Skip to main content
Log in

Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare

  • Original Research Article
  • Published:
Applied Health Economics and Health Policy Aims and scope Submit manuscript

Abstract

Background

Discrete choice experiments (DCEs) and the Juster scale are accepted methods for the prediction of individual purchase probabilities. Nevertheless, these methods have seldom been applied to a social decision-making context.

Objective

To gain an overview of social decisions for a decision-making population through data triangulation, these two methods were used to understand purchase probability in a social decision-making context.

Methods

We report an exploratory social decision-making study of pharmaceutical subsidy in Australia. A DCE and selected Juster scale profiles were presented to current and past members of the Australian Pharmaceutical Benefits Advisory Committee and its Economic Subcommittee.

Results

Across 66 observations derived from 11 respondents for 6 different pharmaceutical profiles, there was a small overall median difference of 0.024 in the predicted probability of public subsidy (p = 0.003), with the Juster scale predicting the higher likelihood. While consistency was observed at the extremes of the probability scale, the funding probability differed over the mid-range of profiles. There was larger variability in the DCE than Juster predictions within each individual respondent, suggesting the DCE is better able to discriminate between profiles. However, large variation was observed between individuals in the Juster scale but not DCE predictions.

Conclusions

It is important to use multiple methods to obtain a complete picture of the probability of purchase or public subsidy in a social decision-making context until further research can elaborate on our findings. This exploratory analysis supports the suggestion that the mixed logit model, which was used for the DCE analysis, may fail to adequately account for preference heterogeneity in some contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Table I
Table II
Table III
Fig. 2
Table IV
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. For useful reviews of the application of the DCE in healthcare, see Ryan and Gerard,[28] Marshall et al.[29] and de Bekker-Grob et al.[30]

References

  1. Harris AH, Buxton M, O’Brien B, et al. Using economic evidence in reimbursement decisions for health technologies: experience of 4 countries. Expert Rev Pharmacoecon Outcomes Res 2001; 1(1): 7–12

    Article  PubMed  CAS  Google Scholar 

  2. Peckham S. Primary care purchasing: are integrated primary care provider/purchasers the way forward? Pharmacoeconomics 1999 Mar; 15(3): 209–16

    Article  PubMed  CAS  Google Scholar 

  3. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics 2008; 26(8): 661–77

    Article  PubMed  Google Scholar 

  4. Viney R, Lancsar E, Louviere J. Discrete choice experiments to measure consumer preferences for health and healthcare. Expert Rev Pharmacoecon Outcomes Res 2002; 2(4): 319–26

    Article  PubMed  Google Scholar 

  5. Dolan P, Olsen JA, Menzel P, et al. An inquiry into the different perspectives that can be used when eliciting preferences in health. Health Econ 2003; 12: 545–51

    Article  PubMed  Google Scholar 

  6. Gold MR, Franks P, Siegelberg T, et al. Does providing cost-effectiveness information change coverage priorities for citizens acting as social decision-makers? Health Policy 2007; 83: 65–72

    Article  PubMed  Google Scholar 

  7. Al MJ, Feenstra T, Brouwer WBF. Decision makers’ views on health care objectives and budget constraints: results from a pilot study. Health Policy 2004 Oct; 70(1): 33–48

    Article  PubMed  Google Scholar 

  8. Menon D, Stafinski T, Stuart G. Access to drugs for cancer: does where you live matter? Can J Public Health 2005 Nov-Dec; 96(6): 454–8

    PubMed  Google Scholar 

  9. Gallego G, Taylor SJ, Brien J-AE. Priority setting for high cost medications (HCMs) in public hospitals in Australia: a case study. Health Policy 2007 Nov; 84(1): 58–66

    Article  PubMed  Google Scholar 

  10. National Institute for Health and Clinical Excellence. Social value judgements: principles for the development of NICE guidance. London: National Institute for Health and Clinical Excellence, 2008

    Google Scholar 

  11. Nord E, Street A, Richardson J, et al. The significance of age and duration of effect in social evaluation of health care. Health Care Anal 1996 May; 4(2): 103–11

    PubMed  CAS  Google Scholar 

  12. Rodriguez-Miguez E, Pinto-Prades JL. Measuring the social importance of concentration or dispersion of individual health benefits. Health Econ 2002 Jan; 11(1): 43–53

