Using co-creation and multi-criteria decision analysis to close service gaps for underserved populations.

BACKGROUND: Navigating treatment pathways remains a challenge for populations with complex needs due to bottlenecks, service gaps and access barriers. The application of novel methods may be required to identify and remedy such problems. OBJECTIVE: To demonstrate a novel approach to identifying persistent service gaps, generating potential solutions and prioritizing action. DESIGN: Co-creation and multi-criteria decision analysis in the context of a larger, mixed methods study. SETTING AND PARTICIPANTS: Community-dwelling sample of older women living alone (OWLA), residing in Melbourne, Australia (n = 13-37). Convenience sample of (n = 11) representatives from providers and patient organizations. INTERVENTIONS: Novel interventions co-created to support health, well-being and independence for OWLA and bridge missing links in pathways to care. MAIN OUTCOME MEASURES: Performance criteria, criterion weights , performance ratings, summary scores and ranks reflecting the relative value of interventions to OWLA. RESULTS: The co-creation process generated a list of ten interventions. Both OWLA and stakeholders considered a broad range of criteria when evaluating the relative merits of these ten interventions and a "Do Nothing" alternative. Combining criterion weights with performance ratings yielded a consistent set of high priority interventions, with "Handy Help," "Volunteer Drivers" and "Exercise Buddies" most highly ranked by both OWLA and stakeholder samples. DISCUSSION AND CONCLUSIONS: The present study described and demonstrated the use of multi-criteria decision analysis to prioritize a set of novel interventions generated via a co-creation process. Application of this approach can add community voice to the policy debate and begin to bridge the gap in service provision for underserved populations.

Economic evaluation of simulated and traditional clinical placements in occupational therapy education.

INTRODUCTION: This economic evaluation complements results of the randomised controlled trial that established non-inferiority of the learning outcomes of a one-week simulated clinical placement (SCP) in occupational therapy qualifying degrees in comparison to an equivalent traditional clinical placement (TCP). This companion study presents detailed cost analyses of two placement alternatives and a cost-benefit study to assess the value for money of SCP. An economic evaluation of simulated versus traditional placements has not previously been conducted in Australia. METHODS: Nine SCP/TCP rounds were conducted by six Australian universities. Costs were collected using study-specific instruments. Public health sector costs were sourced from available literature. Willingness-to-pay for SCP/TCP was estimated using both a Discrete Choice Experiment and a Contingent Valuation method. These methods were employed to assess a comparative 'value' of SCP/TCP from the perspective of heads of occupational therapy departments (N = 28), who were asked to put a monetary value on the broader range of benefits associated with SCP/TCP. RESULTS: From the universities' perspective the average cost per student ranged from AUD$460 to AUD$1511 for simulated and AUD$144 to AUD$1112 for traditional placement. From the health care sector perspective, the difference in costs favoured simulated placements for four implementations and traditional placements for five. In the Discrete Choice Experiment respondents preferred traditional rather than simulated placement and would pay additional AUD$533. The estimated monetary value of simulated placements from a contingent valuation ranged from AUD$200 to AUD$1600. CONCLUSIONS: For universities that procure TCPs predominately at public health care facilities and sustain high administrative overheads, the SCP program could be a cost-saving alternative. From a broader value-for-money perspective, respondents favoured TCP over SCP, yet placed importance on placement availability and opportunity to demonstrate competence for students during the placement. Results should be interpreted with caution and further research with larger sample sizes is required.

Sharing and the Provision of "Cost-Ineffective" Life-Extending Services to Less Severely Ill Patients.

BACKGROUND: Cost-utility analysis prioritizes services using cost, life-years, and the health state utility of the life-years. Nevertheless, a significant body of evidence suggests that the public would prefer more variables to be considered in decision making and at least some sharing of the budget with services for severe conditions that are not cost-effective because of their high cost. OBJECTIVES: To examine whether this preference for sharing persists for less severe conditions when both cost effectiveness and illness severity would indicate that resources should be allocated to other services. METHODS: Survey respondents were asked to divide a budget between two patients facing life-threatening illnesses. The severity of the illnesses differed and the price of treatment was varied. RESULTS: Sharing occurred in all scenarios including scenarios in which the illness was less severe and services were not cost-effective. Results are consistent with behavior commonly observed in other contexts. CONCLUSIONS: Results suggest that sharing per se is important and that the public would support some funding of cost-ineffective services for less severe health problems.

