Discrete-Choice-Experimente zur Messung der Zahlungsbereitschaft für Gesundheitsleistungen – ein anwendungsbezogener Literaturreview

Zusammenfassung

Zielsetzung: Discrete-Choice-Experimente (DCE) sind eine Methode zur Messung der Zahlungsbereitschaft im Kontext von Kosten-Nutzen-Analysen. Verglichen mit herkömmlichen Verfahren bieten DCE vielseitige Ansatzpunkte zur Messung von Präferenzurteilen. Ziel dieser Arbeit war es, die praktischen Anwendungsmöglichkeiten von DCE im Rahmen der Zahlungsbereitschaftsmessung für medizinische Technologien zu untersuchen. Methodik: Literaturreview basierend auf computergestützter Literaturrecherche in medizinischen und wirtschaftswissenschaftlichen Datenbanken (PubMed, EconLit) und bibliografische Suche in Literaturverzeichnissen im Veröffentlichungszeitraum von 01 / 1998 – 05 / 2010. Ergebnisse: Die Nutzenmessung mittels DCE bietet im Gegensatz zu anderen Methoden zwei Vorteile: Zum einen ist das Experiment für die Probanden leicht durchzuführen und zum anderen basieren der Zahlungsbereitschaftsansatz und DCE auf fundierten theoretischen Grundlagen. Aus der Literatur wurden die Validität, Reliabilität, Akzeptanz bei den befragten Personen, Praktikabilität und Wirtschaftlichkeit als Beurteilungskriterien für DCE evaluiert. Auf methodischer Ebene erweisen sich diese als ein Nutzenmaß von hoher Validität und Reliabilität. Besonders die Ergebnisse im Bereich der internen Konsistenz und der theoretischen Validität sind sehr gut. DCE können hilfreiche Anhaltspunkte liefern, insbesondere bei der Identifizierung von nutzenstiftenden Eigenschaften medizinischer Serviceleistungen, bei der Eliminierung von Leistungsbestandteilen, für die keine Zahlungsbereitschaft besteht, und bei der Konzeption von Leistungsangeboten für spezifische Patientengruppen. Die besten Ergebnisse lassen sich erzielen, wenn die befragten Personen mit der Entscheidungssituation vertraut sind. Schwierigkeiten in diesem Zusammenhang bestehen insbesondere in öffentlich finanzierten Gesundheitssystemen, in denen die Preissensitivität der Probanden nicht hinreichend genug ausgeprägt ist. Schlussfolgerung: DCE sind ein leistungsstarkes Verfahren, mit dem neben gesundheitsbezogenen Folgen auch Prozessattribute bewertet und Trade-Offs der Probanden zwischen einzelnen Produktattributen beobachtet werden können. Durch die Nachbildung von alltagstypischen Entscheidungssituationen können insbesondere interventionsspezifische Auswirkungen ermittelt werden. Dennoch erscheint es angebracht, zahlreiche Aspekte einer weiteren empirischen Überprüfung zu unterziehen. Hinsichtlich der Zahlungsbereitschaftsmessung sind Fragen bezüglich des optimalen Designs, psychologischer Aspekte und kognitiver Probleme der Entscheidungsfindung zu berücksichtigen.

