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

Background Health utility data are rarely routinely collected in mental helath services. Mapping models that predict health utility from other outcome measures are typically derived from cross-sectional data but often used to predict longitudinal change. Objective We aimed to develop models to map six psychological measures to adolescent Assessment of Quality of Life – Six Dimensions (AQOL-6D) health utility for youth mental health service clients and assess the ability of mapping models 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 mapping model. Ten-fold cross-validation using R 2 , root mean square error (RMSE) and mean absolute error (MAE) were used to compare models and assess predictive ability of six candidate measures of psychological distress, depression and anxiety. 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 performing 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. Mapping models estimated from cross-sectional data can approximate longitudinal change but may slightly bias health utility predictions. Data Replication code and model catalogues are available at: . ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the National Health and Medical Research Council (NHMRC, APP1076940), Orygen, headspace and an Australian Government Research Training Program (RTP) Scholarship ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was reviewed and granted approval by the University of Melbourne Human Research Ethics Committee, and the local Human Ethics and Advisory Group (1645367.1). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Detailed results in the form of catalogues of the utility mapping models produced by this study and other supporting information are available in the results repository . Tools for finding and using the utility mapping models with new prediction datasets are available as part of the youthu R package (). The TTU () has tools for both replicating the study and generalising our algorithms to develop utility mapping algorithms with other utility measures and predictors. A program to replicate all steps in the study from data ingest to manuscript creation is available at .