Australian Public Preferences for the Funding of New Health Technologies

Jennifer A. Whitty · Julie Ratcliffe · Gang Chen · Paul Scuffham

63 citations0/0 files storedMedical Decision Making

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Health Systems, Economic Evaluations, Quality of Life Economic and Environmental Valuation Delphi Technique in Research

Abstract

BACKGROUND: Ethical, economic, political, and legitimacy arguments support the consideration of public preferences in health technology decision making. The objective was to assess public preferences for funding new health technologies and to compare a profile case best-worst scaling (BWS) and traditional discrete choice experiment (DCE) method. METHODS: An online survey consisting of a DCE and BWS task was completed by 930 adults recruited via an Internet panel. Respondents traded between 7 technology attributes. Participation quotas broadly reflected the population of Queensland, Australia, by gender and age. Choice data were analyzed using a generalized multinomial logit model. RESULTS: The findings from both the BWS and DCE were generally consistent in that respondents exhibited stronger preferences for technologies offering prevention or early diagnosis over other benefit types. Respondents also prioritized technologies that benefit younger people, larger numbers of people, those in rural areas, or indigenous Australians; that provide value for money; that have no available alternative; or that upgrade an existing technology. However, the relative preference weights and consequent preference orderings differed between the DCE and BWS models. Further, poor correlation between the DCE and BWS weights was observed. While only a minority of respondents reported difficulty completing either task (22.2% DCE, 31.9% BWS), the majority (72.6%) preferred the DCE over BWS task. CONCLUSIONS: This study provides reassurance that many criteria routinely used for technology decision making are considered to be relevant by the public. The findings clearly indicate the perceived importance of prevention and early diagnosis. The dissimilarity observed between DCE and profile case BWS weights is contrary to the findings of previous comparisons and raises uncertainty regarding the comparative merits of these stated preference methods in a priority-setting context.

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References

2001Discrete Choice Methods with Simulation 2005Applied Choice Analysis 1977The structure of random utility models 2008Conducting Discrete Choice Experiments to Inform Healthcare Decision Making 1993The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models 2009The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity 2006Best–worst scaling: What it can do for health care research and how to do it 2004National Institute for Clinical Excellence and its value judgments

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