Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best-worst scaling methods

Whitty, Jennifer A., Ratcliffe, Julie, Chen, Gang and Scuffham, Paul A. (2014) Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best-worst scaling methods. Medical Decision Making, 34 5: 638-654. doi:10.1177/0272989X14526640


Author Whitty, Jennifer A.
Ratcliffe, Julie
Chen, Gang
Scuffham, Paul A.
Title Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best-worst scaling methods
Journal name Medical Decision Making   Check publisher's open access policy
ISSN 1552-681X
0272-989X
Publication date 2014-07
Year available 2014
Sub-type Article (original research)
DOI 10.1177/0272989X14526640
Open Access Status
Volume 34
Issue 5
Start page 638
End page 654
Total pages 17
Place of publication Thousand Oaks CA, United States
Publisher Sage Publications
Collection year 2015
Language eng
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.
Keyword Preferences
Discrete choice experiment
Best-worst scaling
Health technology assessment
Australia
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Pharmacy Publications
 
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