Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-24T02:40:54.765Z Has data issue: false hasContentIssue false

Dear policy maker: Have you made up your mind? A discrete choice experiment among policy makers and other health professionals

Published online by Cambridge University Press:  15 April 2010

Marc A. Koopmanschap
Affiliation:
Erasmus University Medical Center
Elly A. Stolk
Affiliation:
Erasmus University Medical Center
Xander Koolman
Affiliation:
Delft University of Technology

Abstract

Objectives: The aim of this study was to get insight in what criteria as presented in Health technology assessment (HTA) studies are important for decision makers in healthcare priority setting.

Methods: We performed a discrete choice experiment among Dutch healthcare professionals (policy makers, HTA experts, advanced HTA students). In twenty-seven choice sets, we asked respondents to elect reimbursement of one of two different healthcare interventions, which represented unlabeled, curative treatments. Both treatments were incrementally compared with usual care. The results of the interventions were normal outputs of HTA studies with a societal perspective. Results were analyzed using a multinomial logistic regression model. Upon completion of the questionnaire, we discussed the exercise with policy makers.

Results: Severity of disease, costs per quality-adjusted life-year gained, individual health gain, and the budget impact were the most decisive decision criteria. A program targeting more severe diseases increased the probability of reimbursement dramatically. Uncertainty related to cost-effectiveness was also important. Respondents preferred health gains that include quality of life improvements over extension of life without improved quality of life. Savings in productivity costs were not crucial in decision making, although these are to be included in Dutch reimbursement dossiers for new drugs. Regarding subgroups, we found that policy makers attached relatively more weight to disease severity than others but less to uncertainty.

Conclusions: Dutch policy makers and other healthcare professionals seem to have reasonably well articulated preferences: six of seven attributes were significant. Disease severity, budget impact, and cost-effectiveness were very important. The results are comparable to international studies, but reveal a larger set of important decision criteria.

Type
POLICIES
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Al, MJ, Feenstra, T, Brouwer, WBF. Decision makers’ views on health care objectives and budget constraints: Results from a pilot study. Health Policy. 2004;70:3348.CrossRefGoogle ScholarPubMed
2. Al, MJ, Feenstra, TL, van Hout, BA. Optimal allocation of resources over health care programmes: Dealing with decreasing marginal utility and uncertainty. Health Econ. 2005;14:655667.CrossRefGoogle ScholarPubMed
3. Baltussen, R, Ten Asbroek, AH, Koolman, X, et al. Priority setting using multiple criteria: Should a lung health programme be implemented in Nepal? Health Policy Plan. 2007;22:178185.CrossRefGoogle ScholarPubMed
4. Council for Public Health and Health Care. Sensible and sustainable care. (English summary of report on www.rvz.net). Zoetermeer: Council for Public Health and Health Care; 2006.Google Scholar
5. Dakin, HA, Devlin, NJ, Odeyemi, IA. 2006. “Yes”, “no” or “yes, but”? multinomial modelling of NICE decision-making. Health Policy. 2006;77:352367.CrossRefGoogle Scholar
6. Devlin, N, Parkin, D. Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis. Health Econ. 2004;13:437452.CrossRefGoogle ScholarPubMed
7. Green, C, Gerard, K. Exploring the social value of health care interventions: A stated preference discrete choice experiment. Health Econ. 2009;18:951976.CrossRefGoogle Scholar
8. Gyrd-Hansen, D. Investigating the social value of healthy changes. J Health Econ. 2004;23:11011116.CrossRefGoogle Scholar
9. Kocur, G, Adler, T, Hyman, W, et al. Guide to forecasting travel demand with direct utility assessment. Washington DC: Urban Mass Transportation Administration, United States Department of Transportation, 1982. Report UMTA-NH-11-001082-1.Google Scholar
10. Lancsar, E, Louviere, J. Conducting discrete choice experiments to inform healthcare decision making: A user's guide. Pharmacoeconomics. 2008;26:661677.CrossRefGoogle ScholarPubMed
11. Niezen, MGH, De Bont, A, Busschbach, JJV, et al. Finding legitimacy for the role of budget impact in drug reimbursement decisions. Int J Technol Assess Health Care. 2009;25:4955.CrossRefGoogle ScholarPubMed
12. Pronk, MH, Bonsel, GJ. Outpatient drug policy by clinical assessment rather than financial constraints? The gate-keeping function of the out patient drug reimbursement system in the Netherlands. Eur J Health Econ. 2004;5:274277.CrossRefGoogle ScholarPubMed
13. Rabinowicz, W. Prioritarianism and uncertainty: On the interpersonal addition theorem and the priority view. http://mora.rente.nhh.no/projects/EqualityExchange/ressurser/articles/rabinowicz2.pdf (accessed 2001).Google Scholar
14. Ratcliffe, J, Bekker, HL, Dolan, P, et al. Examining the attitudes and preferences of health care decision-makers in relation to access, equity and cost-effectiveness: A discrete choice experiment. Health Policy. 2009;90:4557.CrossRefGoogle ScholarPubMed
15 Schwappach DLB, Strasmann, TJ. Quick and dirty numbers? The reliability of a stated-preference technique for the measurement of preferences for resource allocation. J Health Econ. 2006;25:432448.Google Scholar
16. Stolk, EA, van Donselaar, G, Brouwer, WB, et al. Reconciliation of economic concerns and health policy: Illustration of an equity adjustment procedure using proportional shortfall. Pharmacoeconomics. 2004;22:10971107.CrossRefGoogle ScholarPubMed
17. UTS: Science. Software for the construction of optimal stated choice experiments: Theory and methods. http://maths.science.uts.edu.au/maths/wiki/SPExptSoftware.Google Scholar
Supplementary material: File

Koopmanschaf et al. supplementary material

Supplementary tables

Download Koopmanschaf et al. supplementary material(File)
File 12.6 KB