Use of secondary data to assess occupation-specific risk for selected diseases

Status:

ongoing

Aims:

The analysis of sickness absence (SA) data from statutory health insurance enables a differentiated assessment of work‑related burdens across occupational groups and economic sectors. The aim of this study is to systematically capture the extent and structure of work‑related sickness absence using key SA indicators: cases per 100 insured persons, total SA days, and SA days per case. The combination of these metrics allows both the frequency and severity of SA events to be compared across occupations and sectors, thereby identifying specific areas of elevated burden. In addition, the analysis examines the influence of sex and age groups on observed patterns and assesses whether significant differences in SA levels emerge. The findings provide an empirical basis for more precise quantification of work‑related health risks, the development of targeted prevention strategies, and the further refinement of epidemiological assessments of occupational diseases.

Activities/Methods:

Sickness absence (SA) data were obtained from a statutory health insurance fund and prepared for analysis. The dataset includes SA indicators stratified by occupational groups and economic sectors. The evaluation follows an exploratory approach and focuses on key SA metrics, including cases per 100 insured persons, total SA days, and SA days per case, allowing both the frequency and severity of sickness absence to be assessed.

Methodological considerations addressed the analytical potential and limitations of SA data in identifying work‑related health burdens. Particular attention was given to assessing whether SA patterns can be meaningfully compared with officially recognized occupational disease (OD) statistics. While SA data reflect the overall morbidity burden within occupational groups, OD statistics capture only confirmed cases with established occupational causation. As such, both data sources provide complementary but only partially comparable insights.

The analysis includes descriptive evaluations, comparisons across occupational groups, and stratified assessments by sex and age to identify characteristic patterns and areas of elevated burden.

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