Multi-attribute Utility Instruments Research Articles

An Exploratory Study: A Head-to-Head Comparison of the EQ-5D-5L and AQoL-8D for Long-Term Publicly Waitlisted Bariatric Surgery Patients Before and 3 Months After Bariatric Surgery.

BackgroundChoice of a multi-attribute utility instrument (MAUI) that appropriately assesses an intervention’s health-related quality-of-life (HRQoL) impacts is a vital part of healthcare resource allocation and clinical assessment.ObjectiveOur exploratory study compared the EuroQol (EQ)-5D-5L and Assessment of Quality of Life (AQoL)-8D MAUIs, which were used to assess the effect of bariatric surgery for a cohort of long-term publicly waitlisted, severely obese patients.MethodsThe study was conducted at the Hobart Private Hospital (Tasmania, Australia). To compare the sensitivity and instrument content of the two MAUIs, we used dimensional comparisons by investigating the distribution of patient-reported responses (number/percentage) across the MAUIs’ levels and dimensions; summary health-state utility valuations (utilities); and individual/super-dimension scores (AQoL-8D) to investigate discriminatory power and HRQoL improvements preoperatively and 3 months postoperatively.ResultsParticipants’ (n = 23) overall MAUI completion rate was 74%. Postoperative total weight loss was 9.9%. EQ-5D-5L utilities were relatively higher pre- and postoperatively than AQoL-8D utilities [mean standard deviation (SD) EQ-5D-5L 0.70 (0.25) to 0.80 (0.25); AQoL-8D 0.51 (0.24) to 0.61 (0.24)]. AQoL-8D Psychosocial super dimension was relatively low postoperatively [0.37 (0.25)], driving the instrument’s lower utility. These results were supported by the dimensional comparisons that revealed an overall greater dispersion for the AQoL-8D. Nevertheless, there were clinical improvements in utilities for both instruments. AQoL-8D utilities were lower than population norms; not so the EQ-5D-5L utilities. The AQoL-8D dimensions of Happiness, Coping, and Self-worth improved the most.ConclusionsAQoL-8D more fully captured the impact of obesity and bariatric surgery on HRQoL (particularly psychosocial impacts) for long-term waitlisted bariatric surgery patients, even 3 months postoperatively. AQoL-8D preoperative utility revealed our population’s HRQoL was lower than people with cancer or heart disease.Electronic supplementary materialThe online version of this article (doi:10.1007/s41669-017-0060-1) contains supplementary material, which is available to authorized users.

Comparison of the EQ-5D-3L and the SF-6D (SF-12) contemporaneous utility scores in patients with cardiovascular disease.

Multi-attribute utility instruments (MAUIs) are widely used to measure utility weights. This study sought to compare utility weights of two popular MAUIs, the EQ-5D-3L and the SF-6D, and inform researchers in the selection of generic MAUI for use with cardiovascular (CVD) patients. Data were collected in the Young@Heart study, a randomised controlled trial of a nurse-led multidisciplinary home-based intervention compared to standard usual care. Participants (n=598) completed the EQ-5D-3L and the SF-12v2, from which the SF-6D can be constructed, at baseline and at 24-month follow-up. This study examined discrimination, responsiveness, correlation and differences across the two instruments. Both MAUIs were able to discriminate between the NYHA severity classes and recorded similar changes between the two time points although only SF-6D differences were significant. Correlations between the dimensions of the two MAUIs were low. There were significant differences between the two instruments in mild conditions but they were similar in severe conditions. Substantial ceiling and floor effects were observed. Our findings indicate that the EQ-5D and the SF-6D cover different spaces in health due to their classification systems. Both measures were capable of discriminating between severity groups and responsive to quality of life changes in the follow-up. It is recommended to use the EQ-5D-3L in severe and the SF-6D in mild CVD conditions.

Does the choice of tariff matter?: A comparison of EQ-5D-5L utility scores using Chinese, UK, and Japanese tariffs on patients with psoriasis vulgaris in Central South China.

There is an increasing trend globally to develop country-specific tariffs that can theoretically better reflect population's preferences on health states for preference-based health-related quality-of-life instruments, also known as multiattribute utility instruments. This study focused on the most recently developed 5-level version of EuroQol-5 Dimension (EQ-5D) questionnaire, 1 of the world's most well-known multiattribute utility instruments, and aimed to empirically explore the agreements and known-group validities of applying the country-specific tariff versus tariffs developed from other countries using a sample of psoriasis vulgaris patients in Mainland China.A convenience sampling framework was adopted to recruit patients diagnosed with psoriasis vulgaris from Xiangya Hospital, Central South University, China, between May 2014 and February 2015. The 5-level EuroQol-5 dimensions (EQ-5D-5L) utilities were scored by using the Chinese, Japanese, and UK tariffs. Health state utilities were compared using a range of nonparametric test. The intraclass correlation coefficients and Bland-Altman plots were used to examine the agreements among the 3 EQ-5D-5L scores. Health state utility decrements between known groups were investigated using both effect size and a regression analysis.In all, 350 patients (aged 16 years or older) were recruited. There were significant differences among the 3 national tariff sets. Overall, 3 tariffs showed excellent agreements (intraclass correlation coefficient >0.90); however, the wide limits of agreement from the Bland-Altman plots suggest that these tariffs cannot be used interchangeably. The EQ-5D-5L scores using the Chinese-specific tariff showed the best known-group validity than the other 2 tariffs in this Chinese patient sample. The evidence from this study supports the choice of the country-specific tariff to be used in Mainland China.

The Assessment of Quality of Life (AQoL) II instrument: the effect of deliberation and alternative utility weights in a multi-attribute utility instrument

The Assessment of Quality of Life (AQoL) II instrument: the effect of deliberation and alternative utility weights in a multi-attribute utility instrument

\u201cFibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments\u201d

BackgroundThe revised version of the Fibromyalgia Impact Questionnaire (FIQR) is one of the most widely used specific questionnaires in FM studies. However, this questionnaire does not allow calculation of QALYs as it is not a preference-based measure. The aim of this study was to develop mapping algorithm which enable FIQR scores to be transformed into utility scores that can be used in the cost utility analyses.MethodsA cross-sectional survey was conducted. One hundred and 92 Spanish women with Fibromyalgia were asked to complete four general quality of life questionnaires, i.e. EQ-5D-5 L, 15D, AQoL-8D and SF-12, and one specific disease instrument, the FIQR. A direct mapping approach was adopted to derive mapping algorithms between the FIQR and each of the four multi-attribute utility (MAU) instruments. Health state utility was treated as the dependent variable in the regression analysis, whilst the FIQR score and age were predictors.ResultsThe mean utility scores ranged from 0.47 (AQoL-8D) to 0.69 (15D). All correlations between the FIQR total score and MAU instruments utility scores were highly significant (p < 0.0001) with magnitudes larger than 0.5. Although very slight differences in the mean absolute error were found between ordinary least squares (OLS) estimator and generalized linear model (GLM), models based on GLM were better for EQ-5D-5 L, AQoL-8D and 15D.ConclusionMapping algorithms developed in this study enable the estimation of utility values from scores in a fibromyalgia specific questionnaire.

Comparison of the HUI3 and the EQ-5D-3L in a nursing home setting.

BackgroundAccurately assessing changes in the quality of life of older people living permanently in nursing homes is important. The multi-attribute utility instrument most commonly used and recommended to assess health-related quality of life in the nursing home population is the three-level EuroQol EQ-5D-3L. To date, there have been no studies using the Health Utilities Index Mark III (HUI3). The purpose of this study was to compare the level of agreement and sensitivity to change of the EQ-5D-3L and HUI3 in a nursing home population.MethodsEQ-5D-3L and HUI3 scores were measured as part of a cluster randomised controlled trial of nurse led care coordination in a nursing home population in Perth, Western Australia at baseline and 6-month follow up.ResultsBoth measures were completed for 199 residents at baseline and 177 at 6-month follow-up. Mean baseline utility scores for EQ-5D-3L (0.45; 95% CI 0.41–0.49) and HUI3 (0.15; 95% CI 0.10–0.20) were significantly different (Wilcoxon signed rank test, p<0.01) and agreement was poor to moderate between absolute scores from each instrument (intra-class correlation coefficient = 0.63). The EQ-5D-3L appeared more sensitive to change over the 6-month period.ConclusionOur findings show that the EQ-5D-3L and HUI3 estimate different utility scores among nursing home residents. These differences should be taken into account, particularly when considering the implications of the cost-effectiveness of particular interventions and we conclude that the HUI3 is no better suited to measuring health-related quality of life in a nursing home population when compared to the EQ-5D-3L.

