Walters SJ, Brazier JEComparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Qual Life Res 14: 1523-1532

Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, UK.
Quality of Life Research (Impact Factor: 2.49). 09/2005; 14(6):1523-32. DOI: 10.1007/s11136-004-7713-0
Source: PubMed

ABSTRACT The SF-6D and EQ-5D are both preference-based measures of health. Empirical work is required to determine what the smallest change is in utility scores that can be regarded as important and whether this change in utility value is constant across measures and conditions.
To use distribution and anchor-based methods to determine and compare the minimally important difference (MID) for the SF-6D and EQ-5D for various datasets.
The SF-6D is scored on a 0.29-1.00 scale and the EQ-5D on a -0.59-1.00 scale, with a score of 1.00 on both, indicating 'full health'. Patients were followed for a period of time, then asked, using question 2 of the SF-36 as our anchor, if their general health is much better (5), somewhat better (4), stayed the same (3), somewhat worse (2) or much worse (1) compared to the last time they were assessed. We considered patients whose global rating score was 4 or 2 as having experienced some change equivalent to the MID. This paper describes and compares the MID and standardised response mean (SRM) for the SF-6D and EQ-5D from eight longitudinal studies in 11 patient groups that used both instruments.
From the 11 reviewed studies, the MID for the SF-6D ranged from 0.011 to 0.097, mean 0.041. The corresponding SRMs ranged from 0.12 to 0.87, mean 0.39 and were mainly in the 'small to moderate' range using Cohen's criteria, supporting the MID results. The mean MID for the EQ-5D was 0.074 (range -0.011-0.140) and the SRMs ranged from -0.05 to 0.43, mean 0.24. The mean MID for the EQ-SD was almost double that of the mean MID for the SF-6D.
There is evidence that the MID for these two utility measures are not equal and differ in absolute values. The EQ-5D scale has approximately twice the range of the SF-6D scale. Therefore, the estimates of the MID for each scale appear to be proportionally equivalent in the context of the range of utility scores for each scale. Further empirical work is required to see whether or not this holds true for other utility measures, patient groups and populations.

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    • "Higher scores indicate better health status. Differences in PCS and MCS scores exceeding 3 points are considered minimally important differences, as are SF-6D health utilities differences exceeding 0.03 points (Walters and Brazier, 2005; Maruish, 2011). "
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    ABSTRACT: Objectives Individuals with dementia due to Alzheimer's disease often receive care from family members who experience associated burden. This study provides the first broad, population-based account of caregiving-related health outcome burden in Brazil.Methods Data were analyzed from the 2012 National Health and Wellness Survey in Brazil (n = 12 000), an Internet-based survey of adults (aged 18+ years), using stratified sampling by sex and age to ensure demographic representation of Brazil's adult population. Caregivers of individuals with Alzheimer's disease or dementia were compared with non-caregivers on comorbidities, productivity impairment, health-related quality of life, resource utilization, sociodemographic/health characteristics and behaviors, and Charlson comorbidity index scores. Regression models assessed outcomes associated with caregiving, adjusting for potential confounds.ResultsAmong 10 853 respondents, caregivers' (n = 209) average age was 42.1 years, 53% were female, and 52% were married/living with a partner. Caregivers versus non-caregivers (n = 10 644) were more frequently obese, smokers, insured, employed, college-educated, and wealthier and had higher Charlson comorbidity index, all p < 0.05. Adjusting for covariates, caregiving was associated with significantly increased risk of depressive symptoms (odds ratio [OR] = 2.008), major depressive disorder (OR = 1.483), anxiety (OR = 1.714), insomnia (OR = 1.644), hypertension (OR = 1.584), pain (OR = 1.704), and diabetes (OR = 2.103), all p < 0.015. Caregiving was also associated with lower health utilities (−0.024 points) and mental health status (−1.70 points), higher rates of presenteeism-related impairment (32.7% greater) and overall work impairment (35.9% greater), and higher traditional provider visit rates (28.7% greater), all p < 0.035.Conclusions Caregiver status was found to be a factor associated with worse health outcomes and psychiatric and clinical disorders.
    International Journal of Geriatric Psychiatry 05/2015; DOI:10.1002/gps.4309 · 2.87 Impact Factor
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    • "Higher scores indicate better health status. Differences in PCS and MCS scores exceeding 3 points are considered minimally important differences, as are SF-6D health utilities differences exceeding 0.03 points (Walters and Brazier, 2005; Maruish, 2011). "
    Value in Health 11/2014; 17(7):A402. DOI:10.1016/j.jval.2014.08.921 · 3.28 Impact Factor
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    • "It has been argued that the difference must be at least 0.03 in the utility score to be considered clinically meaningful [59-61]. It has also been demonstrated that the MCID differs between the EQ-5D and SF-6D [62]. Drummond (2001) argued that as long as the ultimate objective is to aid resource allocation decisions, it is the difference in incremental cost per QALY and not the improvement in utility that is important [59]. "
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    ABSTRACT: Background The quality-adjusted life-year (QALY) is a recognised outcome measure in health economic evaluations. QALY incorporates individual preferences and identifies health gains by combining mortality and morbidity into one single index number. A literature review was conducted to examine and discuss the use of QALYs to measure outcomes in telehealth evaluations. Methods Evaluations were identified via a literature search in all relevant databases. Only economic evaluations measuring both costs and QALYs using primary patient level data of two or more alternatives were included. Results A total of 17 economic evaluations estimating QALYs were identified. All evaluations used validated generic health related-quality of life (HRQoL) instruments to describe health states. They used accepted methods for transforming the quality scores into utility values. The methodology used varied between the evaluations. The evaluations used four different preference measures (EQ-5D, SF-6D, QWB and HUI3), and utility scores were elicited from the general population. Most studies reported the methodology used in calculating QALYs. The evaluations were less transparent in reporting utility weights at different time points and variability around utilities and QALYs. Few made adjustments for differences in baseline utilities. The QALYs gained in the reviewed evaluations varied from 0.001 to 0.118 in implying a small but positive effect of telehealth intervention on patient’s health. The evaluations reported mixed cost-effectiveness results. Conclusion The use of QALYs in telehealth evaluations has increased over the last few years. Different methodologies and utility measures have been used to calculate QALYs. A more harmonised methodology and utility measure is needed to ensure comparability across telehealth evaluations.
    BMC Health Services Research 08/2014; 14(1):332. DOI:10.1186/1472-6963-14-332 · 1.71 Impact Factor
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