Multi-attribute Utility Instruments Research Articles

Mapping between 6 Multiattribute Utility Instruments.

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. 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. 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. 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. 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.

The cost-effectiveness of biologics for the treatment of rheumatoid arthritis: a systematic review.

Background and ObjectivesEconomic evaluations provide information to aid the optimal utilization of limited healthcare resources. Costs of biologics for Rheumatoid arthritis (RA) are remarkably high, which makes these agents an important target for economic evaluations. This systematic review aims to identify existing studies examining the cost-effectiveness of biologics for RA, assess their quality and report their results systematically.MethodsA literature search covering Medline, Scopus, Cochrane library, ACP Journal club and Web of Science was performed in March 2013. The cost-utility analyses (CUAs) of one or more available biological drugs for the treatment of RA in adults were included. Two independent investigators systematically collected information and assessed the quality of the studies. To enable the comparison of the results, all costs were converted to 2013 euro.ResultsOf the 4890 references found in the literature search, 41 CUAs were included in the current systematic review. While considering only direct costs, the incremental cost-effectiveness ratio (ICER) of the tumor necrosis factor inhibitors (TNFi) ranged from 39,000 to 1 273,000 €/quality adjusted life year (QALY) gained in comparison to conventional disease-modifying antirheumatic drugs (cDMARDs) in cDMARD naïve patients. Among patients with an insufficient response to cDMARDs, biologics were associated with ICERs ranging from 12,000 to 708,000 €/QALY. Rituximab was found to be the most cost-effective alternative compared to other biologics among the patients with an insufficient response to TNFi.ConclusionsWhen 35,000 €/QALY is considered as a threshold for the ICER, TNFis do not seem to be cost-effective among cDMARD naïve patients and patients with an insufficient response to cDMARDs. With thresholds of 50,000 to 100,000 €/QALY biologics might be cost-effective among patients with an inadequate response to cDMARDs. Standardization of multiattribute utility instruments and a validated standard conversion method for missing utility measures would enable better comparison between CUAs.

How Well Do the Generic Multi-attribute Utility Instruments Incorporate Patient and Public Views Into Their Descriptive Systems?

Multi-attribute utility instruments (MAUIs) are increasingly being used to generate utility data, which can be used to calculate quality-adjusted life-years (QALYs). These QALY data can then be incorporated into a cost-utility analysis as part of an economic evaluation, to inform health care resource allocation decisions. Many health care decision-making bodies around the world, such as the National Institute for Health and Care Excellence, require the use of generic MAUIs. Recently, there has been a call for greater input of patients into the development of patient-reported outcome measures, and this is now actively encouraged. By incorporating the views of patients, greater validity of an instrument is expected and it is more likely that patients will be able to self-complete the instrument, which is the ideal when obtaining information about a patient's health-related quality of life. This paper examines the stages of MAUI development and the scope for patient and/or public involvement at each stage. The paper then reviews how much the main generic MAUIs have incorporated the views of patients/the public into the development of their descriptive systems at each of these stages, and the implications of this. The review finds that the majority of MAUIs had very little input from patients/the public. Instead, existing literature and/or the views of experts were used. If we wish to incorporate patient/public views into future development of MAUIs, qualitative methods are recommended.

Why do multi-attribute utility instruments produce different utilities: the relative importance of the descriptive systems, scale and 'micro-utility' effects.

PurposeHealth state utilities measured by the major multi-attribute utility instruments differ. Understanding the reasons for this is important for the choice of instrument and for research designed to reconcile these differences. This paper investigates these reasons by explaining pairwise differences between utilities derived from six multi-attribute utility instruments in terms of (1) their implicit measurement scales; (2) the structure of their descriptive systems; and (3) ‘micro-utility effects’, scale-adjusted differences attributable to their utility formula.Methods The EQ-5D-5L, SF-6D, HUI 3, 15D and AQoL-8D were administered to 8,019 individuals. Utilities and unweighted values were calculated using each instrument. Scale effects were determined by the linear relationship between utilities, the effect of the descriptive system by comparison of scale-adjusted values and ‘micro-utility effects’ by the unexplained difference between utilities and values.ResultsOverall, 66 % of the differences between utilities was attributable to the descriptive systems, 30.3 % to scale effects and 3.7 % to micro-utility effects.DiscussionResults imply that the revision of utility algorithms will not reconcile differences between instruments. The dominating importance of the descriptive system highlights the need for researchers to select the instrument most capable of describing the health states relevant for a study.ConclusionsReconciliation of inconsistent utilities produced by different instruments must focus primarily upon the content of the descriptive system. Utility weights primarily determine the measurement scale. Other differences, attributable to utility formula, are comparatively unimportant.

