Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.

Corné A M Roelen,Ute Bültmann,Jos W R Twisk,Martijn W Heymans,Johan W Groothoff

doi:10.1007/s00420-015-1032-3

Abstract

BackgroundPrognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥30) SA days and high (≥3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures.MethodsThis was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures.ResultsIn total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable.ConclusionIn the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA.

Highlights

Sickness absence (SA) is an increasing problem in developed economies
Reclassification analyses were performed in R (Project for Statistical Computing) using the regression modeling strategies package (Harrell 2013) for calculating Net Reclassification Index (NRI) and the predictABEL package (Kundu et al 2011) for calculating integrated discrimination improvement (IDI)
It is recommended to distinguish between subjects with and without events in reclassification analysis (Pencina et al 2008; Pepe 2011; Kerr et al 2014; Leening et al 2014a)

Summary

Introduction

Sickness absence (SA) is an increasing problem in developed economies. The Organization for Economic Cooperation and Development reported that countries spend on average 2 % of their gross domestic product (GDP) and 10 % of their social expenditures on SA and disability benefits (OECD 2011). Two prognostic models for identifying workers at risk of high SA were developed in a sample of Dutch hospital workers (Roelen et al 2013a) and validated in Dutch office workers (Roelen et al 2013b) and Danish eldercare workers (Roelen et al 2014a). Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥30) SA days and high (≥3) SA episodes during 1-year follow-up. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures. Methods This was a prospective cohort study with 1-year follow-up of 1,137 office workers. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures.

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