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Using Best-Worst Scaling Choice Experiments to Measure Public Perceptions and Preferences for Healthcare Reform in Australia

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  • Jordan Louviere
  • Terry Flynn

Abstract

Background: One of the greatest difficulties in evaluating healthcare system reform in any country is that governments often do not clearly articulate what it is they are attempting to do. In Australia, a recent inquiry set out 15 principles to guide the reform process, but it remains unclear how the Australian public values the principles, how such values vary across the country, and, more fundamentally, if Australians understand the principles. Objectives: To evaluate the Australian healthcare reform principles from the perspective of the Australian public, to test if such preferences are valued consistently across geographic and socioeconomic strata, and to test for the degree of understanding of the principles among the public. Methods: We employed best-worst scaling (BWS), a stated-preference method grounded in random utility theory, to elicit public preference for 15 healthcare reform principles. The BWS tasks were incorporated into an online survey that also gathered geographic and socioeconomic information and included questions relating to the understanding of the reform principles. Respondents were a geographically diverse set of Australians who were randomized to receive one of two versions of the survey, each containing a block of 15 choice tasks. Tasks in block one contained a subset of the choice tasks containing subsets of seven principles based on a balanced incomplete block design, while tasks in block two contained tasks with eight principles defined by the complement of the former. In each BWS task, respondents were simply asked to identify the most and least important principle. Analysis of preference was based on assigning the most valued principles a ‘1’ and the least valued principles ‘−1’, and with each item appearing eight times in each block, preferences were analyzed over a cardinal utility scale bounded by −8 and +8. Analysis was based on simple summary statistics and stratified by geographic and socioeconomic measures. Results: A sample of 204 respondents participated in the survey (a participation rate of 85%). Quality and safety was the most important principle and a culture of reflective improvement and innovation was the least important. Public voice and community engagement was the second least important principle and was also understood by barely half the respondents. Conclusions: This research demonstrates how random-utility-based methods can be used to provide estimates of the importance of reform principles that have known statistical properties. The BWS task used forced respondents to discriminate between the principles on offer, unlike rating scales. Researchers and practitioners in healthcare should consider using BWS tasks in preference to rating scales. Copyright Adis Data Information BV 2010

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  • Jordan Louviere & Terry Flynn, 2010. "Using Best-Worst Scaling Choice Experiments to Measure Public Perceptions and Preferences for Healthcare Reform in Australia," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 275-283, December.
  • Handle: RePEc:spr:patien:v:3:y:2010:i:4:p:275-283
    DOI: 10.2165/11539660-000000000-00000
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    References listed on IDEAS

