IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v34y2014i5p638-654.html
   My bibliography  Save this article

Australian Public Preferences for the Funding of New Health Technologies

Author

Listed:
  • Jennifer A. Whitty
  • Julie Ratcliffe
  • Gang Chen
  • Paul A. Scuffham

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.

Suggested Citation

  • Jennifer A. Whitty & Julie Ratcliffe & Gang Chen & Paul A. Scuffham, 2014. "Australian Public Preferences for the Funding of New Health Technologies," Medical Decision Making, , vol. 34(5), pages 638-654, July.
    • Handle: RePEc:sae:medema:v:34:y:2014:i:5:p:638-654
    DOI: 10.1177/0272989X14526640
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X14526640
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X14526640?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Abelson, Julia & Giacomini, Mita & Lehoux, Pascale & Gauvin, Francois-Pierre, 2007. "Bringing `the public' into health technology assessment and coverage policy decisions: From principles to practice," Health Policy, Elsevier, vol. 82(1), pages 37-50, June.
    3. Paul Dolan & Rebecca Shaw & Aki Tsuchiya & Alan Williams, 2005. "QALY maximisation and people's preferences: a methodological review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 14(2), pages 197-208, February.
    4. Potoglou, Dimitris & Burge, Peter & Flynn, Terry & Netten, Ann & Malley, Juliette & Forder, Julien & Brazier, John E., 2011. "Best-worst scaling vs. discrete choice experiments: An empirical comparison using social care data," Social Science & Medicine, Elsevier, vol. 72(10), pages 1717-1727, May.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    6. Paul Dolan & Jan Abel Olsen & Paul Menzel & Jeff Richardson, 2003. "An inquiry into the different perspectives that can be used when eliciting preferences in health," Health Economics, John Wiley & Sons, Ltd., vol. 12(7), pages 545-551, July.
    7. Colin Green & Karen Gerard, 2009. "Exploring the social value of health‐care interventions: a stated preference discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 18(8), pages 951-976, August.
    8. Mitton, Craig R. & McMahon, Meghan & Morgan, Steve & Gibson, Jennifer, 2006. "Centralized drug review processes: Are they fair?," Social Science & Medicine, Elsevier, vol. 63(1), pages 200-211, July.
    9. Coast, Joanna & Flynn, Terry N. & Natarajan, Lucy & Sproston, Kerry & Lewis, Jane & Louviere, Jordan J. & Peters, Tim J., 2008. "Valuing the ICECAP capability index for older people," Social Science & Medicine, Elsevier, vol. 67(5), pages 874-882, September.
    10. Denzil G. Fiebig & Stephanie Knox & Rosalie Viney & Marion Haas & Deborah J. Street, 2011. "Preferences for new and existing contraceptive products," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 35-52, September.
    11. 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.
    12. Mira Johri & Laura J. Damschroder & Brian J. Zikmund‐Fisher & Peter A. Ubel, 2005. "The importance of age in allocating health care resources: does intervention‐type matter?," Health Economics, John Wiley & Sons, Ltd., vol. 14(7), pages 669-678, July.
    13. Daniels, Norman & Sabin, James E., 2002. "Setting Limits Fairly: Can we learn to share medical resources?," OUP Catalogue, Oxford University Press, number 9780195149364.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Whitty, Jennifer A. & Littlejohns, Peter, 2015. "Social values and health priority setting in Australia: An analysis applied to the context of health technology assessment," Health Policy, Elsevier, vol. 119(2), pages 127-136.
    2. Gu, Yuanyuan & Lancsar, Emily & Ghijben, Peter & Butler, James RG & Donaldson, Cam, 2015. "Attributes and weights in health care priority setting: A systematic review of what counts and to what extent," Social Science & Medicine, Elsevier, vol. 146(C), pages 41-52.
    3. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    4. 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.
    5. Qinxin Guo & Junyi Shen, 2019. "An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice," Discussion Paper Series DP2019-14, Research Institute for Economics & Business Administration, Kobe University.
    6. Denise Doiron & Hong Il Yoo, 2020. "Stated preferences over job characteristics: A panel study," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(1), pages 43-82, February.
    7. Soto, José R. & Adams, Damian C. & Escobedo, Francisco J., 2016. "Landowner attitudes and willingness to accept compensation from forest carbon offsets: Application of best–worst choice modeling in Florida USA," Forest Policy and Economics, Elsevier, vol. 63(C), pages 35-42.
    8. Ghinea, Narcyz & Critchley, Christine & Morrell, Bronwen & Kerridge, Ian & Campbell, Terry & Day, Richard & Gazarian, Madlen & Isaacs, David & Liauw, Winston & Olver, Ian & Pace, Jessica & Pearson, Sa, 2021. "A survey of Australian public attitudes towards funding of high cost cancer medicines," Health Policy, Elsevier, vol. 125(3), pages 327-334.
    9. Marta Trapero-Bertran & Beatriz Rodríguez-Martín & Julio López-Bastida, 2019. "What attributes should be included in a discrete choice experiment related to health technologies? A systematic literature review," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
