NDLI: External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up

Content Provider	Springer Nature : BioMed Central
Author	Roelen, Corné AM Bültmann, Ute van Rhenen, Willem van der Klink, Jac JL Twisk, Jos WR Heymans, Martijn W
Abstract	Background Two models including age, self-rated health (SRH) and prior sickness absence (SA) were found to predict high SA in health care workers. The present study externally validated these prediction models in a population of office workers and investigated the effect of adding gender as a predictor. Methods SRH was assessed at baseline in a convenience sample of office workers. Age, gender and prior SA were retrieved from an occupational health service register. Two pre-defined prediction models were externally validated: a model identifying employees with high (i.e. ≥30) SA days and a model identifying employees with high (i.e. ≥3) SA episodes during 1-year follow-up. Calibration was investigated by plotting the predicted and observed probabilities and calculating the calibration slope. Discrimination was examined by receiver operating characteristic (ROC) analysis and the area under the ROC-curve (AUC). Results A total of 593 office workers had complete data and were eligible for analysis. Although the SA days model showed acceptable calibration (slope = 0.89), it poorly discriminated office workers with high SA days from those without high SA days (AUC = 0.65; 95% CI 0.58–0.71). The SA episodes model showed acceptable discrimination (AUC = 0.76, 95% CI 0.70–0.82) and calibration (slope = 0.96). The prognostic performance of the prediction models did not improve in the population of office workers after adding gender. Conclusion The SA episodes model accurately predicted the risk of high SA episodes in office workers, but needs further multisite validation and requires a simpler presentation format before it can be used to select high-risk employees for interventions to prevent or reduce SA.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/1471-2458-13-105.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/1471-2458-13-105
Journal	BMC Public Health
Issue Number	1
Volume Number	13
Language	English
Publisher	BioMed Central
Publisher Date	2013-02-05
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Absenteeism Forecasting Generalization Office workers Regression prognostics Sick leave Transportability Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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