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A Comparison of a Preliminary Version of the EQ-HWB Short and the 5-Level Version EQ-5D

Open AccessPublished:March 09, 2022DOI:https://doi.org/10.1016/j.jval.2022.01.003

      Highlights

      • Multiattribute utility instruments, as the 5-level version of EQ-5D (EQ-5D-5L), are commonly used as outcome measures in economic evaluations and, although valid and reliable for many applications, have been the target of criticism based on their focus on a narrow range of health domains. Conceptually, the newly developed EQ Health and Wellbeing Short (EQ-HWB-S) overlaps with the EQ-5D-5L while expanding coverage of concepts relevant to health and social care.
      • Empirical evidence is provided on the extent to which the EQ-5D-5L and the EQ-HWB-S overlap and aspects of the EQ-HWB-S that improve the ability to capture certain aspects of health and wellbeing that can inform instrument selection.
      • In terms of discriminative ability, the EQ-HWB-S appears to perform and the EQ-5D-5L among patient groups and may be better able to capture health and wellbeing of caregivers and respondents closer to full health.

      Abstract

      Objectives

      The EQ Health and Wellbeing Short (EQ-HWB-S) is a new broad generic measure of health and wellbeing for use in economic evaluations of interventions across healthcare, social care, and public health. This measure conceptually overlaps with the 5-level version EQ-5D (EQ-5D-5L), while expanding on the coverage of health and social care related dimensions. This study aims to examine the extent to which the EQ-HWB-S and EQ-5D-5L overlap and are different.

      Methods

      A sample of US-based respondents (n = 903; n = 400 cancer survivors and n = 503 general population) completed a survey administered via an online panel. The survey included the EQ-HWB item pool (62 items, including 11 items used in this analysis), EQ-5D-5L, and questions about sociodemographic and health characteristics. The analysis included (Spearman’s) correlations, the comparison of patterns of response (distributions and ceiling effects), and the ability to discriminate between known groups.

      Results

      Moderate to strong associations were found between conceptually overlapping dimensions of the EQ-5D-5L and the EQ-HWB-S (rs > 0.5, P < .001). Among respondents reporting full health on the EQ-5D-5L (n = 161, 18.23%), the EQ-HWB-S identified ceiling effects, particularly with the item “feeling exhausted.” Most EQ-5D-5L and EQ-HWB-S items demonstrated discriminative ability among those with and without physical and mental conditions, yielding medium (> 0.5) to large effect sizes (> 0.8). Nevertheless, only EQ-HWB-S items distinguished between caregivers and noncaregivers and those with low and high caregiver burden, albeit with small effect sizes (0.2-0.5).

      Conclusions

      Results indicate a convergence between the measures, especially between overlapping dimensions, lending support to the validity of the EQ-HWB-S. The EQ-HWB-S performed similarly or better than the EQ-5D-5L among patient groups and is better able to differentiate among caregivers and respondents closer to full health.

      Keywords

      Introduction

      In light of limited resources and rising healthcare costs, a growing number of jurisdictions worldwide rely on economic evaluations to inform resource allocation and disinvestment decisions within the health and social care sectors. This approach to resource allocation combines information on costs and benefits of competing interventions aiming to identify those that maximize an individual’s health and wellbeing.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      • Misselbrook D.W.
      is for wellbeing and the WHO definition of health.
      One of the most commonly used methods to inform reimbursement and investment decision making in health is the estimation of the incremental cost per quality-adjusted life-year (QALY) of new healthcare programs and technologies in cost-utility analysis.
      • Neumann P.J.
      • Goldie S.J.
      • Weinstein M.C.
      Preference-based measures in economic evaluation in health care.
      • Scuffham P.A.
      • Whitty J.A.
      • Mitchell A.
      • Viney R.
      The use of QALY weights for QALY calculations.
      • Whitehead S.J.
      • Ali S.
      Health outcomes in economic evaluation: the QALY and utilities.
      The QALY comprises, in a single index, the measure of a person’s length of life weighted by a valuation of their health-related quality of life (HRQOL) expressed in utility weights.
      • Torrance G.W.
      • Feeny D.
      Utilities and quality-adjusted life years.
      Multiattribute utility instruments (MAUIs) as the EQ-5D and SF-6D can be used as “off-the-shelf” outcome measures to assess HRQOL within a clinical trial or observational study, providing utility values as endpoints, as well as weights for cost-utility analysis.
      • Whitehead S.J.
      • Ali S.
      Health outcomes in economic evaluation: the QALY and utilities.
      ,
      • Brazier J.
      • Roberts J.
      • Deverill M.
      The estimation of a preference-based measure of health from the SF-36.
      Although valid and reliable for many applications, these instruments have been the target of criticism based on their focus on a narrow range of health domains that leaves out important health outcomes, such as cognition and vision problems,
      • Espallargues M.
      • Czoski-Murray C.J.
      • Bansback N.J.
      • et al.
      The impact of age-related macular degeneration on health status utility values.
      ,
      • Krabbe P.F.
      • Stouthard M.E.
      • Essink-Bot M.-L.
      • Bonsel G.J.
      The effect of adding a cognitive dimension to the EuroQol multiattribute health-status classification system.
      and social care outcomes, such as maintaining independence.
      • Netten A.
      • Burge P.
      • Malley J.
      • et al.
      Outcomes of social care for adults: developing a preference-weighted measure.
      ,
      • Brazier J.
      • Tsuchiya A.
      Improving cross-sector comparisons: going beyond the health-related QALY.
      While economic evaluation guidelines adopted by some health technologies assessment agencies (eg, The National Institute for Health and Care Excellence [NICE]) foresee the use of caregiver quality of life as outcomes, this is not a widely adopted practice and the instruments commonly used are limited capturing impacts on caregiver quality of life. Sector-specific QALY instruments, such as the Adult Social Care Outcomes Toolkit (ASCOT) in the field of social care,
      • Johnstone L.
      • Page C.
      Using Adult Social Care Outcomes Toolkit (ASCOT) in the assessment and review process.
      offer a partial solution to some of these limitations by focusing on sector-specific outcomes but are not adequate to support cross-sectoral comparisons.
      • Brazier J.
      • Tsuchiya A.
      Improving cross-sector comparisons: going beyond the health-related QALY.
      The Extending the QALY (E-QALY) project is an international collaboration that started in 2017 led by researchers in the United Kingdom and joined by research teams in Australia, Argentina, China, Germany, and the United States, with the mission of developing a broad generic measure of health and wellbeing for use in economic evaluations of interventions across healthcare, social care, and public health that overcomes the gaps previously identified for other MAUIs.

      Brazier J, Peasgood T, Mukuria C, et al. The EQ-HWB: overview of the development of a measure of health and well-being and key results. Value Health. In press.

      This project applied state-of-the-art/science methods

      Brazier J, Peasgood T, Mukuria C, et al. The EQ-HWB: overview of the development of a measure of health and well-being and key results. Value Health. In press.

      and a structured framework
      • Carlton J.
      • Peasgood T.
      • Khan S.
      • Barber R.
      • Bostock J.
      • Keetharuth A.D.
      An emerging framework for fully incorporating public involvement (PI) into patient-reported outcome measures (PROMs).
      to ensure the development of a measure capable of adequately assessing the impact of conditions and interventions on users of health and social care services, including informal caregivers and others potentially affected. The first 4 stages of the E-QALY project culminated in the development of a 25-item profile measure, the EQ-HWB, and a 9-items classifier, the EQ-HWB-S, which could be valued and disseminated on stages 5 and 6.

      Brazier J, Peasgood T, Mukuria C, et al. The EQ-HWB: overview of the development of a measure of health and well-being and key results. Value Health. In press.

      ,

      Carlton J, PT, Mukuria C, Connell J, et al. Generation, selection and face validation of items for a new generic measure of quality of life, the EQ health and wellbeing (EQ-HWB). Value in Health. In press.

      ,

      Peasgood T, Mukuria C, Brazier J, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ Health and Wellbeing. Value Health. In press.

      The EQ-5D items were considered alongside the pool of EQ-HWB candidate items in the psychometric assessment stage of the E-QALY project, and their performance was not inferior to the remaining item pool. Nevertheless, head-to-head comparisons showed that the EQ-5D items were outperformed by the items chosen to integrate the final version of the EQ-HWB-S health classifier.

      Monteiro A, Kuharic M, Pickard S. ‘I can’t feel my face’: will the E-QALY project measure resemble the EQ-5D? Results from the US arm. Paper Presented at: 1st EuroQol Early Career Researcher Meeting; March 2020; Prague, Czech Republic.