Abstract

Aim: Discrete choice experiments (DCE) are a method to assess willingness-to-pay (WTP) within the framework of cost-benefit analysis. Compared to traditional tools, DCE offer a broad application spectrum for the measurement of preferences. The objective of this paper was to evaluate the application of DCE in the measurement of willingness-to-pay for medical interventions. Method: A literature review was conducted in healthcare and economic databases (PubMed, EconLit), as well as manual search and citation-tracking in bibliographies for papers and books published in the period 01 / 1998 – 05 / 2010. Results: Compared to conventional methods, utility measurement using DCE provides two advantages. First, the experiment is less cognitive demanding for respondents. Second, willingness-to-pay and DCE are based on a valid theoretical basis. From the literature, validity, reliability, acceptance by respondents, practicability, and efficiency were evaluated as criteria for assessing DCE. These criteria proved to be of high methodological validity and reliability. Particularly, the results concerning internal consistency and theoretical validity are very encouraging. DCE provide an informative basis for identifying medical service features which create a higher benefit for patients, eliminating services for which no willingness-to-pay exists, and the conception of medical services offered to specific patient groups. Optimized results may be achieved if the respondents are familiar with the framing of the decision situation. Particularly in healthcare systems where respondents exhibit inadequate price sensitivity, this may be a difficulty. Conclusion: DCE are a versatile tool for WTP measurement in health economics, which enables researchers both to evaluate process attributes and to observe individual trade-offs between service attributes. By mimicking everyday decision-making situations the method is especially suitable for the evaluation of intervention-specific effects. However, numerous criteria require empirical examination. Focusing on WTP measurement, aside from experimental design aspects, particularly psychological aspects and cognitive problems of decision heuristics should be taken into consideration.