Uncertainty and the Undervaluation of Services for Severe Health States in Cost-Utility Analyses.

OBJECTIVES: To test the hypothesis that the "severity effect"-the preference for more than utility-maximizing expenditure on severe health states-may be the result of, or exacerbated by, the uncertainty associated with the chance of contracting the illness that causes the severe health state. METHODS: Survey respondents were asked to imagine that they will contract one of two illnesses and asked to allocate a budget between two insurance policies, each of which provides services for the treatment of one of the illnesses. A person's final health state varied with the amount of insurance purchased for the illness that occurred and therefore with the level of treatment. The relative cost of the two policies was altered and the selected levels of insurance compared with the levels that would be provided by a health authority that sought to maximize total utility or quality-adjusted life-years from its own budget. RESULTS: Respondents selected more than utility-maximizing insurance for protection against severe health states. A number of psychological factors that affect measurement under uncertainty do not affect utility as currently measured. This difference may explain the present results and also explain the "severity paradox" that personal preferences as presently measured imply less expenditure on severe health states than do "social preferences" for the treatment of strangers. CONCLUSIONS: Uncertainty alters preferences. Incorporating these preferences in decision making would result in greater spending on severe health states.

Does a patient's health potential affect the social valuation of health services?

BACKGROUND: Patients with a permanent impairment may be unable to reach full health. Consequently health services which cure illnesses which are unrelated to the impairment may increase health less than services for patients with no impairment. While it has been argued that this should not lead to discrimination against impaired patients there is little evidence to determine whether this equity-efficiency trade-off is consistent with social values. OBJECTIVES: To measure the effect of permanent impairment upon the social valuation of services for unrelated illnesses. METHODS: Social valuations of services for illnesses associated with mobility, depression or pain were assessed and compared for patients with and without a permanent impairment using the Relative Social Willingness to Pay (RS-WTP) instrument. The maximum valuation of services for impaired patients was also compared with the maximum utility which could be gained when utility was measured using three multi attribute utility instruments. RESULTS: Curing the illness of impaired patients was valued 8-11 percent less than the cure of patients with no impairment. Discrimination decreased as the severity of the illness increased. Valuation of health states using the utility instruments implied significantly greater discrimination than the social valuations using the RS-WTP instrument. CONCLUSIONS: Health services are valued less highly when a patient's health potential is impaired. However discrimination is significantly less than would occur if the value of the services were limited to the value of the health state causing the impairment. The argument for disregarding a patient's limited health potential when resources are allocated therefore receives some support from social valuations but the case for completely equal treatment depends upon additional ethical arguments.

Does the use of the proportional shortfall help align the prioritisation of health services with public preferences?

It has been proposed that equity may be included in the economic evaluation of health services using the 'proportional shortfall' (PS)-the proportion of a person's QALY expectation that they would lose because of an illness. The present paper reports the results of a population survey designed to test whether PS helped to explain people's preferences for health services and whether it did this better than the absolute shortfall or the equity related variables that PS seeks to replace. Survey respondents were asked to allocate 100 votes between 13 scenarios and a standard scenario. Variation in the allocation of votes was explained by health gain and different combinations of the equity variables. Differences in votes for the comparisons were significantly related to differences in PS but the relationship was weaker than between votes and the age related variables. Cases were identified where PS suggested a priority ordering of services which was strongly rejected by respondents. It is concluded that the use of PS is unlikely to improve the alignment of priorities with public preferences.

Are Quality-Adjusted Life Years a Good Proxy Measure of Individual Capabilities?

