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PharmacoEconomics

, Volume 37, Issue 3, pp 407–417 | Cite as

Measuring Public Preferences for Health Outcomes and Expenditures in a Context of Healthcare Resource Re-Allocation

  • Nicolas KrucienEmail author
  • Nathalie Pelletier-Fleury
  • Amiram Gafni
Original Research Article

Abstract

Background

The final outcome of any resource allocation decision in healthcare cannot be determined in advance. Thus, decision makers, in deciding which new program to implement (or not), need to accommodate the uncertainty of different potential outcomes (i.e., change in both health and costs) that can occur, the size and nature (i.e., ‘bad’ or ‘good’) of these outcomes, and how they are being valued. Using the decision-making plane, which explicitly incorporates opportunity costs and relaxes the assumptions of perfect divisibility and constant returns to scale of the cost-effectiveness plane, all the potential outcomes of each resource allocation decision can be described.

Objective

In this study, we describe the development and testing of an instrument, using a discrete choice experiment methodology, allowing the measurement of public preferences for potential outcomes falling in different quadrants of the decision-making plane.

Method

In a sample of 200 participants providing 4200 observations, we compared four versions of the preference-elicitation instrument using a range of indicators.

Results

We identified one version that was well accepted by the participants and with good measurement properties.

Conclusion

This validated instrument can now be used in a larger representative sample to study the preferences of the public for potential outcomes stemming from re-allocation of healthcare resources.

Notes

Acknowledgements

We thank all participants in the study. We also thank the two anonymous reviewers for their comments, which helped us to improve the quality of this article.

Author Contributions

Nicolas Krucien, Nathalie Pelletier-Fleury, and Amiram Gafni were involved in the design of the study and the writing of the article. Nicolas Krucien was in charge of the data analysis.

Compliance with Ethical Standards

Funding

Financial support for this study was provided by the French National Institute of Health and Medical Research (INSERM). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, and writing and publishing the article.

Conflict of interest

Nicolas Krucien; Nathalie Pelletier-Fleury, and Amiram Gafni have no conflicts of interest that are directly relevant to the contents of this study.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on request.

Supplementary material

40273_2018_751_MOESM1_ESM.docx (40 kb)
Supplementary material 1 (DOCX 40 kb)
40273_2018_751_MOESM2_ESM.docx (75 kb)
Supplementary material 2 (DOCX 75 kb)

