Evidence map›Paper›PMID 27928758›Full record

ArticlePharmacoEconomics2017

Mapping CHU9D Utility Scores from the PedsQL

Christine Mpundu-Kaambwa, Gang Chen, Remo Russo, Katherine Stevens, Karin Dam Petersen, Julie Ratcliffe

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in PharmacoEconomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Trial
  2. Mapping PedsQL™ scores to CHU9D utility weights for children with chronic conditions in a multi-ethnic and deprived metropolitan population.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Trial
  3. Trial
  4. Article
  5. Article
  6. Development of algorithms for estimating the Child Health Utility 9D from Caregiver Priorities and Child Health Index of Life with Disability.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  7. Article
  8. Article
  9. Mapping the 12-item World Health Organization disability assessment schedule 2.0 (WHODAS 2.0) onto the assessment of quality of life (AQoL)-4D utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  10. Article
  11. Mapping the Peds QLFrontiers in public health · 2023
    Article
  12. Application of validated mapping algorithms between generic PedsQL scores and utility values to individuals with sickle cell disease.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
    Article
  13. Mapping the Haem-A-QoL to the EQ-5D-5L in patients with hemophilia.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
    Article
  14. Deriving health utility indices from a food allergy quality-of-life questionnaire.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2021
    Article
  15. Mapping PedsQL™ Generic Core Scales to EQ-5D-3L utility scores in transfusion-dependent thalassemia patients.The European journal of health economics : HEPAC : health economics in prevention and care · 2021
    Article
  16. Article
  17. Development of algorithms to estimate EQ-5D and derive health utilities from WHOQOL-HIV Bref: a mapping study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Article
  18. Article
  19. Article
  20. Mapping PedsQLQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Christine Mpundu-KaambwaInstitute for Choice, University of South Australia, Business School, Adelaide, South Australia, Australia. mpucc001@mymail.unisa.edu.au.
Gang ChenCentre for Health Economics, Monash Business School, Monash University, Melbourne, Victoria, Australia.
Remo RussoFaculty of Health Sciences, School of Medicine, Flinders University, Adelaide, South Australia, Australia.
Katherine StevensHealth Economics and Decision Science, University of Sheffield, Sheffield, United Kingdom.
Karin Dam PetersenDepartment of Business and Management, Faculty of Social Sciences, Aalborg University, Aalborg East, Denmark.
Julie RatcliffeFlinders Health Economics Group, Flinders University, Adelaide, South Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based.

objectiveThis paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis.

methodsThe PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models.

resultsThe MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE.

conclusionsOur mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research.

Indexed as

Health StatusModels, StatisticalQuality-Adjusted Life YearsQuality of LifeAdolescentAlgorithmsAustraliaCost-Benefit AnalysisFemaleHumansLeast-Squares AnalysisLinear ModelsLogistic ModelsMaleSurveys and Questionnaires

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.