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

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. 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. 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. 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.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach.

BackgroundMulti attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure.AimTo determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure.MethodsQLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis.ResultsCFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA.ConclusionCFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Assessing outcomes for cost-utility analysis in depression: comparison of five multi-attribute utility instruments with two depression-specific outcome measures.

Many mental health surveys and clinical studies do not include a multi-attribute utility instrument (MAUI) that produces quality-adjusted life-years (QALYs). There is also some question about the sensitivity of the existing utility instruments to mental health. To compare the sensitivity of five commonly used MAUIs (Assessment of Quality of Life - Eight Dimension Scale (AQoL-8D), EuroQoL-five dimension (EQ-5D-5L), Short Form 6D (SF-6D), Health Utilities Index Mark 3 (HUI3), 15D) with that of disease-specific depression outcome measures (Depression Anxiety Stress Scales (DASS-21) and the Kessler Psychological Distress Scale (K10)) and develop 'crosswalk' transformation algorithms between the measures. Individual data from 917 people with self-report depression collected as part of the International Multi-Instrument Comparison Survey. All the MAUIs discriminated between the levels of severity measured by the K10 and the DASS-21. The AQoL-8D had the highest correlation with the disease-specific measures and the best goodness-of-fit transformation properties. The algorithms developed in this study can be used to determine cost-effectiveness of services or interventions where utility measures are not collected.

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.

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. 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. 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 instrument's descriptive systems. 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

The 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. Data 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'. Algorithms 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. The 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.

Impact of Keratoconus in the Better Eye and the Worse Eye on Vision-Related Quality of Life

We assessed the impact of keratoconus disease indicators in the better eye and worse eye on quality of life (QoL) using the Vision and Quality of Life Index (VisQoL) multi-attribute utility instrument (MAUI). ?tlsb -.01w?>Patients with keratoconus completed the six-item VisQoL utility measure. Visual acuity was assessed using a logMAR chart, and corneal thickness and the keratometric values were measured by using Scheimpflug imaging (Pentacam). Four indicators of keratoconus disease status were considered in this study, namely best corrected visual acuity (BCVA), average front corneal curvature (Front Km), thinnest corneal location (TCL), and spherical equivalent refractive error (SE). As keratoconus is an asymmetric condition, we considered the disease parameters separately for the better eye and the worse eye. The association between the four keratoconus indicators and VisQoL utilities was assessed using multivariate linear regression. A total of 170 patients with keratoconus completed the VisQoL. Patients' median age was 33 (IQR = 18) years (range, 14-75 years) and 58% (n = 99) were males. The median VisQoL utility value was 0.60 (IQR, 0.46; range, 0.02-0.99). The VisQoL utilities reduced with increasing age (ρ = -0.18, P = 0.02) and were higher for males (median = 0.65, IQR = 0.49) than females (median = 0.51, IQR = 0.40). In univariate analyses, BCVA in the better and worse eye, and Front Km and TCL in the worse eye were associated with lower VisQol utilities. However, after adjusting for relevant covariates, only BCVA in the better eye remained associated significantly with reduced VisQoL utilities (β = -0.20, P = 0.018). Using a vision-specific MAUI, our study demonstrated substantial disutility relating to keratoconus. Worse vision in the better eye (but not the worse eye) was associated independently with a reduction in VisQoL utilities, suggesting that considering VisQoL utilities based on vision in the better eye is an important estimate of the impact of keratoconus from the patients' perspective. Treatment and rehabilitation interventions to retard the progression of vision impairment in the better eye resulting from keratoconus would be most efficacious at an early stage to improve QoL outcomes for patients with this disease.

