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Updated: May 10, 2026

E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
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Published on: August 1, 2019

Predicting productivity based on EQ-5D: an explorative study.

Marieke Krol1, Elly Stolk, Werner Brouwer

  • 1Department of Health Policy and Management, Erasmus University, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands, krol@bmg.eur.nl.

The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care
|June 14, 2013
PubMed
Summary
This summary is machine-generated.

This study developed a method to predict productivity losses using quality-of-life data from the EQ-5D-3L. The predictions showed good accuracy for absenteeism but may overestimate presenteeism.

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Last Updated: May 10, 2026

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Published on: August 1, 2019

Area of Science:

  • Health Economics
  • Outcomes Research
  • Psychometrics

Background:

  • Productivity costs are frequently omitted from economic evaluations.
  • Estimating productivity costs indirectly via quality-of-life data is a proposed method for inclusion.
  • This facilitates a more comprehensive economic assessment of health interventions.

Purpose of the Study:

  • To develop and validate a predictive algorithm for productivity losses.
  • The algorithm utilizes quality-of-life data from the EQ-5D-3L instrument.
  • This aims to enable indirect estimation of productivity costs.

Main Methods:

  • A web-based survey collected productivity expectations (absenteeism, presenteeism) for various EQ-5D-3L health states from 1,100 Dutch individuals.
  • Two generalized estimating equations (GEE) models were built to predict absenteeism and presenteeism.
  • Model validation involved comparing predictions with measured productivity in low back pain patients.

Main Results:

  • Predicted absenteeism closely matched conventionally measured absenteeism.
  • Presenteeism-related productivity losses were slightly overestimated by the prediction model.
  • A moderate, highly significant correlation was found between measured and predicted productivity.

Conclusions:

  • Reasonable productivity predictions can be derived from EQ-5D data.
  • Further research and validation are needed, particularly for presenteeism prediction.
  • This approach offers a promising avenue for incorporating productivity costs into economic evaluations.