Predicting Quality Adjusted Life Years in young people attending primary mental health services

Hamilton, M. P.; Gao, C. X.; Filia, K. M.; Menssink, J. M.; Sharmin, S.; Telford, N.; Herrman, H.; Hickie, I. B.; Mihalopoulos, C.; Rickwood, D. J.; McGorry, P. D.; Cotton, S. M.

Background: Quality Adjusted Life Years (QALYs) are often used in economic evaluations, yet utility weights for deriving them are rarely directly measured in mental health services. Objectives: We aimed to: (i) identify the best Transfer To Utility (TTU) algorithms and predictors for an adolescent specific Multi-Attribute Utility Instrument - the Assessment of Quality of Life - six dimensions (AQoL-6D) and (ii) assess ability of TTU algorithms to predict longitudinal change. Methods: We recruited 1107 young people attending Australian primary mental health services, collecting data at two time points, three months apart. Five linear and three generalised linear models were explored to identify the best TTU algorithm. Forest models were used to explore predictive ability of six candidate measures of psychological distress, depression and anxiety and linear / generalised linear mixed effect models were used to construct longitudinal predictive models for AQoL-6D change. Results: A depression measure (Patient Health Questionnaire-9) was the strongest independent predictor of health utility. Linear regression models with complementary log-log transformation of utility score were the best preforming models. Between-person associations were slightly larger than within-person associations for most of the predictors. Conclusions: Adolescent AQoL-6D utility can be derived from a range of psychological distress, depression and anxiety measures. TTU algorithms estimated from cross-sectional data may slightly bias QALY predictions. Toolkits: The TTU models produced by this study can be searched, retrieved and applied to new data to generate QALY predictions with the Youth Outcomes to Health Utility (youthu) R package - https://ready4-dev.github.io/youthu.