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Patient‐reported outcome measures in the NHS: new methods for analysing and reporting EQ‐5D data

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  • Nancy J. Devlin
  • David Parkin
  • John Browne

Abstract

In a landmark move, the UK Department of Health (DH) has introduced the routine collection of patient‐reported outcome measures (PROMs) to measure the performance of health‐care providers. From April 2009, generic (EQ‐5D) and condition‐specific PROMs are being collected from patients before and after four surgical procedures; eventually this will be extended to include a wide range of other NHS services. The aim of this article is to report analysis of the EQ‐5D data generated from a pilot study commissioned by the DH and to consider the implications for the use of EQ‐5D data in performance indicators and measures of patient benefit. We present two new methods that we have developed for analysing and displaying EQ‐5D profile data: a Paretian Classification of Health Change and a health profile grid. We show that EQ‐5D profile data can be readily analysed to generate insights into the nature of changes in patient‐reported health that would be obscured by summarising these profiles by their index scores, or focusing just on the post operative outcomes. Our methods indicate differences between providers and between sub‐groups of patients. Our results also show striking differences in changes in EQ‐5D profiles between surgical procedures, which require further investigation. Copyright © 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Nancy J. Devlin & David Parkin & John Browne, 2010. "Patient‐reported outcome measures in the NHS: new methods for analysing and reporting EQ‐5D data," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 886-905, August.
    • Handle: RePEc:wly:hlthec:v:19:y:2010:i:8:p:886-905
    DOI: 10.1002/hec.1608
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    1. Nancy Devlin;John Appleby, 2010. "Getting the Most out of PROMs: Putting Health Outcomes at the Heart of NHS Decision-Making," Monograph 000220, Office of Health Economics.
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    1. Cristina Gutierrez-Delgado & Rosa-María Galindo-Suárez & Cesar Cruz-Santiago & Koonal Shah & Manny Papadimitropoulos & Yan Feng & Bernarda Zamora & Nancy Devlin, 2021. "EQ-5D-5L Health-State Values for the Mexican Population," Applied Health Economics and Health Policy, Springer, vol. 19(6), pages 905-914, November.
    2. Knott, R. & Lorgelly, P. & Black, N. & Hollingsworth, B., 2016. "Differential item functioning in the EQ-5D: An exploratory analysis using anchoring vignettes," Health, Econometrics and Data Group (HEDG) Working Papers 16/14, HEDG, c/o Department of Economics, University of York.
    3. Hareth Al-Janabi & Andrea Manca & Joanna Coast, 2017. "Predicting carer health effects for use in economic evaluation," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-18, September.
    4. Shaofan Chen & Bo Burström & Vibeke Sparring & Dongfu Qian & Kristina Burström, 2019. "Differential Impact of an Education-Based Intervention for Patients with Type 2 Diabetes Mellitus in Rural China," IJERPH, MDPI, vol. 16(15), pages 1-12, July.
    5. David Nuttall & David Parkin & Nancy Devlin, 2015. "Inter‐Provider Comparison Of Patient‐Reported Outcomes: Developing An Adjustment To Account For Differences In Patient Case Mix," Health Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 41-54, January.
    6. Ian M. McCarthy, 2015. "Putting the Patient in Patient Reported Outcomes: A Robust Methodology for Health Outcomes Assessment," Health Economics, John Wiley & Sons, Ltd., vol. 24(12), pages 1588-1603, December.
    7. Nils Gutacker & Andrew Street, 2015. "Multidimensional performance assessment using dominance criteria," Working Papers 115cherp, Centre for Health Economics, University of York.
    8. Nils Gutacker & Andrew Street, 2018. "Multidimensional performance assessment of public sector organisations using dominance criteria," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 13-27, February.
    9. Knott, Rachel J. & Lorgelly, Paula K. & Black, Nicole & Hollingsworth, Bruce, 2017. "Differential item functioning in quality of life measurement: An analysis using anchoring vignettes," Social Science & Medicine, Elsevier, vol. 190(C), pages 247-255.
    10. McCarthy, Ian M., 2016. "Eliminating composite bias in treatment effects estimates: Applications to quality of life assessment," Journal of Health Economics, Elsevier, vol. 50(C), pages 47-58.
    11. Marta Rodríguez-Hernández & Juan-José Criado-Álvarez & Ana-Isabel Corregidor-Sánchez & José L. Martín-Conty & Alicia Mohedano-Moriano & Begoña Polonio-López, 2021. "Effects of Virtual Reality-Based Therapy on Quality of Life of Patients with Subacute Stroke: A Three-Month Follow-Up Randomized Controlled Trial," IJERPH, MDPI, vol. 18(6), pages 1-11, March.

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