Estimating Utility Weights for the Vision Related Quality of Life Index.

The VisQoL instrument was constructed as a vision-specific addition to the AQoL-6D multi-attribute utility instrument. The composite instrument, the AQoL-7D, was assigned utility scores that are the basis for now estimating VisQoL utilities when it is used as a stand-alone instrument. This study aimed to construct mapping functions that allow utility scores to be assigned to the Vision Related Quality of Life (VisQoL) instrument, a stand-alone vision-specific quality-of-life measure. A sample of 164 patients completed the AQoL-7D, which includes the VisQoL. Mapping algorithms between VisQoL and AQoL-7D were then derived using two econometric methods, ordinary least squares estimator and generalized linear model (GLM). Two model specifications were considered with either six VisQoL raw item values or VisQoL overall dimension value as the key independent variables. The predictive performance of each method on each model specification was assessed using the mean absolute error (MAE) and intraclass correlation coefficient (ICC). Both internal and external validation tests (using a second, independent sample of 164 patients) were performed. The mapping algorithms derived from the GLM had superior properties to the ordinary least squares-based algorithms in both internal and external validation tests. The ICC values ranged from 0.851 to 0.913, and the MAE ranged from 0.043 to 0.052 for two model specifications, based on two econometric methods. However, predicted utilities tend to over-predict/under-predict the lowest/highest observed utility. Mapping algorithms predicting AQoL-7D utility based on six VisQoL items or VisQoL dimension value have been developed. The algorithm can be used to estimate quality adjusted life years. This allows the VisQoL to be used in cost utility analyses.

Comparison of contemporaneous responses for EQ-5D-3L and Minnesota Living with Heart Failure; a case for disease specific multiattribute utility instrument in cardiovascular conditions

BackgroundThe EQ-5D-3L, a generic multi-attribute utility instrument (MAUI), is widely employed to assist in economic evaluations in health care. The EQ-5D-3L lacks sensitivity when used in conditions such as cardiovascular disease (CVD). Although there are number of CVD specific quality of life instruments, currently, there are no CVD specific MAUIs. The aim of this study is to investigate the discriminative ability and responsiveness of the EQ-5D-3L and the Minnesota Living with Heart Failure Questionnaire (MLHF), a CVD specific quality of life instrument in a group of heart failure patients. MethodsThe psychometric performance of the EQ-5D-3L and the MLHF was assessed using data from a randomised trial for a heart failure management intervention. The two instruments were compared for discrimination, responsiveness and agreement. The severity groups were defined using New York Heart Association functional classes. ResultsThe effect sizes for severe classes were generally similar showing good discrimination. The MLHF recorded better responsiveness between the time points than the EQ-5D-3L which was indicated by higher effect sizes and standardised response means. The change in MLHF summary scores between the time points was significant (p<0.005; paired t-test). The overall agreement between the two measures was low. ConclusionThe low correlation indicates that the two classification systems cover different aspects of health space. Comparison of CVD specific instruments with other generic MAUIs such as EQ-5D-3L and AQOL-8D is recommended for further research.

Assessing outcomes for cost-utility analysis in mental health interventions: mapping mental health specific outcome measure GHQ-12 onto EQ-5D-3L.

BackgroundMany intervention-based studies aiming to improve mental health do not include a multi-attribute utility instrument (MAUI) that produces quality-adjusted life-years (QALYs) and it limits the applicability of the health economic analyses. This study aims to develop ‘crosswalk’ transformation algorithm between a measure for psychological distress General Health Questionnaire (GHQ-12) and MAUI EuroQoL (EQ-5D-3L).MethodsThe study is based on a survey questionnaire sent to a random sample in four counties in Sweden in 2012. The survey included GHQ-12 and EQ-5D instruments, as well as a question about self-rated health. The EQ-5D index was calculated using the UK and the Swedish tariff values. Two OLS models were used to estimate the EQ-5D health state values using the GHQ-12 as exposure, based on the respondents (n = 17, 101) of two counties. The algorithms were applied to the data from two other counties, (n = 15, 447) to check the predictive capacity of the models.ResultsThe final models included gender, age, self-rated health and GHQ-12 scores as a quantitative variable. The regression equations explained 40 % (UK tariff) and 46 % (Swedish tariff) of the variances. The model showed a satisfying predictive capacity between the observed and the predicted EQ-5D index score, with Pearson correlation = 0.65 and 0.69 for the UK and Swedish models, respectively.ConclusionThe algorithms developed in this study can be used to determine cost-effectiveness of services or interventions that use GHQ-12 as a primary outcome where the utility measures are not collected.

Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs).

Economic evaluation of health services commonly requires information regarding health-state utilities. Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated. This paper examines the possibility of mapping a non-utility-based outcome, the Sydney Asthma Quality of Life Questionnaire (AQLQ-S), onto five multi-attribute utility instruments: Assessment of Quality of Life 8 Dimensions (AQoL-8D), EuroQoL 5 Dimensions 5-Level (EQ-5D-5L), Health Utilities Index Mark 3 (HUI3), 15 Dimensions (15D), and the Short-Form 6 Dimensions (SF-6D). Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison (MIC) survey. Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation, distribution of predicted utilities, distribution of residuals, and proportion of predictions with absolute errors <0.0.5. Validation of initial 'primary' models was carried out on a random sample of the MIC data. Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables. The four statistical techniques predicted models that performed differently when assessed by the six criteria; however, the best results, for both the estimation and validation samples, were obtained using a generalised linear model (GLM estimator). It is possible to predict valid utilities from the AQLQ-S using regression methods. We recommend GLM models for this exercise.

Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments.

Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data.

(i) to demonstrate a method which ameliorates the problem of self-selection in the estimation of population norms from web-based data and (ii) to use the method to calculate population norms for two multi-attribute utility (MAU) instruments, the AQoL-6D and AQoL-8D, and population norms for the sub-scales from which they are constructed. A web-based survey administered the AQoL-8D MAU instrument (which subsumes the AQoL-6D questionnaire), to members of the public along with the AQoL-4D which has extant population norms. Age, gender and the AQoL-4D were used as post-stratification auxiliary variables to construct weights to ameliorate the potential effects of self-selection associated with web-based surveys. The weights were used to estimate unbiased population norms. Standard errors from the weighted samples were calculated using Jackknife estimation. For both AQoL-6D and AQoL-8D, physical health dimensions decline significantly with age. In contrast, for the majority of the psycho-social dimensions there is a significant U-shaped profile. The net effect is a shallow U-shaped relationship between age and both the AQoL-6D and AQoL-8D utilities. This contrasts with the almost monotonic decline in the utilities derived from the AQoL-4D and SF-6D MAU instruments. Post-stratification weights were used to ameliorate potential bias in the derivation of norms from web-based data for the AQoL-6D and AQoL-8D. The methods may be used generally to obtain norms when suitable auxiliary variables are available. The inclusion of an enlarged psycho-social component in the two instruments significantly alters the demographic profile.

Public Values for Health States Versus Societal Valuations of Health Improvements: A Critique of Dan Hausman’s ‘Valuing Health’

Daniel Hausman’s book ‘Valuing Health’ is a valuable contribution to our understanding of QALYs and DALYs and to moving health economics to adopting a broader perspective than that taken in conventional cost-effectiveness analysis. Hausman’s attempt at constructing a public value table for health states without having recourse to data from population preferences studies is also a fascinating read. But I have serious concerns about his resulting table. Hausman’s views on which dimensions of health a benevolent liberal state should care about are essentially not different from what has long been emphasized in health economists’ work on valuations of health outcomes. His table would have been helpful as a sketch if it was the first attempt in health economics to quantify numerically the societal value of different types and degrees of health improvement. But research in the field has gone far beyond that. Multi-attribute utility instruments with much more accurate health classification systems than Hausman’s sketch are now at hand. Available also are models of societal valuations of QALYs that (i) are broadly consistent with general population values, (ii) incorporate wider concerns than Hausman’s table does and (iii) do not have the questionable numerical properties that characterize Hausman’s sketch.

A Head-to-Head Comparison of the EQ-5D-5L and AQoL-8D Multi-Attribute Utility Instruments in Patients Who Have Previously Undergone Bariatric Surgery.

