When to Use VR
VR-36© or VR-12©
Figure 5 describes the potential applications of the VR-36© or VR-12©. The effectiveness of the VR-12© in estimating health status and disease burden along with providing a rubric for risk adjustments has been demonstrated in several publications spanning multiple disease systems (Kazis et al 2006). Subsequently, this work has provided applications in the VA for conducting medication effectiveness studies based upon non-randomized prospective quasi-experimental designs that approximate real world clinical conditions. Such applications using the VR-12© have been widely published for medication studies in those diagnosed with hypertension, diabetes, osteoarthritis, low back pain, hip and knee replacement, depression, and schizophrenia.
Kazis, Lewis E., Donald R. Miller, Katherine M. Skinner, Austin Lee, Xinhua S. Ren, Jack A. Clark, William H. Rogers, Avron Spiro, 3rd, Alfredo Selim, Mark Linzer, Susan M. Payne, Dorcas Mansell, and R. Graeme Fincke. “Patient-Reported Measures of Health: The Veterans Health Study.” The Journal of Ambulatory Care Management 27, no. 1 (2004): 70-83. http://www.ncbi.nlm.nih.gov/pubmed/14717468
Generic HRQoL assessment
A generic tool is an instrument that can be used across different populations and diseases. The VR-36© and VR-12© measures are reliable and effective in documenting perceived change in health-related quality of life and health resource utilization in response to poor health behaviors and lifestyle factors. For example, Borzecki et al. (2005) examined the relationship between health behaviors (cigarette smoking, alcohol use, exercise, seat belt use, cholesterol level, and body mass index) and HRQoL among veterans. They found that HRQoL is negatively affected by poor health behaviors. On the other hand, Malinoff et al. (2013) explored the association between obesity prevalence and HRQoL among Medicare Advantage seniors. Results indicate that obese beneficiaries have poorer HRQOL than normal weight beneficiaries as determined by BMI standards and have substantially higher outpatient utilization.
Borzecki, Ann M., Austin Lee, David Kalman, and Lewis E. Kazis. “Do poor health behaviors affect health-related quality of life and healthcare utilization among veterans? The Veterans Health Study.” The Journal of Ambulatory Care Management 28, no. 2 (June 2005): 141–56. http://www.ncbi.nlm.nih.gov/pubmed/15923947
Malinoff, R. L., M. N. Elliott, L. A. Giordano, S. C. Grace, and J. N. Burroughs. “Obesity Utilization and Health-Related Quality of Life in Medicare Enrollees.” J Ambul Care Manage 36, no. 1 (2013): 61-71. http://www.ncbi.nlm.nih.gov/pubmed/23222013
Chronic Disease Burden
A traditional application of health status measures is the evaluation of change in relation to the presence of different comorbid conditions. VR-12© is used to gauge the incremental effects of case mix on health status and make meaningful comparisons about populations with different chronic diseases. Thus, it provides a proxy for disease burden. For example, the Veterans Health Study showed that having angina resulted in a PCS score that is 2.53 points lower (0.25 of 1 standard deviation) than the score of those veterans without angina. Similarly, the presence of depression led to an 8-point reduction in MCS among veterans while controlling for other comorbidities and demographics (Table 1).
Table 1: Average Impact of Medical Conditions on PCS and MCS Observed in the Veterans Health Study
Impact on PCS*
Impact on MCS*
|Chronic low back pain||-5.51||-2.83|
|Chronic Lung Disease||-3.57||–|
*Impact of disease on PCS/MCS controlling for sociodemographic and comorbid conditions
Kazis, Lewis E., Donald R. Miller, Katherine M. Skinner, Austin Lee, Xinhua S. Ren, Jack A. Clark, William H. Rogers, Avron Spiro, 3rd, Alfredo Selim, Mark Linzer, Susan M. Payne, Dorcas Mansell, and R. Graeme Fincke. “Patient-Reported Measures of Health: The Veterans Health Study.” The Journal of ambulatory care management 27, no. 1 (2004): 70-83. http://www.ncbi.nlm.nih.gov/pubmed/14717468
Risk Adjustor in Models
VR-12© PCS and MCS summary measures are used extensively as integral elements of risk adjustment models that were developed to reliably predict mortality in VA patients receiving ambulatory care. In one longitudinal study at the VA, patients with higher VR-12© PCS and MCS scores showed a lower likelihood of dying. The highly significant associations of VR-12© PCS and MCS summary measures with mortality resulted in their inclusion as important predictor variable in the final risk adjustment model. Similar results were found in using a prospective monitoring system of outcomes of veterans receiving ambulatory care in the VHA.
