Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities.

BACKGROUND: The Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure. METHODS: Service users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range. RESULTS: Analyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior. CONCLUSIONS: This study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

Enabling QALY estimation in mental health trials and care settings: mapping from the PHQ-9 and GAD-7 to the ReQoL-UI or EQ-5D-5L using mixture models.

PURPOSE: Patient-reported outcome measures (PROMs) are commonly collected in trials and some care settings, but preference-based PROMs required for economic evaluation are often missing. For these situations, mapping models are needed to predict preference-based (aka utility) scores. Our objective is to develop a series of mapping models to predict preference-based scores from two mental health PROMs: Patient Health Questionnaire-9 (PHQ-9; depression) and Generalised Anxiety Questionnaire-7 (GAD-7; anxiety). We focus on preference-based scores for the more physical-health-focussed EQ-5D (five-level England and US value set, and three-level UK cross-walk) and more mental-health-focussed Recovering Quality-of-Life Utility Index (ReQoL-UI). METHODS: We used trial data from the Improving Access to Psychological Therapies (IAPT) mental health services (now called NHS Talking Therapies), England, with a focus on people with depression and/or anxiety caseness. We estimated adjusted limited dependent variable or beta mixture models (ALDVMMs or Betamix, respectively) using GAD-7, PHQ-9, age, and sex as covariates. We followed ISPOR mapping guidance, including assessing model fit using statistical and graphical techniques. RESULTS: Over six data collection time-points between baseline and 12-months, 1340 observed values (N ≤ 353) were available for analysis. The best fitting ALDVMMs had 4-components with covariates of PHQ-9, GAD-7, sex, and age; age was not a probability variable for the final ReQoL-UI mapping model. Betamix had practical benefits over ALDVMMs only when mapping to the US value set. CONCLUSION: Our mapping functions can predict EQ-5D-5L or ReQoL-UI related utility scores for QALY estimation as a function of variables routinely collected within mental health services or trials, such as the PHQ-9 and/or GAD-7.

Estimating Cost-Effectiveness Using Alternative Preference-Based Scores and Within-Trial Methods: Exploring the Dynamics of the Quality-Adjusted Life-Year Using the EQ-5D 5-Level Version and Recovering Quality of Life Utility Index.

OBJECTIVES: This study aimed to explore quality-adjusted life-year (QALY) and subsequent cost-effectiveness estimates based on the more physical health-focused EQ-5D 5-level version (EQ-5D-5L) value set for England or cross-walked EQ-5D 3-level version UK value set scores or more mental health recovery-focused Recovering Quality of Life Utility Index (ReQoL-UI), when using alternative within-trial statistical methods. We describe possible reasons for the different QALY estimates based on the interaction between item scores, health state profiles, preference-based scores, and mathematical and statistical methods chosen. METHODS: QALYs are calculated over 8 weeks from a case study 2:1 (intervention:control) randomized controlled trial in patients with anxiety or depression. Complete case and with missing cases imputed using multiple-imputation analyses are conducted, using unadjusted and regression baseline-adjusted QALYs. Cost-effectiveness is judged using incremental cost-effectiveness ratios and acceptability curves. We use previously established psychometric results to reflect on estimated QALYs. RESULTS: A total of 361 people (241:120) were randomized. EQ-5D-5L crosswalk produced higher incremental QALYs than the value set for England or ReQoL-UI, which produced similar unadjusted QALYs, but contrasting baseline-adjusted QALYs. Probability of cost-effectiveness <£30 000 per QALY ranged from 6% (complete case ReQoL-UI baseline-adjusted QALYs) to 64.3% (multiple-imputation EQ-5D-5L crosswalk unadjusted QALYs). The control arm improved more on average than the intervention arm on the ReQoL-UI, a result not mirrored on the EQ-5D-5L nor condition-specific (Patient-Health Questionnaire-9, depression; Generalized Anxiety Disorder-7, anxiety) measures. CONCLUSIONS: ReQoL-UI produced contradictory cost-effectiveness results relative to the EQ-5D-5L. The EQ-5D-5L's better responsiveness and "anxiety/depression" and "usual activities" items drove the incremental QALY results. The ReQoL-UI's single physical health item and "personal recovery" construct may have influenced its lower 8-week incremental QALY estimates in this patient sample.

Estimating a Preference-Based Index for Mental Health From the Recovering Quality of Life Measure: Valuation of Recovering Quality of Life Utility Index.

BACKGROUND: There are increasing concerns about the appropriateness of generic preference-based measures to capture health benefits in the area of mental health. OBJECTIVES: The aim of this study is to estimate preference weights for a new measure, Recovering Quality of Life (ReQoL-10), to better capture the benefits of mental healthcare. METHODS: Psychometric analyses of a larger sample of mental health service users (n = 4266) using confirmatory factor analyses and item response theory were used to derive a health state classification system and inform the selection of health states for utility assessment. A valuation survey with members of the UK public representative in terms of age, sex, and region was conducted using face-to-face interviewer administered time-trade-off with props. A series of regression models were fitted to the data and the best performing model selected for the scoring algorithm. RESULTS: The ReQoL-Utility Index (UI) classification system comprises 6 mental health items and 1 physical health item. Sixty-four health states were valued by 305 participants. The preferred model was a random effects model, with significant and consistent coefficients and best model fit. Estimated utilities modeled for all health states ranged from -0.195 (state worse than dead) to 1 (best possible state). CONCLUSIONS: The development of the ReQoL-UI is based on a novel application of item response theory methods for generating the classification system and selecting health states for valuation. Conventional time-trade-off was used to elicit utility values that are modeled to enable the generation of QALYs for use in cost-utility analysis of mental health interventions.

Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness.

PURPOSE: Generic health measures have been questioned for quantifying mental-health-related outcomes. In patients with anxiety and/or depression, our aim is to assess the psychometric properties of the preference-based EQ-5D-5L (generic health) and ReQoL-UI (recovery-focussed quality of life) for economic evaluation against the PHQ-9 (depression) and GAD-7 (anxiety). EQ-5D-5L anxiety/depression item and ReQoL-10 are also assessed. METHODS: A 2:1 (intervention: control) randomised controlled trial collected measures at baseline and 8 weeks post baseline; in the intervention arm, data were also collected 3, 6, 9, and 12-months post baseline. EQ-5D-5L preference-based scores were obtained from the value set for England (VSE) and 'cross-walked' EQ-5D-3L United Kingdom (UK) value set scores. ReQoL-UI preference-based scores were obtained from its UK value set as applied to seven ReQoL-10 items. EQ-5D-5L and ReQoL measures' construct validity and responsiveness were assessed compared against PHQ-9 and GAD-7 scores and group cut-offs. RESULTS: 361 people were randomised to intervention (241) or control (120). ReQoL-UI/-10 had better construct validity with depression severity than the EQ-5D-5L (VSE/cross-walk scores), which had relatively better construct validity with anxiety severity than the ReQoL-UI/-10. Across all intervention-arm time-points relative to baseline, responsiveness was generally better for EQ-5D-5L (VSE in particular) than ReQoL-UI, but worse than ReQoL-10. CONCLUSION: There is insufficient evidence to recommend the ReQoL-UI over EQ-5D-5L for economic evaluations to capture anxiety severity. However, there may be rationale for recommending the ReQoL-UI over the EQ-5D-5L to capture depression severity given its better construct validity, albeit poorer responsiveness, and if recovery-focussed quality of life relative to condition-specific symptomology is the construct of interest.

Measuring health-related quality of life and well-being: a head-to-head psychometric comparison of the EQ-5D-5L, ReQoL-UI and ICECAP-A.

OBJECTIVE: This study aimed to assess the psychometric properties of three generic preference-based measures and compare their performance in a sample of Hong Kong general population. METHODS: Data used for this analysis were obtained from a cross-sectional telephone-based survey in July 2020. Participants were asked to complete several measures, including The EuroQol five-dimensional five levels (EQ-5D-5L), Recovering Quality of Life-Utility Index (ReQoL-UI) and ICEpop CAPability measure for adults (ICECAP-A). Acceptability, reliability, convergent and discriminant validity of three measures were assessed as well as the agreement between these instruments. RESULTS: Based on data from 500 participants to the survey, a lower mean score of the ICECAP-A (mean = 0.85) was observed compared to the other two measures (meanReQoL-UI = 0.92; meanEQ-5D-5L = 0.92). All three measures showed an acceptable internal consistency reliability (Cronbach's alpha = 0.74, 0.82 and 0.77, respectively) as well as good test-retest reliability (intra-class correlation coefficient = 0.74, 0.82 and 0.77, respectively). Correlation analyses confirmed satisfactory convergent validity and the ability of the measures to differentiate between participants with different health or from socioeconomic status groups. The Bland-Altman plot revealed poor agreement between the three measures. CONCLUSIONS: This study confirmed that EQ-5D-5L, ReQoL-UI and ICECAP-A were psychometrically robust to measure HRQoL in the general HK population. The EQ-5D-5L was more suitable for assessing physical HRQoL, whereas the ICECAP-A and ReQoL-UI were more appropriate for measuring interventions aimed at improving people's well-being and mental health.

Psychometric Evaluation of the Chinese Recovering Quality of Life (ReQoL) Outcome Measure and Assessment of Health-Related Quality of Life During the COVID-19 Pandemic.

OBJECTIVE: The primary objective was to translate the Recovering Quality of Life (ReQoL) measures from English to traditional Chinese and assess their psychometric properties in Hong Kong (HK) Chinese population. The secondary objective was to investigate the mental health-related quality of life (HRQoL) of this sample during the coronavirus disease 2019 (COVID-19) pandemic. METHOD: Recovering Quality of Life was translated to Traditional Chinese adhering to standard guideline recommended by the official distributors. Five hundred members of the general population were successfully recruited to participate in a telephone-based survey. The following psychometric properties of the ReQoL were evaluated: construct, convergent, and known-group validity and internal consistency and test-retest reliability. The item measurement invariance was assessed on the basis of differential item functioning (DIF). Multiple regression analysis was used to assess the relationship between respondents' characteristics and mental HRQoL. RESULTS: Results of confirmatory factor analysis (CFA) supported a two-factor structure of the ReQoL. The ReQoL showed significant correlations with the other mental health, quality of life, and well-being measures, which indicated a satisfactory convergent validity. Known-group validity confirmed that ReQoL is able to differentiate between people with different mental health status. The (Cronbach's alpha = 0.91 and 0.76 for positive [PF] and negative [NF] factor), and McDonald's omega of 0.89 (PF = 0.94, NF = 0.82) indicated the ReQoL has good reliability as well as test-retest reliability with an intraclass correlation coefficient of 0.75. Four items showed negligible DIF with respect to age. Respondents who were highly educated and without psychological problems reported a high ReQoL score. CONCLUSION: Traditional Chinese ReQoL was shown to be a valid and reliable instrument to assess the recovery-focused quality of life in HK general population. Future studies are needed to appraise its psychometric properties in local people experiencing mental disorders.