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Background Several multi-attribute utility instruments (MAUIs) are available from which utilities can be derived for use in cost-utility analysis (CUA). This study provides a review of recommendations from national health technology assessment (HTA) agencies regarding the choice of MAUIs. Methods A list was compiled of HTA agencies that provide or refer to published official pharmacoeconomic (PE) guidelines for pricing, reimbursement or market access. The guidelines were reviewed for recommendations on the indirect calculation of utilities and categorized as: a preference for a specific MAUI; providing no MAUI preference, but providing examples of suitable MAUIs and/or recommending the use of national value sets; and recommending CUA, but not providing examples of MAUIs. Results Thirty-four PE guidelines were included for review. MAUIs named for use in CUA: EQ-5D (n = 29 guidelines), the SF-6D (n = 11), HUI (n = 10), QWB (n = 3), AQoL (n=2), CHU9D (n = 1). EQ-5D was a preferred MAUI in 15 guidelines. Alongside the EQ-5D, the HUI was a preferred MAUI in one guideline, with DALY disability weights mentioned in another. Fourteen guidelines expressed no preference for a specific MAUI, but provided examples: EQ-5D (n = 14), SF-6D (n = 11), HUI (n = 9), QWB (n = 3), AQoL (n = 2), CHU9D (n= 1). Of those that did not specify a particular MAUI, 12 preferred calculating utilities using national preference weights. Conclusions The EQ-5D, HUI, and SF-6D were the three MAUIs most frequently mentioned in guidelines. The most commonly cited MAUI (in 85% of PE guidelines) was EQ-5D, either as a preferred MAUI or as an example of a suitable MAUI for use in CUA in HTA.

Objectives Discrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990–2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges. Methods A systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated. Results Of the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels. Conclusions The use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers’ confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.

Background The incremental economic burden of US adults with major depressive disorder (MDD) was estimated at $US210.5 billion in 2010 (year 2012 values). Objective Following a similar methodology, this study updates the previous findings with more recent data to report the economic burden of adults with MDD in 2018. Method This study used a framework for evaluating the incremental economic burden of adults with MDD in the USA that combined original and literature-based estimates, focusing on key changes between 2010 and 2018. The prevalence rates of MDD by sex, age, employment, and treatment status over time were estimated based on the National Survey on Drug Use and Health (NSDUH). The incremental direct and workplace costs per individual with MDD were primarily derived from administrative claims data and NSDUH data using comparative analyses of individuals with and without MDD. Societal direct and workplace costs were extrapolated by multiplying NSDUH estimates of the number of people with MDD by the direct and workplace cost estimates per patient. The suicide-related costs were estimated using a human capital method. Results The number of US adults with MDD increased by 12.9%, from 15.5 to 17.5 million, between 2010 and 2018, whereas the proportion of adults with MDD aged 18–34 years increased from 34.6 to 47.5%. Over this period, the incremental economic burden of adults with MDD increased by 37.9% from $US236.6 billion to 326.2 billion (year 2020 values). All components of the incremental economic burden increased (i.e., direct costs, suicide-related costs, and workplace costs), with the largest growth observed in workplace costs, at 73.2%. Consequently, the composition of 2018 costs changed meaningfully, with 35% attributable to direct costs (47% in 2010), 4% to suicide-related costs (5% in 2010), and 61% to workplace costs (48% in 2010). This increase in the workplace cost share was consistent with more favorable employment conditions for those with MDD. Finally, the proportion of total costs attributable to MDD itself as opposed to comorbid conditions remained stable at 37% (38% in 2010). Conclusion Workplace costs accounted for the largest portion of the growing economic burden of MDD as this population trended younger and was increasingly likely to be employed. Although the total number of adults with MDD increased from 2010 to 2018, the incremental direct cost per individual declined. At the same time, the proportion of adults with MDD who received treatment remained stable over the past decade, suggesting that substantial unmet treatment needs remain in this population. Further research is warranted into the availability, composition, and quality of MDD treatment services.

The terms health, health-related quality of life (HRQoL), and quality of life (QoL) are used interchangeably. Given that these are three key terms in the literature, their appropriate and clear use is important. This paper reviews the history and definitions of the terms and considers how they have been used. It is argued that the definitions of HRQoL in the literature are problematic because some definitions fail to distinguish between HRQoL and health or between HRQoL and QoL. Many so-called HRQoL questionnaires actually measure self-perceived health status and the use of the phrase QoL is unjustified. It is concluded that the concept of HRQoL as used now is confusing. A potential solution is to define HRQoL as the way health is empirically estimated to affect QoL or use the term to only signify the utility associated with a health state.

Background/Aims Discrete choice experiments (DCEs) with either duration included an attribute or with dead included as an option can be used as a stand-alone approach to value health states. This paper reports on a DCE with both of these features to develop an EQ-5D-5L value set for Australia. Methods A DCE was undertaken using a large Australian panel of internet respondents, from which a sample of more than 4000 Australian adults was chosen, stratified to be population representative on age and gender. The DCE contained 500 choice triplets, with two EQ-5D-5L health states with duration, and dead as the third option. Each respondent answered 12 choice sets from the 500, stating both the best and worst options from the three available. The design was constructed to estimate a utility algorithm with main effects plus some key interaction terms. A variety of approaches to parameterising interactions, and to anchoring the value set on the required 0–1 scale, were tested. A preferred Australian adult utility algorithm for use in cost-utility analysis was then generated. Results In total, 4477 people completed at least one choice set and were included in the analysis. The results reflected the monotonic structure of the EQ-5D-5L, in that moving from no problems to extreme problems led to worsening utility in each dimension. Inclusion of interaction terms demonstrates that the disutility of the first dimension moving to a poor level (defined as either level 5, or level 4 or 5) had a large impact, but subsequent dimensions moving to a poor level had a relatively smaller disutility. Discussion This work develops a value set for the EQ-5D-5L in Australia, and also provides a range of methodological insights which can inform future work using a stand-alone DCE to value health in other countries.