Selim et al (2002) developed a risk-adjustment model predicting mortality rates using a national sample of 31,823 patients receiving ambulatory care in the Veterans Health Administration (VHA). Results showed that age, the Charlson index, gender and PCS and MCS were statistically significant predictors of mortality risk. Specifically, those patients with higher PCS or MCS scores (meaning better health) were less likely to die.
Selim, Alfredo J., Dan Berlowitz, Graeme Fincke, William Rogers, Shirley Qian, Austin Lee, Zhongxiao Cong, et al. “Use of risk-adjusted change in health status to assess the performance of integrated service networks in the Veterans Health Administration.” International journal for quality in health care: journal of the International Society for Quality in Health Care / ISQua 18, no. 1 (February 2006): 43–50. http://www.ncbi.nlm.nih.gov/pubmed/16214882
Clinical course of individual patients
Patient-reported outcomes (PROs), such as health status, can potentially serve as a tool to screen for functional problems, monitor disease progression or therapeutic response, and improve provider-patient communication among patients with a particular condition. They may also be used to assess the impact of an intervention. Valderas and colleagues (2008) conducted a systematic literature review of randomized clinical trials evaluating the impact of various PROs on care processes, outcomes and patient and provider satisfaction. Of the reviewed studies, 65% found an improvement in at least one of several processes of care, and 47% and 42% found an improvement in outcomes and satisfaction, respectively. Valderas (2008) and Marshall (2006) underscore lack of clarity in the impact of PROs on health outcomes and in the mechanism by which they might affect them. Further studies are needed, particularly in regard to self-reported health status, to better understand how to use such information to improve patient care. It is possible that some of the limitations of typical randomized controlled studies regarding PROs can be at least partially overcome by the use of “n-of-1” studies which combine elements of individual patient management with methods to aggregate the results of such management across many individuals in order to assess overall effects. “Mobile health” (“mhealth”), which makes use of smart phones and similar devices, may facilitate PRO collection by allowing patients to enter information at their own convenience and at multiple points in time. Repeated measures of health status may be particularly important in managing individual patients in order obtain more accurate trajectories.
The VR-36© and VR-12© summary scores can be calculated for individual patients and compared to benchmarks. The technology that would allow patients completing these instruments before the clinic visit and immediate transfer of the results to the physician is currently under development. The use of the VR-36© and VR-12© in “n-of-1” designs may have important implications for its future use as part of the Electronic Health Records. Repeated values for the same individual facilitate ruling out biases such as regression towards the mean, establishing what may be consistent trends in the data. At least 3 repeated measures are recommended.
Marshall, Susan, Kirstie Haywood, and Ray Fitzpatrick. “Impact of patient-reported outcome measures on routine practice: a structured review.” Journal of Evaluation in Clinical Practice 12, no. 5 (October 2006): 559–68. http://www.ncbi.nlm.nih.gov/pubmed/16987118
Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013 Jun 11;13:211
Varni JW, Burwinkle TM, Lane MM. Health-related quality of life measurement in pediatric clinical practice: an appraisal and precept for future research and application. Health Qual Life Outcomes. 2005 May 16;3:34.