      The constructs covered by the resultant measure overlap with the EQ-5D (ie, dimensions mobility, activities, pain, anxiety, and sadness/depression) while comparatively expanding on the coverage of health and social care related dimensions.
      A growing number of MAUIs make it crucial to evaluate and compare different options to inform instrument selection. Given the EQ-5D prominent role as the most widely used MAUI worldwide
      • Brazier J.
      • Ratcliffe J.
      • Saloman J.
      • Tsuchiya A.
      Measuring and Valuing Health Benefits for Economic Evaluation.
      ,
      • Kennedy-Martin M.
      • Slaap B.
      • Herdman M.
      • et al.
      Which multi-attribute utility instruments are recommended for use in cost-utility analysis? A review of national health technology assessment (HTA) guidelines.
      for health technology assessment, this study aimed to compare the performance of the EQ-HWB-S with the 5-level version EQ-5D (EQ-5D-5L), focusing on content coverage and measurement properties in terms of acceptability, response distributions, ceiling and floor effects, convergent validity, and the ability to discriminate among chronic health conditions, caregiving status, and caregiver burden.

      Methods

      Sampling and Data Collection

      The data were collected from a sample of US-based cancer survivors (CSs) and members of the general population (GP) during the US arm of psychometric assessment and reduction of the EQ-HWB item pool. Respondents were recruited through an internet panel, between August and September 2019. The sample size estimation was based on recommendations regarding the minimum sample size necessary for the development of factor analysis and item response theory models.
      • Jiang S.
      • Wang C.
      • Weiss D.J.
      Sample size requirements for estimation of item parameters in the multidimensional graded response model.
      ,
      • Comrey A.L.
      • Lee H.B.
      A First Course in Factor Analysis.
      Accordingly, our target sample size was 900 respondents, 500 from the GP and 400 CSs. Despite the health and sociodemographic differences between the GP and CS subsamples, their response patterns and overall results were very similar and thus the data were pooled for this analysis. All respondents were quota sampled for age and gender. Eligible respondents that agreed to participate in the study completed a survey administered online in exchange for a reward. The study protocol was approved by the institutional review board at the University of Illinois at Chicago (Institutional Review Board #2019-0184), and all respondents provided an informed consent.

      Survey Questions and Instruments

      Respondents completed the EQ-HWB item pool, EQ-5D-5L, 3-level version of EQ-5D,
      EuroQol Group
      EuroQol--a new facility for the measurement of health-related quality of life. The EuroQol Group.
      ,
      • Herdman M.
      • Gudex C.
      • Lloyd A.
      • et al.
      Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
      the Short Warwick-Edinburgh Mental Wellbeing Scale,
      • Tennant R.
      • Hiller L.
      • Fishwick R.
      • et al.
      The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): development and UK validation.
      ASCOT,
      • Johnstone L.
      • Page C.
      Using Adult Social Care Outcomes Toolkit (ASCOT) in the assessment and review process.
      and additional questions about socioeconomic and health status, caregiver role, and social care utilization. Health questions included self-reported list of chronic physical or mental health conditions. Block randomization was used to generate 6 versions of the questionnaire; the order of presentation of the E-QALY and EQ-5D-5L items was varied. Positive and negatively worded items were presented together to minimize the number of reversals of the response (positively worded items followed by negatively worded items or vice versa).
      • van Sonderen E.
      • Sanderman R.
      • Coyne J.C.
      Ineffectiveness of reverse wording of questionnaire items: let’s learn from cows in the rain.
      More information on the survey design and implementation can be found elsewhere.

      Peasgood T, Mukuria C, Brazier J, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ Health and Wellbeing. Value Health. In press.

      EQ-HWB item pool and EQ-HWB-S descriptive system

      The EQ-HWB item pool included 62 items covering 7 domains (ie, activity, autonomy, cognition, feelings and emotions, relationships, physical sensations, and self-identity) and 26 subdomains. Respondents were asked to score their responses to items on a 5-point Likert frequency scale (ie, not at all, only occasionally, some of the time, often, most or all of the time), severity scale (ie, mild, slight, moderate, severe, very severe or not at all, a little bit, somewhat, quite a bit, very much), or difficulty scale (ie, no difficulty, slight, some, a lot of, unable). The recall period adopted was of 1 week, that is, “how your life has been over the last 7 days.” The psychometric testing efforts narrowed down the item pool, producing a 25-item profile measure (EQ-HWB) and a 9-item classifier amenable to valuation (EQ-HWB-S).

      Peasgood T, Mukuria C, Brazier J, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ Health and Wellbeing. Value Health. In press.

      Because this analysis used data collected during the stage of psychometric assessment and item reduction phase, there are slight differences between the wording of the items used in this analysis and the next-stage EQ-HWB-S experimental version. The correspondence between the items used here and the final EQ-HWB-S is presented in Table 1. This analysis focuses on the 11 items shown in Table 1 that composed the preliminary version of the EQ-HWB-S.
      Table 1EQ-HWB-S experimental version final wording and correspondence with EQ-HWB item pool (preliminary version of the EQ-HWB-S).
      Item IDWording on EQ-HWB item pool (preliminary version)Wording on the final version (experimental version)
      EQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside)
      How well were you able to get around inside your home (D)1. How difficult was it for you to get around inside and outside? (using, for example, walking stick, frame, or wheelchair if you usually use them) (D)
      EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside)
      How well were you able to get around outside your home (D)
      EQ-HWB 2 (activities)How well were you able to do your day-to-day activities (D)2. How difficult was it for you to do day-to-day activities? (for example, working, shopping, housework) (D)
      EQ-HWB 3 (exhausted)I felt exhausted (F)3. I felt exhausted (F)
      EQ-HWB 4 (lonely)I felt lonely (F)4. I felt lonely (F)
      EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly)
      I had trouble thinking clearly (F)5. I had trouble concentrating or thinking clearly (F)
      EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating)
      I found it hard to concentrate (F)
      EQ-HWB 6 (anxious)I felt anxious (F)6. I felt anxious (F)
      EQ-HWB 7 (sad)I felt sad (F)7. I felt sad or depressed (F)
      EQ-HWB 8 (control)I felt I had no control over my day-to-day life (F)8. I felt I had no control over my day-to-day life (had the choice to do things or have things done for you as you liked and when you wanted) (F)
      EQ-HWB 9 (pain)Please tick one box to describe your experience in the last 7 days:
      • I had no physical pain.
      • I had mild physical pain.
      • I had moderate physical pain.
      • I had severe physical pain.
      • I had very severe physical pain.
      9. Please select (x) one box to describe your experience in the last 7 days:
      • I had no physical pain.
      • I had mild physical pain.
      • I had moderate physical pain.
      • I had severe physical pain.
      • I had very severe physical pain.
      Note. EQ-HWB-S response scales: F, frequency scale (1 = no difficulty, 2 = slight difficulty, 3 = some difficulty, 4 = a lot of difficulty, 5 = unable); D, difficulty response scale (1 =none of the time, 2 = only occasionally, 3 = sometimes, 4 = often, 5 = most or all of the time); S, severity scale, shown in the table.
      EQ-HWB indicates EQ Health and Wellbeing; EQ-HWB-S, EQ Health and Wellbeing Short; ID, identification.
      Preliminary versions of the items.

      EQ-5D

      The EQ-5D is a MAUI that assesses health outcomes based on a selection of “common core” HRQOL dimensions known to be relevant across a range of conditions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression.

      Gudex C. The descriptive system of the EuroQOL instrument. EQ-5D Concepts and Methods: A Developmental History. Berlin, Germany: Springer; 2005:19-27.

      This study used the EQ-5D-5L descriptive system (5-level version where “1” stands for no problems, “2” for mild problems, “3” for some/moderate problems, “4” for severe problems, and “5” for unable/extreme problems). The EQ-5D also comprised a EQ-5D Visual Analog Scale that assesses the respondent’s self-rated health on a vertical scale from 0 (worst health you can imagine) to 100 (the best health you can imagine).
      • Rabin R.
      • Oemar M.
      • Oppe M.
      EQ-5D-3L User Guide Basic Information on How to Use the EQ-5D-3L Instrument.
      EQ-5D-5L index values were scored using the US value set based on the EuroQol Valuation Technology.
      • Pickard A.S.
      • Law E.H.
      • Jiang R.
      • et al.
      United States valuation of EQ-5D-5L health states using an international protocol.

