Mapping the Health of Nation Outcomes Scale (HoNOS) onto the Recovering Quality of Life Utility Index (ReQoL-UI)

Aim: The aim of this project is to develop and assess a mapping function to predict ReQoL-UI (a patient-reported mental health-specific preference-based measure) scores from HoNOS scores (clinician-reported measure, Health of Nation Outcomes Score). Methods: Participants were recruited from 14 secondary mental health services in England, UK, and their clinician completed HoNoS. Mapping models were estimated using Ordinary Least Squares (OLS) on individual level and mean level data and different model specifications were explored. Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), percentage of observations with absolute errors greater than 0.1, and plots of the observed and predicted ReQoL-UI utilities and errors. Results: Matched ReQoL-UI and HoNOS scores were collected for 649 participants. The sample comprised 56% inpatients, with overall mean ReQoL-UI utility of 0.683 and range from 1 to -0.195. Correlations between ReQoL-UI (items and utility) and HoNOS scores were moderate (0.2<r<0.4) or small (<0.2). The best model was OLS estimated using mean level data, with lowest MAE (0.046) and RMSE (0.056). Discussion: There is little conceptual overlap between ReQoL-UI and HoNOS. They measure different concepts and, arguably, service users and clinicians, who complete the measures respectively, have different perspectives. Under these circumstances, caution is recommended when applying these estimates

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

Estimating a preference-based index for mental health from the Recovering Quality of Life (ReQoL) measure : valuation of ReQoL-UI

Objectives There are increasing concerns about the appropriateness of generic preference-based measures to capture health benefits in the area of mental health. This study estimates preference weights for a new measure, Recovering Quality of Life (ReQoL-10), to better capture the benefits of mental health care. Methods Psychometric analyses of a larger sample of mental health service users (n = 4266) using confirmatory factor analyses and item response theory (IRT) 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, gender and region was conducted using face-to-face interviewer administered time-trade-off (TTO) 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-UI classification system comprises six mental health items and one physical health (PH) 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 modelled 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 IRT methods for generating the classification system and selecting health states for valuation. Conventional TTO was used to elicit utility values that are modelled to enable the generation of QALYs for use in cost-utility analysis of mental health interventions

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

Developing an Australian Value Set for the Recovering Quality of Life-Utility Index Instrument Using Discrete Choice Experiment With Duration.

OBJECTIVES: The Recovering Quality of Life-Utility Index (ReQoL-UI) instrument was designed to measure the quality-of-life outcomes for people older than 16 years with mental health problems. We aimed to elicit societal preferences for the ReQoL-UI health states to facilitate better decision making in Australia. METHODS: A discrete choice experiment with duration was embedded in a self-completed online survey and administered to a representative sample (n = 1019) of the Australian adult population aged 18 years and older stratified by age, sex, and geographic location. A partial subset design discrete choice experiment was used with 3 fixed attributes and 5 varying attributes, containing 240 choice tasks that were divided into 20 blocks so that each respondent was assigned a block of 12 choice tasks. The value set was modeled using the conditional logit model with utility decrements directly anchored on the 0 to 1 dead-full health scale. Preference heterogeneity was tested using a mixed logit model. RESULTS: The final value set reflects the monotonic nature of the ReQoL-UI descriptive systems where the best health state defined by the descriptive system has a value of 1 and the worst state has a value of -0.585. The most important dimension was physical health problems, whereas the least important attribute was self-perception. Sensitivity and preference heterogeneity analyses revealed the stability of the value set. CONCLUSIONS: The value set, which reflects the preferences of the Australian population, facilitates the calculation of an index for quality-adjusted life-years in mental health intervention cost-utility analyses

Statistical and Health Economic Analysis Plan for a Secure Care Hospital Evaluation of Manualized (interpersonal) Art-psychotherapy: the SCHEMA Randomized Controlled Trial

Background: The SCHEMA trial evaluates whether interpersonal art psychotherapy reduces the frequency/severity of aggressive incidents or patient distress associated with psychiatric symptoms, compared to usual care. Objective To describe the statistical and health economic analysis plan. Methods A multicentre, two-arm, parallel-group, single blind individually randomised controlled trial with 150 adults within NHS secure care who have borderline to mild/moderate intellectual disability. The primary outcome is the frequency/severity of aggressive behaviour, measured on the Modified Overt Aggression Scale (MOAS) 19 weeks post-randomisation, analysed using a linear mixed-effect model, adjusted for baseline MOAS and stratification by gender and psychosis diagnosis. Changes in aggressive behaviour will be evaluated using weekly MOAS scores between 19 and 38 weeks. Patient distress relating to psychiatric symptoms will be assessed using the Brief Symptom Inventory Positive Symptom Distress Index across baseline, 19, and 38 weeks. Health-related quality-of-life will be assessed using self- and proxy-reported EQ-5D three-level (EQ-5D-3L) and Recovering Quality of Life 10-item measures, the latter to estimate the ReQoL Utility Index, across baseline, 19, and 38 weeks. The self-reported EQ-5D-3L is collected using an adapted version for people with intellectual disabilities. Resource-use is collected based on secure care records, to estimate intervention and healthcare costs over 19 and 38 weeks. HRQoL and cost data will inform cost-effectiveness based on the incremental cost per quality-adjusted life year over 38 weeks. Discussion This paper details the planned analyses and discusses recruitment challenges, sample size implications, and effect size assumptions. The plan was developed prior to database lock and unblinding to minimise analytical bias

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

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

Measuring the overall performance of mental healthcare providers

To date there have been no attempts to construct composite measures of healthcare provider performance which reflect preferences for health and non-health benefits, as well as costs. Health and non-health benefits matter to patients, healthcare providers and the general public. We develop a novel provider performance measurement framework that combines health gain, non-health benefit, and cost and illustrate it with an application to 54 English mental health providers. We apply estimates from a discrete choice experiment eliciting the UK general population’s valuation of non-health benefits relative to health gains, to administrative and patient survey data for years 2013-2015 to calculate equivalent health benefit (eHB) for providers. We measure costs as forgone health and quantify the relative performance of providers in terms of equivalent net health benefit (eNHB): the value of the health and non-health benefits minus the forgone benefit equivalent of cost. We compare rankings of providers by eHB, eNHB, and by the rankings produced by the hospital sector regulator. We find that taking account of the non-health benefits in the eNHB measure makes a substantial difference to the evaluation of provider performance. Our study demonstrates that the provider performance evaluation space can be extended beyond measures of health gain and cost, and that this matters for comparison of providers