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Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample

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Abstract

Background

Mapping algorithms have been indicated as a second-best solution for estimating health state utilities for the calculation of quality-adjusted life-years within cost-utility analysis when no generic preference-based measure is incorporated into the study. However, the predictive performance of these algorithms may be variable and hence it is important to assess their external validity before application in different settings.

Objective

The aim of this study was to assess the external validity and generalisability of existing mapping algorithms for predicting preference-based Child Health Utility 9D (CHU9D) utilities from non-preference-based Pediatric Quality of Life Inventory (PedsQL) scores among children and adolescents living with or without disabilities or health conditions.

Methods

Five existing mapping algorithms, three developed using data from an Australian community population and two using data from a UK population with one or more self-reported health conditions, were externally validated on data from the Longitudinal Study of Australian Children (n = 6623). The predictive accuracy of each mapping algorithm was assessed using the mean absolute error (MAE) and the mean squared error (MSE).

Results

Values for the MAE (0.0741–0.2302) for all validations were within the range of published estimates. In general, across all ages, the algorithms amongst children and adolescents with disabilities/health conditions (Australia MAE: 0.2085–0.2302; UK MAE: 0.0854–0.1162) performed worse relative to those amongst children and adolescents without disabilities/health conditions (Australia MAE: 0.1424–0.1645; UK MAE: 0.0741–0.0931).

Conclusions

The published mapping algorithms have acceptable predictive accuracy as measured by MAE and MSE. The findings of this study indicate that the choice of the most appropriate mapping algorithm to apply may vary according to the population under consideration.

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Acknowledgements

This study used confidentialised data from the Longitudinal Study of Australian Children (LSAC). The LSAC is conducted in partnership between the Australian Department of Families, Housing, Community Services and Indigenous Affairs (FAHCSIA), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). We would like to thank the LSAC families for their participation in the study, and the LSAC Research Consortium.

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Authors and Affiliations

  1. Institute for Choice, University of South Australia Business School, Level 3 Way Lee Building, North Terrace, Adelaide, SA, 5001, Australia

    Christine Mpundu-Kaambwa & Julie Ratcliffe

  2. Centre for Health Economics, Monash Business School, Monash University, Melbourne, Australia

    Gang Chen

  3. Department of Health Services Research and Policy, The Australian National University, Canberra, Australia

    Elisabeth Huynh

  4. Faculty of Health Sciences, School of Medicine, Flinders University, Adelaide, Australia

    Remo Russo

  5. Department of Paediatric Rehabilitation, Women’s and Children’s Hospital, Adelaide, Australia

    Remo Russo

  6. Health and Social Care Economics Group, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia

    Julie Ratcliffe

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  1. Christine Mpundu-Kaambwa
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Corresponding author

Correspondence to Christine Mpundu-Kaambwa.

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Funding

No funding has been received for the conduct of this study and/or preparation of this manuscript.

Author contributions

Christine Mpundu-Kaambwa, Julie Ratcliffe and Gang Chen contributed to the study inception. Christine Mpundu-Kaambwa analysed the data, interpreted the results and wrote the first draft of the manuscript. Julie Ratcliffe, Gang Chen, Elisabeth Huynh and Remo Russo contributed to the interpretation of results and revision of the manuscript. All authors have read and approved the final manuscript. Christine Mpundu-Kaambwa is the guarantor of the manuscript.

Conflict of interest

Christine Mpundu-Kaambwa, Gang Chen, Remo Russo and Julie Ratcliffe were authors who developed the mapping algorithms A–C that were validated in this study.

Data availability

The data that support the findings of this study are available from The National Centre for Longitudinal Data, Australian Government Department of Social Services but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Human and animal rights

This manuscript only contains secondary data.

Financial support information

Christine Mpundu-Kaambwa is supported by the Australian Government Research Training Program Scholarship.

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Mpundu-Kaambwa, C., Chen, G., Huynh, E. et al. Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample. PharmacoEconomics 37, 1139–1153 (2019). https://doi.org/10.1007/s40273-019-00808-2

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