A new version of the EQ-5D, the EQ-5D-5L, is available. The aim of this study is to produce a value set to support use of EQ-5D-5L data in decision-making. The study design followed an international research protocol. Randomly selected members of the English general public completed 10 time trade-off and 7 discrete choice experiment tasks in face-to-face interviews.A 20-parameter hybrid model was used to combine time trade-off and discrete choice experiment data to generate values for the 3,125 EQ-5D-5L health states. Valuation data are available for 996 respondents. Face validity of the data has been demonstrated, with more severe health states generally given lower values. Problems with pain/discomfort and anxiety/depression received the greatest weight. Compared to the existing EQ-5D-3L value set, there are considerably fewer "worse than dead" states (5.1%, compared with over one third), and the minimum value is higher. Values range from −0.285 (extreme problems on all dimensions) to 0.950 (for health states 11211 and 21111). Results have important implications for users of the EQ-5D-5L both in England and internationally. Quality-adjusted life year gains from interventions seeking to improve very poor health may be smaller using this value set and may previously have been overestimated. KEYWORDSEQ-5D-5L, NICE, PROMs, quality of life, stated preferences | INTRODUCTIONHealth care decisions are made under uncertainty, whereby any decision may have a range of different outcomes. To make the "best" decision, potential outcomes need ordering and valuing. Such decisions are made both at the individual level, such as choosing the optimal treatment for a patient, and at the national level, such as choosing how to allocate resources between treatments for different patient groups and across different health conditions. Clinical decisions often affect patients' health-related quality of life (HRQL). Evidence on patients' HRQL can be obtained using patient-reported outcome (PRO) measures. These may be condition specific or generic (see Fayers andMachin, 2016, andLongworth et al., 2014, for further information). Condition-specific PROs focus on specific healthThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

BackgroundCost-effectiveness analysis involves the comparison of the incremental cost-effectiveness ratio of a new technology, which is more costly than existing alternatives, with the cost-effectiveness threshold. This indicates whether or not the health expected to be gained from its use exceeds the health expected to be lost elsewhere as other health-care activities are displaced. The threshold therefore represents the additional cost that has to be imposed on the system to forgo 1 quality-adjusted life-year (QALY) of health through displacement. There are no empirical estimates of the cost-effectiveness threshold used by the National Institute for Health and Care Excellence.Objectives(1) To provide a conceptual framework to define the cost-effectiveness threshold and to provide the basis for its empirical estimation. (2) Using programme budgeting data for the English NHS, to estimate the relationship between changes in overall NHS expenditure and changes in mortality. (3) To extend this mortality measure of the health effects of a change in expenditure to life-years and to QALYs by estimating the quality-of-life (QoL) associated with effects on years of life and the additional direct impact on QoL itself. (4) To present the best estimate of the cost-effectiveness threshold for policy purposes.MethodsEarlier econometric analysis estimated the relationship between differences in primary care trust (PCT) spending, across programme budget categories (PBCs), and associated disease-specific mortality. This research is extended in several ways including estimating the impact of marginal increases or decreases in overall NHS expenditure on spending in each of the 23 PBCs. Further stages of work link the econometrics to broader health effects in terms of QALYs.ResultsThe most relevant ‘central’ threshold is estimated to be £12,936 per QALY (2008 expenditure, 2008–10 mortality). Uncertainty analysis indicates that the probability that the threshold is < £20,000 per QALY is 0.89 and the probability that it is < £30,000 per QALY is 0.97. Additional ‘structural’ uncertainty suggests, on balance, that the central or best estimate is, if anything, likely to be an overestimate. The health effects of changes in expenditure are greater when PCTs are under more financial pressure and are more likely to be disinvesting than investing. This indicates that the central estimate of the threshold is likely to be an overestimate for all technologies which impose net costs on the NHS and the appropriate threshold to apply should be lower for technologies which have a greater impact on NHS costs.LimitationsThe central estimate is based on identifying a preferred analysis at each stage based on the analysis that made the best use of available information, whether or not the assumptions required appeared more reasonable than the other alternatives available, and which provided a more complete picture of the likely health effects of a change in expenditure. However, the limitation of currently available data means that there is substantial uncertainty associated with the estimate of the overall threshold.ConclusionsThe methods go some way to providing an empirical estimate of the scale of opportunity costs the NHS faces when considering whether or not the health benefits associated with new technologies are greater than the health that is likely to be lost elsewhere in the NHS. Priorities for future research include estimating the threshold for subsequent waves of expenditure and outcome data, for example by utilising expenditure and outcomes available at the level of Clinical Commissioning Groups as well as additional data collected on QoL and updated estimates of incidence (by age and gender) and duration of disease. Nonetheless, the study also starts to make the other NHS patients, who ultimately bear the opportunity costs of such decisions, less abstract and more ‘known’ in social decisions.FundingThe National Institute for Health Research-Medical Research Council Methodology Research Programme.