BACKGROUND: There is a debate in the health economics literature concerning the most appropriate way of applying Amartya Sen's capability approach in economic evaluation studies. Some suggest that quality-adjusted life years (QALYs) alone are adequate while others argue that this approach is too narrow and that direct measures of capability wellbeing provide a more extensive application of Sen's paradigm. OBJECTIVE: This paper empirically explores whether QALYs provide a good proxy for individual capabilities. METHODS: Data is taken from a multinational cross-sectional survey of individuals with seven health conditions (asthma, arthritis, cancer, depression, diabetes, hearing loss, heart disease) and a healthy population. Each individual completed the ICECAP-A measure of capability wellbeing for adults and six health utility instruments that are used to generate QALYs, including EQ-5D and SF-6D. Primary analysis examines how well health utility instruments can explain variation in the ICECAP-A using ordinary least squares regression. RESULTS: The findings show that all seven health conditions have a negative association on overall capability as measured by the ICECAP-A index. Inclusion of health utility instruments into separate regressions improves the predictive power of capability but on average, explains less than half of the variation in capability wellbeing. Individuals with arthritis appear to be less inhibited in terms of capability losses when accounting for health utility, yet those who have depression record significant reductions in capability relative to the healthy population even after accounting for the most commonly used health utility instruments. CONCLUSION: The study therefore casts doubt on the ability of QALYs to act as a reliable proxy measure of individuals' capability.

Assessing the validity of the ICECAP-A capability measure for adults with depression.

BACKGROUND: Effectiveness and cost-effectiveness are increasingly important considerations in determining which mental health services are funded. Questions have been raised concerning the validity of generic health status instruments used in economic evaluation for assessing mental health problems such as depression; measuring capability wellbeing offers a possible alternative. The aim of this study is to assess the validity of the ICECAP-A capability instrument for individuals with depression. METHODS: Hypotheses were developed using concept mapping. Validity tests and multivariable regression analysis were applied to data from a cross-sectional dataset to assess the performance of ICECAP-A in individuals who reported having a primary condition of depression. The ICECAP-A was collected alongside instruments used to measure: 1. depression using the depression scale of the Depression, Anxiety and Stress Scale (DASS-D of DASS-21); 2. mental health using the Kessler Psychological Distress Scale (K10); 3. generic health status using a common measure collected for use in economic evaluations, the five level version of EQ-5D (EQ-5D-5L). RESULTS: Hypothesised associations between the ICECAP-A (items and index scores) and depression constructs were fully supported in statistical tests. In the multivariable analysis, instruments designed specifically to measure depression and mental health explained a greater proportion of the variation in ICECAP-A than the EQ-5D-5L. CONCLUSION: The ICECAP-A instrument appears to be suitable for assessing outcome in adults with depression for resource allocation purposes. Further research is required on its responsiveness and use in economic evaluation. Using a capability perspective when assessing cost-effectiveness could potentially re-orientate resource provision across physical and mental health care services.

Communal Sharing and the Provision of Low-Volume High-Cost Health Services: Results of a Survey.

INTRODUCTION: This paper suggests and tests a reason why the public might support the funding of services for rare diseases (SRDs) when the services are effective but not cost effective, i.e. when more health could be produced by allocating funds to other services. It is postulated that the fairness of funding a service is influenced by a comparison of the average patient benefit with the average cost to those who share the cost. METHODS: Survey respondents were asked to allocate a budget between cost-effective services that had a small effect upon a large number of relatively well patients and SRDs that benefited a small number of severely ill patients but were not cost effective because of their high cost. RESULTS: Part of the budget was always allocated to the SRDs. The budget share rose with the number sharing the cost. DISCUSSION: Sharing per se appears to characterise preferences. This has been obscured in studies that focus upon cost per patient rather than cost per person sharing the cost.

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

PURPOSE: 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). METHODS: 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. RESULTS: 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). CONCLUSIONS: It is possible to predict valid utilities from the AQLQ-S using regression methods. We recommend GLM models for this exercise.

Age Weights for Health Services Derived from the Relative Social Willingness-to-Pay Instrument.

