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Quality of Life Research
Published in: Quality of Life Research 5/2019

Open Access 01-05-2019 | Brief Communication

A note on the relationship between age and health-related quality of life assessment

Authors: Patricia Cubi-Molla, Koonal Shah, Jamie Garside, Mike Herdman, Nancy Devlin

Published in: Quality of Life Research | Issue 5/2019

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Abstract

Purpose

To extend existing analyses of whether and how the age of respondents is related to their time trade-off (TTO) valuations of hypothetical EQ-5D-3L health states, and to contribute to the existing debate about the rationale and implications for using age-specific utilities in health technology assessment (HTA).

Methods

We use data from the MVH UK valuation study. For each profile, the mean TTO value—adjusted by sex, education, self-reported health and personal experience of serious illness—is pairwise compared across the different age groups. A Bonferroni correction is applied to the multiple testing of significant differences between means. Smile plots illustrate the results. A debate regarding whether there is a case for using age-specific utilities in HTAs complements the analysis.

Results

Results show that the oldest respondents value health profiles lower than younger age groups, particularly for profiles describing problems in the mobility dimension.

Conclusion

The findings raise the possibility of using age-specific value sets in HTAs, since a technology may not be cost-effective on average but cost-effective for a sub-group whose preferences are more closely aligned to the benefits offered by the technology.
Appendix
Available only for authorised users
Footnotes
1
Measurement and valuation of health.
 
2
Although a five-level version of the EQ-5D—the EQ-5D-5L—is now available, at the time of writing NICE continues to recommend use of the EQ-5D-3L/MVH value set.
 
3
Corresponds to the final sample size used for the MVH report (with an initial sample of 3395 respondents). We have used the same exclusion criteria as the MVH Group [10].
 
4
Further details about the sample composition (age distribution of respondents, sample size and average TTO value by age group and health profile) are available in supplementary appendix.
 
5
Bonferroni is a rather conservative procedure, potentially leading to a higher rate of “false negatives” (not detecting actual differences between groups) than “false positives” (detecting non-existing differences between age groups) [11].
 
6
Best fitting generalised linear models (Gaussian, Gamma and Poisson) according to a Modified Park Test [12] were used for this purpose.
 
7
The reference line is named ‘parapet line’ because, using a quote from the authors, “null hypotheses that raise their heads above it are shot down” [13].
 
8
The x-axis in Smile plots assigned to Gamma and Poisson regression models show a function of these differences, so that the figures should not be interpreted as linear differences in TTO values.
 
9
The Bonferroni-corrected overall critical p-values are p = 0.05/(15 * 42) = 0.0000794 and p = 0.0000159 for 95% and 99% level of statistical significance, respectively. Test results not shown here.
 
10
Corrected p-value cut-off: 0.000238 and 0.0000476 for 95% and 99% level of confidence, respectively.
 
11
Note that these data points could also show true significant differences amongst age groups, but we cannot separate them from the expected 5% or 1% of associations found “by chance” (“false positives”).
 
12
At the 99% and 95% level of significance.
 
13
Details of TTO values by age group and health profile can be found in Table S2 in supplementary appendix.
 
Literature
1.
NICE. (2013). Guide to the methods of technology appraisal. London: National Institute for Health and Care Excellence.
2.
Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 1095–1108.CrossRefPubMed
3.
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4.
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5.
Robinson, A., Dolan, P., & Williams, A. (1997). Valuing health status using VAS and TTO: what lies behind the numbers? Social Science & Medicine, 45(8), 1289–1297.CrossRef
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8.
Essink-Bot, M. L., Stuifbergen, M. C., Meerding, W. J., Looman, C. W., & Bonsel, G. J. (2007). Individual differences in the use of the response scale determine valuations of hypothetical health states: an empirical study. BMC Health Services Research, 7(1), 62.CrossRefPubMedCentralPubMed
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Coretti, S., Ruggeri, M., & McNamee, P. (2014). The minimum clinically important difference for EQ-5D index: a critical review. Expert Review of Pharmacoeconomics & Outcomes Research, 14(2), 221–233.CrossRef
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Devlin, N., Shah, K. K., & Buckingham, K. (2017). What is the normative basis for selecting the measure of ‘average’ preferences for use in social choices? Research Paper. London: Office of Health Economics.
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Metadata
Title
A note on the relationship between age and health-related quality of life assessment
Authors
Patricia Cubi-Molla
Koonal Shah
Jamie Garside
Mike Herdman
Nancy Devlin
Publication date
01-05-2019
Publisher
Springer International Publishing
Published in
Quality of Life Research / Issue 5/2019
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-018-2071-5

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