Abstract

Background: Sickness absenteeism has long been an organizational problem due to the consequent loss of productivity (1–2% of gross domestic product in European Union countries) and job overload for workers. We designed and conducted an observational cohort study to describe and analyze the short sickness events and their derived absence days in a large university tertiary hospital, also incorporating the fit for work assessment and the anti-influenza vaccination coverage. Materials and Methods: The anonymous data of workers with at least one day of certified presence during the period 1 January–31 August 2025 were extracted from the hospital information system. Crude and job-specific absence rates were calculated. The incidence rate ratios (IRRs) along with their 99% confidence intervals for the covariates were obtained by a negative binomial model fit. Results: The overall sickness event rate was 0.54 per 100 person-workdays, ranging from 0.22 (physicians) to 0.92 (technical/administrative staff), while the overall sickness day rate was 0.88 per 100 person-workdays, spanning from 0.31 (physicians) to 1.41 (health and social care assistants). The multi-variable model fit of sickness days returned significant IRRs for being vaccinated (0.74; 99% CI: 0.58–0.93), for male gender (0.77; 99% CI: 0.63–0.95) and for limitations regarding the fitness for work assessment (2.14; 99% CI: 1.51–3.05). Conclusions: Our findings indicate that, in a large tertiary university hospital, an anti-influenza vaccination campaign may effectively protect workers from sickness absences, even accounting for gender, age and regardless of job typology and fit for work assessment. Furthermore, the health risk of the technical/administrative job category might be underestimated. Finally, our results seem to suggest that workers with limitations might be “fragile”, or more vulnerable, than expected in a broader sense of the term, both from nosological and temporal points of view. Further research with a higher level of evidence is needed to verify and confirm these findings.

IPC Classification

Keywords