Predictors of Sick Leave among Healthcare Workers in a Lebanese Tertiary Medical Center: A Retrospective Analysis

Abstract

Background:Previous studies have shown that demographic, social, and work-related factors can influence sick-leave rates among healthcare workers. In this multivariable study, we evaluated potential predictors of sick leaves among the employees of a tertiary medical center in Lebanon between the years 2015 and 2018.Methods: This study was a retrospective analysis of sick leave records of the population of healthcare workers who took sick leave between 2015 and 2018. The records included a large sample size of 2,850 employees. Descriptive statistics were run, and the number of sick leave episodes was stratified by sick leave diagnosis. Bivariate analysis was done to investigate the association of the outcome with all predictors. To identify potential predictors of sick leave episodes, a multiple linear regression was conducted, and a p-value<0.05 was considered statistically significant.Results:Infectious diseases and musculoskeletal disorders diagnoses accounted for the highest number of sick leaves throughout the study period. All diagnoses had a short duration of sick leave days, with the mean duration of all diagnoses being 2.60 days. As for predictors of number of sick leave episodes, results from multiple regression analysis showed that the female gender, older age, marital status, lower job grade, job position, smoking status, and some medical conditions (namely gastrointestinal problems, respiratory problems, kidney disease, headache, back pain, and anxiety) were all significant predictors of taking more sick-leave episodes in the study population. Conclusion:Analyzing sick leave is a complex process with multifactorial determinants, and our study found a significant association between multiple demographics, work related, and health related predictors with the number of sick leave episodes. These findings call for further research to evaluate how interventions such as smoking cessation programs, safe patient handling programs, and employee assistant programs could impact sick leave rates among healthcare workers. Such interventions would guide policies for healthier, safer, and more productive workplaces.