Greenhalgh, Joanne. “The applications of PROs in clinical practice: what are they, do they work, and why?” Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation 18, no. 1 (February 2009): 115–23. http://www.ncbi.nlm.nih.gov/pubmed/19105048
Howie, Lynn, Bradford Hirsch, Tracie Locklear, and Amy P. Abernethy. “Assessing The Value Of Patient-Generated Data To Comparative Effectiveness Research.” Health Affairs 33, no. 7 (July 01, 2014). http://www.ncbi.nlm.nih.gov/pubmed/25006149
Zucker, D. R., C. H. Schmid, M. W. McIntosh, R. B. D’Agostino, H. P. Selker, and J. Lau. “Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment.” Journal of Clinical Epidemiology 50, no. 4 (April 1997): 401–10. http://www.ncbi.nlm.nih.gov/pubmed/9179098
Duan, Naihua, Richard L. Kravitz, and Christopher H. Schmid. “Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research.” Journal of Clinical Epidemiology 66, no. 8 Suppl (August 2013): S21–28. doi:10.1016/j.jclinepi.2013.04.006. http://www.ncbi.nlm.nih.gov/pubmed/23849149
Kravitz RL, Duan N, eds, and the DEcIDE Methods Center N-of-1 Guidance Panel (Duan N, Eslick I, Gabler NB, Kaplan HC, Kravitz RL, Larson EB, Pace WD, Schmid CH, Sim I, Vohra S). Design and Implementation of N-of-1 Trials: A User’s Guide. AHRQ Publication No. 13(14)-EHC122-EF. Rockville, MD: Agency for Healthcare Research and Quality; January 2014. http://www.effectivehealthcare.ahrq.gov/N-1-Trials.cfm
Vilardaga R, Bricker J, McDonell M. The promise of mobile technologies and single case designs for the study of individuals in their natural environment. J Contextual Behav Sci. 2014 Apr 1;3(2):148-153. http://www.ncbi.nlm.nih.gov/pubmed/24949285
Swendeman D, Comulada WS, Ramanathan N, Lazar M, Estrin D. Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV. AIDS Behav. 2014 Oct 21. [Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/25331266
Provider and System performance indicators
A core application of the VR-36© is assessing health outcomes and system’s performances in large health care systems. Some applications include:
- Comparison of health outcomes between chronically ill Medicare enrollees in health maintenance organizations (HMS) and fee for service (FFS): The change in VR-36© PCS and MCS measures, included as part of a multidimensional risk adjusted model, served as the basis for calculating and comparing expected versus actual PCS and MCS rates at the individual patient level for each integrated network.
Related ArticleSelim, Alfredo J., Dan Berlowitz, Graeme Fincke, William Rogers, Shirley Qian, Austin Lee, Zhongxiao Cong, Bernardo J. Selim, Xinhua S. Ren, Amy K. Rosen, and Lewis E. Kazis. “Use of Risk-Adjusted Change in Health Status to Assess the Performance of Integrated Service Networks in the Veterans Health Administration.” International journal for quality in health care: journal of the International Society for Quality in Health Care / ISQua 18, no. 1 (2006): 43-50. http://www.ncbi.nlm.nih.gov/pubmed/16214882
- Comparison of health outcomes measured with the VR-12© between Medicare advantage plan enrollees and VHA cohorts.
Related ArticleSelim, Alfredo J., Dan R. Berlowitz, Graeme Fincke, Amy K. Rosen, Xinhua S. Ren, Cindy L. Christiansen, Zhongxhiao Cong, Austin Lee, and Lewis Kazis. “Risk-Adjusted Mortality Rates as a Potential Outcome Indicator for Outpatient Quality Assessments.” Medical care 40, no. 3 (2002): 237-45. http://www.ncbi.nlm.nih.gov/pubmed/16565637
- Since 2006, the VR-12© has been the main endpoint to assess system performance of Medicare Advantage plans. More recently, the VR-12© became part of the Star Rating System model, a plan quality and performance assessment system ranging from 1 (poor performance) to 5 stars (excellent performance). The model used to calculate the star rating is in part based on HEDIS measures, CAHP measures and the VR-12©. The VR-12© is given the larger weights in the model. The Star Rating System is used for assessing reimbursements. It also influences whether a businesses can expand.