      Statistical Analysis

      Acceptability was evaluated by computing the rates of item missingness for all items. A proportion of missing values > 5% was defined a priori as a marker of potential problems, given that a higher proportion of missing values may indicate the item’s lower interpretability and acceptability and warrant the use of multiple imputation techniques.
      • Menard J.C.
      • Hinds P.S.
      • Jacobs S.S.
      • et al.
      Feasibility and acceptability of the patient-reported outcomes measurement information system measures in children and adolescents in active cancer treatment and survivorship.
      ,
      • Schafer J.L.
      Multiple imputation: a primer.
      Response patterns were examined by computing absolute and relative frequencies for each level of each item. Ceiling effects were defined at the item and instrument level as the proportion of respondents reporting “no problems” (or the equivalent option on the response scale) for each item (item level) and in all items of each measure (instrument level). Items with > 50% of respondents in the ceiling were flagged as potentially problematic, using a threshold established by the international teams in other analyses.

      Peasgood T, Mukuria C, Brazier J, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ Health and Wellbeing. Value Health. In press.

      At the instrument level, 15% was adopted as a threshold to signal potential problems.
      • Terwee C.B.
      • Bot S.D.
      • de Boer M.R.
      • et al.
      Quality criteria were proposed for measurement properties of health status questionnaires.
      EQ-5D-5L ceiling effects were further examined using the distribution of EQ-HWB-S responses for those who reported full health on the EQ-5D-5L (no problems on all dimensions, health state 11111). We expected the EQ-HWB-S to present lower instrument-level ceiling effects than the EQ-5D-5L, owing to its comparatively broader content coverage. Floor effects, usually identified when a high proportion of respondents reports their HRQOL using the most extreme end of the response scale, were only reported at the item level. Contrary to classical approaches to the investigation of floor effects, in this study, we flagged very low proportions of endorsement of the most extreme option in the response scale (< 5%).
      The degree of association between instruments was examined using Spearman rank-order correlation (rs). We expected stronger associations between items covering overlapping dimensions (eg, EQ-HWB-S items “inside” and “outside” and EQ-5D-5L item “mobility”).
      Known group comparisons were carried to examine the ability of the items to distinguish among distinct groups of respondents defined according to the presence of self-reported chronic conditions, caregiver status, and caregiver burden. For this analysis, respondents who reported a physical or mental health condition were matched on age and gender with respondents with no reported such conditions
      • Blackwell M.
      • Iacus S.
      • King G.
      • Porro G.
      Cem: coarsened exact matching in Stata.
      ,
      • Bradburn N.M.
      • Sudman S.
      • Wansink B.
      Asking Questions: The Definitive Guide to Questionnaire Design--For Market Research, Political Polls, and Social and Health Questionnaires.
      and no problems in EQ-5D-5L dimensions and had an EQ-5D Visual Analog Scale score > 0.8. The same method was used to compare caregivers and noncaregivers and those with high and low self-reported caregiver burden. Caregiver burden was delineated based on hours spent per week providing care, with low defined as < 20 hours and high if 20 hours or more. We expected the EQ-HWB-S to be better able to differentiate groups defined according to their caregiver status and burden. Because 5 levels were used for responses to items on both measures, we assumed interval-level properties for responses to dimensions to facilitate the calculation of Cohen’s D effect sizes and respective 95% confidence intervals for comparison purposes. We used the following thresholds to classify effect sizes: small (d = 0.2), medium (d = 0.5), large (d = 0.8), and very large (d = 1.40).
      • Cohen J.
      Statistical Power Analysis for the Behavioral Sciences.

