Advertisement

Investigating 5-Level EQ-5D (EQ-5D-5L) Values Based on Preferences of Patients With Heart Disease

Published:November 24, 2021DOI:https://doi.org/10.1016/j.jval.2021.09.010

      Highlights

      • Patients with heart disease have health preferences that are systematically different from those of the general public.
      • This study provides utility values for 5-level EQ-5D health states based on the preferences of patients with heart disease.
      • This value set is useful for clinical decision making and for economic evaluation aiming to use patient preferences to inform treatment selection or reimbursement for patients with heart disease.
      • The utility index derived from this value set could be also used as a measure of health-related quality of life for patients with heart disease.

      Abstract

      Objectives

      Several studies have shown that patients with heart disease value hypothetical health states differently from the general population. We aimed to investigate the health preferences of patients with heart disease and develop a value set for the 5-level EQ-5D (EQ-5D-5L) based on these patient preferences.

      Methods

      Patients with confirmed heart disease were recruited from 2 hospitals in Singapore. A total of 86 EQ-5D-5L health states (10 per patient) were valued using a composite time trade-off method according to the international valuation protocol for EQ-5D-5L; 20-parameter linear models and 8-parameter cross-attribute level effects models with and without an N45 term (indicating whether any health state dimension at level 4 or 5 existed) were estimated. Each model included patient-specific random intercepts. Model performance was evaluated for out-of-sample and in-sample predictive accuracy in terms of root mean square error. The discriminative ability of the utility values was assessed using heart disease-related functional classes.

      Results

      A total of 576 patients were included in the analysis. The preferred model, with the lowest out-of-sample root mean square error, was a 20-parameter linear model including N45. Predicted utility values ranged from −0.727 for the worst state to 1 for full health; the value for the second-best state was 0.981. Utility values demonstrated good discriminative ability in differentiating among patients of varied functional classes.