PurposeTo develop a self-report version of the EQ-5D for younger respondents, named the EQ-5D-Y (Youth); to test its comprehensibility for children and adolescents and to compare results obtained using the standard adult EQ-5D and the EQ-5D-Y.MethodsAn international task force revised the content of EQ-5D and wording to ensure relevance and clarity for young respondents. Children’s and adolescents’ understanding of the EQ-5D-Y was tested in cognitive interviews after the instrument was translated into German, Italian, Spanish and Swedish. Differences between the EQ-5D and the EQ-5D-Y regarding frequencies of reported problems were investigated in Germany, Spain and South Africa.ResultsThe content of the EQ-5D dimensions proved to be appropriate for the measurement of HRQOL in young respondents. The wording of the questionnaire had to be adapted which led to small changes in the meaning of some items and answer options. The adapted EQ-5D-Y was satisfactorily understood by children and adolescents in different countries. It was better accepted and proved more feasible than the EQ-5D. The administration of the EQ-5D and of the EQ-5D-Y causes differences in frequencies of reported problems.ConclusionsThe newly developed EQ-5D-Y is a useful tool to measure HRQOL in young people in an age-appropriate manner.

Over the period 1987–1991 an inter-disciplinary five-country group developed the EuroQol instrument, a five-dimensional three-level generic measure subsequently termed the ‘EQ-5D’. It was designed to measure and value health status. The salient features of its development and its consolidation and expansion are discussed. Initial expansion came, in particular, in the form of new language versions. Their development raised translation and semantic issues, experience with which helped feed into the design of two further instruments, the EQ-5D-5L and the youth version EQ-5D-Y. The expanded usage across clinical programmes, disease and condition areas, population surveys, patient-reported outcomes, and value sets is outlined. Valuation has been of continued relevance for the Group as this has allowed its instruments to be utilised as part of the economic appraisal of health programmes and their incorporation into health technology assessments. The future of the Group is considered in the context of: (1) its scientific strategy, (2) changes in the external environment affecting the demand for EQ-5D, and (3) a variety of issues it is facing in the context of the design of the instrument, its use in health technology assessment, and potential new uses for EQ-5D outside of clinical trials and technology appraisal.Electronic supplementary materialThe online version of this article (doi:10.1007/s40258-017-0310-5) contains supplementary material, which is available to authorized users.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions.

The decisions made by the National Institute for Clinical Excellence (NICE) give rise to two questions: how is cost-effectiveness evidence used to make judgements about the 'value for money' of health technologies? And how are factors other than cost-effectiveness taken into account? The aim of this paper is to explore NICE's cost-effectiveness threshold(s) and the tradeoffs between cost effectiveness and other factors apparent in its decisions. Binary choice analysis is used to reveal the preferences of NICE and to consider the consistency of its decisions. For each decision to accept or reject a technology, explanatory variables include: the cost per life year or per QALY gained; uncertainty regarding cost effectiveness; the net cost to the NHS; the burden of disease; the availability (or not) of alternative treatments; and specific factors indicated by NICE. Results support the broad notion of a threshold, where the probability of rejection increases as the cost per QALY increases. Cost effectiveness, together with uncertainty and the burden of disease, explain NICE decisions better than cost effectiveness alone. The results suggest a threshold somewhat higher than NICEs stated 'range of acceptable cost effectiveness' of pound 20,000-30,000 British pounds per QALY--although the exact meaning of a 'range' in this context remains unclear.

The EuroQol Group has developed a standard protocol, with accompanying digital aid and interviewer training materials, that can be used to create value sets for the EQ-5D-5L questionnaire. The use of a well-described, consistent protocol across all countries enhances the comparability of value sets between countries, and allows the exploration of the influence of cultural and other factors on health state values.