IDEAS home Printed from
   My bibliography  Save this paper

Analysing Hospital Variation in Health Outcome at the Level of EQ-5D Dimensions


  • Nils Gutacker

    (Centre for Health Economics, University of York, UK)

  • Chris Bojke

    (Centre for Health Economics, University of York, UK)

  • Silvio Daidone

    (Centre for Health Economics, University of York, UK)

  • Nancy Devlin

    (Office for Health Economics, London, UK)

  • Andrew Street

    (Centre for Health Economics, University of York, UK)


The English Department of Health has introduced routine collection of patient-reported health outcome data for selected surgical procedures (hip and knee replacement, hernia repair, varicose vein surgery) to facilitate patient choice and increase provider accountability. The EQ-5D has been chosen as the preferred generic instrument and the current risk-adjustment methodology is based on the EQ-5D index score to measure variation across hospital providers. There are two potential problems with this. First, using a population value set to generate the index score may not be appropriate for purposes of provider performance assessment because it introduces an exogenous source of variation and assumes identical preferences for health dimensions among patients. Second, the multimodal distribution of the index score creates statistical problems that are not yet resolved. Analysing variation for each dimension of the EQ-5D dimensions (mobility, self care, usual activities, pain/discomfort, anxiety/depression) seems therefore more appropriate and promising. For hip replacement surgery, we explore a) the impact of treatment on each EQ-5D dimension b) the extent to which treatment impact varies across providers c) the extent to which treatment impact across EQ-5D dimensions is correlated within providers. We combine information on pre- and post-operative EQ-5D outcomes with Hospital Episode Statistics for the financial year 2009/10. The overall sample consists of 25k patients with complete pre- and post-operative responses. We employ multilevel ordered probit models that recognise the hierarchical nature of the data (measurement points nested in patients, which themselves are nested in hospital providers) and the response distributions. The treatment impact is modelled as a random coefficient that varies at hospital-level. We obtain provider-specific Empirical Bayes (EB) estimates of this coefficient. We estimate separate models for each of the five EQ-5D dimensions and analyse correlations of the EB estimates across dimensions. Our analysis suggests that hospital treatment is indeed associated with improvements in health and that variability in treatment impact is generally more pronounced on the dimensions mobility, usual activity and pain/discomfort than on others. The pairwise correlation between the provider EB estimates is substantial, suggesting a) that certain providers are better in improving health across multiple EQ-5D dimensions than others and b) multivariate models are appropriate and should be further investigated.

Suggested Citation

  • Nils Gutacker & Chris Bojke & Silvio Daidone & Nancy Devlin & Andrew Street, 2012. "Analysing Hospital Variation in Health Outcome at the Level of EQ-5D Dimensions," Working Papers 074cherp, Centre for Health Economics, University of York.
  • Handle: RePEc:chy:respap:74cherp

    Download full text from publisher

    File URL:
    File Function: First version, 2011
    Download Restriction: no

    References listed on IDEAS

    1. Propper, Carol & Croxson, Bronwyn & Shearer, Arran, 2002. "Waiting times for hospital admissions: the impact of GP fundholding," Journal of Health Economics, Elsevier, vol. 21(2), pages 227-252, March.
    2. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
    3. Peter C. Smith & Nigel Rice & Roy Carr-Hill, 2001. "Capitation funding in the public sector," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 217-257.
    4. Mark Dusheiko & Hugh Gravelle & Rowena Jacobs & Peter C Smith, "undated". "The Effect of Budgets on Doctor Behaviour: Evidence From A Natural Experiment," Discussion Papers 03/04, Department of Economics, University of York.
    5. Roy Carr-Hill & Geoffrey Hardman & Stephen Martin & Stuart Peacock & Trevor Sheldon & Peter Smith, 1994. "A formula for distributing NHS revenues based on small area use of hospital beds," Working Papers 022cheop, Centre for Health Economics, University of York.
    6. J. M. C. Santos Silva & Silvana Tenreyro, 2006. "The Log of Gravity," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 641-658, November.
    7. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    8. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
    9. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
    10. Croxson, B. & Propper, C. & Perkins, A., 2001. "Do doctors respond to financial incentives? UK family doctors and the GP fundholder scheme," Journal of Public Economics, Elsevier, vol. 79(2), pages 375-398, February.
    11. Mark Dusheiko & Hugh Gravelle & Rowena Jacobs, 2004. "The effect of practice budgets on patient waiting times: allowing for selection bias," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 941-958.
    12. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
    13. Mark Dusheiko & Maria Goddard & Hugh Gravelle & Rowena Jacobs, 2008. "Explaining trends in concentration of healthcare commissioning in the English NHS," Health Economics, John Wiley & Sons, Ltd., vol. 17(8), pages 907-926.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Ian M. McCarthy, 2014. "Eliminating Aggregation Bias when Estimating Treatment Effects on Combined Outcomes with Applications to Quality of Life Assessment," Emory Economics 1409, Department of Economics, Emory University (Atlanta).
    2. Giulia Cavrini & J. Zamberletti & M. Zoli, 2016. "Could the EQ-5D be Used to Predict Mortality and Hospitalization Over a Long Term Period?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(2), pages 813-834, September.
    3. 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.

    More about this item


    hospital care; patient outcomes; PROMs; EQ-5D; performance assessment; provider profiling; hierarchical ordered probit;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:chy:respap:74cherp. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gill Forder). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.