Munir Ahmed Khan

Validity and Reliability of the Assessment of Quality of Life (AQoL)-8D Multi-Attribute Utility Instrument

ObjectiveThe purpose of this paper was to report tests of the validity and reliability of a new instrument, the Assessment of Quality of Life (AQoL)-8D, which was constructed to improve the evaluation of health services that have an impact upon the psychosocial aspects of the quality of life.MethodsAustralian and US data from a large multi-instrument comparison survey were used to conduct tests of convergent, predictive and content validity using as comparators five other multi-attribute utility (MAU) instruments—the EQ-5D, SF-6D, Health Utilities Index (HUI) 3, 15D and the Quality of Well-Being (QWB)—as well as four non-utility instruments—the SF-36 and three measures of subjective well-being (SWB). A separate three part Australian survey was used to assess test–retest reliability.ResultsResults indicate that AQoL-8D correlates more highly with both the SWB instruments and the psychosocial dimensions of the SF-36, and that it is similar to the other MAU instruments in terms of its convergent and predictive validity. The second Australian survey demonstrated high test–retest reliability.ConclusionsThe results indicate that the AQoL-8D is a reliable and valid instrument which offers an alternative to the MAU instruments presently used in economic evaluation studies, and one which is particularly suitable when psychosocial elements of health are of importance.

Comparing and Explaining Differences in the Magnitude, Content, and Sensitivity of Utilities Predicted by the EQ-5D, SF-6D, HUI 3, 15D, QWB, and AQoL-8D Multiattribute Utility Instruments

Background. Cost utility analysis permits the comparison of disparate health services by measuring outcomes in comparable units, namely, quality-adjusted life-years, which equal life-years times the utility of the health state. However, comparability is compromised when different utility instruments predict different utilities for the same health state. The present paper measures the extent of, and reason for, differences between the utilities predicted by the EQ-5D-5L, SF-6D, HUI 3, 15D, QWB, and AQoL-8D. Methods. Data were obtained from patients in seven disease areas and members of the healthy public in six countries. Differences between public and patient utilities were estimated using each of the instruments. To explain discrepancies between the estimates, the measurement scales and content of the instruments were compared. The sensitivity of instruments to independently measured health dimensions was measured in pairwise comparisons of all combinations of the instruments. Results. The difference between public and patient utilities varied with the choice of instrument by more than 50% for every disease group and in four of the seven groups by more than 100%. Discrepancies were associated with differences in both the instrument content and their measurement scales. Pairwise comparisons of instruments found that variation in the sensitivity to physical and psychosocial dimensions of health closely reflected the items in the instruments descriptive systems. Discussion. Results indicate that instruments measure related but different constructs. They imply that commonly used instruments systematically discriminate against some classes of services, most notably mental health services. Differences in the instrument scales imply the need for transformations between the instruments to increase the comparability of measurement.

Modelling utility weights for the Assessment of Quality of Life (AQoL)-8D

AbstractPurposeThe objective of this paper is to describe the four-stage methodology used to obtain utility scores for the Assessment of Quality of Life (AQoL)-8D, a 35-item 8 dimension multi-attribute utility instrument, which was created to achieve a high degree of sensitivity to psycho-social health.MethodsData for the analyses were obtained from a representative group of 347 members of the Australian public and from 323 mental health patients each of whom provided VAS and time trade-off valuations of multiple health states. Data were used initially to create multiplicative scoring algorithms for each of the instrument’s 8 dimensions and for the overall instrument. Each of the algorithms was then subject to a second-stage econometric ‘correction’.ResultsAlgorithms were successfully created for each of the AQoL-8D’s dimensions, for physical and mental ‘super-dimensions’ and for the overall AQoL-8D instrument. The final AQoL-8D algorithm has good predictive power with respect to the TTO valuations.ConclusionsThe AQoL-8D is a suitable instrument for researchers conducting cost utility analyses generally but, in particular, for the analysis of services affecting psycho-social health.

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

Background. 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. Aims. 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. Methods. 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. Results. 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.

