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Are Health State Valuations from the General Public Biased? A Test of Health State Reference Dependency Using Self‐assessed Health and an Efficient Discrete Choice Experiment

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  • Marcel F. Jonker
  • Arthur E. Attema
  • Bas Donkers
  • Elly A. Stolk
  • Matthijs M. Versteegh
Abstract
Health state valuations of patients and non‐patients are not the same, whereas health state values obtained from general population samples are a weighted average of both. The latter constitutes an often‐overlooked source of bias. This study investigates the resulting bias and tests for the impact of reference dependency on health state valuations using an efficient discrete choice experiment administered to a Dutch nationally representative sample of 788 respondents. A Bayesian discrete choice experiment design consisting of eight sets of 24 (matched pairwise) choice tasks was developed, with each set providing full identification of the included parameters. Mixed logit models were used to estimate health state preferences with respondents' own health included as an additional predictor. Our results indicate that respondents with impaired health worse than or equal to the health state levels under evaluation have approximately 30% smaller health state decrements. This confirms that reference dependency can be observed in general population samples and affirms the relevance of prospect theory in health state valuations. At the same time, the limited number of respondents with severe health impairments does not appear to bias social tariffs as obtained from general population samples. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Marcel F. Jonker & Arthur E. Attema & Bas Donkers & Elly A. Stolk & Matthijs M. Versteegh, 2017. "Are Health State Valuations from the General Public Biased? A Test of Health State Reference Dependency Using Self‐assessed Health and an Efficient Discrete Choice Experiment," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1534-1547, December.
    • Handle: RePEc:wly:hlthec:v:26:y:2017:i:12:p:1534-1547
    DOI: 10.1002/hec.3445
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    References listed on IDEAS

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    2. Himmler, Sebastian & Jonker, Marcel & van Krugten, Frédérique & Hackert, Mariska & van Exel, Job & Brouwer, Werner, 2022. "Estimating an anchored utility tariff for the well-being of older people measure (WOOP) for the Netherlands," Social Science & Medicine, Elsevier, vol. 301(C).
    3. Marcel F. Jonker & Bas Donkers & Esther de Bekker‐Grob & Elly A. Stolk, 2019. "Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments," Health Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 350-363, March.
    4. Elliott, Jack & Tsuchiya, Aki, 2022. "Do they just know more, or do they also have different preferences? An exploratory analysis of the effects of self-reporting serious health problems on health state valuation," Social Science & Medicine, Elsevier, vol. 315(C).
    5. Brendan Mulhern & Richard Norman & Deborah J. Street & Rosalie Viney, 2019. "One Method, Many Methodological Choices: A Structured Review of Discrete-Choice Experiments for Health State Valuation," PharmacoEconomics, Springer, vol. 37(1), pages 29-43, January.
    6. Hajji, Assma & Trukeschitz, Birgit & Malley, Juliette & Batchelder, Laurie & Saloniki, Eirini & Linnosmaa, Ismo & Lu, Hui, 2020. "Population-based preference weights for the Adult Social Care Outcomes Toolkit (ASCOT) for service users for Austria: Findings from a best-worst experiment," Social Science & Medicine, Elsevier, vol. 250(C).
    7. Marcel F. Jonker & Richard Norman, 2022. "Not all respondents use a multiplicative utility function in choice experiments for health state valuations, which should be reflected in the elicitation format (or statistical analysis)," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 431-439, February.
    8. Stöckel, Jannis & van Exel, Job & Brouwer, Werner B.F., 2023. "Adaptation in life satisfaction and self-assessed health to disability - Evidence from the UK," Social Science & Medicine, Elsevier, vol. 328(C).
    9. Edward J. D. Webb & John O’Dwyer & David Meads & Paul Kind & Penny Wright, 2020. "Transforming discrete choice experiment latent scale values for EQ-5D-3L using the visual analogue scale," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 787-800, July.
    10. Bram Roudijk & A. Rogier T. Donders & Peep F. M. Stalmeier, 2019. "Cultural Values: Can They Explain Differences in Health Utilities between Countries?," Medical Decision Making, , vol. 39(5), pages 605-616, July.
    11. Ruixuan Jiang & Eleanor Pullenayegum & James W. Shaw & Axel Mühlbacher & Todd A. Lee & Surrey Walton & Thomas Kohlmann & Richard Norman & A. Simon Pickard, 2023. "Comparison of Preferences and Data Quality between Discrete Choice Experiments Conducted in Online and Face-to-Face Respondents," Medical Decision Making, , vol. 43(6), pages 667-679, August.

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