      Results

      Data from 903 US-based respondents were collected (n = 400 CS and n = 503 GP). The overall sample was 51.3% male, with a mean age of 53.8 (SD 17.7) years old. Approximately 22% of respondents identified as caregivers, whereas 9.5% reported being social care users. A detailed breakdown of the respondents’ characteristics for the whole sample and the subsamples (ie, GP and CS) is presented in Table 2. The 3 most prevalent self-reported chronic health conditions in our sample were high blood pressure (30%), arthritis (17.8%), and diabetes (13.5%). Mean EQ-5D-5L scores were 0.76 (SD 0.26). Notably, patients with cancer reported higher scores on the EQ-5D-5L and ASCOT than those in the GP.
      Table 2Characteristics of the study sample (N = 903).
      CharacteristicsWhole sample (N = 903)General population (n = 500)Patients with cancer (n = 403)
      Age, mean (SD)53.8117.4645.8316.9563.8012.13
      Age groupn%n%n%
       18-24586.455611.2020.50
       25-3411012.2410020.00102.51
       35-4411212.469318.60194.76
       45-5412413.798316.604110.28
       55-6417319.247915.809423.56
       65-7423926.597014.0016942.36
       75-84819.01193.806215.54
       85+20.2200.0020.54
      Gender
       Male46351.3023646.9222756.75
       Female43648.3026352.2917343.25
      Other40.4040.8000.00
      Education
       Did not finish high school202.21203.9800.00
       Completed high school or equivalent17719.6013727.244010.00
       Completed some college30834.1117234.1913634.00
       Completed a bachelor’s degree22925.3611021.8711929.75
       Completed a professional or graduate degree16918.726412.7210526.25
      Caregiver (yes/no)
       No70078.1338377.2231779.25
       Yes19621.8711322.788320.75
      Hours spent as caregiver (last 7 days)
      Among caregivers.
       1-199250.274442.314860.76
       20-494826.232927.881924.05
       ≥504323.503129.811215.19
      Social care user (yes/no)
       No81590.4645690.8435989.97
       Yes869.54469.164010.03
      Long-term condition
       No long-term conditions28031.0128056.0000
       Asthma768.40397.75379.25
       Arthritis16117.865711.3310426.00
       Heart condition869.50316.165513.75
       Stroke192.1391.79102.50
       Emphysema212.3061.19153.75
       Hyperthyroidism141.63397.7592.25
       Hypothyroidism687.50132.585513.75
       Chronic bronchitis212.30142.7871.75
       Any kind of liver condition232.5991.79143.50
       HIV/AIDS60.7010.2051.25
       Diabetes12213.50458.957719.25
       Epilepsy40.4030.6010.25
       High blood pressure27630.607615.1120050.00
       Irritable bowel syndrome495.40295.77205.00
       Depression11312.507414.71399.75
       Generalized anxiety disorder9810.926512.92338.25
       Disability10211.335611.134611.50
       Other physical condition19221.347114.1212130.25
       Other mental condition525.82397.75133.25
      HRQOL and wellbeing
       EQ-5D-5L, mean (SD)0.760.260.760.270.760.25
       EQ VAS, mean (SD)72.3319.3872.4220.7972.1317.56
       ASCOT, mean (SD)0.840.190.810.210.880.14
       SWEMWBS, mean (SD)25.756.7424.177.0527.005.77
      AIDS indicates acquired immunodeficiency syndrome; ASCOT, Adult Social Care Outcomes Toolkit; EQ VAS, EQ-5D Visual Analog Scale; EQ-5D-5L, 5-level version EQ-5D; HIV, human immunodeficiency virus; HRQOL, health-related quality of life; SWEMWBS, Short Warwick-Edinburgh Mental Wellbeing Scale.
      Among caregivers.
      Overall, there were very low levels of missing data across all items (0%-2.5%). Most items presented a right-skewed distribution of responses (Table 3). Evidence of item-level floor effects (ie, < 5% of the participants reported having extreme problems) was found for all 5L dimensions. Several EQ-HWB-S items (ie, items assessing exhaustion, loneliness, trouble concentrating, anxiety, and sadness) yielded > 5% endorsement of the most extreme end of the response scale (ie, problems most or all of the time). Ceiling effects (ie, > 50% of the participants reported having no problems) were identified on the EQ-5D-5L descriptive systems in all dimensions except for the dimension pain. Ceiling effects were also found on the EQ-HWB-S items “inside” (75%), “outside” (64%), “activities” (58%), “lonely” (54%), “thinking clearly” (54%), and “control” (56%). The distribution of responses to the EQ-5D-5L item “pain/discomfort” and the EQ-HWB-S “pain” was notably similar. Additionally, the EQ-HWB-S items “anxious” and “sad” had fewer ceiling and floor effects than the EQ-5D-5L item “anxiety/depression.”
      Table 3Distribution of item responses for the EQ-5D-5L and EQ-HWB-S.
      ItemsDistribution
      CeilingFloor
      12345Missing
      EQ-5D-5L
       EQ-5D-5L mobility61.221.712.22.91.10.9
       EQ-5D-5L self-care81.011.15.11.00.61.3
       EQ-5D-5L usual activities57.726.610.03.71.20.9
       EQ-5D-5L pain/discomfort29.243.020.04.52.30.9
       EQ-5D-5L anxiety/depression50.924.114.16.13.90.9
      EQ-HWB-S
       EQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside) (D)
      75.013.36.93.00.81.1
       EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside) (D)
      63.918.710.45.01.01.0
       EQ-HWB 2 (activities) (D)57.621.413.55.01.90.7
       EQ-HWB 3 (exhausted) (F)25.327.920.015.410.31.1
       EQ-HWB 4 (lonely) (F)54.418.211.78.57.00.2
       EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly) (F)
      53.620.515.56.04.20.2
       EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating) (F)
      36.228.619.89.16.00.3
       EQ-HWB 6 (anxious) (F)44.424.013.011.06.61.0
       EQ-HWB 7 (sad) (F)36.732.014.210.26.40.6
       EQ-HWB 8 (control) (F)56.419.711.18.24.10.6
       EQ-HWB 9 (pain) (S)26.544.420.86.31.90.1
      Note. EQ-5D-5L response scales: 1 = no problems, 2 = slight problems; 3 = moderate problems; 4 = severe problems; 5 = extreme problems or unable; EQ-HWB-S response scales: F, frequency scale: 1 = no difficulty, 2 = slight difficulty, 3 = some difficulty, 4 = a lot of difficulty, 5 = unable; D, difficulty response scale: 1- = none of the time, 2 = only occasionally, 3 = sometimes, 4 = often, 5 = most or all of the time; S, severity response scale: 1 = no physical pain, 2 = mild physical pain, 3 = moderate physical pain, 4 = severe physical pain, 5 = very severe physical pain. Values above the threshold of 50% in the column ceiling or below the thresholds 5% for the column “floor” appear bolded.
      EQ-5D-5L indicates 5-level version EQ-5D; EQ-HWB, EQ Health and Wellbeing; EQ-HWB-S, EQ Health and Wellbeing Short.
      Preliminary versions of the items.
      The strength of correlation between EQ-5D-5L and EQ-HWB-S items indicated convergent validity between conceptually related items (Table 4). Moderate to strong associations were found between the physical function and pain dimension of the EQ-5D-5L descriptive systems and the EQ-HWB-S items related to the ability to do day-to-day activities, ability to get around inside, ability to get around outside, and pain (rs > 0.5). Likewise, the EQ-5D-5L dimension “anxiety/depression” appears to be moderate to strongly correlated with the EQ-HWB-S items assessing feelings of exhaustion, loneliness, anxiety, sadness, control, and trouble thinking clearly and concentrating (rs > 0.6). All coefficients were significant (P < .001).
      Table 4Correlation matrices between the EQ-5D-5L and the EQ-HWB-S.
      EQ-HWB-SEQ-5D-5L
      MobilitySelf-careUsual activitiesPain/discomfortAnxiety/depression
      EQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside)
      0.550.530.550.410.31
      EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside)
      0.670.530.650.490.34
      EQ-HWB 2 (activities)0.580.510.680.500.40
      EQ-HWB 3 (exhausted)0.240.240.360.360.56
      EQ-HWB 4 (lonely)0.180.320.260.270.62
      EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly)
      0.180.280.280.290.59
      EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating)
      0.160.280.260.280.60
      EQ-HWB 6 (anxious)0.150.260.230.270.76
      EQ-HWB 7 (sad)0.150.300.280.280.67
      EQ-HWB 8 (control)0.240.350.350.330.62
      EQ-HWB 9 (pain)0.530.360.530.810.35
      Note. All P < .001.
      0-0.19 Very weak
      0.20-0.39 Weak
      0.40-0.59 Moderate
      0.60-0.79 Strong
      0.80-1.0 Very strong
      EQ-5D-5L indicates 5-level version EQ-5D; EQ-HWB, EQ Health and Wellbeing; EQ-HWB-S, EQ Health and Wellbeing Short.
      Preliminary versions of the items.
      The EQ-HWB-S displayed fewer instrument-level ceiling effects (n = 57, 6.3%) than the EQ-5D-5L (n = 161, 18.2%). Among the 161 individuals who reported full health on the EQ-5D-5L (Tables 5 and 6), certain items on the EQ-HWB-S identified notable ceiling effects: “exhausted” (n = 75, 47%), “concentrating” (n = 58, 36%), and “sad” (n = 54, 34%).
      Table 5Distribution of EQ-HWB-S item responses among respondents reporting full health on EQ-5D-5L.
      LevelEQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside)
      EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside)
