The CONSTANCES job exposure matrix based on self-reported exposure to physical risk factors: development and evaluation

Abstract

Objectives: Job exposure matrices (JEMs) can be constructed from expert-rated assessments, direct measurement and self-reports. This paper describes the construction of a general population JEM based on self-reported physical exposures, its ability to create homogeneous exposure groups (HEG) and the use of different exposure metrics to express job-level estimates.

Methods: The JEM was constructed from physical exposure data obtained from the Cohorte des consultants des Centres d'examens de santé (CONSTANCES). Using data from 35 526 eligible participants, the JEM consisted of 27 physical risk factors from 407 job codes. We determined whether the JEM created HEG by performing non-parametric multivariate analysis of variance (NPMANOVA). We compared three exposure metrics (mean, bias-corrected mean, median) by calculating within-job and between-job variances, and by residual plots between each metric and individual reported exposure.

Results: NPMANOVA showed significantly higher between-job than within-job variance among the 27 risk factors (F(253,21964)=61.33, p<0.0001, r²=41.1%). The bias-corrected mean produced more favourable HEG as we observed higher between-job variance and more explained variance than either means or medians. When compared with individual reported exposures, the bias-corrected mean led to near-zero mean differences and lower variance than other exposure metrics.

Conclusions: CONSTANCES JEM using self-reported data yielded HEGs, and can thus classify individual participants based on job title. The bias-corrected mean metric may better reflect the shape of the underlying exposure distribution. This JEM opens new possibilities for using unbiased exposure estimates to study the effects of workplace physical exposures on a variety of health conditions within a large general population study.

Keywords: ergonomics; exposure assessment; musculoskeletal disorders; occupational biomechanical exposure.

Figure 1

Multidimensional scaling plots of exposure vectors for all PCS codes with 95% confidence ellipses based on Monte Carlo simulations. Colour coded by PCS subgroup (first digit of PCS). PCS, Profession et Catégorie Sociale.

Figure 2

Example of box plots of the differences between individual-level reports and group-level exposure estimates (individual JEM) at each exposure intensity level for three exposure metrics: (A) JEM mean, (B) JEM bias-corrected mean and (C) JEM median. Distributions of individual (top axis) and JEM (right axis) are plotted. Bias-corrected mean determined using empirical quantile mapping (EQM) methods. The exposure variable in this example is ‘Repetition’.