Predicting Quality Adjusted Life Years in young people attending primary mental health services
Matthew Phillip Hamilton,
Caroline X Gao,
Kate Maree Filia,
Jana Marcelle Menssink,
Ian B Hickie,
Debra Janet Rickwood,
Patrick Dennistoun McGorry,
Sue Maree Cotton
Posted 08 Jul 2021
medRxiv DOI: 10.1101/2021.07.07.21260129
Posted 08 Jul 2021
BackgroundQuality Adjusted Life Years (QALYs) are often used in economic evaluations, yet utility weights for deriving them are rarely directly measured in mental health services. ObjectivesWe 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. MethodsWe 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. ResultsA 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. ConclusionsAdolescent 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. ToolkitsThe 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.
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