Corné Roelen1,2, Sannie Thorsen3, Martijn Heymans2, Jos Twisk2, Ute Bültmann1,3, Jakob Bjørner3,4. 1. a Department of Health Sciences, Community and Occupational Medicine , University Medical Center Groningen, University of Groningen, Groningen , Groningen , The Netherlands. 2. b Department of Epidemiology and Biostatistics , VU University Medical Center, VU University , Amsterdam , The Netherlands. 3. c National Research Center for the Working Environment , Copenhagen , Demark. 4. d Department of Public Health , University of Copenhagen , Copenhagen , Denmark.
Abstract
PURPOSE: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. MATERIALS AND METHODS: Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. RESULTS: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. CONCLUSIONS: A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience of rehabilitation professionals, to improve long-term sickness absence risk profiles.
PURPOSE: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. MATERIALS AND METHODS: Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. RESULTS: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. CONCLUSIONS: A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience of rehabilitation professionals, to improve long-term sickness absence risk profiles.
Entities:
Keywords:
Clinical prediction models; general working population; risk assessment; sick leave; work disability prevention
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