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. 2025 Jun;35(2):181-214.
doi: 10.1007/s10926-024-10205-y. Epub 2024 May 16.

Prognostic Factors and Models for Predicting Work Absence in Adults with Musculoskeletal Conditions Consulting a Healthcare Practitioner: A Systematic Review

Gwenllian Wynne-Jones  1 , Elaine Wainwright  2   3 , Nicola Goodson  4 , Joanne L Jordan  5 , Amardeep Legha  5 , Millie Parchment  6   7 , Ross Wilkie  5 , George Peat  8
Affiliations

Prognostic Factors and Models for Predicting Work Absence in Adults with Musculoskeletal Conditions Consulting a Healthcare Practitioner: A Systematic Review

Gwenllian Wynne-Jones et al. J Occup Rehabil. 2025 Jun.

Abstract

Purpose: It is difficult to predict which employees, in particular those with musculoskeletal pain, will return to work quickly without additional vocational advice and support, which employees will require this support and what levels of support are most appropriate. Consequently, there is no way of ensuring the right individuals are directed towards the right services to support their occupational health needs. The aim of this review will be to identify prognostic factors for duration of work absence in those already absent and examine the utility of prognostic models for work absence.

Methods: Eight databases were search using a combination of subject headings and key words focusing on work absence, musculoskeletal pain and prognosis. Two authors independently assessed the eligibility of studies, extracted data from all eligible studies and assessed risk of bias using the QUIPS or PROBAST tools, an adapted GRADE was used to assess the strength of the evidence. To make sense of the data prognostic variables were grouped according to categories from the Disability Prevention Framework and the SWiM framework was utilised to synthesise findings.

Results: A total of 23 studies were included in the review, including 13 prognostic models and a total of 110 individual prognostic factors. Overall, the evidence for all prognostic factors was weak, although there was some evidence that older age and better recovery expectations were protective of future absence and that previous absence was likely to predict future absences. There was weak evidence for any of the prognostic models in determining future sickness absence.

Conclusion: Analysis was difficult due to the wide range of measures of both prognostic factors and outcome and the differing timescales for follow-up. Future research should ensure that consistent measures are employed and where possible these should be in-line with those suggested by Ravinskaya et al. (2023).

Keywords: Absence; Musculoskeletal pain; Prognosis; Systematic review; Work absence.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Overarching groups and categories of prognostic factors
Fig. 2
Fig. 2
Flow diagram of study selection. From Page et al. [17]. For more information, visit: http://www.prisma-statement.org/
Fig. 3
Fig. 3
Risk of bias (QUIPS)—domain summary assessments
Fig. 4
Fig. 4
Risk of bias (PROBAST) summary judgements
See this image and copyright information in PMC

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