Visual Impairment as a Function of Visual Acuity in Both Eyes and Its Impact on Patient Reported Preferences

PurposeTo assess the impact of VA loss on patient reported utilities taking both eyes into account compared to taking only the better or the worse eye into account.MethodsIn this cross-sectional study 1085 patients and 254 controls rated preferences with the generic health-related (EQ-5D; n = 868) and vision-specific (Vision and Quality of Life Index (VisQoL); n = 837) multi-attribute utility instruments (MAUIs). Utilities were calculated for three levels of VA in the better and worse eyes, as well as for 6 different vision states based on combinations of the better and worse eye VA.ResultsUsing the VisQoL, utility scores decreased significantly with deteriorating vision in both the better and worse eyes when analysed separately. When stratified by the 6 vision states, VisQoL utilities decreased as VA declined in the worse eye despite stable VA in the better eye. Differences in VisQoL scores were statistically significant for cases where the better eye had no vision impairment and the worse seeing fellow eye had mild, moderate or severe vision impairment. In contrast, the EQ-5D failed to capture changes in better or worse eye VA, or any of the six vision states.ConclusionsCalculating utilities based only on better eye VA or using a generic MAUI is likely to underestimate the impact of vision impairment, particularly when the better eye has no or little VA loss and the worse eye is moderately to severely visually impaired. These findings have considerable implications for the assessment of overall visual impairment as well as economic evaluations within eye health.

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.

Valuing SF-6D Health States Using a Discrete Choice Experiment

SF-6D utility weights are conventionally produced using a standard gamble (SG). SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques. Recent advances in discrete choice methods have allowed estimation of utility weights. The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment (DCE) methods in this context. We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D. We designed an online DCE and administered it to an Australia-representative online panel (n = 1017). A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a random-effects probit model. The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0, respectively, to provide an algorithm for Australian cost-utility analyses. Physical functioning, pain, mental health, and vitality were the largest drivers of utility weights. Combining levels to remove illogical orderings did not lead to a poorer model fit. Relative to international SG-derived weights, the range of utility weights was larger with 5% of health states valued below zero. s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments. Australian cost-utility analyses can now use domestic SF-6D weights. The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality-adjusted life-year calculations should be further investigated.

Vision and Quality of Life Index: Validation of the Indian Version Using Rasch Analysis

A multi-attribute utility instrument (MAUI) consists of a descriptive system in which the items and responses seek information about a concept of the universe of health-related quality of life (QoL), and responses to these items then are weighted and combined to produce the index. To our knowledge, the 6-item Vision and Quality of Life Index (VisQoL) is the only available vision-related MAUI, developed and validated in Australia, specifically for visually impaired (VI) populations. To our knowledge, the psychometric properties of the VisQoL have not yet been investigated in an Indian VI sample; this was the aim of our study. The Indian VisQoL was administered to 349 VI adults face-to-face by a trained interviewer at the Vision Rehabilitation Centres of a tertiary eye care facility, South India. Rasch analysis was used to assess the psychometric properties. Rescoring was necessary for all except one item before ordered thresholds were obtained. All items fit the Rasch model and unidimensionality was confirmed. Person separation was acceptable (2.01), indicating that the instrument can discriminate among three strata of participants" vision-related QoL (VRQoL). The VisQoL items were targeted substantially to the participants" VRQoL (-0.69 logits). One item ("ability to have friendships") demonstrated large differential item functioning by work status; working participants reported the item to be more difficult (-1.13 logits) relative to other items when compared to the nonworking participants. The 6-item Indian VisQoL satisfies unidimensional Rasch model expectations in VI patients. Disordering of response categories was evident; replication is required before a common rescoring option should be considered.