      EQ-HWB 2 (activities)EQ-HWB 3 (exhausted)EQ-HWB 4 (lonely)EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly)
      EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating)
      EQ-HWB 6 (anxious)EQ-HWB 7 (sad)EQ-HWB 8 (control)EQ-HWB 9 (pain)
      195.696.395.053.485.183.164.078.866.586.382.6
      22.51.93.127.39.910.623.015.626.18.716.2
      30.61.20.615.54.44.411.24.46.24.41.2
      40.60.60.62.50.61.90.01.30.00.60.0
      50.60.00.61.20.00.01.90.01.20.00.0
      Total (N)160161161161160160161160161161161
      EQ-5D-5L indicates 5-level version EQ-5D; EQ-HWB, EQ Health and Wellbeing; EQ-HWB-S, EQ Health and Wellbeing Short.
      Preliminary versions of the items.
      Table 6Distribution of EQ-5D-5L item responses among respondents reporting full health on EQ-HWB.
      LevelEQ-5D-5LEQ-5D-5LEQ-5D-5LEQ-5D-5LEQ-5D-5L
      MobilitySelf-careUsual activitiesPain/discomfortAnxiety/depression
      193.498.210094.598.2
      23.61.80.05.51.8
      30.00.00.00.00.0
      40.00.00.00.00.0
      50.00.00.00.00.0
      Total (N)5554555556
      EQ-5D-5L indicates 5-level version EQ-5D; EQ-HWB, EQ Health and Wellbeing.
      EQ-HWB-S items were generally better at distinguishing between respondents with and without mental health conditions than those with and without physical health conditions, except for those with disability. All EQ-5D-5L and EQ-HWB-S items were able to discriminate among those with and without physical and mental conditions, yielding medium (> 0.5) to large (> 0.8) effect sizes and 95% confidence intervals not containing 0 (Table 7). When respondents were categorized in terms of caregiving status and hours of care, EQ-5D-5L items were unable to distinguish between those who reported being caregivers and those who did not, as well as those with low and high burden of care. In contrast, several EQ-HWB-S items were able to distinguish between caregivers and noncaregivers (ie, items “exhausted,” “thinking clear,” “concentration,” “anxious,” and “sad”) and those with low versus high caregiver burden (ie, “anxious” and “pain”), albeit with small effect sizes (−0.20 to −0.38).
      Table 7Effect sizes for health conditions compared with a healthy group + effect sizes for other group comparisons.
      Physical health conditions
      ArthritisCancer diagnosisBlood pressureHeart conditionAsthmaDiabetesDisabilityIrritable bowel syndromeOther physical health conditions
      EQ-5D-5LES95% CIES95% CIES95% CIES95% CIES95% CIES95% CIES95% CIES95% CIES95% CI
      EQ-5D-5L mobility−1.69−2.12 to −1.25−1.06−1.42 to −0.68−1.33−1.71 to −0.94−1.42−1.88 to −0.94−1.33−1.73 to −0.94−1.15−1.54 to −0.75−2.08−2.55 to −1.60−1.42−1.89 to −0.94−1.35−1.74 to −0.95
      EQ-5D-5L self-care−1.05−1.45 to −0.65−0.60−0.95 to −0.24−0.86−1.22 to −0.49−1.16−1.61 to −0.70−0.69−1.05 to −0.32−0.92−1.30 to −0.53−1.37−1.79 to −0.94−0.95−1.40 to −0.50−0.85−1.22 to −0.47
      EQ-5D-5L usual activities−1.74−2.17 to −1.30−1.34−1.72 to −0.96−1.35−1.73 to −0.96−1.33−1.79 to −0.87−1.35−1.74 to −0.95−1.30−1.70 to −0.90−1.82−2.27 to −1.36−1.49−1.97 to −1.01−1.44−1.84 to −1.03
      EQ-5D-5L pain/discomfort−2.80−3.32 to −2.27−2.30−2.75 to −1.85−2.17−2.61 to −1.73−2.50−3.05 to −1.93−1.91−2.33 to −1.47−1.98−2.42 to −1.53−2.37−2.86 to −1.86−2.18−2.72 to −1.63−1.83−2.26 to −1.40
      EQ-5D-5L anxiety/depression−1.72−2.16 to −1.28−1.53−1.92 to −1.13−1.37−1.76 to −0.98−1.44−1.91 to −0.97−1.41−1.81 to −1.01−1.12−1.51 to −0.73−1.67−2.11 to −1.22−1.76−2.26 to −1.25−1.48−1.88 to −1.07
      EQ-HWB-S
      EQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside)
      −1.25−1.65 to −0.84−0.71−1.07 to −0.35−0.92−1.28 to −0.55−1.19−1.64 to −0.73−1.01−1.39 to −0.63−0.96−1.34 to −0.57−1.38−1.80 to −0.95−0.92−1.37 to −0.48−1.02−1.40 to −0.64
      EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside)
      −1.43−1.85 to −1.01−1.01−1.38 to −0.64−1.19−1.57 to −0.81−1.53−2.01 to −1.05−1.36−1.75 to −0.96−1.24−1.64 to −0.84−1.82−2.27 to −1.37−1.44−1.92 to −0.97−1.16−1.54 to −0.77
      EQ-HWB 2 (activities)−1.66−2.08 to −1.22−1.11−1.48 to −0.74−1.30−1.68 to −0.91−1.39−1.85 to −0.92−1.35−1.74 to −0.95−1.24−1.64 to −0.84−2.07−2.54 to −1.59−1.65−2.14 to −1.15−1.36−1.76 to −0.96
      EQ-HWB 3 (exhausted)−1.31−1.72 to −0.89−0.99−1.35 to −0.62−1.12−1.50 to −0.75−1.00−1.44 to −0.55−1.33−1.72 to −0.94−0.89−1.28 to −0.50−1.44−1.87 to −1.01−1.36−1.83 to −0.88−1.03−1.41 to −0.64
      EQ-HWB 4 (lonely)−1.29−1.69 to −0.88−0.75−1.11 to −0.39−0.96−1.33 to −0.59−1.15−1.60 to −0.70−1.11−1.48 to −0.72−0.86−1.24 to −0.48−1.33−1.74 to −0.90−1.10−1.56 to −0.65−1.06−1.44 to −0.67
      EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly)
      −1.26−1.66 to −0.85−0.77−1.13 to −0.41−0.90−1.26 to −0.53−1.09−1.54 to −0.64−1.18−1.56 to −0.79−0.92−1.30 to −0.53−1.21−1.62 to −0.79−1.19−1.65 to −0.73−0.89−1.27 to −0.52
      EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating)
      −1.21−1.61 to −0.80−0.94−1.30 to −0.57−0.96−1.33 to −0.59−1.38−1.84 to −0.91−1.12−1.50 to −0.74−1.02−1.41 to −0.63−1.32−1.74 to −0.89−1.32−1.79 to −0.85−0.86−1.24 to −0.49
      EQ-HWB 6 (anxious)−1.42−1.83 to −1.00−1.00−1.37 to −0.63−1.22−1.60 to −0.84−1.24−1.70 to −0.77−1.28−1.68 to −0.89−1.01−1.41 to −0.61−1.74−2.18 to −1.28−1.63−2.12 to −1.14−1.18−1.57 to −0.79
      EQ-HWB 7 (sad)−1.30−1.71 to −0.89−0.84−1.20 to −0.48−1.08−1.45 to −0.70−1.22−1.67 to −0.75−1.02−1.39 to −0.64−0.88−1.26 to −0.49−1.30−1.72 to −0.87−1.29−1.76 to −0.82−1.20−1.58 to −0.81
      EQ-HWB 8 (control)−1.27−1.67 to −0.86−0.83−1.19 to −0.47−0.79−1.15 to −0.43−1.18−1.63 to −0.72−1.02−1.39 to −0.64−0.66−1.03 to −0.28−1.31−1.73 to −0.89−1.49−1.96 to −1.01−0.96−1.34 to −0.58
      EQ-HWB 9 (pain)−2.34−2.81 to −1.85−1.77−2.17 to −1.35−1.92−2.34 to −1.49−2.27−2.81 to −1.73−1.71−2.12 to −1.29−1.78−2.21 to −1.35−2.07−2.54 to −1.60−1.93−2.43 to −1.41−1.91−2.34 to −1.48
      Mental health conditionsCaregiving
      AnxietyDepressionOther mental health conditionsCaregiversHours of care
      EQ-5D-5LES95% CIES95% CIES95% CIES95% CIES95% CI
      EQ-5D-5L mobility−1.45−1.83 to 1.06−1.26−1.62 to −0.88−1.17−1.62 to −0.71−0.12−0.32 to 0.08−0.05−0.40 to 0.30
      EQ-5D-5L self-care−0.91−1.28 to −0.55−0.99−1.35 to −0.63−0.90−1.34 to −0.46−0.07−0.27 to 0.130.10−0.26 to 0.45
      EQ-5D-5L usual activities−1.62−2.01 to −1.22−1.45−1.83 to −1.07−1.23−1.69 to −0.77−0.13−0.32 to 0.07−0.05−0.40 to 0.30
      EQ-5D-5L pain/discomfort−1.97−2.40 to −1.55−2.02−2.44 to −1.60−1.79−2.29 to −1.29−0.04−0.23 to 0.16−0.15−0.50 to 0.20
      EQ-5D-5L anxiety/depression−2.25−2.69 to −1.80−2.50−2.98 to −2.04−2.01−2.53 to −1.49−0.11−0.31 to 0.09−0.25−0.60 to 0.10
      EQ-HWB-S
      EQ-HWB 1
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (inside)
      −0.89−1.24 to −0.52−0.86−1.21 to −0.51−0.83−1.23 to −0.36−0.17−0.37 to 0.03−0.10−0.45 to 0.25
      EQ-HWB 1
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (outside)
      −1.38−1.76 to −0.99−1.34−1.71 to −0.96−1.11−1.55 to −0.66−0.12−0.32 to 0.08−0.14−0.49 to 0.21
      EQ-HWB 2 (activities)−1.46−1.85 to −1.07−1.36−1.73 to −0.98−1.06−1.51 to −0.61−0.10−0.29 to 0.10−0.24−0.59 to 0.11
      EQ-HWB 3 (exhausted)−1.68−2.08 to −1.28−1.33−1.70 to −0.95−1.30−1.76 to −0.83−0.20−0.40 to −0.01−0.15−0.50 to 0.20
      EQ-HWB 4 (lonely)−1.64−2.03 to −1.24−1.40−1.78 to −1.02−1.66−2.15 to −1.18−0.13−0.33 to 0.07−0.14−0.49 to 0.21
      EQ-HWB 5
      Preliminary versions of the items.
      • Drummond M.F.
      • Sculpher M.J.
      • Claxton K.
      • Stoddart G.L.
      • Torrance G.W.
      Methods for the Economic Evaluation of Health Care Programmes.
      (thinking clearly)
      −1.61−2.00 to −1.21−1.46−1.84 to −1.07−1.54−2.01 to −1.06−0.21−0.41 to −0.01−0.23−0.58 to 0.12
      EQ-HWB 5
      Preliminary versions of the items.
      • Kind P.
      • Hardman G.
      • Leese B.
      Measuring health status: information for primary care decision making.
      (concentrating)
      −1.51−1.90 to −1.12−1.58−1.97 to −1.19−1.29−1.75 to −0.83−0.26−0.46 to −0.06−0.13−0.48 to 0.22
      EQ-HWB 6 (anxious)−2.32−2.77 to −1.87−1.88−2.29 to −1.47−1.84−2.34 to −1.32−0.24−0.44 to −0.04−0.38−0.73 to −0.03
      EQ-HWB 7 (sad)−1.64−2.04 to −1.24−1.63−2.02 to −1.23−1.57−2.05 to −1.09−0.21−0.40 to −0.01−0.19−0.54 to 0.16
      EQ-HWB 8 (control)−1.34−1.71 to −0.95−1.38−1.75 to −1.00−1.55−2.02 to −1.07−0.15−0.35 to 0.04−0.15−0.50 to 0.20
      EQ-HWB 9 (pain)−1.99−2.41 to −1.57−1.92−2.33 to −1.51−1.64−2.12 to −1.15−0.13−0.33 to 0.07−0.35−0.70 to − 0.01
      Note. ES – Cohen’s D effect sizes; hours of care (low burden < 20 hours, high burden ≥ 20 hours). Boldened 95% confidence intervals imply statistical significance.
      0 to −0.19 Very small
      −0.20 to −0.49 Small
      −0.50 to −0.79 Medium
      −0.80 to max Large
      CI indicates confidence interval; EQ-5D-5L, 5-level version EQ-5D; EQ-HWB, EQ Health and Wellbeing; EQ-HWB-S, EQ Health and Wellbeing Short; ES, effect sizes; max, maximum.
      Preliminary versions of the items.