Psychosocial health status is an important and dynamic outcome for bariatric/metabolic surgery patients, as acknowledged in recent international standardised outcomes reporting guidelines. Multi-attribute utility-instruments (MAUIs) capture and assess an individual's health-related quality-of-life (HRQoL) within a single valuation, their utility. Neither MAUIs nor utilities were discussed in the guidelines. Many MAUIs (e.g. EQ-5D) target physical health. Not so the AQoL-8D. Our objective was to explore agreement between, and suitability of, the EQ-5D-5L and AQoL-8D for assessing health state utility, and to determine whether either MAUI could be preferentially recommended for metabolic/bariatric surgery patients. Utilities for post-surgical private-sector patients (n=33) were assessed using both instruments and summary statistics expressed as mean [standard deviation (SD)] and median [interquartile range (IQR)]. Interchangeability of the MAUIs was assessed with Bland-Altman analysis. Discriminatory attributes were investigated through floor/ceiling effects and dimension-to-dimension comparisons. Spearman's rank measured associations between the instruments' utility values and with the body mass index (BMI). Mean (SD) EQ-5D-5L utility value was 0.84 (0.15) and median 0.84 (IQR 0.75-1.00). Mean (SD) AQoL-8D utility value was 0.76 (0.17) and median 0.81 (IQR 0.63-0.88). Spearman's rank was r=0.68; (p<0.001); however, Bland-Altman analysis revealed fundamental differences. Neither instrument gave rise to floor effects. A ceiling effect was observed with the EQ-5D-5L, with 36% of participants obtaining a utility value of 1.00 (perfect health). These same participants obtained a mean utility of 0.87 on the AQoL-8D, primarily driven by the mental-super-dimension score (0.52). The AQoL-8D preferentially captures psychosocial aspects of metabolic/bariatric surgery patients' HRQoL. We recommend the AQoL-8D as a preferred MAUI for these patients given their complex physical/psychosocial needs.

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.

To derive a health state classification system (HSCS) from the cancer-specific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multi-attribute utility instrument. The conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori selection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75 % observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions. CFA supported the conceptual model and its generalisability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems. The HSCS created from QLQ-C30 items is known as the EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The next phase of the QLU-C10D's development involves valuation studies, currently planned or being conducted across the globe.

Comparison of the Multiattribute Utility Instruments EQ-5D and SF-6D in a Europe-Wide Population-Based Cohort of Patients with Inflammatory Bowel Disease 10 Years after Diagnosis.

Background. The treatment of chronic inflammatory bowel disease (IBD) is costly, and limited resources call for analyses of the cost effectiveness of therapeutic interventions. The present study evaluated the equivalency of the Short Form 6D (SF-6D) and the Euro QoL (EQ-5D), two preference-based HRQoL instruments that are broadly used in cost-effectiveness analyses, in an unselected IBD patient population. Methods. IBD patients from seven European countries were invited to a follow-up visit ten years after their initial diagnosis. Clinical and demographic data were assessed, and the Short Form 36 (SF-36) was employed. Utility scores were obtained by calculating the SF-6D index values from the SF-36 data for comparison with the scores obtained with the EQ-5D questionnaire. Results. The SF-6D and EQ-5D provided good sensitivities for detecting disease activity-dependent utility differences. However, the single-measure intraclass correlation coefficient was 0.58, and the Bland-Altman plot indicated numerous values beyond the limits of agreement. Conclusions. There was poor agreement between the measures retrieved from the EQ-5D and the SF-6D utility instruments. Although both instruments may provide good sensitivity for the detection of disease activity-dependent utility differences, the instruments cannot be used interchangeably. Cost-utility analyses performed with only one utility instrument must be interpreted with caution.

Measuring the Sensitivity and Construct Validity of 6 Utility Instruments in 7 Disease Areas.

