Quality of Life Research

, Volume 18, Issue 1, pp 43–52 | Cite as

Updated U.S. population standard for the Veterans RAND 12-item Health Survey (VR-12)

  • Alfredo J. SelimEmail author
  • William Rogers
  • John A. Fleishman
  • Shirley X. Qian
  • Benjamin G. Fincke
  • James A. Rothendler
  • Lewis E. Kazis



The purpose of this project was to develop an updated U.S. population standard for the Veterans RAND 12-item Health Survey (VR-12).


We used a well-defined and nationally representative sample of the U.S. population from 52,425 responses to the Medical Expenditure Panel Survey (MEPS) collected between 2000 and 2002. We applied modified regression estimates to update the non-proprietary 1990 scoring algorithms. We applied the updated standard to the Medicare Health Outcomes Survey (HOS) to compute the VR-12 physical (PCS(MEPS standard)) and mental (MCS(MEPS standard)) component summaries based on the MEPS. We compared these scores to PCS and MCS based on the 1990 U.S. population standard.


Using the updated U.S. population standard, the average VR-12 PCS(MEPS standard) and MCS(MEPS standard) scores in the Medicare HOS were 39.82 (standard deviation [SD] = 12.2) and 50.08 (SD = 11.4), respectively. For the same Medicare HOS, the average PCS and MCS scores based on the 1990 standard were 1.40 points higher and 0.99 points lower in comparison to VR-12 PCS and MCS, respectively.


Changes in the U.S. population between 1990 and today make the old standard obsolete for the VR-12, so the updated standard developed here is widely available to serve as such a contemporary standard for future applications for health-related quality of life (HRQoL) assessments.


Health-related quality of life Veterans RAND 12-item Health Survey Veterans Standard-based scoring 



The research in this article was supported by the Centers for Medicare & Medicaid Services (CMS) and the National Committee for Quality Assurance (NCQA) under contract numbers 500-00-0055 with the NCQA, Office of Quality and Performance (OQP) of the Department of Veterans Affairs, The Center for Health Quality, Outcomes and Economic Research (CHQOER), Department of Veterans Affairs, and Boston University School of Public Health.