Mapping between 6 Multiattribute Utility Instruments

Background: Cost-utility analyses commonly employ a multiattribute utility (MAU) instrument to estimate the health state utilities, which are needed to calculate quality-adjusted life years. Different MAU instruments predict significantly different utilities, which makes comparison of results from different evaluation studies problematical. Aim: This article presents mapping functions (“crosswalks”) from 6 MAU instruments (EQ-5D-5L, SF-6D, Health Utilities Index 3 [HUI 3], 15D, Quality of Well-Being [QWB], and Assessment of Quality of Life 8D [AQoL-8D]) to each of the other 5 instruments in the study: a total of 30 mapping functions. Methods: Data were obtained from a multi-instrument comparison survey of the public and patients in 7 disease areas conducted in 6 countries (Australia, Canada, Germany, Norway, United Kingdom, and United States). The 8022 respondents were administered each of the 6 study instruments. Mapping equations between each instrument pair were estimated using 4 econometric techniques: ordinary least squares, generalized linear model, censored least absolute deviations, and, for the first time, a robust MM-estimator. Results: Goodness-of-fit indicators for each of the results are within the range of published studies. Transformations reduced discrepancies between predicted utilities. Incremental utilities, which determine the value of quality-related health benefits, are almost perfectly aligned at the sample means. Conclusion: Transformations presented here align the measurement scales of MAU instruments. Their use will increase confidence in the comparability of evaluation studies, which have employed different MAU instruments.

Can multi-attribute utility instruments adequately account for subjective well-being?

Introduction: The quality of life is included in cost utility analyses by weighting the relevant years of life by health state utilities. However, the utilities predicted by multi-attribute utility instruments (MAUIs) for this purpose do not correlate highly with the subjective well-being (SWB) of people experiencing the health states. This suggests that MAUIs may not take account of the SWB experienced by patients. This article explores an alternative hypothesis: that a failure of an MAUI to account for variation in SWB is primarily a result of the failure of its descriptive system to include the elements of health that determine SWB and that cannot therefore be included in assessment of the health state utility. Methods: Survey data are used to determine the extent to which 6 MAUIs with significantly different descriptive systems explain differences between the SWB of the healthy public and patients in 7 disease areas. Results: The EQ-5D-5L takes least account and AQoL-8D most account of SWB. AQoL-8D overpredicts the loss of SWB in 2 cases where hedonic adaptation is known to occur. Discussion: Results suggest that, to a large extent, utility can account for variation in SWB. The case for replacing utility with SWB in economic evaluation studies has arisen, in part, because elements of importance for SWB have been omitted from the descriptive systems of commonly used MAUIs.

Utility weights for the vision-related Assessment of Quality of Life (AQoL)-7D instrument.

Purpose: To obtain utility weights consistent with the needs of economic evaluation for the Assessment of Quality of Life (AQoL)-7D, a generic instrument created to increase the sensitivity of the measurement of quality of life amongst people with impaired vision. Methods: Two extant instruments were combined, the Vision-related Quality of Life Index (VisQoL) and the AQoL-6D. Utilities were obtained from patients with visual impairment and from the general population using time trade-off (TTO) methodology. Dimensions were combined and an econometric adjustment used to eliminate the effects of instrument redundancy. Bias was tested by comparison of holistic TTO values with utility scores predicted from the AQoL-7D scoring formula. Results: The AQoL-7D instrument consists of 26 items and 7 dimensions each with good psychometric properties. Their combination into a single instrument resulted in significant redundancy which was successfully eliminated. Utility formulae for both the public and patients produced bias-free estimates of the utility of holistic health states describing visual impairment. Results imply differing valuations of health states by the public and by people with impaired vision. Conclusions: The AQoL-7D can detect changes in health states affecting people with impaired vision which are likely to be overlooked by other generic instruments due to content insensitivity. The utilities it produces are generated using a “mainstream” methodology, the TTO. Quality-adjusted life year values based on the AQoL-7D may therefore be used for economic evaluation of programs.

Deriving health utilities from the MacNew Heart Disease Quality of Life Questionnaire

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system.

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

OBJECTIVE To 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 METHODS Self-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). RESULTS MAU 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. CONCLUSIONS Utilities 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.