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

Abstract

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. Sixtyfour 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 20.195 (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.

Instruments to assess quality of life in people with mental health problems: a systematic review and dimension analysis of generic, domain- and disease-specific instruments

“…One important quality of life instrument, specifically designed for use in the mental health field, that has become available since the completion of our review is the Recovering Quality of Life (ReQoL) measure [ 60 ]. The ReQoL measure is a preference-based [ 61 ] patient reported outcome measure that was explicitly designed to cover all seven dimensions of the evaluation framework used in the current study. The development of the ReQoL measure highlights the need and search for outcome instruments that adequately measure and value the benefits of mental healthcare interventions.…”

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

“…This finding was not unexpected because a great number of studies have confirmed the good performance of the EQ-5D-5L in HK Chinese population [ 34 – 37 ]. However, no empirical evidence about the other two measures in HK population was found, especially that this is the first paper using the ReQoL-UI in the HK population [ 15 ]. Additionally, the ICECAP-A and EQ-5D-5L exhibited acceptable to good test–retest reliability but the ICC for the ReQoL-UI was poor.…”

“…In the absence of HK specific preference weights, we used the UK preference weights to calculate the utility score in this study. The weights were estimated from a sample of 305 UK general population using the time trade-off method [ 15 ]. The ReQoL-UI utility score ranges between −0.195 and 1, which reflects people’s worst and best recovered HRQoL, respectively.…”

“…The Recovering Quality of Life-Utility Index (ReQoL-UI) is a new GPBM that aims to capture changes in mental HRQoL [ 14 ]. It was developed on the basis of the theoretical framework established with considerable input from mental health service users, which is believed to provide different perspectives to the comparability across evaluations undertaken in physical and mental health [ 15 ]. The developers indicated that the ReQoL-UI has the advantage to detect psychometrically changes in HRQoL over time and differences across treatments.…”

Mapping psychological distress, depression and anxiety measures to adolescent AQoL-6D utility using data from a sample of young people presenting to primary mental health services

“…These results may be useful for service system planners in helping to prioritise which measures should be included in routine data collection. Although direct measurement of health utility with measures such as the ReQoL [29] may be feasible in some mental health services, relying on clinical measures that can also map to health utility may be an attractive alternative.…”