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

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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.

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Fig. 1

Source: Sendi et al. [7]

Fig. 2
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Notes

  1. The DMP can also be extended to the case where more than one existing treatment has to be replaced to free up resources for the implement of the new treatment.

  2. Those who prefer to use quality-adjusted life-years as a measure of health outcome can use the methodology described in this paper but will need to change the description of the health outcome in the instrument.

  3. We used the same experimental design for V1–3 because we specified null preferences for the ΔE and ΔC attributes, thus making the D-efficiency measure insensitive to changes in the magnitude only of the attribute levels. The purpose of V4 was to investigate whether a ‘better’ (i.e., statistically more efficient) design would allow building a better PEI. The gain in statistical efficiency was obtained by relaxing the assumption of null preferences for ΔE and ΔC, using V3 as non-null priors for the designing of V4.

  4. The V4 was administered 2 months after the three other versions because we first needed to analyze data obtained from V3 before being able to improve the statistical efficiency of the V4 design (by using V3 results as non-null priors).

  5. The formulae is for choice proportions and it allows the testing of whether observed proportions significantly differ from proportions that would be obtained by chance (in our case, 33% as there are three choice options per task): H0: proportion = 33%; H1: proportion ≠ 33%.

  6. Summary information about all model specifications can be found in the Electronic Supplementary Material (ESM).

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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 information

Authors and Affiliations

  1. Health Economics Research Unit, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, AB25 2QN, Scotland, UK

    Nicolas Krucien

  2. Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, Villejuif, France

    Nathalie Pelletier-Fleury

  3. Department of Health Research Methods, Evaluation and Impact, Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ON, Canada

    Amiram Gafni

Authors
  1. Nicolas Krucien
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  2. Nathalie Pelletier-Fleury
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  3. Amiram Gafni
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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.

Corresponding author

Correspondence to Nicolas Krucien.

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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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 40 kb)

Supplementary material 2 (DOCX 75 kb)

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Krucien, N., Pelletier-Fleury, N. & Gafni, A. Measuring Public Preferences for Health Outcomes and Expenditures in a Context of Healthcare Resource Re-Allocation. PharmacoEconomics 37, 407–417 (2019). https://doi.org/10.1007/s40273-018-0751-1

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  • DOI: https://doi.org/10.1007/s40273-018-0751-1

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