      Discussion

      This is the first study to compare the preliminary version of the EQ-HWB-S with the well-established and widely used EQ-5D-5L. Our results showed similar acceptability and substantial overlap between these measures, with a high degree of convergence and similar response patterns observed across most items, primarily those conceptually related. Nevertheless, the EQ-HWB-S appeared to outperform the EQ-5D-5L descriptive system in terms of ceiling effects and the ability to discriminate between known groups. These improvements were observed in healthier individuals (ie, respondents closer to full health) and caregivers.
      Given that the EQ-HWB-S and EQ-5D-5L share a group of core dimensions, with items covering mobility, activities, pain, anxiety, and depression in both measures, we expected to observe a significant level of overlap. Indeed, moderate to strong associations were found between the physical function and pain dimensions of the EQ-5D-5L and EQ-HWB-S classifier (rs > 0.5) and among items assessing mental health, cognition, exhaustion, loneliness, and control (rs > 0.6). The high degree of convergence lends support to both instruments construct validity.
      • Brazier J.
      • Ratcliffe J.
      • Saloman J.
      • Tsuchiya A.
      Measuring and Valuing Health Benefits for Economic Evaluation.
      ,
      • Streiner D.L.
      • Norman G.R.
      • Cairney J.
      Health Measurement Scales: A Practical Guide to Their Development and Use.
      Compared with the EQ-5D-5L, the EQ-HWB-S items displayed fewer limitations in terms of proportion of respondents reporting no problems, although > 50% of respondents reported no problems/issues on items referring to difficulty to walk inside and outside (ie, “inside,” “outside”), “activities,” “lonely,” “thinking clearly,” and “control.” The presence of problematic ceiling and floor effects was less evident across the items “exhausted,” “concentrating,” “anxious,” “sad,” and “pain.” Notably, the EQ-HWB-S items “anxious” and “sad” presented lower ceiling and fewer “very low” floor effects than its EQ-5D-5L counterpart, “anxiety/depression.” The EQ-5D-5L dimension encompasses both anxiety and depression; therefore, it is expected to be endorsed at least as much, if not more, as the items related to anxiety and sadness separately. Nevertheless, even among those reporting full health on the EQ-5D-5L, 21% reported feeling anxious and 34% reported feeling sad at least occasionally, suggesting that the disaggregated presentation of the dimension “anxiety/depression” may be easier to endorse. Nevertheless, this finding should be interpreted carefully as the EQ-HWB used the term “sadness” rather than depression, and arguably sadness is a different and more commonly experienced emotional state. Furthermore, the EQ-5D-5L and EQ-HWB-S have different recall periods, and it is plausible that respondents experienced more anxiety and sadness/depression over the last 7 days than on the day of the survey. Different recall periods have been shown to be associated with differences in self-reported HRQOL
      • Kularatna S.
      • Senanayake S.
      • Gunawardena N.
      • Graves N.
      Comparison of the EQ-5D 3L and the SF-6D (SF-36) contemporaneous utility scores in patients with chronic kidney disease in Sri Lanka: a cross-sectional survey.
      even when the same instrument is used.
      • Luyten J.
      • Marais C.
      • Hens N.
      • De Schrijver K.
      • Beutels P.
      Imputing QALYs from single time point health state descriptions on the EQ-5D and the SF-6D: a comparison of methods for hepatitis A patients.
      Furthermore, the item wording is slightly different between instruments. Although the EQ-5D-5L words the item in terms of “being anxious/depressed” using a severity scale, the EQ-HWB-S items are worded in terms of “feeling anxious” and “feeling sad” using a frequency scale. Findings from the E-QALY project face validity stage, in which participants related that the term depression has a clinical connotation, suggesting that it can be more difficult to endorse.

      Carlton J, PT, Mukuria C, Connell J, et al. Generation, selection and face validation of items for a new generic measure of quality of life, the EQ health and wellbeing (EQ-HWB). Value in Health. In press.

      ,
      • Pickard A.S.
      • Monteiro A.
      • Kuharic M.
      • Mukuria C.
      • Peasgood T.
      • Brazier J.
      Extending the QALY project in the United States: face and content validity of items for a new preference-based measure.
      Finally, the final wording of the EQ-HWB-S item related to feelings of sadness brings back the term depression (ie, “I felt sad or depressed”); therefore, the response patterns observed may not hold in future iterations of this instrument.
      The relative differences in item-level ceiling effects coupled with the EQ-HWB-S broader content coverage entail an improvement in instrument-level ceiling effects. Indeed, the EQ-HWB-S yielded the lowest instrument-level ceiling effects (EQ-HWB-S 6.3% vs EQ-5D-5L 18.2 %). Furthermore, EQ-HWB-S items, in particular, the items “exhausted,” “thinking clearly,” and “sad,” were able to differentiate among those who reported full health using the EQ-5D-5L. These results suggest that, compared with the EQ-5D-5L, the EQ-HWB-S content “registers” more with well populations, which indicates a better content validity in those populations.
      • Patrick D.L.
      • Erickson P.
      Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation.
      The EQ-5D-5L focuses on core HRQOL domains such as mobility and self-care that, although important to gauge physical function among patient populations with substantial symptoms and disability, may not be as relevant to discriminate respondents from respondents closer to full health.
      • Patrick D.L.
      • Erickson P.
      Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation.
      Alternatively, the EQ-HWB-S expands on the EQ-5D-5L by including items covering health and wellbeing aspects such as cognitive problems, exhaustion, loneliness, and control, which have been shown to improve the instrument’s ability to discriminate and detect changes in well populations.
      • Patrick D.L.
      • Erickson P.
      Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation.
      • Goranitis I.
      • Coast J.
      • Day E.
      • et al.
      Measuring health and broader well-being benefits in the context of opiate dependence: the psychometric performance of the ICECAP-A and the EQ-5D-5L.
      • Geraerds A.J.L.M.
      • Bonsel G.J.
      • Janssen M.F.
      • et al.
      The added value of the EQ-5D with a cognition dimension in injury patients with and without traumatic brain injury.
      Therefore, the ability to discriminate among respondents in the ceiling of the EQ-5D-5L will likely translate into a relatively higher sensitivity and responsiveness to improvements on the higher end of the HRQOL continuum.
      • Brazier J.
      • Ratcliffe J.
      • Saloman J.
      • Tsuchiya A.
      Measuring and Valuing Health Benefits for Economic Evaluation.
      ,
      • Patrick D.L.
      • Erickson P.
      Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation.
      ,
      • Howorka K.
      • Pumprla J.
      • Schlusche C.
      • Wagner-Nosiska D.
      • Schabmann A.
      • Bradley C.
      Dealing with ceiling baseline treatment satisfaction level in patients with diabetes under flexible, functional insulin treatment: assessment of improvements in treatment satisfaction with a new insulin analogue.
      Differences between the measures extend to their ability to discriminate between caregiver groups. The effect sizes observed for physical and mental conditions ranged from medium to very high for almost all items, which indicates that all items were able to discriminate between respondents with and without long-standing health conditions. As expected, HQ-HWB-S items generally performed better than the EQ-5D-5L when it comes to differentiating between groups defined by caregiving status and caregiving burden. These findings suggest that the EQ-HWB-S may potentially yield more sensitive estimates of gains from health and social care interventions targeting caregivers, although it remains to be seen whether this translates into differences between the indices generated by these 2 measures after EQ-HWB-S valuation work is completed.
      There are several limitations to this study. First, there are slight differences between the wording of the items used in this analysis and the final version of the EQ-HWB-S. The combined presentation of items and the inclusion of examples and the term depression may lead to changes in response patterns, although we do not anticipate these changes to fundamentally alter the implications of this study. Additionally, the extent to which these results may be generalized to broader populations may be limited. Samples obtained from online panels may be systematically different from those obtained through alternative modes of data collection. US online panels tend to be disproportionately white and unrepresentative of minorities,
      • Kennedy C.
      • Mercer A.
      • Keeter S.
      • Hatley N.
      • McGeeney K.
      • Gimenez A.
      • Duffy B.
      • Smith K.
      • Terhanian G.
      • Bremer J.
      Comparing data from online and face-to-face surveys.
      • Baker R.
      • Blumberg S.J.
      • Brick J.M.
      • et al.
      Research synthesis: AAPOR report on online panels.
      and although we imposed quota-based sampling based on age and sex in the GP cohort, we did not include race and ethnicity. Furthermore, there may be limitations in generalizability of the results, given that previous research has found that self-reported health and HRQOL differed when surveys were self-administered (ie, in online panels) versus interviewer administered (via telephone or in face-to-face interviews).
      • Hanmer J.
      • Hays R.D.
      • Fryback D.G.
      Mode of administration is important in US national estimates of health-related quality of life.
      ,
      • Jiang R.
      • Janssen M.F.B.
      • Pickard A.S.
      US population norms for the EQ-5D-5L and comparison of norms from face-to-face and online samples.
      The cross-sectional nature of this study limited our ability to explore the instruments’ sensitivity to changes over time, which is an important aspect of the performance of measures. Finally, the absence of a value set available for the EQ-HWB-S meant that we could only compare self-reported health classifications, with no reference to index scores. Given that utility weights are important independent predictors of differences between instrument scores,
      • Richardson J.
      • Iezzi A.
      • Khan M.A.
      Why do multi-attribute utility instruments produce different utilities: the relative importance of the descriptive systems, scale and ‘micro-utility’effects.
      even if primarily via scale effects, our ability to assess and discuss how the differences between these measures impact the estimates of gains from health and social care interventions is limited. Nevertheless, previous research also argues that the differences between different MAUIs utilities are primarily attributable to differences in the instruments’ descriptive systems
      • Richardson J.
      • Iezzi A.
      • Khan M.A.
      Why do multi-attribute utility instruments produce different utilities: the relative importance of the descriptive systems, scale and ‘micro-utility’effects.
      ; therefore, we expect that overall patterns will likely hold. Future studies should focus on how the indices generated by these instruments vary across the range of ill health and its impacts on the estimation of QALY gains and losses.