    10. Nicolas Krucien & Verity Watson & Mandy Ryan, 2017. "Is Best–Worst Scaling Suitable for Health State Valuation? A Comparison with Discrete Choice Experiments," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1-16, December.
    11. Lesley Chim & Glenn Salkeld & Patrick Kelly & Wendy Lipworth & Dyfrig A Hughes & Martin R Stockler, 2017. "Societal perspective on access to publicly subsidised medicines: A cross sectional survey of 3080 adults in Australia," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
    12. Soekhai, V. & Donkers, B. & Levitan, B. & de Bekker-Grob, E.W., 2021. "Case 2 best-worst scaling: For good or for bad but not for both," Journal of choice modelling, Elsevier, vol. 41(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessandro Mengoni & Chiara Seghieri & Sabina Nuti, 2013. "The application of discrete choice experiments in health economics: a systematic review of the literature," Working Papers 201301, Scuola Superiore Sant'Anna of Pisa, Istituto di Management.
    2. Lien Nguyen & Hanna Jokimäki & Ismo Linnosmaa & Eirini-Christina Saloniki & Laurie Batchelder & Juliette Malley & Hui Lu & Peter Burge & Birgit Trukeschitz & Julien Forder, 2022. "Valuing informal carers’ quality of life using best-worst scaling—Finnish preference weights for the Adult Social Care Outcomes Toolkit for carers (ASCOT-Carer)," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(3), pages 357-374, April.
    3. 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.
    4. Kaambwa, Billingsley & Lancsar, Emily & McCaffrey, Nicola & Chen, Gang & Gill, Liz & Cameron, Ian D. & Crotty, Maria & Ratcliffe, Julie, 2015. "Investigating consumers' and informal carers' views and preferences for consumer directed care: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 140(C), pages 81-94.
    5. Sivey, Peter & Scott, Anthony & Witt, Julia & Joyce, Catherine & Humphreys, John, 2012. "Junior doctors’ preferences for specialty choice," Journal of Health Economics, Elsevier, vol. 31(6), pages 813-823.
    6. 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.
    7. Terry N. Flynn & Elisabeth Huynh & Tim J. Peters & Hareth Al‐Janabi & Sam Clemens & Alison Moody & Joanna Coast, 2015. "Scoring the Icecap‐a Capability Instrument. Estimation of a UK General Population Tariff," Health Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 258-269, March.
    8. Yuanyuan Gu & Arne Risa Hole & Stephanie Knox, 2013. "Fitting the generalized multinomial logit model in Stata," Stata Journal, StataCorp LP, vol. 13(2), pages 382-397, June.
    9. Whitty, Jennifer A. & Littlejohns, Peter, 2015. "Social values and health priority setting in Australia: An analysis applied to the context of health technology assessment," Health Policy, Elsevier, vol. 119(2), pages 127-136.
    10. Chandoevwit, Worawan & Wasi, Nada, 2020. "Incorporating discrete choice experiments into policy decisions: Case of designing public long-term care insurance," Social Science & Medicine, Elsevier, vol. 258(C).
    11. Marti, Joachim, 2012. "A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking," Social Science & Medicine, Elsevier, vol. 75(1), pages 87-97.
    12. T.N. Flynn & A.A.J. Marley, 2014. "Best-worst scaling: theory and methods," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 8, pages 178-201, Edward Elgar Publishing.
    13. Martin, Inès & Vranken, Liesbet & Ugás, Roberto, 2021. "Farmers’ Preferences to Cultivate Threatened Crop Varieties: Evidence from Peru," 2021 Conference, August 17-31, 2021, Virtual 315216, International Association of Agricultural Economists.
    14. Yuanyuan Gu & Richard Norman & Rosalie Viney, 2014. "Estimating Health State Utility Values From Discrete Choice Experiments—A Qaly Space Model Approach," Health Economics, John Wiley & Sons, Ltd., vol. 23(9), pages 1098-1114, September.
    15. Davis, Katrina J & Burton, Michael & Kragt, Marit E, 2016. "Discrete choice models: scale heterogeneity and why it matters," Working Papers 235373, University of Western Australia, School of Agricultural and Resource Economics.
    16. Nicolas Krucien & Verity Watson & Mandy Ryan, 2017. "Is Best–Worst Scaling Suitable for Health State Valuation? A Comparison with Discrete Choice Experiments," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1-16, December.
    17. Kassie, Girma T. & Abdulai, Awudu & Greene, William H. & Shiferaw, Bekele & Abate, Tsedeke & Tarekegne, Amsal & Sutcliffe, Chloe, 2017. "Modeling Preference and Willingness to Pay for Drought Tolerance (DT) in Maize in Rural Zimbabwe," World Development, Elsevier, vol. 94(C), pages 465-477.
    18. Lutzeyer, Sanja & Phaneuf, Daniel J. & Taylor, Laura O., 2018. "The amenity costs of offshore wind farms: Evidence from a choice experiment," Energy Economics, Elsevier, vol. 72(C), pages 621-639.
    19. N. Flynn, Terry & J. Peters, Tim & Coast, Joanna, 2013. "Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data," Journal of choice modelling, Elsevier, vol. 6(C), pages 34-43.
    20. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:34:y:2014:i:5:p:638-654. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.