Supplementary file 1_Simulated Improvements in Influence at Work and Reduction in Sickness

301,185 young Danish employees aged 15–30 who entered the labor market between 2010 and 2018 were followed for a mean of 2.6 years. This dataset was used to simulate the effect of a one standard deviation increase in job influence on sickness absence days, predicting a reduction of 0.16 days per person annually. The study, authored by Jeppe Karl Sørensen and published on figshare, used a job-exposure matrix and Poisson regression for its analysis.

Use Cases

• Modeling the relationship between psychosocial work factors and health outcomes based on job-exposure matrix data.
• Simulating public health intervention impacts on workforce productivity using the parametric g-formula method.
• Analyzing sector-specific effects on sickness absence, as the description notes the largest reductions were in care work and education.

Strengths

• Based on a large, nationwide register cohort of 301,185 individuals.
• Uses a longitudinal design with a mean follow-up period of 2.6 years.
• Provides specific, quantified results, such as a rate ratio of 0.71 and an estimated reduction of 126,400 sickness absence days.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a 164.3 KB DOCX document, suggesting the underlying dataset itself is not directly provided or is very small in scale.

Provenance

Source

Danish Work Life Course Cohort register data.

Collection Method

Register-based study using a job-exposure matrix to assign influence levels and Poisson regression for simulation.

Time Range

Covers individuals entering the labor market between 2010 and 2018.

Freshness

Last updated 2026-04-23 05:01:16; freshness should be verified.

Geography

Denmark (nationwide).