    Article  PubMed  Google Scholar 

  13. Ubel PA, Loewenstein G, Scanlon D, et al. Individual utilities are inconsistent with rationing choices: a partial explanation of why Oregon’s cost-effectiveness list failed. Med Decis Making 1996 Apr-Jun; 16(2): 108–16

    Article  PubMed  CAS  Google Scholar 

  14. Green C. On the societal value of health care: what do we know about the person trade-off technique? Health Econ 2001 Apr; 10(3): 233–43

    Article  PubMed  CAS  Google Scholar 

  15. Bryan S, Roberts T, Heginbotham C, et al. QALY-maximisation and public preferences: results from a general population survey. Health Econ 2002 Dec; 11(8): 679–93

    Article  PubMed  Google Scholar 

  16. Schwappach DLB. Does it matter who you are or what you gain? An experimental study of preferences for resource allocation. Health Econ 2003 Apr; 12(4): 255–67

    Article  PubMed  Google Scholar 

  17. Johnson FR, Backhouse M. Eliciting stated preferences for health-technology adoption criteria using paired comparisons and recommendation judgements. Value Health 2006; 9(5): 303–11

    Article  PubMed  Google Scholar 

  18. Tappenden P, Brazier J, Ratcliffe J, et al. A stated preference binary choice experiment to explore NICE decision making. Pharmacoeconomics 2007; 25(8): 685–93

    Article  PubMed  Google Scholar 

  19. Green C, Gerard K. Exploring the social value of health-care interventions: a stated preference discrete choice experiment. Health Econ 2009 Nov 25; 18(8): 951–76

    Article  PubMed  Google Scholar 

  20. Swancutt DR, Greenfield SM, Wilson S. Women’s colposcopy experience and preferences: a mixed methods study. BMC Womens Health 2008; 8: 2

    Article  PubMed  Google Scholar 

  21. Pitchforth E, Watson V, Tucker J, et al. Models of intrapartum care and women’s trade-offs in remote and rural Scotland: a mixed-methods study. BJOG 2008 Apr; 115(5): 560–9

    Article  PubMed  CAS  Google Scholar 

  22. Ryan M, Scott DA, Reeves C, et al. Eliciting public preferences for healthcare: a systematic review of techniques. Health Technol Assess 2001 Mar; 5(5): 1–186

    PubMed  CAS  Google Scholar 

  23. Furnham A. Factors relating to the allocation of medical resources. J Soc Behav Pers 1996; 11(3): 615–24

    PubMed  Google Scholar 

  24. Furnham A, Meader N, McClelland A. Factors affecting nonmedical participants’ allocation of scarce medical resources. J Soc Behav Pers 1998; 13(4): 735–46

    PubMed  CAS  Google Scholar 

  25. Bowling A, Jacobson B, Southgate L. Explorations in consultation of the public and health professionals on priority setting in an inner London health district. Soc Sci Med 1993 Oct; 37(7): 851–7

    Article  PubMed  CAS  Google Scholar 

  26. Olsen JA, Donaldson C. Helicopters, hearts and hips: using willingness to pay to set priorities for public sector health care programmes. Soc Sci Med 1998 Jan; 46(1): 1–12

    Article  PubMed  CAS  Google Scholar 

  27. Ratcliffe J. Public preferences for the allocation of donor liver grafts for transplantation. Health Econ 2000 Mar; 9(2): 137–48

    Article  PubMed  CAS  Google Scholar 

  28. Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy 2003; 2(1): 55–64

    PubMed  Google Scholar 

  29. Marshall D, Bridges JFP, Hauber B, et al. Conjoint analysis applications in health: how are studies being designed and reported? Patient 2010; 3(4): 249–56

    Article  PubMed  Google Scholar 

  30. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. Epub 2010 Dec 19

    Google Scholar 

  31. Farrar S, Ryan M, Ross D, et al. Using discrete choice modelling in priority-setting: an application to clinical service developments. Soc Sci Med 2000; 50: 63–75

    Article  PubMed  CAS  Google Scholar 

  32. Juster TF. Consumer buying intentions and purchase probability: an experiment in survey design. J Am Stat Assoc 1966 Sep; 61(315): 658–96