Schlüsselwörter

Präferenzmessung - Discrete-Choice-Experiment - Zahlungsbereitschaft

Key words

preferences - discrete choice experiment - willingness-to-pay

Literatur

• 1 Cookson R. Willingness to pay methods in health care: a sceptical view.  Health Econ. 2003;  12 891-894
• 2 Ryan M, Gerard K, Amaya-Amaya M (Hrsg.).. Using Discrete Choice Experiments to Value Health and Health Care. Springer Netherlands. Dordrecht; 2008
• 3 Pol van der M, Shiell A, Au F et al. Eliciting individual preferences for health care: a case study of perinatal care.  Health Expect. 2010;  13 4-12
• 4 McIntosh E. Using discrete choice experiments within a cost-benefit analysis framework: some considerations.  Pharmacoeconomics. 2006;  24 855-868
• 5 Ryan M, Bate A, Eastmond C J et al. Use of discrete choice experiments to elicit preferences.  Qual Health Care. 2001;  10 Suppl 1 i55-60
• 6 Ratcliffe J. The use of conjoint analysis to elicit willingness-to-pay values. Proceed with caution?.  Int J Technol Assess Health Care. 2000;  16 270-275
• 7 Slothuus Skjoldborg U, Gyrd-Hansen D. Conjoint analysis. The cost variable: an Achilles’ heel?.  Health Econ. 2003;  12 479-491
• 8 Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide.  Pharmacoeconomics. 2008;  26 661-677
• 9 Carlsson F, Martinsson P. Design techniques for stated preference methods in health economics.  Health Econ. 2003;  12 281-294
• 10 Ryan M, Scott D A, Reeves C et al. Eliciting public preferences for healthcare: a systematic review of techniques.  Health Technol Assess. 2001;  5 1-186
• 11 Ryan M. A role for conjoint analysis in technology assessment in health care?.  Int J Technol Assess Health Care. 1999;  15 443-457
• 12 Donaldson C, Shackley P. Willingness to Pay for Health Care. In Scott A, Maynard A, Elliott R, (Hrsg.) Advances in Health Economics.. Chichester: Wiley & Sons; 2003
• 13 Garrod G, Willis K. Economic Valuation of the Environment, Methods and Case Studies. Edward Elgar Publishing Limited. Cheltenham UK; 1999
• 14 Luce R D, Tukey J W. Simultaneous conjoint measurement: A new type of fundamental measurement.  Journal of Mathematical Psychology. 1964;  1 1-27
• 15 Louviere J J, Woodworth G. Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data.  Journal of Marketing Research (JMR). 1983;  20 350-367
• 16 Lancaster K. Consumer Demand. A New Approach. Columbia University Press. New York; 1971
• 17 Gravelle H, Rees R. Microeconomics. Longman. New York; 1992 2nd ed
• 18 McFadden D. Conditional logit analysis of qualitative choice behaviour. In Zarembka P, (Hrsg.) Frontiers in Econometrics.. New York: Academic Press; 1974: 105-142
• 19 Lloyd A J. Threats to the estimation of benefit: are preference elicitation methods accurate?.  Health Econ. 2003;  12 393-402
• 20 Ryan M, Netten A, Skatun D et al. Using discrete choice experiments to estimate a preference-based measure of outcome – an application to social care for older people.  J Health Econ. 2006;  25 927-944
• 21 Ryan M, Hughes J. Using conjoint analysis to assess women’s preferences for miscarriage management.  Health Econ. 1997;  6 261-273
• 22 Emery D R, Barron F H. Axiomatic and numerical conjoint measurement: an evaluation of diagnostic efficacy.  Psychometrika. 1979;  44 195-210
• 23 Ryan M. A comparison of stated preference methods for estimating monetary values.  Health Econ. 2004;  13 291-296
• 24 McFadden D. Econometric Models of Probabilistic Choice. In Manski C, McFadden D, (Hrsg.) Structural Analysis of Discrete Data with Econometric Applications.. Cambridge: MIT Press; 1981
• 25 Train K E. Recreation Demand Models with Taste Differences Over People.  Land Economics. 1998;  74 230-239
• 26 Hausman J A, Wise D A. A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences.  Econometrica. 1978;  46 403-426
• 27 McIntosh E, Ryan M. Using discrete choice experiments to derive welfare estimates for the provision of elective surgery: Implications of discontinuous preferences.  Journal of Economic Psychology. 2002;  23 367
• 28 Scott A. Identifying and analysing dominant preferences in discrete choice experiments: An application in health care.  Journal of Economic Psychology. 2002;  23 383
• 29 Train K E, Sonnier G. Mixed logit with bounded distributions of correlated partworths. In Alberini A, Scarpa R, (Hrsg.) Application of Simulation Methods in Environmental und Resource Economics.. Dordrecht: Springer; 2005: 117-134
