Prediction of long-term and frequent sickness absence using company data

C. R.L. Boot, A. Van Drongelen, I. Wolbers, H. Hlobil, A. J. Van der Beek, T. Smid

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background More insight into predictive factors is needed to identify employees at risk for future sickness absence. Companies register potentially relevant information regarding sickness absence in their human resources and work schedule administration.Aims To investigate which combination of administrative company data best predicts long-term and frequent sickness absence in airline employees.Methods Socio-demographic and work-related variables between 2005 and 2008 were retrieved from the administrative data of an airline company. Logistic regression analyses were used to build prediction models for long-term (>42 consecutive days) and frequent (more than three episodes) sickness absence in 2009. Both models were internally validated.Results Data on 7652 employees were available for analysis. Long-term sickness absence was predicted by a combination of higher age, recent pregnancy, having a parking permit, having 'aggravated working conditions' and previous sickness absence. Recent marriage appeared to reduce the risk. Frequent sickness absence was predicted by being single, not having children of 16 years and older, not having a company parking permit, no shift work, having a job with special operational requirements and previous sickness absence. The long-term and frequent sickness absence models had a discriminative ability of 0.72 and 0.73, and an explained variance of 10.9 and 14.2%, respectively.Conclusions The results show that it is possible to compose prediction models for employees at risk of sickness absence using only administrative company data. However, as the explained variance was low, additional factors should be identified to predict risk of future sickness absence.

Original languageEnglish
Pages (from-to)176-181
Number of pages6
JournalOccupational Medicine
Volume67
Issue number3
DOIs
Publication statusPublished - 1 Apr 2017

Cite this

Boot, C. R.L. ; Van Drongelen, A. ; Wolbers, I. ; Hlobil, H. ; Van der Beek, A. J. ; Smid, T. / Prediction of long-term and frequent sickness absence using company data. In: Occupational Medicine. 2017 ; Vol. 67, No. 3. pp. 176-181.
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abstract = "Background More insight into predictive factors is needed to identify employees at risk for future sickness absence. Companies register potentially relevant information regarding sickness absence in their human resources and work schedule administration.Aims To investigate which combination of administrative company data best predicts long-term and frequent sickness absence in airline employees.Methods Socio-demographic and work-related variables between 2005 and 2008 were retrieved from the administrative data of an airline company. Logistic regression analyses were used to build prediction models for long-term (>42 consecutive days) and frequent (more than three episodes) sickness absence in 2009. Both models were internally validated.Results Data on 7652 employees were available for analysis. Long-term sickness absence was predicted by a combination of higher age, recent pregnancy, having a parking permit, having 'aggravated working conditions' and previous sickness absence. Recent marriage appeared to reduce the risk. Frequent sickness absence was predicted by being single, not having children of 16 years and older, not having a company parking permit, no shift work, having a job with special operational requirements and previous sickness absence. The long-term and frequent sickness absence models had a discriminative ability of 0.72 and 0.73, and an explained variance of 10.9 and 14.2{\%}, respectively.Conclusions The results show that it is possible to compose prediction models for employees at risk of sickness absence using only administrative company data. However, as the explained variance was low, additional factors should be identified to predict risk of future sickness absence.",
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Prediction of long-term and frequent sickness absence using company data. / Boot, C. R.L.; Van Drongelen, A.; Wolbers, I.; Hlobil, H.; Van der Beek, A. J.; Smid, T.

In: Occupational Medicine, Vol. 67, No. 3, 01.04.2017, p. 176-181.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Prediction of long-term and frequent sickness absence using company data

AU - Boot, C. R.L.

AU - Van Drongelen, A.

AU - Wolbers, I.

AU - Hlobil, H.

AU - Van der Beek, A. J.

AU - Smid, T.

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AB - Background More insight into predictive factors is needed to identify employees at risk for future sickness absence. Companies register potentially relevant information regarding sickness absence in their human resources and work schedule administration.Aims To investigate which combination of administrative company data best predicts long-term and frequent sickness absence in airline employees.Methods Socio-demographic and work-related variables between 2005 and 2008 were retrieved from the administrative data of an airline company. Logistic regression analyses were used to build prediction models for long-term (>42 consecutive days) and frequent (more than three episodes) sickness absence in 2009. Both models were internally validated.Results Data on 7652 employees were available for analysis. Long-term sickness absence was predicted by a combination of higher age, recent pregnancy, having a parking permit, having 'aggravated working conditions' and previous sickness absence. Recent marriage appeared to reduce the risk. Frequent sickness absence was predicted by being single, not having children of 16 years and older, not having a company parking permit, no shift work, having a job with special operational requirements and previous sickness absence. The long-term and frequent sickness absence models had a discriminative ability of 0.72 and 0.73, and an explained variance of 10.9 and 14.2%, respectively.Conclusions The results show that it is possible to compose prediction models for employees at risk of sickness absence using only administrative company data. However, as the explained variance was low, additional factors should be identified to predict risk of future sickness absence.

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KW - Retrospective exposure assessment

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