References

  1. 1.
    Williams A. The economic role of health indicators. In: Teeling Smith G, editor. Measuring the social benefits of medicine. London: Office of Health Economics; 1983. p. 63–7.Google Scholar
  2. 2.
    Weinstein M, Zeckhauser R. Critical ratios and efficient allocation. J Public Econ. 1973;2(2):147–57.Google Scholar
  3. 3.
    Gafni A, Birch S. Incremental cost-effectiveness ratios (ICERs): the silence of the lambda. Soc Sci Med. 2006;62(9):2091–100.Google Scholar
  4. 4.
    Birch S, Gafni A. Cost effectiveness/utility analyses: do current decision rules lead us to where we want to be? J Health Econ. 1992;11(3):279–96.Google Scholar
  5. 5.
    Gafni A, Birch S. Guidelines for the adoption of new technologies: a prescription for uncontrolled growth in expenditures and how to avoid the problem. Can Med Assoc J. 1993;148(6):913–7.Google Scholar
  6. 6.
    Eckermann S, Pekarsky B. Can the real opportunity cost stand up: displaced services, the straw man outside the room. Pharmacoeconomics. 2014;32(4):319–25.Google Scholar
  7. 7.
    Sendi P, Gafni A, Birch S. Opportunity costs and uncertainty in the economic evaluation of health care interventions. Health Econ. 2002;11(1):23–31.Google Scholar
  8. 8.
    Gafni A, Walter S, Birch S. Uncertainty and the decision maker: assessing and managing the risk of undesirable outcomes: uncertainty and the decision maker. Health Econ. 2013;22(11):1287–94.Google Scholar
  9. 9.
    de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72.Google Scholar
  10. 10.
    Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883–902.Google Scholar
  11. 11.
    Gafni A, Birch S. QALYs and HYEs (healthy years equivalent): spotting the differences. J Health Econ. 1997;16(5):601–8.Google Scholar
  12. 12.
    Louviere JJ, Street D, Burgess L, Wasi N, Islam T, Marley AAJ. Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information. J Choice Model. 2008;1(1):128–64.Google Scholar
  13. 13.
    Mørkbak MR, Christensen T, Gyrd-Hansen D. Choke price bias in choice experiments. Environ Resour Econ. 2010;45(4):537–51.Google Scholar
  14. 14.
    Rose JM, Bliemer MCJ. Constructing efficient stated choice experimental designs. Transp Rev. 2009;29(5):587–617.Google Scholar
  15. 15.
    Reed Johnson F, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13.Google Scholar
  16. 16.
    Louviere JJ, Hensher DA, Swait JD, Adamowicz W. Stated choice methods: analysis and applications. Cambridge: Cambridge University Press; 2010.Google Scholar
  17. 17.
    McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in econometrics. New York (NY): Academic Press; 1974. p. 105–42.Google Scholar
  18. 18.
    Train K. Discrete choice methods with simulation. 2nd ed. Cambridge: Cambridge University Press; 2009.Google Scholar
  19. 19.
    Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica. 1979;47(2):263.Google Scholar
  20. 20.
    Samuelson W, Zeckhauser R. Status quo bias in decision making. J Risk Uncertain. 1988;1(1):7–59.Google Scholar
  21. 21.
    Kahneman D, Knetsch JL, Thaler RH. Anomalies: the endowment effect, loss aversion, and status quo bias. J Econ Perspect. 1991;5(1):193–206.Google Scholar
  22. 22.
    Bonsall P, Lythgoe B. Factors affecting the amount of effort expended in responding to questions in behavioural choice experiments. J Choice Model. 2009;2(2):216–36.Google Scholar
  23. 23.
    Louviere JJ, Islam T, Wasi N, Street D, Burgess L. Designing discrete choice experiments: do optimal designs come at a price? J Consum Res. 2008;35(2):360–75.Google Scholar
  24. 24.
    Bateman IJ, Burgess D, Hutchinson WG, Matthews DI. Learning design contingent valuation (LDCV): NOAA guidelines, preference learning and coherent arbitrariness. J Environ Econ Manag. 2008;55(2):127–41.Google Scholar
  25. 25.
    Day B, Ian JB, Richard TC et al. Ordering effects and choice set awareness in repeat-response stated preference studies. J Environ Econ Manag. 2012;63(1):73–91.Google Scholar
  26. 26.
    Yao RT, Scarpa R, Rose JM, Turner JA. Experimental design criteria and their behavioural efficiency: an evaluation in the field. Environ Resour Econ. 2015;62(3):433–55.Google Scholar
  27. 27.
    Viney R, Savage E, Louviere J. Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Econ. 2005;14(4):349–62.Google Scholar
  28. 28.
    Diederich A, Swait J, Wirsik N. Citizen participation in patient prioritization policy decisions: an empirical and experimental study on patients’ characteristics. PLoS One. 2012;7(5):e36824.Google Scholar
  29. 29.
    Erdem S, Thompson C. Prioritising health service innovation investments using public preferences: a discrete choice experiment. BMC Health Serv Res. 2014;28(14):360.Google Scholar
  30. 30.
    Lim MK, Bae EY, Choi S-E, Lee EK, Lee T-J. Eliciting public preference for health-care resource allocation in South Korea. Value Health. 2012;15(1 Suppl.):S91–4.Google Scholar
  31. 31.
    Scuffham PA, Julie R, Elizabeth K et al. Engaging the public in healthcare decision-making: quantifying preferences for healthcare through citizens’ juries. BMJ Open. 2014;4(5):e005437.Google Scholar
  32. 32.
    Schwappach DLB, Strasmann TJ. Quick and dirty numbers? J Health Econ. 2006;25(3):432–48.Google Scholar
  33. 33.
    Schwappach DLB. Does it matter who you are or what you gain? An experimental study of preferences for resource allocation. Health Econ. 2003;12(4):255–67.Google Scholar
  34. 34.
    Green C, Gerard K. Exploring the social value of health-care interventions: a stated preference discrete choice experiment. Health Econ. 2009;18(8):951–76.Google Scholar
  35. 35.
    Skedgel CD, Wailoo AJ. Akehurst RL. Choosing vs. allocating: discrete choice experiments and constant-sum paired comparisons for the elicitation of societal preferences. Health Expect. 2015;18(5):1227–40.Google Scholar
  36. 36.
    Skedgel C, Wailoo A, Akehurst R. Societal preferences for distributive justice in the allocation of health care resources: a latent class discrete choice experiment. Med Decis Making. 2015;35(1):94–105.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Health Economics Research UnitInstitute of Applied Health Sciences, University of AberdeenAberdeenUK
  2. 2.Centre de Recherche en Epidémiologie et Santé des PopulationsUniversité Paris-Sud, UVSQ, INSERM, Université Paris-SaclayVillejuifFrance
  3. 3.Department of Health Research Methods, Evaluation and Impact, Centre for Health Economics and Policy AnalysisMcMaster UniversityHamiltonCanada

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