      Conclusions

      An EQ-5D-5L value set representing the preferences of patients with heart disease was developed. The value set could be used for patient-centric economic evaluation and health-related quality of life assessment for patients with heart disease.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Value in Health
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. World population ageing 2017. Union Nations, Department of Economic and Social Affairs Population Division.
        (Accessed January 2021)
        • Drummond M.
        • Torbica A.
        • Tarricone R.
        Should health technology assessment be more patient centric? If so, how?.
        Eur J Health Econ. 2020; 21: 1117-1120
      2. 2015 global survey on health technology assessment by national authorities. World health organization.
        • Helgesson G.
        • Ernstsson O.
        • Åström M.
        • Burström K.
        Whom should we ask? A systematic literature review of the arguments regarding the most accurate source of information for valuation of health states.
        Qual Life Res. 2020; 29: 1465-1482
        • Cubi-Molla P.
        • Shah K.
        • Burström K.
        Experience-based values: a framework for classifying different types of experience in health valuation research.
        Patient Patient Centered Outcomes Res. 2018; 11: 253-270
      3. General guidelines for economic evaluations from the Dental and Pharmaceutical Benefits Agency (TLVAR 2017:1). TLV.
        (Accessed January 2021)
      4. Drug Evaluation Methods and Process Guide. 2nd version ACE.
        (Accessed January 2021)
      5. Medical Technologies Methods and Process Guide. 1st version. ACE.
        (Accessed January 2021)
      6. World health statistics. World Health Organization.
        (Accessed January 2021)
        • Gandhi M.
        • San Tan R.
        • Ng R.
        • et al.
        Comparison of health state values derived from patients and individuals from the general population.
        Qual Life Res. 2017; 26: 3353-3363
        • Gandhi M.
        • Thumboo J.
        • Luo N.
        • Wee H.L.
        • Cheung Y.B.
        Do chronic disease patients value generic health states differently from individuals with no chronic disease? A case of a multicultural Asian population.
        Health Qual Life Outcomes. 2015; 13: 8
        • Pickard A.S.
        • Tawk R.
        • Shaw J.W.
        The effect of chronic conditions on stated preferences for health.
        Eur J Health Econ. 2013; 14: 697-702
        • Roth G.A.
        • Johnson C.
        • Abajobir A.
        • et al.
        Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990-2015.
        J Am Coll Cardiol. 2017; 70: 1-25
        • Janssen M.
        • Pickard A.S.
        • Golicki D.
        • et al.
        Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: a multi-country study.
        Qual Life Res. 2013; 22: 1717-1727
      7. Census of population 2010 statistical release 1. Demographic characteristics, education, language and religion. Singapore Department of Statistics.
        (Accessed January 2021; Published 2010)
        • Ramos-Goñi J.M.
        • Oppe M.
        • Slaap B.
        • Busschbach J.J.
        • Stolk E.
        Quality control process for EQ-5D-5L valuation studies.
        Value Health. 2017; 20: 466-473
        • Oppe M.
        • Devlin N.J.
        • van Hout B.
        • Krabbe P.F.
        • de Charro F.
        A program of methodological research to arrive at the new international EQ-5D-5L valuation protocol.
        Value Health. 2014; 17: 445-453
        • Oppe M.
        • Rand-Hendriksen K.
        • Shah K.
        • Ramos-Goñi J.M.
        • Luo N.
        EuroQol protocols for time trade-off valuation of health outcomes.
        Pharmacoeconomics. 2016; 34: 993-1004
      8. EQ-5D-5L user guide. EuroQol Research Foundation.
        (Accessed May 2021)
        • Luo N.
        • Wang Y.
        • How C.H.
        • Tay E.G.
        • Thumboo J.
        • Herdman M.
        Interpretation and use of the 5-level EQ-5D response labels varied with survey language among Asians in Singapore.
        J Clin Epidemiol. 2015; 68: 1195-1204
        • Luo N.
        • Wang Y.
        • How C.
        • et al.
        Cross-cultural measurement equivalence of the EQ-5D-5L items for English-speaking Asians in Singapore.
        Qual Life Res. 2015; 24: 1565-1574
        • Oldridge N.
        • Höfer S.
        • McGee H.
        • et al.
        The HeartQoL: Part I. Development of a new core health-related quality of life questionnaire for patients with ischemic heart disease.
        Eur J Prev Cardiol. 2014; 21: 90-97
        • Dolgin M.
        • Fox A.
        • Gorlin R.
        • Levin R.
        • New York Heart Association, Criteria Committee
        Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels.
        9th ed. Lippincott Williams and Wilkins, Boston, MA1994
        • Campeau L.
        Grading of angina pectoris.
        Circulation. 1976; 54: 522-523
        • Gandhi M.
        • Xu Y.
        • Luo N.
        • Cheung Y.B.
        Sample size determination for EQ-5D-5L value set studies.
        Qual Life Res. 2017; 26: 3365-3376
        • Rand-Hendriksen K.
        • Ramos-Goñi J.M.
        • Augestad L.A.
        • Luo N.
        Less is more: cross-validation testing of simplified nonlinear regression model specifications for EQ-5D-5L health state values.
        Value Health. 2017; 20: 945-952
        • Dolan P.
        Modeling valuations for EuroQol health states.
        Med Care. 1997; 35: 1095-1108
        • Luo N.
        • Liu G.
        • Li M.
        • Guan H.
        • Jin X.
        • Rand-Hendriksen K.
        Estimating an EQ-5D-5L value set for China.
        Value Health. 2017; 20: 662-669
        • Rand K.
        xreg: flexible, multi-data.frame maximum likelihood-based regression functions in R. GitHub.
        (Accessed January 2021)
        • Team R.C.
        R: A language and environment for statistical computing. R Foundation for Statistical Computing.
        (Accessed January 2021)
        • Luo N.
        • Wang P.
        • Thumboo J.
        • Lim Y.-W.
        • Vrijhoef H.J.
        Valuation of EQ-5D-3L health states in Singapore: modeling of time trade-off values for 80 empirically observed health states.
        Pharmacoeconomics. 2014; 32: 495-507
        • Xie F.
        • Pullenayegum E.
        • Gaebel K.
        • et al.
        A time trade-off-derived value set of the EQ-5D-5L for Canada.
        Med Care. 2016; 54: 98
        • Devlin N.J.
        • Shah K.K.
        • Feng Y.
        • Mulhern B.
        • van Hout B.
        Valuing health-related quality of life: an EQ-5 D-5 L value set for E ngland.
        Health Econ. 2018; 27: 7-22
        • Versteegh M.M.
        • Vermeulen K.M.
        • Evers S.M.
        • de Wit G.A.
        • Prenger R.
        • Stolk E.A.
        Dutch tariff for the five-level version of EQ-5D.
        Value Health. 2016; 19: 343-352
        • Picco L.
        • Subramaniam M.
        • Abdin E.
        • Vaingankar J.A.
        • Chong S.A.
        Prevalence and correlates of heart disease among adults in Singapore.
        Asian J Psychiatry. 2016; 19: 37-43
        • King M.T.
        • Costa D.
        • Aaronson N.
        • et al.
        QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.
        Qual Life Res. 2016; 25: 625-636
        • King M.T.
        • Viney R.
        • Pickard A.S.
        • et al.
        Australian utility weights for the EORTC QLU-C10D, a multi-attribute utility instrument derived from the cancer-specific quality of life questionnaire, EORTC QLQ-C30.
        Pharmacoeconomics. 2018; 36: 225-238
        • Norman R.
        • Mercieca-Bebber R.
        • Rowen D.
        • et al.
        UK utility weights for the EORTC QLU-C10D.
        Health Econ. 2019; 28: 1385-1401