EQ-5D-5L value set for Norway: a hybrid model using cTTO and DCE data

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Abstract

Purpose

To develop the Norwegian value set for the EQ-5D-5L based on interviews with a representative sample of the Norwegian adult population.

Methods

Random and quota sampling were used to recruit the sample of adults (age> 18 years) representative of the Norwegian general population. Data collection followed EQ-VT 2.1 undertaken before and after the COVID-19 pandemic from November 2019 to December 2022, using PC-assisted and video conferencing interviews, respectively. Each respondent valued 10 health states using composite time trade-off (cTTO) and 7 health states using a discrete choice experiment (DCE). Different statistical models were assessed for logical consistency and predictive accuracy using cTTO and DCE data alone or in combination as hybrid models.

Results

Of the 1,321 respondents, 1,237 met inclusion criteria. All statistical models demonstrated logical consistency. The weighted hybrid model combining both cTTOand DCE data was preferred and had the highest predictive accuracy. Predicted values ranged from -0.453 to 1, and the dimension of anxiety/depression was the most highly valued by respondents, followed by pain/discomfort, self-care, mobility, and usual activities. These findings are not dissimilar to those for most Western European countries, and regression coefficients are closest to those for other Scandinavian countries.

Conclusion

This study provides the Norwegian value set for the EQ-5D-5L based on health state values obtained from members of the adult general population in Norway. This is an important contribution to economic evaluation and the broader application ofthe EQ-5D-5L in Norway including clinical and health services research, and quality measurement.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11136-024-03837-3.

Related collections

Most cited references 34

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Modeling valuations for EuroQol health states.

Paul Dolan (1997)

It has become increasingly common for preference-based measures of health-related quality of life to be used in the evaluation of different health-care interventions. For one such measure, The EuroQol, designed to be used for these purposes, it was necessary to derive a single index value for each of the 243 health states it generates. The problem was that it was virtually impossible to generate direct valuations for all of these states, and thus it was necessary to find a procedure that allows the valuations of all EuroQol states to be interpolated from direct valuations on a subset of these. In a recent study, direct valuations were elicited for 42 EuroQol health states (using the time trade-off method) from a representative sample of the UK population. This article reports on the methodology that was adopted to build up a "tariff" of EuroQol values from this data. A parsimonious model that fits the data well was defined as one in which valuations were explained in terms of the level of severity associated with each dimension, an intercept associated with any move away from full health, and a term that picked up whether any dimension in the state was at its most severe level. The model presented in this article appears to predict the values of the states for which there are direct observations and, thus, can be used to interpolate values for the states for which no direct observations exist.

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Dutch Tariff for the Five-Level Version of EQ-5D.

Matthijs M Versteegh, Karin M Vermeulen, Silvia M A A Evers … (2016)

In 2009, a new version of the EuroQol five-dimensional questionnaire (EQ-5D) was introduced with five rather than three answer levels per dimension. This instrument is known as the EQ-5D-5L. To make the EQ-5D-5L suitable for use in economic evaluations, societal values need to be attached to all 3125 health states.

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Valuing health‐related quality of life: An EQ ‐5 D ‐5 L value set for E ngland

Nancy J Devlin, Koonal K Shah, Yan Feng … (2017)

Abstract A new version of the EQ‐5D, the EQ‐5D‐5L, is available. The aim of this study is to produce a value set to support use of EQ‐5D‐5L data in decision‐making. The study design followed an international research protocol. Randomly selected members of the English general public completed 10 time trade‐off and 7 discrete choice experiment tasks in face‐to‐face interviews. A 20‐parameter hybrid model was used to combine time trade‐off and discrete choice experiment data to generate values for the 3,125 EQ‐5D‐5L health states. Valuation data are available for 996 respondents. Face validity of the data has been demonstrated, with more severe health states generally given lower values. Problems with pain/discomfort and anxiety/depression received the greatest weight. Compared to the existing EQ‐5D‐3L value set, there are considerably fewer “worse than dead” states (5.1%, compared with over one third), and the minimum value is higher. Values range from −0.285 (extreme problems on all dimensions) to 0.950 (for health states 11211 and 21111). Results have important implications for users of the EQ‐5D‐5L both in England and internationally. Quality‐adjusted life year gains from interventions seeking to improve very poor health may be smaller using this value set and may previously have been overestimated.

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Author and article information

Contributors

Andrew M. Garratt:

ORCID: http://orcid.org/0000-0002-1000-4780

andrew.garratt@fhi.no

Journal

Journal ID (nlm-ta): Qual Life Res

Journal ID (iso-abbrev): Qual Life Res

Title: Quality of Life Research

Publisher: Springer New York (New York )

ISSN (Print): 0962-9343

ISSN (Electronic): 1573-2649

Publication date (Electronic): 20 November 2024

Publication date PMC-release: 20 November 2024

Publication date (Print): 2025

Volume: 34

Issue: 2

Pages: 417-427

Affiliations

[1 ]Division for Health Services, Norwegian Institute of Public Health, ( https://ror.org/046nvst19) Oslo, Norway

[2 ]Health Services Research Unit, Akershus University Hospital, ( https://ror.org/0331wat71) Nordbyhagen, Norway

[3 ]Institute of Clinical Medicine, University of Oslo, ( https://ror.org/01xtthb56) Oslo, Norway

[4 ]Department of Pulmonary Medicine, Medical Division, Akershus University Hospital, ( https://ror.org/0331wat71) Nordbyhagen, Norway

[5 ]Bristol Myers Squibb, ( https://ror.org/00gtmwv55) Princeton, NJ USA

Author information

Andrew M. Garratt http://orcid.org/0000-0002-1000-4780

Knut Stavem http://orcid.org/0000-0003-4512-8000

James W. Shaw http://orcid.org/0000-0002-2893-3211

Kim Rand http://orcid.org/0000-0001-7692-4099

Article

Publisher ID: 3837

DOI: 10.1007/s11136-024-03837-3

PMC ID: 11865167

PubMed ID: 39565555

SO-VID: 12cab3ad-f2dd-4d80-b1f1-73c37ce944a4

License:

Open Access This 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/.

History

Date accepted : 7 November 2024

Funding

Funded by: Akershus University Hospital (AHUS)

Open Access :

Open access funding provided by Akershus University Hospital (AHUS)

Custom metadata

ScienceOpen disciplines: Public health

Keywords: eq-5d-5l,general population,outcomes,quality of life,utilities,value set

Data availability: