Skip to main content

Advertisement

Log in

Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population

  • Published:
Journal of Occupational Rehabilitation Aims and scope Submit manuscript

Abstract

Purpose This study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders. Methods Cohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples. Results 30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692–0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615–0.804). Conclusions An 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Organization of Economic Cooperation and Development. Fit mind, fit job. From evidence to practice in mental health and work. Paris: OECD Publishing; 2015.

    Google Scholar 

  2. Nicholson PJ. Common mental disorders and work. Br Med Bull. 2018;126(1):113–121.

    Article  PubMed  Google Scholar 

  3. Henderson M, Harvey SB, Øverland S, Mykletun A, Hotopf M. Work and common psychiatric disorders. J R Soc Med. 2011;104(5):198–207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Roelen CA, Hoedeman R, van Rhenen W, Groothoff JW, van der Klink JJ, Bültmann U. Mental health symptoms as prognostic risk markers of all-cause and psychiatric sickness absence in office workers. Eur J Public Health. 2013;24(1):101–105.

    Article  PubMed  Google Scholar 

  5. Van Hoffen MFA, Joling CI, Heymans MW, Twisk JW, Roelen CA. Mental health symptoms identify workers at risk of long-term sickness absence due to mental disorders: prospective cohort study with 2-year follow-up. BMC Public Health. 2015;15(1):1235.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Van Hoffen MFA, Twisk JWR, Heymans MW, De Bruin J, Joling CI. Roelen CAM Psychological distress screener for risk of future mental sickness absence in non-sicklisted employees. Eur J Public Health. 2016;26(3):510–512.

    Article  PubMed  Google Scholar 

  7. Lidwall U, Bill S, Palmer E, Olsson Bohlin C. Mental disorder sick leave in Sweden: a population study. Work. 2018;59(2):259–272.

    Article  PubMed  Google Scholar 

  8. Foss L, Gravseth HM, Kristensen P, Claussen B, Mehlum IS, Skyberg K. Risk factors for long-term absence due to psychiatric sickness: a register-based 5-year follow-up from the Oslo health study. J Occup Environ Med. 2010;52(7):698–705.

    Article  PubMed  Google Scholar 

  9. Nieuwenhuijsen K, Bruinvels D, Frings-Dresen M. Psychosocial work environment and stress-related disorders, a systematic review. Occup Med. 2010;60(4):277–286.

    Article  CAS  Google Scholar 

  10. Airaksinen J, Jokela M, Virtanen M, Oksanen T, Koskenvuo M, Pentti J, Vahtera J, Kivimäki M. Prediction of long-term absence due to sickness absence in employees: development and validation of a multifactorial risk score in two cohort studies. Scand J Work Environ Health. 2018;44(3):274–282.

    Article  PubMed  Google Scholar 

  11. Roelen CAM, van Hoffen MFA, Waage S, Schaufeli WB, Twisk JWR, Bjorvatn B, Moen BE, Pallesen S. Psychosocial work environment and mental health-related long-term sickness absence among nurses. Int Arch Occup Environ Health. 2018;91(2):195–203.

    Article  PubMed  Google Scholar 

  12. Loh WY. Fifty years of classification and regression trees. Int Stat Rev. 2014;82(3):329–348.

    Article  Google Scholar 

  13. Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med. 2003;26(3):172–181.

    Article  PubMed  Google Scholar 

  14. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–W73.

    Article  Google Scholar 

  15. Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–781.

    Article  CAS  PubMed  Google Scholar 

  16. van Veldhoven MV, Jonge JD, Broersen S, Kompier M, Meijman T. Specific relationships between psychosocial job conditions and job-related stress: a three-level analytic approach. Work Stress. 2002;16(3):207–228.

    Article  Google Scholar 

  17. McAuley E, Duncan T, Tammen VV. Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis. Res Q Exerc Sport. 1989;60(1):48–58.

    Article  CAS  PubMed  Google Scholar 

  18. Schouten LS, Bültmann U, Heymans MW, Joling CI, Twisk JW, Roelen CA. Shortened version of the work ability index to identify workers at risk of long-term sickness absence. Eur J Public Health. 2016;26(2):301–305.

    Article  PubMed  Google Scholar 

  19. Schaufeli WB, Bakker AB, Salanova M. The measurement of work engagement with a short questionnaire: a cross-national study. Educ Psychol Meas. 2006;66(4):701–716.

    Article  Google Scholar 

  20. Bakker AB, Demerouti E, Schaufeli WB. Validation of the Maslach burnout inventory—general survey: an internet study. Anx Stress Coping. 2002;15(3):245–260.

    Article  Google Scholar 

  21. Terluin B, van Rhenen W, Schaufeli WB, de Haan M. The Four-Dimensional Symptom Questionnaire (4DSQ): measuring distress and other mental health problems in a working population. Work Stress. 2004;18(3):187–207.

    Article  Google Scholar 

  22. Terluin B, van Marwijk HW, Adèr HJ, de Vet HC, Penninx BW, Hermens ML, van Boeijen CA, van Balkom AJ, van der Klink JJ, Stalman WA. The Four-Dimensional Symptom Questionnaire (4DSQ): a validation study of a multidimensional self-report questionnaire to assess distress, depression, anxiety and somatization. BMC Psychiatry. 2006;6(1):34.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Terluin B, Terluin M, Prince K, van Marwijk H. The Four-Dimensional Symptom Questionnaire (4 DSQ) detects psychological problems. Huisarts en Wet. 2008;51(2):251–255.

    Article  Google Scholar 

  24. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–1379.

    Article  CAS  PubMed  Google Scholar 

  25. Stiglic G, Kocbek S, Pernek I, Kokol P. Comprehensive decision tree models in bioinformatics. PLoS ONE. 2012;7(3):e33812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Burdorf A. Prevention strategies for sickness absence: sick individuals or sick populations? Scand J Work Environ Health. 2019;45(2):101–102.

    Article  PubMed  Google Scholar 

Download references

Funding

The study was not funded.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marieke F. A. van Hoffen.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical Approval

The Medical Ethics Committee of the University Medical Center Groningen reviewed the study and granted ethical clearance.

Informed Consent

All occupational health survey participants agreed to the use of their questionnaire results for scientific research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Hoffen, M.F.A., Norder, G., Twisk, J.W.R. et al. Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population. J Occup Rehabil 30, 308–317 (2020). https://doi.org/10.1007/s10926-019-09852-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10926-019-09852-3

Keywords

Navigation