Australian Public Preferences for the Funding of New Health Technologies: A Comparison of Discrete Choice and Profile Case Best-Worst Scaling Methods.
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.



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.



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.



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.
Centre for Applied Health Economics, School of Medicine, Griffith Health Institute, Griffith University, Logan, Australia (JAW, PAS)
Jennifer A
JA
Whitty
Flinders Health Economics Group, School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Flinders University, Adelaide, Australia (JR, GC).
Julie
J
Ratcliffe
Flinders Health Economics Group, School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Flinders University, Adelaide, Australia (JR, GC).
Gang
G
Chen
Centre for Applied Health Economics, School of Medicine, Griffith Health Institute, Griffith University, Logan, Australia (JAW, PAS)
Paul A
PA
Scuffham
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D001315:Australia; D020811:Biomedical Technology / Q000191:economics*; D002755:Choice Behavior; D003657:Decision Making; D003661:Decision Support Techniques; D005260:Female; D015276:Health Care Rationing / Q000191:economics*; D006304:Health Status; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011639:Public Opinion; D012737:Sex Factors; D012959:Socioeconomic Factors; D055815:Young Adult
Australia; best-worst scaling; discrete choice experiment; health technology assessment; preferences