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.

Methods: Job satisfaction was assessed on the first day of sickness absence and associated with the duration of sickness absence using a multiple logistic regression model to which demographics, working conditions, and the week day on which sick-leave was taken were stepwise added as covariates. Results: Workers with low job satisfaction scores had higher odds (odds ratio [OR] = 3.85; 95% confidence interval [CI] 1.74 to 8.51) of being > 3 days absent from work due to the common cold compared to satisfied workers. However, the duration of sickness absence due to the common cold was more strongly related to the day of taking sick-leave with workers who reported sick on Monday or Tuesday being longer absent (OR = 5.36; 95% CI 1.44 to 9.90) than those who reported sick on Friday. Conclusions: When having a common cold, dissatisfied workers are longer absent from work although the duration of short episodes of sickness absence seems to be determined by the week day on which sick-leave is taken rather than working conditions or perceptions about work.

Self-reported reduced work ability is associated with both frequent and long-term sickness absence, suggesting that frequent sickness absence is not only driven by motivational processes.

BackgroundFrequent sickness absence—that is, 3 or more episodes of sickness absence in 1 year—is a problem for employers and employees. Many employees who have had frequent sickness absence in a prior year also have frequent sickness absence in subsequent years: 39% in the first follow-up year and 61% within 4 years. Moreover, 19% have long-term sickness absence (≥6 weeks) in the first follow-up year and 50% within 4 years. We developed an electronic health (eHealth) intervention, consisting of fully automated feedback and advice, to use either as a stand-alone tool (eHealth intervention–only) or combined with consultation with an occupational physician (eHealth intervention–occupational physician).ObjectiveThis study aimed to evaluate the effect of the eHealth intervention, with or without additional occupational physician consultation, to reduce sickness absence frequency for employees with frequent sickness absence, versus care as usual (CAU).MethodsThis study was a three-armed randomized controlled trial. Employees with frequent sickness absence received invitational letters, which were distributed by their employers. The primary outcome measure was the number of register-based sickness absence episodes 12 months after completing the baseline questionnaire. Secondary outcome measures were register-based total sickness absence days and self-assessed burnout, engagement, and work ability. In a process evaluation 3 months after baseline, we examined adherence to the intervention and additional actions such as general practitioner and occupational physician visit, communication with the manager, and lifestyle change.ResultsA total of 82 participants were included in the analyses, 21 in the eHealth intervention–only group, 31 in the eHealth intervention–occupational physician group, and 30 in the CAU group. We found no significant difference in sickness absence frequency between the groups at 1-year follow-up. Sickness absence frequency decreased in the eHealth intervention–only group from 3 (interquartile range, IQR 3-4) to 1 episode (IQR 0.3-2.8), in the eHealth intervention–occupational physician group from 4 (IQR 3-5) to 3 episodes (IQR 1-4), and in the CAU group from 3 (IQR 3-4) to 2 episodes (IQR 1-3). For secondary outcomes, we found no significant differences between the intervention groups and the control group. The process evaluation showed that only 3 participants from the eHealth intervention–occupational physician group visited the occupational physician on invitation.ConclusionsAmong employees with frequent sickness absence, we found no effect from the eHealth intervention as a stand-alone tool in reducing sickness absence frequency, nor on total sickness absence days, burnout, engagement, or work ability. This might be due to low adherence to the intervention because of insufficient urgency to act. We cannot draw any conclusion on the effect of the eHealth intervention tool combined with an occupational physician consultation (eHealth intervention–occupational physician), due to very low adherence t...