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Tytuł:
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External validation of a prediction model and decision tree for sickness absence due to mental disorders.
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Autorzy:
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van Hoffen MFA; Department of Research and Development, Human Total Care, Utrecht, The Netherlands. .; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands. .; HumanCapitalCare, Laan van Nieuw Oost-Indië 133-G, 2593 BM, Den Haag, The Netherlands. .
Norder G; Department of Research and Development, Human Total Care, Utrecht, The Netherlands.
Twisk JWR; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands.
Roelen CAM; Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands.; Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
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Źródło:
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International archives of occupational and environmental health [Int Arch Occup Environ Health] 2020 Nov; Vol. 93 (8), pp. 1007-1012. Date of Electronic Publication: 2020 May 11.
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Typ publikacji:
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Journal Article; Validation Study
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Język:
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English
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Imprint Name(s):
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Original Publication: Berlin, New York : Springer-Verlag.
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MeSH Terms:
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Decision Trees*
Surveys and Questionnaires*
Mental Disorders/*epidemiology
Occupational Health Services/*methods
Sick Leave/*statistics & numerical data
Adult ; Cohort Studies ; Female ; Humans ; Job Satisfaction ; Male ; Mental Disorders/etiology ; Middle Aged ; Models, Statistical ; Netherlands ; Prospective Studies ; Social Support ; Stress, Psychological
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References:
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PLoS One. 2012;7(3):e33812. (PMID: 22479449)
J R Soc Med. 2011 May;104(5):198-207. (PMID: 21558098)
BMC Public Health. 2015 Dec 12;15:1235. (PMID: 26655203)
Eur J Public Health. 2014 Feb;24(1):101-5. (PMID: 23487550)
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Radiology. 1982 Apr;143(1):29-36. (PMID: 7063747)
J Occup Rehabil. 2020 Sep;30(3):308-317. (PMID: 31420790)
Eur J Public Health. 2016 Jun;26(3):510-2. (PMID: 27037332)
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Ann Intern Med. 2015 Jan 6;162(1):W1-73. (PMID: 25560730)
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Contributed Indexing:
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Keywords: Health surveys; Mental health; ROC analysis; Reproducibility of results; Validation studies
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Entry Date(s):
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Date Created: 20200513 Date Completed: 20210319 Latest Revision: 20210319
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Update Code:
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20240105
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PubMed Central ID:
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PMC7519895
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DOI:
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10.1007/s00420-020-01548-z
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PMID:
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32394071
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Purpose: A previously developed prediction model and decision tree were externally validated for their ability to identify occupational health survey participants at increased risk of long-term sickness absence (LTSA) due to mental disorders.
Methods: The study population consisted of N = 3415 employees in mobility services who were invited in 2016 for an occupational health survey, consisting of an online questionnaire measuring the health status and working conditions, followed by a preventive consultation with an occupational health provider (OHP). The survey variables of the previously developed prediction model and decision tree were used for predicting mental LTSA (no = 0, yes = 1) at 1-year follow-up. Discrimination between survey participants with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC).
Results: A total of n = 1736 (51%) non-sick-listed employees participated in the survey and 51 (3%) of them had mental LTSA during follow-up. The prediction model discriminated (AUC = 0.700; 95% CI 0.628-0.773) between participants with and without mental LTSA during follow-up. Discrimination by the decision tree (AUC = 0.671; 95% CI 0.589-0.753) did not differ significantly (p = 0.62) from discrimination by the prediction model.
Conclusion: At external validation, the prediction model and the decision tree both poorly identified occupational health survey participants at increased risk of mental LTSA. OHPs could use the decision tree to determine if mental LTSA risk factors should be explored in the preventive consultation which follows after completing the survey questionnaire.
