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Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population

PURPOSE: The Child Health Utility-9 Dimensions (CHU9D) is a patient-reported outcome measure to generate Quality-Adjusted Life Years (QALYs), recommended for economic evaluations of interventions to inform funding decisions. When the CHU9D is not available, mapping algorithms offer an opportunity to...

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Autores principales: Kelly, Clare B., Soley-Bori, Marina, Lingam, Raghu, Forman, Julia, Cecil, Lizzie, Newham, James, Wolfe, Ingrid, Fox-Rushby, Julia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241699/
https://www.ncbi.nlm.nih.gov/pubmed/36814010
http://dx.doi.org/10.1007/s11136-023-03359-4
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author Kelly, Clare B.
Soley-Bori, Marina
Lingam, Raghu
Forman, Julia
Cecil, Lizzie
Newham, James
Wolfe, Ingrid
Fox-Rushby, Julia
author_facet Kelly, Clare B.
Soley-Bori, Marina
Lingam, Raghu
Forman, Julia
Cecil, Lizzie
Newham, James
Wolfe, Ingrid
Fox-Rushby, Julia
author_sort Kelly, Clare B.
collection PubMed
description PURPOSE: The Child Health Utility-9 Dimensions (CHU9D) is a patient-reported outcome measure to generate Quality-Adjusted Life Years (QALYs), recommended for economic evaluations of interventions to inform funding decisions. When the CHU9D is not available, mapping algorithms offer an opportunity to convert other paediatric instruments, such as the Paediatric Quality of Life Inventory™ (PedsQL), onto the CHU9D scores. This study aims to validate current PedsQL to CHU9D mappings in a sample of children and young people of a wide age range (0 to 16 years of age) and with chronic conditions. New algorithms with improved predictive accuracy are also developed. METHODS: Data from the Children and Young People’s Health Partnership (CYPHP) were used (N = 1735). Four regression models were estimated: ordinal least squared, generalized linear model, beta-binomial and censored least absolute deviations. Standard goodness of fit measures were used for validation and to assess new algorithms. RESULTS: While previous algorithms perform well, performance can be enhanced. OLS was the best estimation method for the final equations at the total, dimension and item PedsQL scores levels. The CYPHP mapping algorithms include age as an important predictor and more non-linear terms compared with previous work. CONCLUSION: The new 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 required. Trial registration number NCT03461848; pre-results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11136-023-03359-4.
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spelling pubmed-102416992023-06-07 Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population Kelly, Clare B. Soley-Bori, Marina Lingam, Raghu Forman, Julia Cecil, Lizzie Newham, James Wolfe, Ingrid Fox-Rushby, Julia Qual Life Res Article PURPOSE: The Child Health Utility-9 Dimensions (CHU9D) is a patient-reported outcome measure to generate Quality-Adjusted Life Years (QALYs), recommended for economic evaluations of interventions to inform funding decisions. When the CHU9D is not available, mapping algorithms offer an opportunity to convert other paediatric instruments, such as the Paediatric Quality of Life Inventory™ (PedsQL), onto the CHU9D scores. This study aims to validate current PedsQL to CHU9D mappings in a sample of children and young people of a wide age range (0 to 16 years of age) and with chronic conditions. New algorithms with improved predictive accuracy are also developed. METHODS: Data from the Children and Young People’s Health Partnership (CYPHP) were used (N = 1735). Four regression models were estimated: ordinal least squared, generalized linear model, beta-binomial and censored least absolute deviations. Standard goodness of fit measures were used for validation and to assess new algorithms. RESULTS: While previous algorithms perform well, performance can be enhanced. OLS was the best estimation method for the final equations at the total, dimension and item PedsQL scores levels. The CYPHP mapping algorithms include age as an important predictor and more non-linear terms compared with previous work. CONCLUSION: The new 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 required. Trial registration number NCT03461848; pre-results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11136-023-03359-4. Springer International Publishing 2023-02-23 2023 /pmc/articles/PMC10241699/ /pubmed/36814010 http://dx.doi.org/10.1007/s11136-023-03359-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kelly, Clare B.
Soley-Bori, Marina
Lingam, Raghu
Forman, Julia
Cecil, Lizzie
Newham, James
Wolfe, Ingrid
Fox-Rushby, Julia
Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title_full Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title_fullStr Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title_full_unstemmed Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title_short Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
title_sort mapping pedsql™ scores to chu9d utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241699/
https://www.ncbi.nlm.nih.gov/pubmed/36814010
http://dx.doi.org/10.1007/s11136-023-03359-4
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