Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain

Lisa C. Bosman; Jos W.R. Twisk; Anna S. Geraedts; Martijn W. Heymans

doi:https://doi.org/10.1097/JOM.0000000000001749

Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain

Lisa C. Bosman, Jos W.R. Twisk, Anna S. Geraedts, Martijn W. Heymans

Research output: Contribution to journal › Article › Academic › peer-review

7 Citations (Scopus)

Abstract

Objective:The aim of this study was to develop a prediction model for the prognosis of sick leave due to low back pain (LBP).Methods:This is a cohort study with 103 employees sick-listed due to non-specific LBP and spinal disc herniation. A prediction model was developed based on questionnaire data and registered sick leave data with follow up of 180 days.Results:At follow up 31 (30.1%) employees were still sick-listed due to LBP. Forward selection procedure resulted in a model with: catastrophizing, musculoskeletal work load, and disability. The explained variance was 27.3%, calibration was adequate and discrimination was fair with area under the ROC-curve (AUC) = 0.761 (interquartile range [IQR]: 0.755-0.770).Conclusion:The prediction model of this study can adequately predict LBP sick leave after 180 days and could be used for employees sick listed due LBP.

Original language	English
Pages (from-to)	1065-1071
Number of pages	7
Journal	Journal of occupational and environmental medicine
Volume	61
Issue number	12
DOIs	https://doi.org/10.1097/JOM.0000000000001749
Publication status	Published - 1 Dec 2019

Keywords

absenteeism
low back pain
prediction models
prognostic research
return to work

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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https://doi.org/10.1097/JOM.0000000000001749

Cite this

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title = "Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain",

abstract = "Objective:The aim of this study was to develop a prediction model for the prognosis of sick leave due to low back pain (LBP).Methods:This is a cohort study with 103 employees sick-listed due to non-specific LBP and spinal disc herniation. A prediction model was developed based on questionnaire data and registered sick leave data with follow up of 180 days.Results:At follow up 31 (30.1%) employees were still sick-listed due to LBP. Forward selection procedure resulted in a model with: catastrophizing, musculoskeletal work load, and disability. The explained variance was 27.3%, calibration was adequate and discrimination was fair with area under the ROC-curve (AUC) = 0.761 (interquartile range [IQR]: 0.755-0.770).Conclusion:The prediction model of this study can adequately predict LBP sick leave after 180 days and could be used for employees sick listed due LBP.",

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AU - Bosman, Lisa C.

AU - Twisk, Jos W.R.

AU - Geraedts, Anna S.

AU - Heymans, Martijn W.

PY - 2019/12/1

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N2 - Objective:The aim of this study was to develop a prediction model for the prognosis of sick leave due to low back pain (LBP).Methods:This is a cohort study with 103 employees sick-listed due to non-specific LBP and spinal disc herniation. A prediction model was developed based on questionnaire data and registered sick leave data with follow up of 180 days.Results:At follow up 31 (30.1%) employees were still sick-listed due to LBP. Forward selection procedure resulted in a model with: catastrophizing, musculoskeletal work load, and disability. The explained variance was 27.3%, calibration was adequate and discrimination was fair with area under the ROC-curve (AUC) = 0.761 (interquartile range [IQR]: 0.755-0.770).Conclusion:The prediction model of this study can adequately predict LBP sick leave after 180 days and could be used for employees sick listed due LBP.

AB - Objective:The aim of this study was to develop a prediction model for the prognosis of sick leave due to low back pain (LBP).Methods:This is a cohort study with 103 employees sick-listed due to non-specific LBP and spinal disc herniation. A prediction model was developed based on questionnaire data and registered sick leave data with follow up of 180 days.Results:At follow up 31 (30.1%) employees were still sick-listed due to LBP. Forward selection procedure resulted in a model with: catastrophizing, musculoskeletal work load, and disability. The explained variance was 27.3%, calibration was adequate and discrimination was fair with area under the ROC-curve (AUC) = 0.761 (interquartile range [IQR]: 0.755-0.770).Conclusion:The prediction model of this study can adequately predict LBP sick leave after 180 days and could be used for employees sick listed due LBP.

KW - absenteeism

KW - low back pain

KW - prediction models

KW - prognostic research

KW - return to work

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Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain

Abstract

Keywords

UN SDGs

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