed}\,{\rm data}\right)\):

$$\begin{array}{l}p\left( {\theta _{11}^{{\rm obs}}, \ldots ,\theta _{In_I}^{{\rm obs}},\,\theta _{11}^{{\rm mis}}, \ldots ,\theta _{In_I}^{{\rm mis}},\theta _1, \ldots ,}\right. \\ \left.{ \theta _I,\mu ,\tau ^2, \sigma ^2{\mathrm{|}}{\rm observed}\,{\rm data}} \right) \\ \propto p\left( {{\rm observed}\,{\rm data}{\mathrm{|}}\theta _{11}^{{\rm obs}}, \ldots ,\theta _{In_I}^{{\rm obs}},\,\theta _{11}^{{\rm mis}}, \ldots ,}\right. \\ \left.{\theta _{In_I}^{{\rm mis}},\theta _1, \ldots ,\theta _I,\mu ,\tau ^2,\sigma ^2} \right)\\ \cdot \left\{{\mathop {\prod}\limits_{i = 1}^I \mathop {\prod}\limits_{j = 1}^{n_i^{{\rm obs}}} p(\theta _{ij}^{{\rm obs}}|\theta _i,\,\sigma ^2)} \right\} \cdot \left\{{\mathop {\prod}\limits_{i = 1}^I \mathop {\prod}\limits_{k = 1}^{n_i^{{\rm mis}}} p(\theta _{ik}^{{\rm obs}}|\theta _i,\,\sigma ^2)} \right\} \\ \cdot \left\{{\mathop {\prod}\limits_{i = 1}^I p(\theta _i|\mu ,\tau ^2)} \right\} \cdot \pi (\mu ) \cdot \pi (\tau ^2) \cdot \pi (\sigma ^2)\end{array}.$$

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

$$\left( {\theta _{ij}^{{\rm obs}}{\mathrm{|}}{\rm observed}\,{\rm data},{\rm all}\,{\rm other}\,{\rm para}} \right) \propto \\ {\rm e}{\mathrm{xp}}\left( { - \frac{{\left( {Y_{ij}^{{\rm obs}} - \theta _{ij}^{{\rm obs}}} \right)^2}}{{2\frac{{\left( {s_{ij}^{{\rm obs}}} \right)^2}}{{n_{ij}^{{\rm obs}}}}}}} \right) \cdot {\mathrm{exp}} \left( { - \frac{{\left( {\theta _{ij}^{{\rm obs}} - \theta _i} \right)^2}}{{2\sigma ^2}}} \right).$$

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:

$$\theta _{ij}^{{\rm obs}}\sim N\left( {\mu _{ij}^{{\rm obs}},(\sigma _{ij}^{{\rm obs}})^2} \right),$$

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

$$\theta _{ik}^{{\rm mis}}\sim N(\theta _i,\sigma ^2).$$

The conditional distribution of \(\theta _i\) is also normal distribution

$$\theta _i\sim N(\mu _i,\tau _i^2),$$

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

$$\mu \sim N\left( {\frac{{\mathop {\sum}\nolimits_{i = 1}^I \theta _i\gamma _0^2 + \mu _0\tau ^2}}{{I\gamma _0^2 + \tau ^2}},\frac{{\tau ^2\gamma _0^2}}{{I\gamma _0^2 + \tau ^2}}} \right).$$

The conditional distribution of \(\tau ^2\) will be inverse gamma distribution

$$\tau ^2\sim {\rm Inv} - {\rm Gamma}\left( {\frac{{I + \eta _0}}{2},\frac{{\mathop {\sum}\nolimits_{i = 1}^I (\theta _i - \mu )^2 + \eta _0\tau _0^2}}{2}} \right).$$

The conditional distribution of \(\sigma ^2\) will be inverse gamma distribution

$$ \sigma ^2{\mathrm{\sim }}{\rm Inv} \\ - {\rm Gamma}\left( \begin{array}{l}\frac{{\mathop {\sum }\nolimits_{i = 1}^I n_i^{{\rm obs}} + \mathop {\sum }\nolimits_{i = 1}^I n_i^{{\rm mis}} + \upsilon _0}}{2} ,\\ \frac{{\mathop {\sum }\nolimits_{i = 1}^I \mathop {\sum }\nolimits_{j = 1}^{n_i^{{\rm obs}}} \left( {\theta _{ij}^{{\rm obs}} - \theta _i} \right)^2{\mathrm{ + }}\mathop {\sum }\nolimits_{i = 1}^I \mathop {\sum }\nolimits_{k = 1}^{n_i^{{\rm mis}}} \left( {\theta _{ik}^{{\rm mis}} - \theta _i} \right)^2 + \upsilon _0\sigma _0^2}}{2}\end{array} \right).$$

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:

  1. 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

  2. 2.

    Posterior step: sample \(\mu ^{(s + 1)}\) from \(\mu |\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{\left( {s + 1} \right)},\tau ^{2(s)}\)

  3. 3.

    Posterior step: sample \(\tau ^{2(s + 1)}\) from \(\tau ^2|\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{\left( {s + 1} \right)},\,\mu ^{(s + 1)}\)

  4. 4.

    Posterior step: sample \(\sigma ^{2(s + 1)}\) from \(\sigma ^2|\theta _{11}^{{\rm obs}\left( s \right)}, \ldots ,\)\(\theta _{In_I}^{{\rm obs}\left( s \right)},\theta _{11}^{{\rm mis}\left( s \right)}, \ldots ,\theta _{In_I}^{{\rm mis}\left( s \right)},\theta _1^{\left( {s + 1} \right)}, \ldots ,\theta _I^{(s + 1)}\)

  5. 5.

    Posterior step: sample \(\theta _{ij}^{{\rm obs}(s + 1)},i = 1, \ldots ,I,j = 1, \ldots ,n_i^{{\rm obs}}\) from \(\theta _{ij}^{{\rm obs}}|\theta _i^{(s + 1)},\sigma ^{2\left( {s + 1} \right)}\)

  6. 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

Table 6 Predicted noise exposure based on model results
Full size table

Figures 6 and 7

Fig. 6
figure 6

Major SOCs that are decreasing over time

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Fig. 7
figure 7

Major SOCs that are not decreasing over time

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Appendix 3 – Full Time Trend Results

Table 7

Table 7 Full model results for the temporal analysis for major SOCs
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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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