AN AUSTRALIAN DISCRETE CHOICE EXPERIMENT TO VALUE EQ‐5D HEALTH STATES

Conventionally, generic quality-of-life health states, defined within multi-attribute utility instruments, have been valued using a Standard Gamble or a Time Trade-Off. Both are grounded in expected utility theory but impose strong assumptions about the form of the utility function. Preference elicitation tasks for both are complicated, limiting the number of health states that each respondent can value and, therefore, that can be valued overall. The usual approach has been to value a set of the possible health states and impute values for the remainder. Discrete Choice Experiments (DCEs) offer an attractive alternative, allowing investigation of more flexible specifications of the utility function and greater coverage of the response surface. We designed a DCE to obtain values for EQ-5D health states and implemented it in an Australia-representative online panel (n = 1,031). A range of specifications investigating non-linear preferences with respect to time and interactions between EQ-5D levels were estimated using a random-effects probit model. The results provide empirical support for a flexible utility function, including at least some two-factor interactions. We then constructed a preference index such that full health and death were valued at 1 and 0, respectively, to provide a DCE-based algorithm for Australian cost-utility analyses.

Jeopardizing Validity by Mismeasurement of Quality of Life

Practitioners and policy-makers have a common interest in an instrument that will measure the value people place on different health states. For practitioners, a patient’s valuation can guide individual decisions. For senior managers and policy-makers the information can help them decide which services to provide and which technologies to install. It is therefore disappointing to encounter results, such as those from the Leung study in this issue of The Journal 1, that show 2 reputable and widely used instruments that purport to provide such information giving different results. It raises a number of questions: How can such an outcome arise? What should practitioners and policy-makers do in view of such a finding? Measuring patient quality of life (QOL) is a relatively new venture — in particular, measurement using multi-attribute utility (MAU) instruments, like those used in the Leung study. MAU are questionnaires in which response categories are scored using utility weights that purport to measure the strength of preference for a health state. In principle, the application of these weights permits comparison of dissimilar health states. A higher score for health state A versus health state B does not have clinical meaning. Rather, a higher score indicates that there is a subjective preference for health state A by the person or persons whose judgment was used to create the utility weights. MAU instruments are now widely employed in the calculation of quality-adjusted life-years (QALY) or, more correctly, preference-adjusted life-years. Used in economic evaluation studies, QALY serve to rank dissimilar services (cost utility analysis). A variant of the QALY — the disability-adjusted life-year — is used to estimate the burden of disease (for example, the recent World Health Organization Burden of Disease Study2). The field is currently dominated by a limited number of MAU instruments. … Address correspondence to N.A. Day; E-mail: neilathertonday{at}bigpond.com

A Pilot Discrete Choice Experiment to Explore Preferences for EQ-5D-5L Health States

The EQ-5D-5L has recently been developed to improve the sensitivity of the widely used three-level version. Valuation studies are required before the use of this new instrument can be adopted. The use of discrete choice experiments (DCEs) in this area is a promising area of research. To test the plausibility and acceptability of estimating an Australian algorithm for the newly developed five-level version of the EQ-5D using a DCE. A choice experiment was designed, consisting of 200 choice sets blocked such that each respondent answered 10 choice sets. Each choice set presented two health state-duration combinations, and an immediate death option. The experiment was implemented in an online Australian-representative sample. A random-effects probit model was estimated. To explore the feasibility of the approach, an indicative algorithm was developed. The algorithm is transformed to a 0 to 1 scale suitable for use to estimate quality-adjusted life-year weights for use in economic evaluation. A total of 973 respondents undertook the choice experiment. Respondents were slightly younger and better educated than the general Australian population. Of the 973 respondents, 932 (95.8 %) completed all ten choice sets, and a further 12 completed some of the choice sets. In choice sets in which one health state-duration combination dominated another, the dominant option was selected on 89.5 % of occasions. The mean and median completion times were 17.9 and 9.4 min, respectively, exhibiting a highly skewed distribution. The estimation results are broadly consistent with the monotonic nature of the EQ-5D-5L. Utility is increasing in life expectancy (i.e., respondents tend to prefer health profiles with longer life expectancy), and mainly decreases in higher levels in each dimension of the instrument. A high proportion of respondents found the task clear and relatively easy to complete. DCEs are a feasible approach to the estimation of utility weights for more complex multi-attribute utility instruments such as the EQ-5D-5L.