    Article  Google Scholar 

  33. Day D, Gan B, Gendall P, et al. Predicting purchase behaviour. Market Bull 1991 May; 2: 18–30

    Google Scholar 

  34. McDonald H, Alpert F. Using the Juster Scale to predict adoption of an innovative product. Australia and New Zealand Marketing Academy Conference; 2001 Dec 1-5; Auckland NZ

  35. Wright M, MacRae M. Bias and variability in purchase intention scales. J Acad Market Sci 2007 Dec; 35(4): 617–24

    Article  Google Scholar 

  36. Street DJ, Burgess L, Louviere J. Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments. Int J Res Market 2005; 22: 459–70

    Article  Google Scholar 

  37. Hensher DA, Rose JM, Greene WH. Applied choice analysis: a primer. New York: Cambridge University Press, 2005

    Book  Google Scholar 

  38. Wright M, Sharp A, Sharp B. Market statistics for the Dirichlet model: using the Juster scale to replace panel data. Int J Res Market 2002 Mar; 19(1): 81–90

    Article  Google Scholar 

  39. Uncles M, Lee D. Brand purchasing by older consumers: an investigation using the Juster scale and the Dirichlet model. Market Lett 2006; 17(1): 17–29

    Article  Google Scholar 

  40. Brennan M. Constructing demand curves from purchase probability data: an application of the Juster scale. Market Bull 1995; 6: 51–8

    Google Scholar 

  41. Brennan M, Charbonneau J. Constructing demand curves: a comparison of two procedures using the Juster scale. Market Bull 2005; 16 (Research note 7): 1–6

    Google Scholar 

  42. Brennan M, Esslemont D. The accuracy of the Juster Scale for predicting purchase rates of branded, fast-moving consumer goods. Market Bull 1994; 5 (Research note 1): 47–52

    Google Scholar 

  43. Garland R. Estimating customer defection in personal retail banking. Int J Bank Market 2002; 20(7): 317–24

    Article  Google Scholar 

  44. Patterson PG. Demographic correlates of loyalty in a service context. J Serv Market 2007; 21(2): 112–21

    Article  Google Scholar 

  45. Reid M, Wood A. An investigation into blood donation intentions among non-donors. Int J Nonprofit Volunt Sector Market 2008; 13: 31–43

    Article  Google Scholar 

  46. Hoek J, Maubach N, Gendall P. Effects of cigarette on-pack warning labels on smokers’ perceptions and behaviour. In: Lees MC, Davis T, Gregory G, editors. Asia-Pacific advances in consumer research. Sydney, Australia: Association for Consumer Research, 2006: 173–80

    Google Scholar 

  47. McFadden D. Conditional logit analysis of qualitative choice behaviour. In: Zarembka P, editor. Frontiers in econometrics. New York: Academic Press, 1973: 105–42

    Google Scholar 

  48. McFadden D. Econometric models of probabilistic choice. In: Manski C, McFadden D, editors. Structural analysis of discrete data with economic applications. Boston: MIT Press, 1981

    Google Scholar 

  49. Thurstone L. A law of comparative judgement. Psychol Rev 1927; 4: 273–86

    Article  Google Scholar 

  50. Manski CF. The structure of random utility models. Theory Decision 1977; 8: 229–54

    Article  Google Scholar 

  51. Baumgartner H, Steenkamp J-BE. Response Styles in Marketing Research: A Cross-National Investigation. Journal of Marketing Research 2001; 38(2): 143–56

    Article  Google Scholar 

  52. Brazier J, Green C, McCabe C, et al. Use of visual analog scales in economic evaluation. Expert Rev Pharmacoecon Outcomes Res 2003 Jun; 3(3): 293–302

    Article  PubMed  Google Scholar 

  53. AIHW. Australia’s health 2008. Canberra: Australian Institute of Health and Welfare (AIHW), 2008. Report No.: AUS 99

    Google Scholar 

  54. OECD. Social expenditure: aggregated data. OECD social expenditure statistics database [online]. Available from URL: http://www.oecd-ilibrary.org/social-issues-migration-health/data/social-expenditure/aggregated-data_data-00166-en?isPartOf=/content/datacollection/els-socx-data-en [Accessed 2011 Nov 30]

  55. Australian Bureau of Statistics. Household expenditure survey, Australia: summary of results, 2003–04. Canberra: Australian Bureau of Statistics, 2006 Feb 15. Report No.: 6530.0

