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Estimating Age- and Sex-Specific Utility Values from the CHU9D Associated with Child and Adolescent BMI z-Score

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Abstract

Objective

Our objective was to identify age- and sex-specific utilities for children and adolescents by body mass index (BMI) z-score.

Methods

We used data from 6822 participants and 12,094 observations from two cohorts and two waves of interviews from the Longitudinal Study of Australian Children. We fit linear models using generalised estimating equations to investigate associations between Child Health Utility 9D and BMI z-score in girls and boys aged 10–17 years. We initially fit models for each sex, fully adjusted for known predictors of health-related quality of life, including socioeconomic position, long-term medical condition and maternal smoking status and also included an interaction between age and BMI z-score to examine age-specific effects. Finally, we derived a minimal model for each sex by eliminating interaction terms with P > 0.01 and predictors with P > 0.05.

Results

Our adjusted results show different utility patterns in girls and boys. In girls, utility decrements for each unit increase in BMI z-score changed with age (P < 0.01 for interaction between age and BMI z-score). At age 10 years, the mean utility decrement for each unit increase in BMI z-score was 0.002 (95% confidence interval [CI] 0.011 decrement to 0.006 increment), but, by age 17 years, this utility decrement was 0.023 (95% CI 0.013 to 0.032). In boys, small non-significant decrements were found in utility for each unit increase in BMI z-score, with no observable change with age.

Conclusion

Our analyses demonstrated that age and sex should be considered when attributing utility values and decrements to BMI z-scores.

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Data Availability

The data that support the findings of this study are available from the Department of Social Services (DSS), Australian Government, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. However, data are available upon application to the DSS. The code used to analyse the data is available on request to the corresponding author.

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Acknowledgements

The authors thank the DSS, Australian Government, for providing access to data collected from the LSAC. We also thank Emma Frew and the International Health Economics Association’s Economics of Obesity Special Interest Group for their comments and feedback on an earlier draft of this paper.

Author information

Authors and Affiliations

  1. Faculty of Medicine and Health, School of Public Health, Edward Ford Building A27, University of Sydney, Sydney, NSW, 2006, Australia

    Anagha Killedar, Thomas Lung, Armando Teixeira-Pinto & Alison Hayes

  2. The George Institute for Global Health, University of New South Wales, Sydney, NSW, 2042, Australia

    Thomas Lung

  3. Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK

    Stavros Petrou

Authors
  1. Anagha Killedar
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  2. Thomas Lung
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  4. Armando Teixeira-Pinto
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Contributions

AK, AH, TL and AT-P contributed to the study design. AK analysed the data and wrote the initial draft of the manuscript. AK, AH, TL and SP interpreted the analyses, and all authors commented on and made revisions to manuscript drafts. AK will act as the overall guarantor.

Corresponding author

Correspondence to Anagha Killedar.

Ethics declarations

Conflict of interest

Anagha Killedar, Thomas Lung, Stavros Petrou, Armando Teixeira-Pinto and Alison Hayes have no conflicts of interest that are directly relevant to the content of this article.

Funding

Anagha Killedar is supported by the Kassulke Scholarship and the National Health and Medical Research Council (NHMRC) scholarship (APP1169039) for PhD study. Thomas Lung is supported by an NHMRC Early Career Fellowship (APP1141392) and a National Heart Foundation Postdoctoral Fellowship (award ID 101956).

Ethical approval

Ethics approval for our study was obtained from the University of Sydney Human Ethics Committee (project number 2018/726).

Informed consent

This analysis used secondary data from the LSAC.

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Killedar, A., Lung, T., Petrou, S. et al. Estimating Age- and Sex-Specific Utility Values from the CHU9D Associated with Child and Adolescent BMI z-Score. PharmacoEconomics 38, 375–384 (2020). https://doi.org/10.1007/s40273-019-00866-6

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  • DOI: https://doi.org/10.1007/s40273-019-00866-6

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