Browse > Article
http://dx.doi.org/10.4332/KJHPA.2009.19.4.098

Eliciting stated preferences for drugs reimbursement decision criteria in South Korea  

Lim, Min-Kyoung (Institute of Health & Environment, School of Public HEalth, Seoul NAtional University)
Bae, Eun-Young (Department of HEalth Policy and Management, sangji University)
Publication Information
Health Policy and Management / v.19, no.4, 2009 , pp. 98-120 More about this Journal
Abstract
The purpose of this study is to elicit preference for drug listing decision criteria and to estimate the ICER threshold in South Korea using the discrete choice experiment (DCE) method. To collect the data, a DCE survey was administered to a subject sample either educated in the principle concepts of pharmacoeconomics or were decision makers within that field. Subjects chose between alternative drug profiles differing in four attributes: ICER, uncertainty, budget impact and severity of disease. The orthogonal and balanced designs were determined through computer algorithm to take the optimal set of drug profiles. The survey employed 15 hypothetical choice sets. A random effect probit model was used to analyze the relative importance of attributes and the probabilities of a recommendation response. Parameter estimates from the models indicated that three attributes (ICER, Impact, Severity of disease) influenced respondents' choice significantly(p${\pm}$0.001). In addition, each parameter displayed an expected sign. The Lower the ICER, the higher the probability of choosing that alternative. Respondents also preferred low levels of uncertainty and smaller impact on health service budget. They were also more likely to choose drugs for serious diseases rather than mild or moderate ones. Uncertainty however is not statistically significant. The ICER threshold, at which the probability of a recommendation was 0.5, was 29,000,000 KW/QALY in expert group and 46,500,000 KW/QALY in industry group. We also found that those in our sample were willing to accept high ICER to get medication for severe diseases. This study demonstrates that the cost-effectiveness, budget impact and severity of disease are the main reimbursement decision criteria in South Korea, and that DCE can be a useful tool in analyzing the decision making process where a variety of factors are considered and prioritized.
Keywords
drug reimbursement; reference; discrete choice experiment;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 정형선, 김주경, 이규식, 신의철. 건강보험 기본급여의 우선순위. 보건행정학회지 2004;14(2):34-57   과학기술학회마을   DOI
2 최상은. 우리나라 의약품 경제성평가의 현황과 과제. 예방의학회지 2008;41(2):74-79   과학기술학회마을   DOI
3 Anell A. Norinder A. Health outcome used in cost-effectiveness studies:a review of original articles published between 1986 and 1996. Health Policy 2000;51:89-99
4 Eicher H, Kong S, Gerth W, Mavros P, Jonsson B, Use of cost-effectiveness analysis in health-care resource allocation decision-making: How are cost-effectiveness thresholds expected to emerge. Value in Health 2004;7(5)518-528   DOI   ScienceOn
5 Farrar S, Ryan M, Ross D, Ludbrook A. Using discrete choice modelling in priority setting: an application to clinical service developments. Social Science and Medicine 2000;50:63-75.   DOI   ScienceOn
6 Ham C. Priority setting in health care: learning from international experience. Health Policy 1997:49-66.
7 Hasman A. Elicting Reasons: Empirical Methods in Proerity Setting. Health Care Analysis 2003:11(1):41-58.   DOI   ScienceOn
8 Hensher D, Rose J, Greene W, Applied Choice Analysis: A Primer. New York:Cambridge University press;2005
9 건강보험심사평가원. 내부자료. 2008
10 건강보험심사평가원. 신약 등 협상대상 약제의 세부평가기준. 서울:건강보헙심사평가원;2008
11 배은영, 임민경. 약제급여결정기준에 관한 연구. 서울:건강보헙심사평가원;2007
12 배은영. 약품비 구성요소별 기여율 분석. 서울:건강보험심사평가원;2007
13 Ratcliffe J, Bekker H, Dolan P, Edlin R. 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(1):45-57   DOI   ScienceOn
14 Noorani H, Husereau D, Boudreau R, Skidmore B. Priority setiing for health rechnology assesment: A systemic review of current practical approaches. International Journal of Technology Assessment in Health Care 2007;23(3):310-315.   DOI
15 Norheim O, Ekeberg Om Evensen S, Halvorsen M, Kvernebo K. Adoption of new health care services in Norway (1993-1997): specialists' self-assesment according to national criteria for priority setting. Health Policy 2001;56(1):65-79   DOI   ScienceOn
16 Pol M, shiell A, Au F, Johnson D, Tough S. Convergent validity between a discrete choice experiment and a direct, oper-ended method: comparison of preferred attribute levels and willingness to pay estimates. Social Science and Medicine 2008;67:2043-2050   DOI   ScienceOn
17 Baltussen R, Ten Asbreak AH, Koolman X, Shrestha N, Bhattarai P, Nissen L. Priority setting using multiple criteria: Should a lung health programme be omplemented in Nepal?. Health Policy plan 2007;22(3):178-185   DOI   ScienceOn
18 Cappelen A, Norheim O, Responsibility, fairness and rationing in health care. Health Policy 2006;76(3):312-319   DOI   ScienceOn
19 Cookson R, Dolan P, Principle of justice in health care rationing. Jounal of Medical Ethics 2000;26:323-329   DOI   ScienceOn
20 Devlin N, Parkin D. Does NICE have a cost-effectiveness threshold and what other factors influence its dection? A binary Choice analysis. Economic Evaluation 2004;13:437-452
21 Kapiriri L, Norhem O. Criteria for priority-setting in health care in Uganda: exploration of stakeholder's values. Bulletin of the World Health Organization 2004:82(3)172-179
22 Schreyogg J, Stargardt T, Velasco-Garrido M, Busse R, Defining the "Health Benefit Basket" in nine European coutries: Evidence from the European Union Health BASKET Project. The Eupean Journal of Health Economics 2005;6 suppl 1:2-10
23 Shani S, Siebzebner M, Luxenburg O, Shemer J, Setting Priorities for the adoption of health technologies on a national level-the Israeli experience. Health Policy 2000;54:196-185   DOI   ScienceOn
24 Johnson F. Backgouse M. Eliciting Stated Preferences for Health-Technology Adoption Criteria Using Paired Comparisons and Recommendation Judgements. Value in Health 2006:9(5)303-311   DOI   ScienceOn