    Google Scholar 

  56. Australian Government Productivity Commission. Impacts of advances in medical technology in Australia: research report. Melbourne: Australian Government Productivity Commission, 2005

    Google Scholar 

  57. Taylor RS, Drummond MF, Salkeld G, et al. Development of fourth hurdle policies around the world. In: Freemantle N, Hill S, editors. Evaluating pharmaceuticals for health policy and reimbursement. Oxford: Blackwell Publishing Ltd, 2004: 67–87

    Chapter  Google Scholar 

  58. Mitchell A. Antipodean assessment: activities, actions and achievements. Int J Technol Assess Health Care 2002; 18: 203–12

    Article  PubMed  Google Scholar 

  59. Hjelmgren J, Berggren F, Andersson F. Health economic guidelines: similarities, differences and some implications. Value Health 2001 May-Jun; 4(3): 225–50

    Article  PubMed  CAS  Google Scholar 

  60. Pharmaceutical Benefits Advisory Committee. Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (version 4.3). Canberra: Australian Government Department of Health and Ageing, 2008 Dec

    Google Scholar 

  61. Sansom L. The subsidy of pharmaceuticals in Australia: processes and challenges. Aust Health Rev 2004 Nov 8; 28(2): 194–205

    Article  PubMed  Google Scholar 

  62. Harris AH, Hill SR, Chin G, et al. The role of value for money in public insurance coverage decisions for drugs in Australia: a retrospective analysis 1994-2004. Med Decis Making 2008 Sep-Oct; 28(5): 713–22

    Article  PubMed  Google Scholar 

  63. Department of Health and Ageing. Pharmaceutical Benefits Advisory Committee, 2010 [online]. Available from URL: http://www.health.gov.au/internet/main/publishing.nsf/Content/health-pbs-general-listing-committee2.htm [Accessed 2010 Sep 9]

    Google Scholar 

  64. Whitty JA, Scuffham PA, Rundle-Thiele SR. Public and decision maker stated preferences for pharmaceutical subsidy decisions: a pilot study. Appl Health Econ Health Policy 2011; 9(2): 73–9

    Article  PubMed  Google Scholar 

  65. Greene W. NLOGIT [computer program]. version 4.0.1. Plainview, NY: Econometric Software, Inc., 2007

    Google Scholar 

  66. Greene WH. NLOGIT version 4.0: reference guide. Plain-view, NY: Econometric Software, Inc., 2007

    Google Scholar 

  67. Drummond M, McGuire A, editors. Economic evaluation in health care: merging theory with practice. Oxford: Oxford University Press, 2001

    Google Scholar 

  68. Dolan P. Modeling valuations for Euroqol health states. Med Care 1997; 35(11): 1095–108

    Article  PubMed  CAS  Google Scholar 

  69. Scuffham PA, Whitty JA, Mitchell A, et al. The use of QALY weights for QALY calculations: a review of industry submissions requesting listing on the Australian Pharmaceutical Benefits Scheme 2002-4. Pharmacoeconomics 2008; 26(4): 297–310

    Article  PubMed  Google Scholar 

  70. Torrance GW. Social preferences for health states: an empirical evaluation of three measurement technques. Socioecon Plann Sci 1976; 10: 129–36

    Article  Google Scholar 

  71. Fiebig DG, Keane MP, Louviere JJ, et al. The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Marketing Science 2010; 29: 393–421

    Article  Google Scholar 

Download references

Acknowledgements

This study was undertaken without external funding. Jennifer Whitty gratefully acknowledges the support of Griffith University and School of Medicine research scholarships to undertake this study. We thank Dorte Gyrd-Hansen and two anonymous reviewers for providing valuable suggestions on an earlier draft of this paper.

Conflict of Interest Disclosures: The authors are not aware of any conflicts of interest that are directly relevant to the content of this article. Paul Scuffham leads a research group contracted to undertake evaluations for the PBAC and Jennifer Whitty undertakes evaluations for that group.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jennifer A. Whitty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Whitty, J.A., Rundle-Thiele, S.R. & Scuffham, P.A. Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare. Appl Health Econ Health Policy 10, 113–126 (2012). https://doi.org/10.2165/11597100-000000000-00000

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/11597100-000000000-00000

Keywords

Navigation