  1. 1.
    Testa, M. A., & Simonson, D. C. (1996). Assessment of quality-of-life outcomes. The New England Journal of Medicine, 334, 835–840. doi: 10.1056/NEJM199603283341306.PubMedCrossRefGoogle Scholar
  2. 2.
    Patrick, D. L. (1997). Finding health-related quality of life outcomes sensitive to health-care organization and delivery. Medical Care, 35(11 Suppl), NS49–NS57. doi: 10.1097/00005650-199711001-00006.PubMedCrossRefGoogle Scholar
  3. 3.
    Fleishman, J. A., Cohen, J. W., Manning, W. G., & Kosinski, M. (2006). Using the SF-12 health status measure to improve predictions of medical expenditures. Medical Care, 44(5 Suppl), I54–I63.PubMedGoogle Scholar
  4. 4.
    Hays, R. D., Sherbourne, C. D., & Mazel, R. M. (1993). The RAND 36-item Health Survey 1.0. Health Economics, 2, 217–227. doi: 10.1002/hec.4730020305.PubMedCrossRefGoogle Scholar
  5. 5.
    Kazis, L. E., Selim, A., Rogers, W. H., Ren, X. S., Lee, A., & Miller, D. R. (2006). Dissemination of methods and results from the veterans health study: Final comments and implications for future monitoring strategies within and outside the veterans healthcare system. The Journal of Ambulatory Care Management, 29, 310–319.PubMedGoogle Scholar
  6. 6.
    Ware, J. E., Jr., Kosinski, M., & Dewey, J. E. (2000). How to score version 2 of the SF-36 Health Survey. Lincoln, RI: QualityMetric Incorporated.Google Scholar
  7. 7.
    Lohr, K. N. (2000). Health outcomes methodology symposium: Summary and recommendations. Medical Care, 38(9 Suppl), II194–II208.PubMedGoogle Scholar
  8. 8.
    Schraeder, C., Shelton, P., Britt, T., Parker, R., & Leonard, J. (1997). Population-based research data as a means to address health outcomes. The Journal of Ambulatory Care Management, 20, 39–46.PubMedGoogle Scholar
  9. 9.
    Kazis, L. E., Miller, D. R., Skinner, K. M., Lee, A., Ren, X. S., Rogers, W. H., et al. (2006). Applications of methodologies of the Veterans Health Study in the VA healthcare system: Conclusions and summary. The Journal of Ambulatory Care Management, 29, 182–188.PubMedGoogle Scholar
  10. 10.
    Medicare Health Outcomes Survey. Home page at:
  11. 11.
    Ware, J. E., Jr., Kosinski, M., & Keller, S. K. (1994). SF-36 physical and mental health summary scales: A users’s manual. Boston, MA: The Health Institute, New England Medical Center.Google Scholar
  12. 12.
    Ware, J. E., Jr., Kosinski, M., Turner-Bowker, D. M., & Gandek, B. (2002). How to score version 2 of the SF-12 ® Health Survey (with a supplement documenting version 1). Lincoln, RI: QualityMetric Incorporated.Google Scholar
  13. 13.
    All Across the U.S.A. (2000). Population distribution and composition, 2000. Available online at:
  14. 14.
    American Psychological Association. (2002). Racial/ethnic diversity in the United States and psychology. Available online at:
  15. 15.
    Ware, J. E., Jr. (2000). SF-36 health survey update. Spine, 25, 3130–3139. doi: 10.1097/00007632-200012150-00008.PubMedCrossRefGoogle Scholar
  16. 16.
    Ware, J. E., Jr., Kosinski, M., Bayliss, M. S., McHorney, C. A., Rogers, W. H., & Raczek, A. (1995). Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: Summary of results from the Medical Outcomes Study. Medical Care, 33, AS264–AS279. doi: 10.1097/00005650-199501001-00005.PubMedCrossRefGoogle Scholar
  17. 17.
    Burdine, J. N., Felix, M. R., Abel, A. L., Wiltraut, C. J., & Musselman, Y. J. (2000). The SF-12 as a population health measure: An exploratory examination of potential for application. Health Services Research, 35, 885.PubMedGoogle Scholar
  18. 18.
    Jenkinson, C., Layte, R., Jenkinson, D., Lawrence, K., Petersen, S., Paice, C., et al. (1997). A shorter form health survey: Can the SF-12 replicate results from the SF-36 in longitudinal studies? Journal of Public Health Medicine, 19, 179–186.PubMedGoogle Scholar
  19. 19.
    Kazis, L. E., Selim, A., Rogers, W., Ren, X. S., Lee, A., & Miller, D. R. Veterans RAND 12 item Health Survey (VR-12): A white paper summary. Available online at:
  20. 20.
    Kazis, L. E., Miller, D. R., Clark, J. A., Skinner, K. M., Lee, A., Ren, X. S., et al. (2004). Improving the response choices on the veterans SF-36 health survey role functioning scales: Results from the Veterans Health Study. The Journal of Ambulatory Care Management, 27, 263–280.PubMedGoogle Scholar
  21. 21.
    Kazis, L. E., Lee, A., Spiro, A., 3rd, Rogers, W., Ren, X. S., Miller, D. R., et al. (2004). Measurement comparisons of the medical outcomes study and veterans SF-36 health survey. Health Care Financing Review, 25, 43–58.PubMedGoogle Scholar
