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A preference-based measure of health: the VR-6D derived from the veterans RAND 12-Item Health Survey

Quality of Life Research volumeĀ 20,Ā pages 1337ā€“1347 (2011)Cite this article

Abstract

Purpose

The Veterans RAND 12-Item Health Survey (VR-12) is currently the major endpoint used in the Medicare managed care outcomes measure in the Healthcare Effectiveness Data and Information Set (HEDISĀ®), referred to as the Health Outcomes Survey (HOS). The purpose of this study is to adapt the Brazier SF-6D utility measure to the VR-12 to generate a single utility index.

Methods

We used the HOS cohorts 2 and 3 for SF-36 data and 9 for VR-12 data. We calculated SF-6D scores from the SF-36 using the algorithms developed by Brazier and colleagues. The values of the Brazier SF-6D were used to estimate utility scores from the VR-12 using a mapping approach based on a 2-stage mapping procedure, named as VR-6D.

Results

The VR-6D derived from the VR-12 has similar distributional properties as the SF-6D. The change in VR-6D showed significant variations across disease groups with different levels of morbidity and mortality.

Conclusions

This study produced a utility measure for the VR-12 that is comparable to the SF-6D and responsive to change. The VR-6D can be used in evaluations of health care plans and cost-effectiveness analysis to compare the health gains that health care interventions can achieve.

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Fig.Ā 1
Fig.Ā 2

Abbreviations

SF-36:

Short form 36-Item Health Survey

VR-36:

Veterans RAND 36-Item Health Survey

MRE:

Modified regression estimate

VR-12:

Veterans RAND 12-Item Health Survey

HEDIS:

Healthcare Effectiveness Data And Information Set

MA:

Medicare advantage

HOS:

Health Outcomes Survey

MRE:

Modified regression estimation

QALYs:

Quality adjusted life years

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Acknowledgments

The research in this article was supported in part by the Centers for Medicare and Medicaid Services and the National Committee for Quality Assurance (NCQA) under contract number HHSM-500-2009-00051C; the Center for the Assessment of Pharmaceutical Practices (CAPP) Boston University School of Public Health, Boston MA and the Center for Health Quality, Outcomes and Economic Research (CHQOER), Veterans Administration Medical Center, Bedford MA.

Author information

Authors and Affiliations

  1. The Center for the Assessment of Pharmaceutical Practices (CAPP), Department of Health Policy and Management, Boston University School of Public Health, 715 Albany Street, T3Ā W, Boston, MA, 02118, USA

    Alfredo J. Selim,Ā Shirley X. QianĀ &Ā Lewis E. Kazis

  2. The Center for Health Quality, Outcomes, and Economic Research (CHQOER), A Health Services Research and Development Field Program, VA Medical Center, Bedford, MA, USA

    Alfredo J. Selim,Ā William Rogers,Ā Shirley X. QianĀ &Ā Lewis E. Kazis

  3. The Section of General Internal Medicine and Emergency Services, Boston VA Health Care System, West Roxbury, MA, USA

    Alfredo J. Selim

  4. The Boston University School of Medicine, Boston, MA, USA

    Alfredo J. Selim

  5. The Health Institute, New England Medical Center, Boston, MA, USA

    William Rogers

  6. The School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

    John Brazier

Authors
  1. Alfredo J. Selim
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  2. William Rogers
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  3. Shirley X. Qian
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  4. John Brazier
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  5. Lewis E. Kazis
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Corresponding author

Correspondence to Lewis E. Kazis.

Additional information

SF-36Ā® and SF-12Ā® are registered trademarks of the Medical Outcomes Trust.

Appendix 1

Appendix 1

In the figure below, the Y-axis is the actual value. The X-axis is the predicted value. The red curve is the regression of the SF-6D on 7 SF-36 scales (without General Health scale). The blue curve is the regression of the SF-6D on 8 SF-36 scales (with General Health). The dark green curve is the Ara and Brazier SF-6D [23]. The orange line is the line from (0,0) to (1,1). For each type of prediction, the predicted values were lumped into buckets 0.4% wide and the actual SF-6D values are averaged within them. The lines represent the non-linear regression lines organized by predicted values. The values by the original SF-6D algorithm and the regression method tracked each other in the mean utility up to about .8, and from there, the Brazier SF-6D goes up to 1 at roughly twice the rate of the linear version. The curvature in the values reflected the non-linearity of the SF-6D.

We estimated the transformation (T) coefficients using variables defined as follows utility prediction (p) estimated from step 1 and sf6dhat:

  • p2Ā =Ā p^2;

  • p3Ā =Ā p^3;

  • p4Ā =Ā Max(p-86, 0);

  • p42Ā =Ā p4^2;

  • p5Ā =Ā Min(p-42, 0);

and a constraint in place that the maximum predicted value should yield a utility of 1.

sf6dhat

T Coef.

SE

t

PĀ >Ā |t| [95% Conf. Interval]

p

4.002

.022

180.06

0.000 3.958 4.045

p2

āˆ’.0571

.001

āˆ’161.00

0.000 āˆ’.0579 āˆ’.056

p3

.001

1.850

181.93

0.000 .000 .001

p4

1.895

.042

45.33

0.000 1.813 1.977

p42

.186

.016

11.73

0.000 .156 .218

p5

āˆ’.051

.005

āˆ’10.06

0.000 āˆ’.062 āˆ’.042

cons

āˆ’47.536

.453

āˆ’104.78

0.000 āˆ’48.425 āˆ’46.647

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Selim, A.J., Rogers, W., Qian, S.X. et al. A preference-based measure of health: the VR-6D derived from the veterans RAND 12-Item Health Survey. Qual Life Res 20, 1337ā€“1347 (2011). https://doi.org/10.1007/s11136-011-9866-y

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