Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jun;31(2):383-392.
doi: 10.1007/s10926-020-09928-5. Epub 2020 Oct 14.

Subgroups of Long-Term Sick-Listed Based on Prognostic Return to Work Factors Across Diagnoses: A Cross-Sectional Latent Class Analysis

Martin Inge Standal  1 , Lene Aasdahl  2   3 , Chris Jensen  2   4 , Vegard Stolsmo Foldal  2 , Roger Hagen  5 , Egil Andreas Fors  6 , Marit Solbjør  2 , Odin Hjemdal  5 , Margreth Grotle  7   8 , Ingebrigt Meisingset  2
Affiliations

Subgroups of Long-Term Sick-Listed Based on Prognostic Return to Work Factors Across Diagnoses: A Cross-Sectional Latent Class Analysis

Martin Inge Standal et al. J Occup Rehabil. 2021 Jun.

Abstract

Comorbidity is common among long-term sick-listed and many prognostic factors for return to work (RTW) are shared across diagnoses. RTW interventions have small effects, possibly due to being averaged across heterogeneous samples. Identifying subgroups based on prognostic RTW factors independent of diagnoses might help stratify interventions. The aim of this study was to identify and describe subgroups of long-term sick-listed workers, independent of diagnoses, based on prognostic factors for RTW. Latent class analysis of 532 workers sick-listed for eight weeks was used to identify subgroups based on seven prognostic RTW factors (self-reported health, anxiety and depressive symptoms, pain, self-efficacy, work ability, RTW expectations) and four covariates (age, gender, education, physical work). Four classes were identified: Class 1 (45% of participants) was characterized by favorable scores on the prognostic factors; Class 2 (22%) by high anxiety and depressive symptoms, younger age and higher education; Class 3 (16%) by overall poor scores including high pain levels; Class 4 (17%) by physical work and lack of workplace adjustments. Class 2 included more individuals with a psychological diagnosis, while diagnoses were distributed more proportionate to the sample in the other classes. The identified classes illustrate common subgroups of RTW prognosis among long-term sick-listed individuals largely independent of diagnosis. These classes could in the future assist RTW services to provide appropriate type and extent of follow-up, however more research is needed to validate the class structure and examine how these classes predict outcomes and respond to interventions.

Keywords: Common mental disorder; Pain; Return to work; Sick leave; Vocational rehabilitation.

PubMed Disclaimer

Conflict of interest statement

Authors Martin Inge Standal, Lene Aasdahl, Chris Jensen, Vegard Stolsmo Foldal, Roger Hagen, Egil A. Fors, Marit Solbjør, Odin Hjemdal, Margreth Grotle, and Ingebrigt Meisingset declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Latent class model. Indicator variables (anxiety and depression, pain, health, workplace adjustment latitude, work ability, return to work self-efficacy, and return to work expectations) and covariates (age, gender, education and physically demanding work)
Fig. 2
Fig. 2
Normalized class profiles. Variables are normalized on a scale from 0 to 1, where 1 indicates poorer scores. In this representation, mean scores were divided by the variable’s full range and reversed where higher numbers originally indicated favorable scores
Fig. 3
Fig. 3
Distribution of diagnostic groups based on ICPC-2 diagnoses set by the worker’s general practitioner. Percentages within each class and in the total sample
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Waddell G, Burton AK. Is work good for your health and wellbeing? London: The Stationery Office; 2006.
    1. Waddel G, Burton AK, Kendall NA. Vocational rehabilitation—what works, for whom, and when? London: The Stationery Office; 2008.
    1. Hoefsmit N, Houkes I, Nijhuis FJ. Intervention characteristics that facilitate return to work after sickness absence: a systematic literature review. J Occup Rehabil. 2012;22(4):462–477. - PMC - PubMed
    1. Cullen K, Irvin E, Collie A, Clay F, Gensby U, Jennings P, et al. Effectiveness of workplace interventions in return-to-work for musculoskeletal, pain-related and mental health conditions: an update of the evidence and messages for practitioners. J Occup Rehabil. 2018;28(1):1–15. - PMC - PubMed
    1. Vogel N, Schandelmaier S, Zumbrunn T, Ebrahim S, de Boer WE, Busse JW, et al. Return-to-work coordination programmes for improving return to work in workers on sick leave. Cochrane Database Syst Rev. 2017;3:CD011618. - PMC - PubMed

Publication types

LinkOut - more resources