The effect of a patient's age on the social valuation of health services remains controversial, with empirical results varying in magnitude and implying a different age-value profile. This article employs a new methodology to re-examine these questions. Data were obtained from 2 independent Web-based surveys that administered the Relative Social Willingness to Pay instrument. In the first survey, the age of the patient receiving a life-saving service was varied. Patients were left with either poor mental or physical health. In the second survey, patient age was varied for a service that fully cured the patient's poor mental or physical health. In total, therefore, 4 sets of age weights were obtained: weights for life-extending services with poor physical or mental health outcomes and weights for quality-of-life improvement for patients in poor mental or physical health. Results were consistent. Increasing age was associated in each case with a monotonic decrease in the social valuation of the services. The decrease in value was quantitatively small until age 60 years. By age 80 years, the social value of services had declined by about 50%. The decline commenced at an earlier age in the context of physical health, although the magnitude of the decrement by age 80 years was unrelated to the type of service. With 1 exception, there was little difference in the valuation of services by the age of the survey respondent. Respondents aged >60 years placed a lower, not higher, value on quality-of-life improvement for elderly individuals than other respondents. There was no difference in the valuation of life-extending services.

How important is severity for the evaluation of health services: new evidence using the relative social willingness to pay instrument.

The 'severity hypothesis' is that a health service which increases a patient's utility by a fixed amount will be valued more highly when the initial health state is more severe. Supporting studies have employed a limited range of analytical techniques and the objective of the present paper is to test the hypothesis using a new methodology, the Relative Social Willingness to Pay. Three subsidiary hypotheses are: (1) that the importance of the 'severity effect' varies with the type of medical problem; (2) that the relationship between value and utility varies with the severity of the initial health state; and (3) that there is a threshold beyond which severity effects are insignificant. For each of seven different health problems respondents to a web-based survey were asked to allocate a budget to five services which would, cumulatively, move a person from near death to full health. The time trade-off utilities of health states before and after the service were estimated. The social valuation of the service measured by the budget allocation was regressed upon the corresponding increase in utility and severity as measured by the pre-service health state utility. Results confirm the severity hypothesis and support the subsidiary hypotheses. However, the effects identified are quantitatively significant only for the most severe health states. This implies a relatively limited redistribution of resources from those with less severe to those with more severe health problems.

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.

The Relative Impacts of Disease on Health Status and Capability Wellbeing: A Multi-Country Study.

BACKGROUND: Evaluations of the impact of interventions for resource allocation purposes commonly focus on health status. There is, however, also concern about broader impacts on wellbeing and, increasingly, on a person's capability. This study aims to compare the impact on health status and capability of seven major health conditions, and highlight differences in treatment priorities when outcomes are measured by capability as opposed to health status. METHODS: The study was a cross-sectional four country survey (n = 6650) of eight population groups: seven disease groups with: arthritis, asthma, cancer, depression, diabetes, hearing loss, and heart disease and one health population 'comparator' group. Two simple self-complete questionnaires were used to measure health status (EQ-5D-5L) and capability (ICECAP-A). Individuals were classified by illness severity using condition-specific questionnaires. Effect sizes were used to estimate: (i) the difference in health status and capability for those with conditions, relative to a healthy population; and (ii) the impact of the severity of the condition on health status and capability within each disease group. FINDINGS: 5248 individuals were included in the analysis. Individuals with depression have the greatest mean reduction in both health (effect size, 1.26) and capability (1.22) compared to the healthy population. The effect sizes for capability for depression are much greater than for all other conditions, which is not the case for health. For example, the arthritis group effect size for health (1.24) is also high and similar to that of depression, whereas for the same arthritis group, the effect size for capability is much lower than that for depression (0.55). In terms of severity within disease groups, individuals categorised as 'mild' have similar capability levels to the healthy population (effect sizes <0.2, excluding depression) but lower health status than the healthy population (≥0.4). CONCLUSION: Significant differences exist in the relative effect sizes across diseases when measured by health status and capability. In terms of treating morbidity, a shift in focus from health gain to capability gain would increase funding priorities for patients with depression specifically and severe illnesses more generally.

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.

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

PURPOSE: Health 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. RESULTS: Overall, 66 % of the differences between utilities was attributable to the descriptive systems, 30.3 % to scale effects and 3.7 % to micro-utility effects. DISCUSSION: Results 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. CONCLUSIONS: Reconciliation 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?

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.

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 instrument's 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.

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

BACKGROUND: 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. AIMS: 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. METHOD: Individual data from 917 people with self-report depression collected as part of the International Multi-Instrument Comparison Survey. RESULTS: 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. CONCLUSIONS: The algorithms developed in this study can be used to determine cost-effectiveness of services or interventions where utility measures are not collected.