      Conclusions

      The EQ-HWB-S is an emerging MAUI that represents a new option for use in cross-sectoral economic evaluations of interventions in healthcare, social care, and public health. It is important to note that the EQ-HWB-S is not intended as a replacement for the EQ-5D-5L, but rather as an alternative. Previous studies show that the choice of MAUI partially explains differences in HRQOL estimates obtained across several disease areas,
      • Richardson J.
      • Khan M.A.
      • Iezzi A.
      • Maxwell A.
      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.
      ultimately affecting the results of cost-utility analysis.
      • Richardson J.
      • Khan M.A.
      • Iezzi A.
      • Maxwell A.
      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.
      ,
      • Sach T.H.
      • Barton G.R.
      • Jenkinson C.
      • Doherty M.
      • Avery A.J.
      • Muir K.R.
      Comparing cost-utility estimates: does the choice of EQ-5D or SF-6D matter?.
      Thus, discussions surrounding the selection of the most adequate MAUI to capture the full impacts of diseases and interventions are of utmost importance and should consider how alternative instruments compare in terms of acceptability, content covered, and context-specific sensitivity. In this article, we present the first comparisons between the EQ-5D-5L and the EQ-HWB-S seeking to inform such discussions. Our results showed that although there is a considering overlap between both EQ-5D-5L and EQ-HWB-S descriptive systems, the EQ-HWB-S performed and the EQ-5D-5L among patient groups may be better able to capture the impact of programs and interventions in caregivers and respondents closer to full health and suggests that the EQ-HWB-S may fulfill its potential as a measure that captures broader impact of intervention to inform decision around health and social care. Ultimately, whether these potential advantages translate at the utility level will depend on valuation studies.

      Article and Author Information

      Author Contributions: Concept and design: Monteiro, Pickard
      Acquisition of data: Monteiro, Kuharic, Pickard
      Analysis and interpretation of data: Monteiro, Pickard
      Drafting of the manuscript: Monteiro
      Critical revision of the paper for important intellectual content: Monteiro, Kuharic, Pickard
      Statistical analysis: Monteiro
      Provision of study materials or patients: Monteiro, Kuharic
      Obtaining funding: Monteiro, Pickard
      Administrative, technical, or logistic support: Monteiro, Kuharic
      Supervision: Monteiro, Pickard
      Conflict of Interest Disclosures: All authors reported receiving grants from the EuroQol Research Foundation during the conduct of the study. Dr Kuharic reported receiving fellowship support for graduate studies from Takeda Pharmaceuticals USA. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of those acknowledged or our funding sources, National Institute of Health and Care Excellence, or the Department of Health and Social Care.
      Funding/Support: All phases of this project were supported by the EuroQol Research Foundation, Netherlands. Maja Kuharic was supported by the 2018-2021 University of Illinois at Chicago/Takeda Health Economics and Outcomes Research Fellowship.
      Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

      Acknowledgment

      The authors acknowledge the EuroQol Research Foundation for financial support. Furthermore, the authors thank National Institute for Health and Care Excellence for highlighting the methodological research need to the MRC that resulted in the funding call entitled “Beyond the QALY,” which led to this research being funded. The authors acknowledge the support of the National Institute for Health Research Yorkshire and Humber Applied Research Collaboration (formerly CLAHRC) and the National Institute for Health Research Clinical Research Network. The authors also thank the members of the project steering group, advisory group, and public and patient involvement and engagement groups for their invaluable contributions. The authors also thank members of the EuroQol Group Association for their input at plenary and academy meetings. Finally, the authors also extend their acknowledgments to all the members of the international E-QALY consortium. The E-QALY international project is composed of Tessa Peasgood, Clara Mukuria, and John Brazier (UK team—project leaders); Brendan Mulhern, and Lidia Engel (Australian team); Wolfgang Greiner, Ole Marten, Simone Kreimeier, and Kristina Ludwig (German team); Nan Luo and Zhihao Yang (Chinese team); and Federico Augustovski and Maria Belizan (Argentinian team).

      References

        • Drummond M.F.
        • Sculpher M.J.
        • Claxton K.
        • Stoddart G.L.
        • Torrance G.W.
        Methods for the Economic Evaluation of Health Care Programmes.
        Oxford University Press, Oxford, United Kingdom2015
        • Kind P.
        • Hardman G.
        • Leese B.
        Measuring health status: information for primary care decision making.
        Health Policy. 2005; 71: 303-313
        • Misselbrook D.W.
        is for wellbeing and the WHO definition of health.
        Br J Gen Pract. 2014; 64: 582
        • Neumann P.J.
        • Goldie S.J.
        • Weinstein M.C.
        Preference-based measures in economic evaluation in health care.
        Annu Rev Public Health. 2000; 21: 587-611
        • Scuffham P.A.
        • Whitty J.A.
        • Mitchell A.
        • Viney R.
        The use of QALY weights for QALY calculations.
        Pharmacoeconomics. 2008; 26: 297-310
        • Whitehead S.J.
        • Ali S.
        Health outcomes in economic evaluation: the QALY and utilities.
        Br Med Bull. 2010; 96: 5-21
        • Torrance G.W.
        • Feeny D.
        Utilities and quality-adjusted life years.
        Int J Technol Assess Health Care. 1989; 5: 559-575
        • Brazier J.
        • Roberts J.
        • Deverill M.
        The estimation of a preference-based measure of health from the SF-36.
        J Health Econ. 2002; 21: 271-292
        • Espallargues M.
        • Czoski-Murray C.J.
        • Bansback N.J.
        • et al.
        The impact of age-related macular degeneration on health status utility values.
        Invest Ophthalmol Vis Sci. 2005; 46: 4016-4023
        • Krabbe P.F.
        • Stouthard M.E.
        • Essink-Bot M.-L.
        • Bonsel G.J.
        The effect of adding a cognitive dimension to the EuroQol multiattribute health-status classification system.
        J Clin Epidemiol. 1999; 52: 293-301
        • Netten A.
        • Burge P.
        • Malley J.
        • et al.
        Outcomes of social care for adults: developing a preference-weighted measure.
        Health Technol Assess. 2012; 16: 1-166
        • Brazier J.
        • Tsuchiya A.
        Improving cross-sector comparisons: going beyond the health-related QALY.
        Appl Health Econ Health Policy. 2015; 13: 557-565
        • Johnstone L.
        • Page C.
        Using Adult Social Care Outcomes Toolkit (ASCOT) in the assessment and review process.
        Res Policy Plan. 2013; 14: 3
      1. Brazier J, Peasgood T, Mukuria C, et al. The EQ-HWB: overview of the development of a measure of health and well-being and key results. Value Health. In press.