  22. 22.
    The Medical Expenditure Panel Survey. Survey Background. Available online at:
  23. 23.
    Agency for Healthcare Research and Quality. (2004, April). MEPS HC-060: 2001 full year consolidated data file. Available online at:
  24. 24.
    The Medicare Health Outcomes Survey. Survey results. Available online at:
  25. 25.
    Allen, H. M., Jr., & Rogers, W. H. (1997). The consumer health plan value survey: Round two. Health Affairs (Project Hope), 16, 156–166. doi: 10.1377/hlthaff.16.4.156.CrossRefGoogle Scholar
  26. 26.
    Jones, D., Kazis, L., Lee, A., Rogers, W., Skinner, K., Cassar, L., et al. (2001). Health status assessments using the Veterans SF-12 and SF-36: Methods for evaluating outcomes in the Veterans Health Administration. The Journal of Ambulatory Care Management, 24, 68–86.PubMedGoogle Scholar
  27. 27.
    Spiro, A., 3rd, Rogers, W. H., Qian, S., & Kazis, L. E. (2004) Imputing physical and mental summary scores (PCS and MCS) for the Veterans SF-12 Health Survey in the context of missing data. Available online at:
  28. 28.
    Greene, W. H. (2007). Econometric analysis (6th ed.). New Jersey: Prentice-Hall Inc.Google Scholar
  29. 29.
    Fleishman, J. A., & Lawrence, W. F. (2003). Demographic variation in SF-12 scores: True differences or differential item functioning? Medical Care, 41, III-75–III-86.Google Scholar
  30. 30.
    Keller, S. D., Ware, J. E., Jr., Bentler, P. M., Aaronson, N. K., Alonso, J., Apolone, G., et al. (1998). Use of structural equation modeling to test the construct validity of the SF-36 Health Survey in ten countries: Results from the IQOLA Project. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51, 1179–1188. doi: 10.1016/S0895-4356(98)00110-3.PubMedCrossRefGoogle Scholar
  31. 31.
    Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  32. 32.
    Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks, CA: Sage.Google Scholar
  33. 33.
    Kisinski, M., Bayliss, M., Bjorner, J. B., & Ware, J. E., Jr. (2000). Improving estimates of SF-36® Health Survey scores for respondents with missing data. Available online at:
  34. 34.
    Rogers, W. H., Qian, S., & Kazis, L. (2004). Imputing the physical and mental summary scores (PCS and MCS) for the MOS SF-36 and the Veterans SF-36 Health Survey in the presence of missing data. Available online at:
  35. 35.
    Perlin, J., Kazis, L. E., Skinner, K., Ren, X. S., Lee A., Rogers W., et al. (2000, May). Health status and outcomes of veterans: physical and mental component summary scores, veterans SF-36. 1999 large health survey of veteran enrollees, Executive Report. Washington D.C., Bedford, Massachusetts: Office of Quality and Performance (10Q) and Center for Health Quality, Outcomes and Economic Research, HSR&D Field Program.Google Scholar
  36. 36.
    Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1996). A 12-item Short-Form Health Survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233. doi: 10.1097/00005650-199603000-00003.PubMedCrossRefGoogle Scholar
  37. 37.
    Amir, M., Lewin-Epstein, N., Becker, G., & Buskila, D. (2002). Psychometric properties of the SF-12 (Hebrew version) in a primary care population in Israel. Medical Care, 40, 918–928. doi: 10.1097/00005650-200210000-00009.PubMedCrossRefGoogle Scholar
  38. 38.
    Iglesias, C. P., Birks, Y. F., & Torgerson, D. J. (2001). Improving the measurement of quality of life in older people: The York SF-12. The Quarterly Journal of Medicine, 94, 695–698. doi: 10.1093/qjmed/94.12.695.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Alfredo J. Selim
    • 1
    • 2
    • 3
    • 4
    Email author
  • William Rogers
    • 1
    • 5
  • John A. Fleishman
    • 6
  • Shirley X. Qian
    • 1
  • Benjamin G. Fincke
    • 1
    • 2
    • 4
  • James A. Rothendler
    • 1
    • 2
  • Lewis E. Kazis
    • 1
    • 2
  1. 1.Center for Health Quality, Outcomes, and Economic Research, A Health Services Research and Development Center of ExcellenceVA Medical CenterBedfordUSA
  2. 2.Center for the Assessment of Pharmaceutical Practices (CAPP), Department of Health Policy and ManagementBoston University School of Public HealthBostonUSA
  3. 3.Section of Emergency ServicesBoston VA Health Care SystemWest RoxburyUSA
  4. 4.Boston University School of MedicineBostonUSA
  5. 5.Health InstituteNew England Medical CenterBostonUSA
  6. 6.Center for Financing, Access, and Cost Trends (CFACT)Agency for Healthcare Research and QualityRockvilleUSA

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