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    1. Emily Lancsar & Jordan Louviere, 2008. "Conducting Discrete Choice Experiments to Inform Healthcare Decision Making," PharmacoEconomics, Springer, vol. 26(8), pages 661-677, August.
    2. Flynn, Terry Nicholas & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2010. "Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters," Social Science & Medicine, Elsevier, vol. 70(12), pages 1957-1965, June.
    3. Flynn, Terry N. & Louviere, Jordan J. & Peters, Tim J. & Coast, Joanna, 2007. "Best-worst scaling: What it can do for health care research and how to do it," Journal of Health Economics, Elsevier, vol. 26(1), pages 171-189, January.
    4. Ben-Akiva, Moshe & Morikawa, Takayuki & Shiroishi, Fumiaki, 1991. "Analysis of the reliability of preference ranking data," Journal of Business Research, Elsevier, vol. 23(3), pages 253-268, November.
    5. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    6. Helen Mason & Michael Jones‐Lee & Cam Donaldson, 2009. "Modelling the monetary value of a QALY: a new approach based on UK data," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 933-950, August.
    7. Steenkamp, Jan-Benedict E M & Baumgartner, Hans, 1998. "Assessing Measurement Invariance in Cross-National Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(1), pages 78-90, June.
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    2. Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    3. Tatenda T Yemeke & Elizabeth E Kiracho & Aloysius Mutebi & Rebecca R Apolot & Anthony Ssebagereka & Daniel R Evans & Sachiko Ozawa, 2020. "Health versus other sectors: Multisectoral resource allocation preferences in Mukono district, Uganda," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
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    6. Loureiro, Maria L. & Rahmani, Djamel, 2016. "The incidence of calorie labeling on fast food choices: A comparison between stated preferences and actual choices," Economics & Human Biology, Elsevier, vol. 22(C), pages 82-93.
    7. Emily Lancsar & Peter Burge, 2014. "Choice modelling research in health economics," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 28, pages 675-687, Edward Elgar Publishing.
    8. Rausch, Theresa Maria & Baier, Daniel & Wening, Stefanie, 2021. "Does sustainability really matter to consumers? Assessing the importance of online shop and apparel product attributes," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    9. Jennifer A Whitty & Ruth Walker & Xanthe Golenko & Julie Ratcliffe, 2014. "A Think Aloud Study Comparing the Validity and Acceptability of Discrete Choice and Best Worst Scaling Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    10. Qingmeng Tong & Lu Zhang & Junbiao Zhang, 2017. "Evaluation of GHG Mitigation Measures in Rice Cropping and Effects of Farmer’s Characteristics: Evidence from Hubei, China," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    11. Yangui, Ahmed & Akaichi, Faical & Costa-Font, Montserrat & Gil, Jose Maria, 2019. "Comparing results of ranking conjoint analyses, best–worst scaling and discrete choice experiments in a nonhypothetical context," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(2), April.
    12. Lancsar, Emily & Louviere, Jordan & Donaldson, Cam & Currie, Gillian & Burgess, Leonie, 2013. "Best worst discrete choice experiments in health: Methods and an application," Social Science & Medicine, Elsevier, vol. 76(C), pages 74-82.
    13. Zander, Kerstin K., 2020. "Unrealised opportunities for residential solar panels in Australia," Energy Policy, Elsevier, vol. 142(C).
    14. Áron Török & Ching-Hua Yeh & Davide Menozzi & Péter Balogh & Péter Czine, 2023. "Consumers' preferences for processed meat: a best–worst scaling approach in three European countries," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-24, December.
    15. Saeideh Khosroshahi & Lin Crase & Bethany Cooper & Michael Burton, 2021. "Matching customers’ preferences for tariff reform with managers’ appetite for change: The case of volumetric‐only tariffs in Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(2), pages 449-471, April.
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    17. Katrina J Davis & Marit E Kragt & Stefan Gelcich & Michael Burton & Steven Schilizzi & David J Pannell, 2017. "Why are Fishers not Enforcing Their Marine User Rights?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(4), pages 661-681, August.
    18. Erdem, Seda & Rigby, Dan & Wossink, Ada, 2012. "Using best–worst scaling to explore perceptions of relative responsibility for ensuring food safety," Food Policy, Elsevier, vol. 37(6), pages 661-670.
    19. Shittu, A. & Kehinde, M., 2018. "Willingness to Accept Incentives for a Shift to Climate – Smart Agriculture among Smallholder Farmers in Southwest and Northcentral Nigeria," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275983, International Association of Agricultural Economists.
    20. Aizaki, Hideo & Fogarty, James, 2023. "R packages and tutorial for case 1 best–worst scaling," Journal of choice modelling, Elsevier, vol. 46(C).
    21. Kayode Ajewole & Elliott Dennis & Ted C. Schroeder & Jason Bergtold, 2021. "Relative valuation of food and non‐food risks with a comparison to actuarial values: A best–worst approach," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 927-943, November.
    22. Elizabeth Kinter & Thomas Prior & Christopher Carswell & John Bridges, 2012. "A Comparison of Two Experimental Design Approaches in Applying Conjoint Analysis in Patient-Centered Outcomes Research," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 279-294, December.
    23. Loureiro, Maria L. & Dominguez Arcos, Fernando, 2012. "Applying Best–Worst Scaling in a stated preference analysis of forest management programs," Journal of Forest Economics, Elsevier, vol. 18(4), pages 381-394.
    24. Hall, Natasha Yvonne & Le, Long & Abimanyi-Ochom, Julie & Mihalopoulos, Cathy, 2023. "Measuring the importance of different barriers to opioid agonist treatment using best-worst scaling in an Australian setting," Health Policy, Elsevier, vol. 138(C).

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