        • Carlton J.
        • Peasgood T.
        • Khan S.
        • Barber R.
        • Bostock J.
        • Keetharuth A.D.
        An emerging framework for fully incorporating public involvement (PI) into patient-reported outcome measures (PROMs).
        J Patient Rep Outcomes. 2020; 4: 4
      2. Carlton J, PT, Mukuria C, Connell J, et al. Generation, selection and face validation of items for a new generic measure of quality of life, the EQ health and wellbeing (EQ-HWB). Value in Health. In press.

      3. Peasgood T, Mukuria C, Brazier J, et al. Developing a new generic health and wellbeing measure: psychometric survey results for the EQ Health and Wellbeing. Value Health. In press.

      4. Monteiro A, Kuharic M, Pickard S. ‘I can’t feel my face’: will the E-QALY project measure resemble the EQ-5D? Results from the US arm. Paper Presented at: 1st EuroQol Early Career Researcher Meeting; March 2020; Prague, Czech Republic.

        • Brazier J.
        • Ratcliffe J.
        • Saloman J.
        • Tsuchiya A.
        Measuring and Valuing Health Benefits for Economic Evaluation.
        Oxford University Press, Oxford, United Kingdom2016
        • Kennedy-Martin M.
        • Slaap B.
        • Herdman M.
        • et al.
        Which multi-attribute utility instruments are recommended for use in cost-utility analysis? A review of national health technology assessment (HTA) guidelines.
        Eur J Health Econ. 2020; 21: 1245-1257
        • Jiang S.
        • Wang C.
        • Weiss D.J.
        Sample size requirements for estimation of item parameters in the multidimensional graded response model.
        Front Psychol. 2016; 7: 109
        • Comrey A.L.
        • Lee H.B.
        A First Course in Factor Analysis.
        Psychology Press, Hove, United Kingdom2013
        • EuroQol Group
        EuroQol--a new facility for the measurement of health-related quality of life. The EuroQol Group.
        Health Policy. 1990; 16: 199-208
        • Herdman M.
        • Gudex C.
        • Lloyd A.
        • et al.
        Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L).
        Qual Life Res. 2011; 20: 1727-1736
        • Tennant R.
        • Hiller L.
        • Fishwick R.
        • et al.
        The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): development and UK validation.
        Health Qual Life Outcomes. 2007; 5: 63
        • van Sonderen E.
        • Sanderman R.
        • Coyne J.C.
        Ineffectiveness of reverse wording of questionnaire items: let’s learn from cows in the rain.
        PLoS One. 2013; 8e68967
      5. Gudex C. The descriptive system of the EuroQOL instrument. EQ-5D Concepts and Methods: A Developmental History. Berlin, Germany: Springer; 2005:19-27.

        • Rabin R.
        • Oemar M.
        • Oppe M.
        EQ-5D-3L User Guide Basic Information on How to Use the EQ-5D-3L Instrument.
        EuroQol Group, Rotterdam, The Netherlands2011
        • Pickard A.S.
        • Law E.H.
        • Jiang R.
        • et al.
        United States valuation of EQ-5D-5L health states using an international protocol.
        Value Health. 2019; 22: 931-941
        • Menard J.C.
        • Hinds P.S.
        • Jacobs S.S.
        • et al.
        Feasibility and acceptability of the patient-reported outcomes measurement information system measures in children and adolescents in active cancer treatment and survivorship.
        Cancer Nurs. 2014; 37: 66-74
        • Schafer J.L.
        Multiple imputation: a primer.
        Stat Methods Med Res. 1999; 8: 3-15
        • Terwee C.B.
        • Bot S.D.
        • de Boer M.R.
        • et al.
        Quality criteria were proposed for measurement properties of health status questionnaires.
        J Clin Epidemiol. 2007; 60: 34-42
        • Blackwell M.
        • Iacus S.
        • King G.
        • Porro G.
        Cem: coarsened exact matching in Stata.
        STATA J. 2009; 9: 524-546
        • Bradburn N.M.
        • Sudman S.
        • Wansink B.
        Asking Questions: The Definitive Guide to Questionnaire Design--For Market Research, Political Polls, and Social and Health Questionnaires.
        John Wiley & Sons, Chichester, United Kingdom2004
        • Cohen J.
        Statistical Power Analysis for the Behavioral Sciences.
        Academic Press, Cambridge, United Kingdom2013
        • Streiner D.L.
        • Norman G.R.
        • Cairney J.
        Health Measurement Scales: A Practical Guide to Their Development and Use.
        Oxford University Press, New York, NY2015
        • Kularatna S.
        • Senanayake S.
        • Gunawardena N.
        • Graves N.
        Comparison of the EQ-5D 3L and the SF-6D (SF-36) contemporaneous utility scores in patients with chronic kidney disease in Sri Lanka: a cross-sectional survey.
        BMJ Open. 2019; 9e024854
        • Luyten J.
        • Marais C.
        • Hens N.
        • De Schrijver K.
        • Beutels P.
        Imputing QALYs from single time point health state descriptions on the EQ-5D and the SF-6D: a comparison of methods for hepatitis A patients.
        Value Health. 2011; 14: 282-290
        • Pickard A.S.
        • Monteiro A.
        • Kuharic M.
        • Mukuria C.
        • Peasgood T.
        • Brazier J.
        Extending the QALY project in the United States: face and content validity of items for a new preference-based measure.
        Qual Life Res. 2019; 28 (:S165)
        • Patrick D.L.
        • Erickson P.
        Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation.
        Oxford University Press, New York, NY1993
        • Goranitis I.
        • Coast J.
        • Day E.
        • et al.
        Measuring health and broader well-being benefits in the context of opiate dependence: the psychometric performance of the ICECAP-A and the EQ-5D-5L.
        Value Health. 2016; 19: 820-828
        • Geraerds A.J.L.M.
        • Bonsel G.J.
        • Janssen M.F.
        • et al.
        The added value of the EQ-5D with a cognition dimension in injury patients with and without traumatic brain injury.
        Qual Life Res. 2019; 28: 1931-1939
        • Howorka K.
        • Pumprla J.
        • Schlusche C.
        • Wagner-Nosiska D.
        • Schabmann A.
        • Bradley C.
        Dealing with ceiling baseline treatment satisfaction level in patients with diabetes under flexible, functional insulin treatment: assessment of improvements in treatment satisfaction with a new insulin analogue.
        Qual Life Res. 2000; 9: 915-930
        • Kennedy C.
        • Mercer A.
        • Keeter S.
        • Hatley N.
        • McGeeney K.
        • Gimenez A.
        Evaluating online nonprobability surveys. Pew Research.
        (Accessed April 2021)
        • Duffy B.
        • Smith K.
        • Terhanian G.
        • Bremer J.
        Comparing data from online and face-to-face surveys.
        Int J Mark Res. 2005; 47: 615-639
        • Baker R.
        • Blumberg S.J.
        • Brick J.M.
        • et al.
        Research synthesis: AAPOR report on online panels.
        Public Opin Q. 2010; 74: 711-781
        • Hanmer J.
        • Hays R.D.
        • Fryback D.G.
        Mode of administration is important in US national estimates of health-related quality of life.
        Med Care. 2007; 45: 1171-1179
        • Jiang R.
        • Janssen M.F.B.
        • Pickard A.S.
        US population norms for the EQ-5D-5L and comparison of norms from face-to-face and online samples.
        Qual Life Res. 2021; 30: 803-816
        • Richardson J.
        • Iezzi A.
        • Khan M.A.
        Why do multi-attribute utility instruments produce different utilities: the relative importance of the descriptive systems, scale and ‘micro-utility’effects.
        Qual Life Res. 2015; 24: 2045-2053
        • Richardson J.
        • Khan M.A.
        • Iezzi A.
        • Maxwell A.
        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.
        Med Decis Making. 2015; 35: 276-291
        • Sach T.H.
        • Barton G.R.
        • Jenkinson C.
        • Doherty M.
        • Avery A.J.
        • Muir K.R.
        Comparing cost-utility estimates: does the choice of EQ-5D or SF-6D matter?.
        Med Care. 2009; 47: 889-894