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.