Abstract
ObjectiveTo study the performance of a developed job exposure matrix (JEM) for the assessment of psychosocial factors at work in terms of accuracy, possible misclassification bias and predictive ability to detect known associations with depression and low back pain (LBP).Materials and MethodsWe utilized two large population surveys (the Health 2000 Study and the Finnish Work and Health Surveys), one to construct the JEM and another to test matrix performance. In the first study, information on job demands, job control, monotonous work and social support at work was collected via face-to-face interviews. Job strain was operationalized based on job demands and job control using quadrant approach. In the second study, the sensitivity and specificity were estimated applying a Bayesian approach. The magnitude of misclassification error was examined by calculating the biased odds ratios as a function of the sensitivity and specificity of the JEM and fixed true prevalence and odds ratios. Finally, we adjusted for misclassification error the observed associations between JEM measures and selected health outcomes.ResultsThe matrix showed a good accuracy for job control and job strain, while its performance for other exposures was relatively low. Without correction for exposure misclassification, the JEM was able to detect the association between job strain and depression in men and between monotonous work and LBP in both genders.ConclusionsOur results suggest that JEM more accurately identifies occupations with low control and high strain than those with high demands or low social support. Overall, the present JEM is a useful source of job-level psychosocial exposures in epidemiological studies lacking individual-level exposure information. Furthermore, we showed the applicability of a Bayesian approach in the evaluation of the performance of the JEM in a situation where, in practice, no gold standard of exposure assessment exists.
Highlights
During the past three decades, the effects of psychosocial factors at work on health have received considerable attention in research
Our results suggest that job exposure matrix (JEM) more accurately identifies occupations with low control and high strain than those with high demands or low social support
We showed the applicability of a Bayesian approach in the evaluation of the performance of the JEM in a situation where, in practice, no gold standard of exposure assessment exists
Summary
During the past three decades, the effects of psychosocial factors at work on health have received considerable attention in research. Psychosocial factors at work are numerous, with psychological job demands, job control (decision latitude), efforts and rewards [1,2] comprising the key dimensions. Another factor of importance is social support at work [3]. The job strain model has been successfully used to predict the risk of cardiovascular disease [5,6], major mental disorders [7], type II diabetes [8] and musculoskeletal diseases [9]. The effects of the individual components of the job strain model on health have been evaluated, the results have often been inconsistent across the studies and health outcomes [7,9]
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