Dimensional Sensitivity of the 15D Multiattribute Utility Instrument in Diabetic Retinopathy

The purpose of this study was to further investigate the previously suggested high sensitivity of the 15D instrument to diabetic retinopathy (DR) by examining: i) its dimensional sensitivity in patients differing in DR severity, and ii) its dimensional relationship with a well-known vision-targeted health status instrument. Eighty five diabetes patients with non-proliferative or proliferative DR completed the 15D and the National Eye Institute Visual Function Questionnaire 25-item (NEI-VFQ-25) instruments. Dimensional response distributions were examined with chi-square, and mean VFQ25 and 15D dimension score differences with ANCOVA. Dimensional relationships between the instruments were assessed with correlations and ordinary least squares (OLS) regressions. Patients with proliferative DR scored significantly lower (P 0.50) and significant (P 0.85 in 10/13 models), with ocular pain being the only exception (R2 = 0.44). The 15D’s seeing dimension was a significant predictor of 10 VFQ25 scales, whereas speech was not significant in any model. 15D utilities discriminated between the DR subsamples, supporting its appropriateness in this group. Its dimensions (seeing in particular), explained large portions of variance in NEI-VFQ25 scales, implying a strong association with this vision-targeted instrument. These findings might help to explain why 15D performs well in DR and support its use for QALY calculations. However, the need remains for further testing in larger and more diverse samples.

Australian health‐related quality of life population norms derived from the SF‐6D

To investigate population health-related quality of life norms in an Australian general sample by age, gender, BMI, education and socioeconomic status. The SF-36 was included in the 2009/10 wave of the Household, Income and Labour Dynamics in Australia (HILDA) survey (n=17,630 individuals across 7,234 households), and converted into SF-6D utility scores. Trends across the various population subgroups were investigated employing population weights to ensure a balanced panel, and were all sub-stratified by gender. SF-6D scores decline with age beyond 40 years, with decreasing education and by higher levels of socioeconomic disadvantage. Scores were also lower at very low and very high BMI levels. Males reported higher SF-6D scores than females across most analyses. This study reports Australian population utility data measured using the SF-6D, based on a national representative sample. These results can be used in a range of policy settings such as cost-utility analysis or exploration of health-related inequality. In general, the patterns are similar to those reported using other multi-attribute utility instruments and in different countries.

Evaluation of a Vision-Related Utility Instrument: The German Vision and Quality of Life Index

Multi-attribute utility instruments (MAUIs), which contain a descriptive system, including several health dimensions with associated levels of increasing severity, are used commonly to measure utilities. However, the validity of the descriptive systems rarely is examined using modern psychometric theory. Therefore, we evaluated the psychometric properties of the German version of the Vision and Quality of Life Index (VisQol), a six-item vision-related MAUI. The German VisQol was self-administered to 340 patients and 280 controls. All subjects underwent a full ophthalmologic examination, including best-corrected visual acuity (VA) testing. The psychometric properties of the VisQoL were assessed using Rasch analysis. The VisQoL's descriptive system did not function in controls. In patients, after collapsing response categories to resolve disordered thresholds and omitting misfitting persons, the measurement properties (i.e., precision, unidimensionality, and targeting) of the German VisQoL were satisfactory. Most person misfit related to unexpected responses to item 4 ("organizing assistance"). Rasch-generated person estimates were not different between age categories, sex, or underlying ocular condition, but decreased significantly with presence of visual impairment in the better eye (LogMAR ≥ 0.5, 1.20 ± 4.62 compared to 3.46 ± 3.52, P < 0.001). The German VisQoL's descriptive system displayed adequate fit to the Rasch model after removal of a large proportion of patients with poor fit statistics. However, the wording of item four should be revised to reduce respondent confusion and measurement "noise." The scale's descriptive system does not function in a sample of visually unimpaired persons, most likely due to a lack of variance in the measured trait.