Publication

Predicting long-term sickness absence among employees with frequent sickness absence

Notenbomer, A., van Rhenen, W., Groothoff, J. W. & Roelen, C. A. M., May-2019, In : International Archives of Occupational and Environmental Health. 92, 4, p. 501-511 11 p.

Research output: Contribution to journalArticleAcademicpeer-review

PurposeFrequent absentees are at risk of long-term sickness absence (SA). The aim of the study is to develop prediction models for long-term SA among frequent absentees.MethodsData were obtained from 53,833 workers who participated in occupational health surveys in the period 2010-2013; 4204 of them were frequent absentees (i.e., employees with 3 SA spells in the year prior to the survey). The survey data of the frequent absentees were used to develop two prediction models: model 1 including job demands and job resources and model 2 including burnout and work engagement. Discrimination between frequent absentees with and without long-term SA during follow-up was assessed with the area under the receiver operating characteristic curve (AUC); (AUC)0.75 was considered useful for practice.ResultsA total of 3563 employees had complete data for analyses and 685 (19%) of them had long-term SA during 1-year follow-up. The final model 1 included age, gender, education, marital status, prior long-term SA, work pace, role clarity and learning opportunities. Discrimination between frequent absentees with and without long-term SA was significant (AUC 0.623; 95% CI 0.601-0.646), but not useful for practice. Model 2 showed comparable discrimination (AUC 0.624; 95% CI 0.596-0.651) with age, gender, education, marital status, prior long-term SA, burnout and work engagement as predictor variables. Differentiating by gender or sickness absence cause did not result in better discrimination.ConclusionsBoth prediction models discriminated significantly between frequent absentees with and without long-term SA during 1-year follow-up, but have to be further developed for use in healthcare practice.

Original languageEnglish
Pages (from-to)501-511
Number of pages11
JournalInternational Archives of Occupational and Environmental Health
Volume92
Issue number4
Publication statusPublished - May-2019

    Keywords

  • Absenteeism, Sick leave, Prediction model, ROC analysis, Occupational health, Health surveillance, MASLACH BURNOUT INVENTORY, WORK ENGAGEMENT, JOB DEMANDS, RESOURCES, RISK

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