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

Abstract

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.

Predicting the duration of sickness absence spells due to back pain: a population-based study from Sweden

“…Several studies have identified previous (long-term) SA as a strong risk factor for future SA17–19; we also found this to be a predictor and included it in our model. We also accounted for other factors such as age, sex, educational level and family composition based on their well-known importance for SA 19 32.…”

“…Moreover, the use of high-quality register-based information26 28 40 (not self-reports) on the possible predictors, no dropouts, the complete and very large study group, meant that our data were not hampered by recall bias or selection bias and made it possible to validate our model for the whole population. However, it also limited our possibility to include some predictors that have been shown to be of importance for occurrence of SA but which are usually obtained through surveys, such as: self-rated health, sleep problems, body mass index, smoking or social support 17 18. Other studies might be able to investigate if such factors can increase the discriminative capacity of the model.…”

“…Several studies have developed and validated decision support tools to predict future work incapacity, whether ending in SA or not, in general or related to specific diagnoses 14–18. Most of these studies were limited by low sample sizes,19 20 specific study populations,17 19 short follow-up periods17 21 or lack of information on SA diagnosis 17–19. The grounds for the selection of predictors in those prediction models have varied from using theoretical frameworks, that is, the job-demand-control model,17 mathematical modelling of survey data,14 to selection based on literature 19.…”

“…Most of these studies were limited by low sample sizes,19 20 specific study populations,17 19 short follow-up periods17 21 or lack of information on SA diagnosis 17–19. The grounds for the selection of predictors in those prediction models have varied from using theoretical frameworks, that is, the job-demand-control model,17 mathematical modelling of survey data,14 to selection based on literature 19. Only one previous study developed a model to predict duration of SA among those already on SA—with focus on SA due to stress-related diagnoses 22.…”

Efficacy of an indicated prevention strategy on sickness absence and termination of the employment contract: a 5-year follow-up study

“…The analyses were adjusted for the following covariates: age, gender, job function/education level (available data differed between RCT I and RCT II), and long-term illness previous to the screening questionnaire. These covariates were chosen due to their important predicting ability for SA ( 36 , 37 ). A Chi-square test was used to investigate if SA or LTSA were precursors for the departure of employees from the company.…”