• 30 Hall J, Fiebig D G, King M T et al. What influences participation in genetic carrier testing? Results from a discrete choice experiment.  J Health Econ. 2006;  25 520-537
• 31 Eberth B, Watson V, Ryan M et al. Does one size fit all? Investigating heterogeneity in men’s preferences for benign prostatic hyperplasia treatment using mixed logit analysis.  Med Decis Making. 2009;  29 707-715
• 32 Lancsar E, Savage E. Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory.  Health Econ. 2004;  13 901-907
• 33 Kimman M L, Dellaert B G, Boersma L J et al. Follow-up after treatment for breast cancer: one strategy fits all? An investigation of patient preferences using a discrete choice experiment.  Acta Oncol. 2010;  49 328-337
• 34 Ryan M, Gerard K. Using Discrete Choice Experiments in Health Economics: Moving Forward. In Scott A, Maynard A, Elliott R, (Hrsg.) Advances in Health Economics.. Chichester: Wiley & Sons; 2003
• 35 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;  115 560-569
• 36 Salkeld G, Ryan M, Short L. The veil of experience: do consumers prefer what they know best?.  Health Econ. 2000;  9 267-270
• 37 Thaler R H. Toward a positive theory of consumer choice.  Journal of Economic Behavior and Organization. 1980;  1 39-60
• 38 Grutters J P, Kessels A G, Dirksen C D et al. Willingness to Accept versus Willingness to Pay in a Discrete Choice Experiment.  Value Health. 2008;  11 1110-1119
• 39 Ryan M, Major K, Skatun D. Using discrete choice experiments to go beyond clinical outcomes when evaluating clinical practice.  J Eval Clin Pract. 2005;  11 328-338
• 40 Telser H. Nutzenmessung im Gesundheitswesen. Die Methode der Discrete-Choice-Experimente. Verlag Dr. Kovač. Hamburg; 2002
• 41 Johnson F R, Manjunath R, Mansfield C A et al. High-risk individuals’ willingness to pay for diabetes risk-reduction programs.  Diabetes Care. 2006;  29 1351-1356
• 42 Taylor S J, Armour C L. Acceptability of willingness to pay techniques to consumers.  Health Expect. 2002;  5 341-356
• 43 Roux L, Ubach C, Donaldson C et al. Valuing the benefits of weight loss programs: an application of the discrete choice experiment.  Obes Res. 2004;  12 1342-1351
• 44 Aristides M, Weston A R, FitzGerald P et al. Patient preference and willingness-to-pay for Humalog Mix25 relative to Humulin 30 / 70: a multicountry application of a discrete choice experiment.  Value Health. 2004;  7 442-454
• 45 Louviere J J, Hensher D A, Swait J D. Stated Choice Methods. Analysis and Application. Cambridge University Press. Cambridge; 2000
• 46 Mangham L J, Hanson K, McPake B. How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country.  Health Policy Plan. 2009;  24 151-158
• 47 Farrar S, Ryan M. Response-ordering effects: a methodological issue in conjoint analysis.  Health Econ. 1999;  8 75-79
• 48 Kjaer T, Bech M, Gyrd-Hansen D et al. Ordering effect and price sensitivity in discrete choice experiments: need we worry?.  Health Econ. 2006;  15 1217-1228
• 49 Tversky A, Kahneman D. Judgement under uncertainty: heuristics and biases.  Science. 1974;  185 1124-1130
• 50 Morkbak M R, Christensen T, Gyrd-Hansen D. Choke Price Bias in Choice Experiments.  Environ Resource Econ. 2010;  45 537-551
• 51 Ratcliffe J, Longworth L. Investigating the structural reliability of a discrete choice experiment within health technology assessment.  Int J Technol Assess Health Care. 2002;  18 139-144
• 52 Cheraghi-Sohi S, Bower P, Mead N et al. Making sense of patient priorities: applying discrete choice methods in primary care using ‘think aloud’ technique.  Fam Pract. 2007;  24 276-282
• 53 Ryan M, Watson V, Entwistle V. Rationalising the ‘irrational’: a think aloud study of discrete choice experiment responses.  Health Econ. 2009;  18 321-336
• 54 Bryan S, Buxton M, Sheldon R et al. Magnetic resonance imaging for the investigation of knee injuries: an investigation of preferences.  Health Econ. 1998;  7 595-603
• 55 Ryan M, Skatun D. Modelling non-demanders in choice experiments.  Health Econ. 2004;  13 397-402
• 56 Bateman I J, Carson R T, Day B et al. Economic evaluation with stated preference techniques, a manual. Edward Elgar Publishing Ltd. Cheltenham; 2002
• 57 Miguel F S, Ryan M, Amaya-Amaya M. ‘Irrational’ stated preferences: a quantitative and qualitative investigation.  Health Econ. 2005;  14 307-322
