Skip to main content

Research Repository

Advanced Search

Predicting prolonged work absence due to musculoskeletal disorders: development, validation, and clinical usefulness of prognostic prediction models.

Rysstad, Tarjei; Grotle, Margreth; Traeger, Adrian C; Aasdahl, Lene; Vigdal, Ørjan Nesse; Aanesen, Fiona; Øiestad, Britt Elin; Pripp, Are Hugo; Wynne-Jones, Gwenllian; Dunn, Kate M; Fors, Egil A; Linton, Steven J; Tveter, Anne Therese

Authors

Tarjei Rysstad

Margreth Grotle

Adrian C Traeger

Lene Aasdahl

Ørjan Nesse Vigdal

Fiona Aanesen

Britt Elin Øiestad

Are Hugo Pripp

Egil A Fors

Steven J Linton

Anne Therese Tveter



Abstract

Given the lack of robust prognostic models for early identification of individuals at risk of work disability, this study aimed to develop and externally validate three models for prolonged work absence among individuals on sick leave due to musculoskeletal disorders. We developed three multivariable logistic regression models using data from 934 individuals on sick leave for 4-12 weeks due to musculoskeletal disorders, recruited through the Norwegian Labour and Welfare Administration. The models predicted three outcomes: (1) > 90 consecutive sick days, (2) > 180 consecutive sick days, and (3) any new or increased work assessment allowance or disability pension within 12 months. Each model was externally validated in a separate cohort of participants (8-12 weeks of sick leave) from a different geographical region in Norway. We evaluated model performance using discrimination (c-statistic), calibration, and assessed clinical usefulness using decision curve analysis (net benefit). Bootstrapping was used to adjust for overoptimism. All three models showed good predictive performance in the external validation sample, with c-statistics exceeding 0.76. The model predicting > 180 days performed best, demonstrating good calibration and discrimination (c-statistic 0.79 (95% CI 0.73-0.85), and providing net benefit across a range of decision thresholds from 0.10 to 0.80. These models, particularly the one predicting > 180 days, may facilitate secondary prevention strategies and guide future clinical trials. Further validation and refinement are necessary to optimise the models and to test their performance in larger samples. [Abstract copyright: © 2025. The Author(s).]

Citation

Rysstad, T., Grotle, M., Traeger, A. C., Aasdahl, L., Vigdal, Ø. N., Aanesen, F., Øiestad, B. E., Pripp, A. H., Wynne-Jones, G., Dunn, K. M., Fors, E. A., Linton, S. J., & Tveter, A. T. (2025). Predicting prolonged work absence due to musculoskeletal disorders: development, validation, and clinical usefulness of prognostic prediction models. International Archives of Occupational and Environmental Health, https://doi.org/10.1007/s00420-025-02129-8

Journal Article Type Article
Acceptance Date Feb 11, 2025
Online Publication Date Apr 8, 2025
Publication Date Apr 8, 2025
Deposit Date Apr 29, 2025
Journal International archives of occupational and environmental health
Print ISSN 0340-0131
Electronic ISSN 1432-1246
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s00420-025-02129-8
Keywords Musculoskeletal disorders, External validation, Multivariable logistic regression, Prediction model, Prolonged work absence, Clinical utility
Public URL https://keele-repository.worktribe.com/output/1200222
Publisher URL https://link.springer.com/article/10.1007/s00420-025-02129-8