AI Article Synopsis
- The study explores how unobserved personal traits affect the link between work factors and sickness absence (both full-time and part-time) in Finland over 2005-2016.
- Researchers used panel data and different statistical models to differentiate between general trends and individual changes over time.
- Findings showed that while the public sector and physically demanding jobs were associated with higher sickness absence rates, these effects diminished or even reversed when accounting for people's unmeasured characteristics, particularly among women.
Article Abstract
Objective: We aimed to investigate the influence of unobserved individual characteristics in explaining the effects of work-related factors on full (fSA) and part-time sickness absence (pSA).
Methods: We used register-based panel data for the period 2005-2016 on a 70% random sample of the Finnish working-age population. The relationships between employment sector and occupational exposures (% exposed to physically heavy work and job control score based on job exposure matrices) and the annual onset of fSA and pSA were investigated among men and women. First, random effects (RE) models were applied controlling for observed sociodemographic factors and then fixed effects (FE) models that examine within-individual changes over time and thereby further account for unobserved time-invariant individual characteristics.
Results: In the RE analyses, public employment sector, physically heavy work and lower job control each increased the use of fSA and pSA among both genders. When unobserved individual characteristics were controlled for with the FE models, the effects on fSA attenuated. For pSA, the effects of employment sector and physical heaviness of work among women even reversed. The effect of lower job control on pSA remained especially among women.
Conclusions: The role of individuals' unobserved characteristics in explaining the effects of work-related factors on SA should not be neglected. The effects of work-related factors are likely to be overestimated when using traditional approaches that do not account for unobserved confounding, ie, selection of individuals with a high likelihood of SA into particular work environments.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9045233 | PMC |
| http://dx.doi.org/10.5271/sjweh.4003 | DOI Listing |
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