- The VR-36© was administered to 2425 veterans receiving ambulatory care as part of the 1999 Large Health Survey of Veterans Enrollees.
Related ArticleSelim, Alfredo J., Dan R. Berlowitz, Graeme Fincke, Amy K. Rosen, Xinhua S. Ren, Cindy L. Christiansen, Zhongxhiao Cong, Austin Lee, and Lewis Kazis. “Risk-Adjusted Mortality Rates as a Potential Outcome Indicator for Outpatient Quality Assessments.” Medical care 40, no. 3 (2002): 237-45. http://www.ncbi.nlm.nih.gov/pubmed/10446661
- The VR-12© has been administered at the Veterans Health Administration since 2003. The program was the basis for the original development of the VR versions and later adopted by the VA in the evaluation of the work in quality improvement as part of the Survey of Health Care Experience of Patients (SHEP)
Clinical trials-outcomes assessment
The VR-36© and VR-12© have been used as the primary end point for assessing health outcomes in several clinical trials. Some of these can be seen below:
Donta, Sam T., Charles C. Engel, Jr., Joseph F. Collins,et al. “Benefits and Harms of Doxycycline Treatment for Gulf War Veterans’ Illnesses: A Randomized, Double-Blind, Placebo-Controlled Trial.” Annals of internal medicine 141, no. 2 (2004): 85-94. http://www.ncbi.nlm.nih.gov/pubmed/15262663
Dobscha, Steven K., Kathryn Corson, David H. Hickam, Nancy A. Perrin, Dale F. Kraemer, and Martha S. Gerrity. “Depression Decision Support in Primary Care: A Cluster Randomized Trial.” Annals of internal medicine 145, no. 7 (2006): 477-87. http://www.ncbi.nlm.nih.gov/pubmed/17015865
Mularski, R. A., B. A. Munjas, K. A. Lorenz, S. Sun, S. J. Robertson, W. Schmelzer, A. C. Kim, and P. G. Shekelle. “Randomized Controlled Trial of Mindfulness-Based Therapy for Dyspnea in Chronic Obstructive Lung Disease.” J Altern Complement Med 15, no. 10 (2009): 1083-90. http://www.ncbi.nlm.nih.gov/pubmed/19848546
Ottomanelli, Lisa, Lance Goetz, Charles McGeough, Alina Suris, Jennifer Sippel, Patricia Sinnott, Todd H. Wagner, and Daisha J. Cipher. “Methods of a Multisite Randomized Clinical Trial of Supported Employment among Veterans with Spinal Cord Injury.” Journal of rehabilitation research and development 46, no. 7 (2009): 919-30. http://www.rehab.research.va.gov/jour/09/46/7/Ottomanelli.html
Yueh, Bevan, Margaret P. Collins, Pamela E. Souza, Edward J. Boyko, Carl F. Loovis, Patrick J. Heagerty, Chuan-Fen Liu, and Susan C. Hedrick. “Long-Term Effectiveness of Screening for Hearing Loss: The Screening for Auditory Impairment–Which Hearing Assessment Test (Sai-What) Randomized Trial.” Journal of the American Geriatrics Society 58, no. 3 (2010): 427-34. http://www.ncbi.nlm.nih.gov/pubmed/20398111
Krystal, John H., Robert A. Rosenheck, Joyce A. Cramer, Jennifer C. Vessicchio, et al. “Adjunctive Risperidone Treatment for Antidepressant-Resistant Symptoms of Chronic Military Service-Related Ptsd: A Randomized Trial.” JAMA : the journal of the American Medical Association 306, no. 5 (2011): 493-502. http://www.ncbi.nlm.nih.gov/pubmed/21813427
