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.


Figure 5: Application of the VR-36 and VR-12
Figure 5: Application of the VR-36 and VR-12

 

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.

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

Condition
Impact on PCS*
Impact on MCS*
Hypertension -0.60 -0.50
Angina -2.53 -0.64
Diabetes -3.05 -0.08
Osteoarthritis -4.78 -2.05
Chronic low back pain -5.51 -2.83
Chronic Lung Disease -3.57 –
Depression – -8.00
Alcohol Disorders – -6.59

*Impact of disease on PCS/MCS controlling for sociodemographic and comorbid conditions

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.

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.

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.
  • Comparison of health outcomes measured with the VR-12© between Medicare advantage plan enrollees and VHA cohorts.
  • 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.
  • 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:

Ongoing clinical trials using the VR instruments: