Mapping CHU9D Utility Scores from the PEDSQLTM for Children with Chronic Conditions in an Ethnically Diverse and Deprived Metropolitan Population
Author(s)
Soley-Bori M1, Kelly C2, Lingam R3, Forman J4, Cecil L4, Newham J5, Wolfe I4, Fox-Rushby J6
1King's College London, LONDON, LON, UK, 2Queen's University Belfast, Belfast, UK, 3UNSW, South Wales, NSW, Australia, 4King's College London, London, HI, UK, 5Northumbria University, Newcastle upon Tyne, UK, 6King's College London, London, UK
OBJECTIVES: The Child Health Utility 9D (CHU9D) is a paediatric preference-based questionnaire to measure health related quality of life (HRQoL) commonly used in cost-utility analyses. When not available, mapping algorithms between the Paediatric Quality of Life InventoryTM (PedsQL) into the CHU9D exist. Current mappings are based on populations with limited age ranges and medical conditions and, thus, require further external validation. This study externally validates the most recent PedsQL to CHU9D mappings and develops new mappings based on a sample with a wide age range (0 to 16 years of age) and chronic conditions (asthma, eczema, or constipation).
METHODS: Data from the Children and Young People’s Health Partnership (CYPHP) Evelina London Model of Care, a new integrated paediatric healthcare delivery model, was used (N=1,775 participants). PedsQL responses were imputed into existing mappings and predicted CHU9D values obtained and compared to observed values based on goodness of fit measures (R-squared, mean absolute errors-MAE, mean squared errors, and the percentage of observations with an absolute error smaller than 0.05). To develop new mapping algorithms, the study sample was randomly divided into estimation (80%) and validation (20%) groups.
RESULTS: Existing mappings show an acceptable performance in the CYPHP sample. All MAEs (lower values indicate better fit) are between 0.056 (5-7 years, dimension score) and 0.11 (multimorbidity, total score) and, thus, within the lower bound of previously published estimates, which ranged from 0.074 to 0.230. The new CYPHP mappings perform better for the study sample compared to previous versions.
CONCLUSIONS: The new CYPHP mapping algorithms can predict CHU9D scores from PedsQL scores with good accuracy. These algorithms outperformed those also externally validated in this study. The CYPHP mappings are particularly relevant for samples with children and young people with chronic conditions living in deprived and urban settings. Further validation in an external sample is needed.
Conference/Value in Health Info
Value in Health, Volume 25, Issue 6, S1 (June 2022)
Acceptance Code
P52
Topic
Methodological & Statistical Research, Patient-Centered Research
Topic Subcategory
Health State Utilities, Instrument Development, Validation, & Translation, Patient-reported Outcomes & Quality of Life Outcomes, PRO & Related Methods
Disease
no-additional-disease-conditions-specialized-treatment-areas