Health services that affect quality of life (QoL) are increasingly evaluated using cost utility analyses (CUA). These commonly employ one of a small number of multiattribute utility instruments (MAUI) to assess the effects of the health service on utility. However, the MAUI differ significantly, and the choice of instrument may alter the outcome of an evaluation. The present article has 2 objectives: 1) to compare the results of 3 measures of the sensitivity of 6 MAUI and the results of 6 tests of construct validity in 7 disease areas and 2) to rank the MAUI by each of the test results in each disease area and by an overall composite index constructed from the tests. Patients and the general public were administered a battery of instruments, which included the 6 MAUI, disease-specific QoL instruments (DSI), and 6 other comparator instruments. In each disease area, instrument sensitivity was measured 3 ways: by the unadjusted mean difference in utility between public and patient groups, by the value of the effect size, and by the correlation between MAUI and DSI scores. Content and convergent validity were tested by comparison of MAUI utilities and scores from the 6 comparator instruments. These included 2 measures of health state preferences, measures of subjective well-being and capabilities, and generic measures of physical and mental QoL derived from the SF-36. The apparent sensitivity of instruments varied significantly with the measurement method and by disease area. Validation test results varied with the comparator instruments. Notwithstanding this variability, the 15D, AQoL-8D, and the SF-6D generally achieved better test results than the QWB and EQ-5D-5L.

PRM190 - Assessing Outcomes for Cost-Utility Analyses in Mental Health Interventions: Comparison of Multi-Attribute Utility Instrument Eq-5d with Mental Health Specific Outcomes Ghq12

PRM190 - Assessing Outcomes for Cost-Utility Analyses in Mental Health Interventions: Comparison of Multi-Attribute Utility Instrument Eq-5d with Mental Health Specific Outcomes Ghq12

A Review of the Development and Application of Generic Multi-Attribute Utility Instruments for Paediatric Populations.

Multi-attribute utility instruments (MAUIs) are increasingly being used as a means of quantifying utility for the calculation of quality-adjusted life-years within the context of cost utility analysis. Traditionally, MAUIs have been developed and applied in adult populations. However, increasingly, researchers in health economics and other disciplines are recognising the importance of the measurement and valuation of health in both children and adolescents. Presently, there are nine generic MAUIs available internationally that have been used in paediatric populations: the Quality of Well-Being Scale (QWB), the Health Utility Index Mark 2 (HUI2), the HUI3, the Sixteen-dimensional measure of health-related quality of life (HRQoL) (16D), the Seventeen-dimensional measure of HRQoL (17D), the Assessment of Quality of Life 6-Dimension (AQoL-6D) Adolescent, the Child Health Utility 9D (CHU9D), the EQ-5D Youth version (EQ-5D-Y) and the Adolescent Health Utility Measure (AHUM). This paper critically reviews the development and application of the above nine MAUIs and discusses the specific challenges of health utility measurement in children and adolescents. Areas for further research relating to the development and application of generic MAUIs in paediatric populations are highlighted.

Diabetes and quality of life: Comparing results from utility instruments and Diabetes-39

ObjectiveTo compare the Diabetes-39 (D-39) with six multi-attribute utility (MAU) instruments (15D, AQoL-8D, EQ-5D, HUI3, QWB, and SF-6D), and to develop mapping algorithms which could be used to transform the D-39 scores into the MAU scores. Research design and methodsSelf-reported diabetes sufferers (N=924) and members of the healthy public (N=1760), aged 18 years and over, were recruited from 6 countries (Australia 18%, USA 18%, UK 17%, Canada 16%, Norway 16%, and Germany 15%). Apart from the QWB which was distributed normally, non-parametric rank tests were used to compare subgroup utilities and D-39 scores. Mapping algorithms were estimated using ordinary least squares (OLS) and generalised linear models (GLM). ResultsMAU instruments discriminated between diabetes patients and the healthy public; however, utilities varied between instruments. The 15D, SF-6D, AQoL-8D had the strongest correlations with the D-39. Except for the HUI3, there were significant differences by gender. Mapping algorithms based on the OLS estimator consistently gave better goodness-of-fit results. The mean absolute error (MAE) values ranged from 0.061 to 0.147, the root mean square error (RMSE) values 0.083 to 0.198, and the R-square statistics 0.428 and 0.610. Based on MAE and RMSE values the preferred mapping is D-39 into 15D. R-square statistics and the range of predicted utilities indicate the preferred mapping is D-39 into AQoL-8D. ConclusionsUtilities estimated from different MAU instruments differ significantly and the outcome of a study could depend upon the instrument used. The algorithms reported in this paper enable D-39 data to be mapped into utilities predicted from any of six instruments. This provides choice for those conducting cost-utility analyses.