ArticleInternational archives of occupational and environmental health2025
Predicting prolonged work absence due to musculoskeletal disorders: development, validation, and clinical usefulness of prognostic prediction models.
Article in International archives of occupational and environmental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeGiven 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.
methodsWe 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.
resultsAll 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.
conclusionsThese 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.
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