25 Kuhfeld W. Marketing Research Method in SAS. USA;SAS institute:2005.
26 Louviere J. Analyzing decision making: metric conjoint analysis. Newbury Park. CA:sage publications. 1988. cited from Johnson F, Backhoouse M. Elicting states Preferences for Health-Technology Adoption Criteria Using Paired Comparisions and Recommendation Judgments. Value in Health 2006;9(5)303-311   DOI   ScienceOn
27 New B. Defining a package of health care services the NHS is responsible for. The case for. BMJ 1997;314:503-5   DOI   ScienceOn
28 Ryan M. A role for conjoint analysis in technology assessment in health care?. International Journal of Technology assessment in Health Care 1999;15(3):443-457
29 Ryynanen O, Myllykangas M, Vaskilampi T, Takala J. Random paired Scenarios-a method for investigating attitudes to prioritisation in medicine. Journal of Medical Ethics 1996;22(4):238-242   DOI   ScienceOn
30 Roberts T, Bryan S, Heginbotham C, McMallum A. Public involvoment in health care priority setting: an economic perspective. Health Exprctations 1999;2:235-244   DOI   ScienceOn
31 Rutten F, Busschbach J. How to Define a Basic Package of Health Services for a Tax Funded or Social Insurance Based Health Care System?. The European Journal of Health Economics 2001;2(2):45-46   DOI
32 Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ 2000;320:1530-1533   DOI   ScienceOn
33 Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Applied Health Economics and Health Policy 2003;2(1):55-64
34 Ryan M, Sketun D, Major K. Using discrete choice experiments to go beyond clinical outcomes when cvaluating clinical practice. In : Ryan M, Gerard K, Amaya-Amaya M, Editors. Using discrete choice experiments to value health and care. Dordrecht:Springer;2008. pp101-116
35 World Bank. Gross domestic product 2007. Available from:URL : http://ddp-ext.worldbank.org/ext/DDPQQ/member.do?method=getMembers&userid=1&queryID=135
36 Stolk E, Poley M. Criteria for determining a basic health services package. The European Journal of Health Economics 2005;6(1):2-7   DOI   ScienceOn
37 Tappenden P, Brazier J, Ratcliffe J, Chilcott J. A stated preference binary choice experiment to explore NICE decision making. Pharmacoemonomics 2007;25(8):685-693   DOI   ScienceOn
38 Weinstein M. Fron cost-effectiveness ratios to resource allocation: where to draw the line? In: Solan F, editor. Valuing Health Care: Costs, Benefits, and Effectivess of Pharmaceuticals and Other Medical Techologies. New York: Cambridge University Press;1995.
39 Baltussen R. Niessen L. Priority setting og health intervention: the need for multi-criteria dection analysis. Cost Effectiveness Resource Allocation 2006;4:14   DOI   ScienceOn
40 cites from Eicher H, Kong S, gerth W, Mavros P, Jonsson B. Use of cost-effectiveness analysis in health-care resource allocation decision-making:How are cost-sffectiveness thresholds expected to emerge. Value in Health 2004;8(5):518-528
41 Baltussen R, Stolk E, Chisholm D, Aikins M, Towards a multi-criteria appoach for priority setting: an application to Ghana. Health Rconomics 2006;15:689-696.   DOI   ScienceOn
42 보건복지가족부. 건강보험 약제비 적정화 방안. 서울;보건복지가족부;2007
43 이태진. 의약품 보험 급여 및 가격 결정과 경제성평가의 활용. 예방의학회지 2008;41(2)69-73   과학기술학회마을   DOI
44 Donaldon C and the Social Value of a QALY (SVQ) Reasearch Team. Weighting and valuing quality adjusted life years: preliminary results from the social value of a QALY project. England:National for Clinical Excellence(NICE) and National Co-ordination Centre for Research Methodology(NCCRM);2008