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Predicting long-term sickness absence among employees with frequent sickness absence
PURPOSE: Frequent 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. METHODS: Data were obtained from 53,833 workers who participated in occupational health surveys in the period 2010–2013; 4204 of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435617/ https://www.ncbi.nlm.nih.gov/pubmed/30474733 http://dx.doi.org/10.1007/s00420-018-1384-6 |
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author | Notenbomer, Annette van Rhenen, Willem Groothoff, Johan W. Roelen, Corné A. M. |
author_facet | Notenbomer, Annette van Rhenen, Willem Groothoff, Johan W. Roelen, Corné A. M. |
author_sort | Notenbomer, Annette |
collection | PubMed |
description | PURPOSE: Frequent 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. METHODS: Data 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. RESULTS: A 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. CONCLUSIONS: Both 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. |
format | Online Article Text |
id | pubmed-6435617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-64356172019-04-15 Predicting long-term sickness absence among employees with frequent sickness absence Notenbomer, Annette van Rhenen, Willem Groothoff, Johan W. Roelen, Corné A. M. Int Arch Occup Environ Health Original Article PURPOSE: Frequent 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. METHODS: Data 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. RESULTS: A 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. CONCLUSIONS: Both 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. Springer Berlin Heidelberg 2018-11-24 2019 /pmc/articles/PMC6435617/ /pubmed/30474733 http://dx.doi.org/10.1007/s00420-018-1384-6 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Notenbomer, Annette van Rhenen, Willem Groothoff, Johan W. Roelen, Corné A. M. Predicting long-term sickness absence among employees with frequent sickness absence |
title | Predicting long-term sickness absence among employees with frequent sickness absence |
title_full | Predicting long-term sickness absence among employees with frequent sickness absence |
title_fullStr | Predicting long-term sickness absence among employees with frequent sickness absence |
title_full_unstemmed | Predicting long-term sickness absence among employees with frequent sickness absence |
title_short | Predicting long-term sickness absence among employees with frequent sickness absence |
title_sort | predicting long-term sickness absence among employees with frequent sickness absence |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435617/ https://www.ncbi.nlm.nih.gov/pubmed/30474733 http://dx.doi.org/10.1007/s00420-018-1384-6 |
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