Predicting Productivity Losses from Health-Related Quality of Life Using Patient Data

  • Clara Mukuria
  • Donna Rowen
  • Mónica Hernández-Alava
  • Simon Dixon
  • Roberta Ara
Original Research Article

DOI: 10.1007/s40258-017-0326-x

Cite this article as:
Mukuria, C., Rowen, D., Hernández-Alava, M. et al. Appl Health Econ Health Policy (2017). doi:10.1007/s40258-017-0326-x

Abstract

Objective

This paper estimates productivity loss using the health of the patient in order to allow indirect estimation of these costs for inclusion in economic evaluation.

Methods

Data from two surveys of inpatients [Health outcomes data repository (HODaR) sample (n = 42,442) and health improvement and patient outcomes (HIPO) sample (n = 6046)] were used. The number of days off paid employment or normal activities (excluding paid employment) was modelled using the health of the patients measured by the EQ-5D, international classification of diseases (ICD) chapters, and other health and sociodemographic data. Two-part models (TPMs) and zero-inflated negative binomial (ZINB) models were identified as the most appropriate specifications, given large spikes at the minimum and maximum days for the dependent variable. Analysis was undertaken separately for the two datasets to account for differences in recall period and identification of those who were employed.

Results

Models were able to reflect the large spike at the minimum (zero days) but not the maximum, with TPMs doing slightly better than the ZINB model. The EQ-5D was negatively associated with days off employment and normal activities in both datasets, but ICD chapters only had statistically significant coefficients for some chapters in the HODaR.

Conclusions

TPMs can be used to predict productivity loss associated with the health of the patient to inform economic evaluation. Limitations include recall and response bias and identification of who is employed in the HODaR, while the HIPO suffers from a small sample size. Both samples exclude some patient groups.

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Clara Mukuria
    • 1
    • 2
  • Donna Rowen
    • 1
    • 2
  • Mónica Hernández-Alava
    • 1
    • 2
  • Simon Dixon
    • 1
    • 2
  • Roberta Ara
    • 1
    • 2
  1. 1.School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
  2. 2.Health Economics and Decision ScienceUniversity of SheffieldSheffieldUK

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