Nakamura, Yoshio, David L. Lipschitz, Richard Landward, Renee Kuhn, and Gavin West. “Two Sessions of Sleep-Focused Mind-Body Bridging Improve Self-Reported Symptoms of Sleep and Ptsd in Veterans: A Pilot Randomized Controlled Trial.” Journal of psychosomatic research 70, no. 4 (2011): 335-45. http://www.ncbi.nlm.nih.gov/pubmed/21414453
Hsiao, An-Fu, Robyn York, Ian Hsiao, Ed Hansen, Ron D. Hays, John Ives, and Ian D. Coulter. “A Randomized Controlled Study to Evaluate the Efficacy of Noninvasive Limb Cover for Chronic Phantom Limb Pain among Veteran Amputees.” Archives of physical medicine and rehabilitation 93, no. 4 (2012): 617-22. http://www.ncbi.nlm.nih.gov/pubmed/22464089
Bishawi, M., A. L. Shroyer, J. S. Rumsfeld, J. A. Spertus, J. H. Baltz, J. F. Collins, J. A. Quin, G. H. Almassi, F. L. Grover, and B. Hattler. “Changes in Health-Related Quality of Life in Off-Pump Versus on-Pump Cardiac Surgery: Veterans Affairs Randomized on/Off Bypass Trial.” Ann Thorac Surg 95, no. 6 (2013): 1946-51. http://www.ncbi.nlm.nih.gov/pubmed/23453761
Goertz, C. M., S. A. Salsbury, R. D. Vining, C. R. Long, A. A. Andresen, M. E. Jones, K. J. Lyons, M. A. Hondras, L. Z. Killinger, F. D. Wolinsky, and R. B. Wallace. “Collaborative Care for Older Adults with Low Back Pain by Family Medicine Physicians and Doctors of Chiropractic (Cocoa): Study Protocol for a Randomized Controlled Trial.” Trials 14, no. 18 (2013): 1745-6215. http://www.ncbi.nlm.nih.gov/pubmed/23324133
Ortiz-Declet, V.R., Iacobelli, D.A., Yuen, L.C., Perets, I., Chen, A.W., Domb, B.G., 2017. Birmingham Hip Resurfacing vs Total Hip Arthroplasty: A Matched-Pair Comparison of Clinical Outcomes. J Arthroplasty. https://www.ncbi.nlm.nih.gov/pubmed/28711342
Kilbourne, A.M., Barbaresso, M.M., Lai, Z., Nord, K.M., Bramlet, M., Goodrich, D.E., Post, E.P., Almirall, D., Bauer, M.S., 2017. Improving Physical Health in Patients with Chronic Mental Disorders: 12-Month Results from a Randomized Controlled Collaborative Care Trial. J Clin Psychiatry 78, 129–137. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5272777/
Berlowitz, D.R., Foy, C.G., Kazis, L.E., Bolin, L.P., Conroy, M.B., Fitzpatrick, P., Gure, T.R., Kimmel, P.L., Kirchner, K., Morisky, D.E., Newman, J., Olney, C., Oparil, S., Pajewski, N.M., Powell, J., Ramsey, T., Simmons, D.L., Snyder, J., Supiano, M.A., Weiner, D.E., Whittle, J., 2017. Effect of Intensive Blood-Pressure Treatment on Patient-Reported Outcomes. New England Journal of Medicine 377, 733–744. doi:10.1056/NEJMoa1611179
Ongoing clinical trials using the VR instruments:
- Wake Forest Baptist Health. Systolic Blood Pressure Intervention Trial (SPRINT)
- Bronx Veterans Medical Research Foundation, Inc. A Controlled Trial of Mifepristone in Gulf War Veterans with Chronic Multisymptom Illness
- OrthoCarolina Research Institute, Inc. Multimodal Analgesia vs. Routine Care Pain Management for Lumbar Spine Fusion Surgery: A Prospective Randomized Study
- Department of Veterans Affairs. A Study of Dog Adoption in Veterans With Posttraumatic Stress Disorder (PTSD)
- Major Extremity Trauma Research Consortium. Outcomes Following Severe Distal Tibia, Ankle and/or Foot Trauma: Comparison of Limb Salvage Versus Transtibial Amputation Protocol