Objectives The aim of this study was to estimate the relationship between EQ-5D-3L and EQ-5D-5L, in both directions, using a single model. Methods An online survey containing both variants of EQ-5D, with randomised ordering, was administered to a large UK sample in 2020. A joint statistical model of the ten EQ-5D responses (five at 5L, five at 3L), using a multi-equation ordinal regression framework was estimated. The joint model ensures mappings in either direction are fully consistent with the information in the sample and satisfy Bayes’ rule. Three extensions enhance model flexibility: a copula specification allows differing degrees of correlation between the 3L and 5L responses at the upper and lower extremes of health; a normal mixture residual distribution gives flexibility in the distributional form of responses; and a common factor captures correlations in responses across the five dimensions. Results Almost 50,000 responses were received. Thirty-five percent of respondents reported an existing medical condition. Ninety percent of possible 3L and 43% of possible 5L health states were observed. The preferred model specification includes age, sex and the responses to the EQ-5D instrument. Close alignment to the observed data was observed both in within-sample and out-of-sample comparisons. Conclusion The results from this study provide a means of translating evidence to or from EQ-5D-3L to or from 5L based on a large-scale UK population survey with randomised ordering. Mapping can be performed either using descriptive system responses, individual utility scores or summary statistics.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc.). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

Objectives The objective of this study was to develop a value set for EQ-5D-5L based on the societal preferences of the German population. As the first country to do so, the study design used the improved EQ-5D-5L valuation protocol 2.0 developed by the EuroQol Group, including a feedback module as internal validation and a quality control process that was missing in the first wave of EQ-5D-5L valuation studies. Methods A representative sample of the general German population ( n  = 1158) was interviewed using a composite time trade-off and a discrete choice experiment under close quality control. Econometric modeling was used to estimate values for all 3125 possible health states described by EQ-5D-5L. The value set was based on a hybrid model including all available information from the composite time trade-off and discrete choice experiment valuations without any exclusions due to data issues. Results The final German value set was constructed from a combination of a conditional logit model for the discrete choice experiment data and a censored at −1 Tobit model for the composite time trade-off data, correcting for heteroskedasticity. The value set had logically consistent parameter estimates ( p  < 0.001 for all coefficients). The predicted EQ-5D-5L index values ranged from −0.661 to 1. Conclusions This study provided values for the health states of the German version of EQ-5D-5L representing the preferences of the German population. The study successfully employed for the first time worldwide the improved protocol 2.0. The value set enables the use of the EQ-5D-5L instrument in economic evaluations and in clinical studies.

While the focus of attention currently is on developing a vaccine against the Coronavirus SARS-CoV-2 to protect against the disease COVID-19, policymakers should prepare for the next challenge: uptake of the vaccine among the public. Having a vaccine does not automatically imply it will be used. Compliance with the anti-H1N1 vaccine during the 2009 influenza pandemic, for instance, was low [1], and in the decade since, vaccination rates have remained an issue of concern [2] while vaccination hesitancy has become more prevalent, leading to increases in disease outbreaks in multiple countries [3]. It is, therefore, important to understand whether or not people are willing to be vaccinated against COVID-19, as this can have large consequences for the success a vaccination programme—with potentially large health and economic consequences. In this editorial, we provide some first insights into this willingness to be vaccinated, based on a multi-country European study [4], which hopefully result in more attention for this important issue.

Objective This study describes the first empirical head-to-head comparison of EQ-5D-3L (3L) and EQ-5D-5L (5L) value sets for multiple countries. Methods A large multinational dataset, including 3L and 5L data for eight patient groups and a student cohort, was used to compare 3L versus 5L value sets for Canada, China, England/UK (5L/3L, respectively), Japan, The Netherlands, South Korea and Spain. We used distributional analyses and two methods exploring discriminatory power: relative efficiency as assessed by the F statistic, and an area under the curve for the receiver-operating characteristics approach. Differences in outcomes were explored by separating descriptive system effects from valuation effects, and by exploring distributional location effects. Results In terms of distributional evenness, efficiency of scale use and the face validity of the resulting distributions, 5L was superior, leading to an increase in sensitivity and precision in health status measurement. When compared with 5L, 3L systematically overestimated health problems and consequently underestimated utilities. This led to bias, i.e. over- or underestimations of discriminatory power. Conclusion We conclude that 5L provides more precise measurement at individual and group levels, both in terms of descriptive system data and utilities. The increased sensitivity and precision of 5L is likely to be generalisable to longitudinal studies, such as in intervention designs. Hence, we recommend the use of the 5L across applications, including economic evaluation, clinical and public health studies. The evaluative framework proved to be useful in assessing preference-based instruments and might be useful for future work in the development of descriptive systems or health classifications.