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Assignment errors and the valuation of EQ-5D health states-do responses mean what we think they mean?

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Author Info

  • Richard Edlin

    ()
    (Health Systems, School of Population Health, University of Auckland)

  • Christopher McCabe

    (School of Community Medicine, University of Alberta, Edmonton, Canada)

  • David Meads

    (Academic Unit of Health Economics, LIHS, University of Leeds)

Abstract

Cost-utility analysis is used within the health technology assessment processes of many countries. For these analyses, patients typically indicate the health state that they are in based on a pre-defined descriptive classification. Each health state corresponds to a utility value; these values are obtained from members of the general public who are asked what they would be willing to give up to avoid spending time in that health state. If people struggle to do this, they might imagine (and so value) different states to those they are prompted with. Here, they assign a different meaning to the prompt than intended, which we designate as an ‘assignment error’. This paper formally defines these errors for the EQ-5D-3L and explores MVH dataset used to construct the UK EQ-5D-3L tariff. We modify the regressions used to form this tariff to include potential assignment errors and find that these errors are significant predictors in the regressions. For some states, there is evidence that over half the respondents answering valuation questions may make an assignment error. As these errors will affect some states more than others, they are a potential source of bias and hence distortion in resource allocation. The size of this distortion is explored in the UK context using the regressions identified by this paper, all of which suggest that less weight be given to curing moderate illness and more weight be given to curing more severe illness.

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File URL: http://medhealth.leeds.ac.uk/download/239/auhe_wp13_04
File Function: First version, 2013
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Bibliographic Info

Paper provided by Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds in its series Working Papers with number 1304.

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Length: 20 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:lee:wpaper:1304

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Related research

Keywords: quality-adjusted life years; health related quality of life; assignment errors; estimation;

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References

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  1. Lindeboom, Maarten & van Doorslaer, Eddy, 2004. "Cut-Point Shift and Index Shift in Self-Reported Health," IZA Discussion Papers 1286, Institute for the Study of Labor (IZA).
  2. John Brazier & Jennifer Roberts & Aki Tsuchiya & Jan Busschbach, 2004. "A comparison of the EQ-5D and SF-6D across seven patient groups," Health Economics, John Wiley & Sons, Ltd., vol. 13(9), pages 873-884.
  3. McCabe, C & Stevens, K & Roberts, J & Brazier, JE, 2003. "Health state values for the HUI 2 descriptive system: results from a UK survey," MPRA Paper 29744, University Library of Munich, Germany.
  4. Nancy J. Devlin & Paul Hansen & Paul Kind & Alan Williams, 2003. "Logical inconsistencies in survey respondents' health state valuations - a methodological challenge for estimating social tariffs," Health Economics, John Wiley & Sons, Ltd., vol. 12(7), pages 529-544.
  5. Sprangers, Mirjam A. G. & Schwartz, Carolyn E., 1999. "Integrating response shift into health-related quality of life research: a theoretical model," Social Science & Medicine, Elsevier, vol. 48(11), pages 1507-1515, June.
  6. Crossley, Thomas F. & Kennedy, Steven, 2002. "The reliability of self-assessed health status," Journal of Health Economics, Elsevier, vol. 21(4), pages 643-658, July.
  7. John Brazier & Mark Deverill, 1999. "A checklist for judging preference-based measures of health related quality of life: Learning from psychometrics," Health Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 41-51.
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