• 58 Haaijer R, Kamakura W, Wedel M. The No-Choice Alternative in Conjoint Choice Experiments.  International Journal of Market Research. 2001;  43 93-106
• 59 Herbild L, Bech M, Gyrd-Hansen D. Estimating the Danish populations’ preferences for pharmacogenetic testing using a discrete choice experiment. The case of treating depression.  Value Health. 2009;  12 560-567
• 60 Kenny P, Hall J, Viney R et al. Do participants understand a stated preference health survey? A qualitative approach to assessing validity.  Int J Technol Assess Health Care. 2003;  19 664-681
• 61 Fraenkel L. Conjoint Analysis at the Individual Patient Level: Issues to Consider as We Move from a Research to a Clinical Tool.  Patient. 2008;  1 251-253
• 62 Lichtenstein S, Slovic P. The construction of preference. Cambridge University Press. Cambridge; 2006
• 63 Tversky A, Kahneman D. The framing of decisions and the psychology of choice.  Science. 1981;  211 453-458
• 64 Howard K, Salkeld G. Does Attribute Framing in Discrete Choice Experiments Influence Willingness to Pay? Results from a Discrete Choice Experiment in Screening for Colorectal Cancer.  Value Health. 2009;  12 354-363
• 65 Brocke M. Präferenzmessung durch die Discrete Choice-Analyse. Effekte der Aufgabenkomplexität. Gabler. Wiesbaden; 2006
• 66 Zwerina K. Discrete Choice Experiments in Marketing. Use of Priors in Efficient Choice Designs and Their Application to Individual Preference Measurement. Physica-Verlag. Heidelberg; 1997
• 67 Sandor Z, Wedel M. Heterogeneous conjoint choice designs.  Journal of Marketing Research. 2005;  42 210-218
• 68 DesignDecisionWiki .Software for discrete choice model estimation. http://ddl.me.cmu.edu/ddwiki/index.php/Software_for_discrete_choice_model_estimation
• 69 Royal Economic Society .Econometric Software Links. http://www.feweb.vu.nl/econometricLinks/software.html
• 70 Hauber A B. Issues that May Affect the Validity and Reliability of Willingness-to-Pay Estimates in Stated-Preference Studies.  The Patient: Patient-Centered Outcomes Research. 2008;  1 249-250
• 71 Telser H, Becker K, Zweifel P. Validity and Reliability of Willingness-to-Pay Estimates: Evidence from Two Overlapping Discrete-Choice Experiments.  The Patient: Patient-Centered Outcomes Research. 2008;  1 283-298
• 72 Schwappach D L, Strasmann T J. „Quick and dirty numbers”? The reliability of a stated-preference technique for the measurement of preferences for resource allocation.  J Health Econ. 2006;  25 432-448
• 73 Seston E M, Elliott R A, Noyce P R et al. Women’s preferences for the provision of emergency hormonal contraception services.  Pharm World Sci. 2007;  29 183-189
• 74 Lancsar E, Louviere J. Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences?.  Health Econ. 2006;  15 797-811
• 75 Bryan S, Dolan P. Discrete choice experiments in health economics. For better or for worse?.  Eur J Health Econ. 2004;  5 199-202
• 76 Lloyd A, Doyle S, Dewilde S et al. Preferences and utilities for the symptoms of moderate to severe allergic asthma.  Eur J Health Econ. 2008;  9 275-284
• 77 Bryan S, Gold L, Sheldon R et al. Preference measurement using conjoint methods: an empirical investigation of reliability.  Health Econ. 2000;  9 385-395
• 78 Skjoldborg U S, Lauridsen J, Junker P. Reliability of the discrete choice experiment at the input and output level in patients with rheumatoid arthritis.  Value Health. 2009;  12 153-158
• 79 Payne J W, Bettmann J R, Luce M F. et al .Behavioral Decision Research. An Overview. In Birnbaum M E, (Hrsg.) Measurement, Judgment and Decision Making. San Diego: Academic Press; 1998: 303-359
• 80 Gigerenzer G, Todd P. ABC Research Group .Simple Heuristics that Make us Smart. Oxford University Press. New York; 1999
• 81 Til J A, Stiggelbout A M, Ijzerman M J. The effect of information on preferences stated in a choice-based conjoint analysis.  Patient Educ Couns. 2009;  74 264-271
• 82 Zeliadt S B, Ramsey S D, Penson D F et al. Why do men choose one treatment over another? A review of patient decision making for localized prostate cancer.  Cancer. 2006;  106 1865-1874
• 83 Bech van M, Kjaer T, Lauridsen J. Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment.  Health Econ. 2010;  Feb 8. [Epub ahead of print]
• 84 Witt J, Scott A, Osborne R H. Designing choice experiments with many attributes. An application to setting priorities for orthopaedic waiting lists.  Health Econ. 2009;  18 681-696
• 85 Sculpher M, Bryan S, Fry P et al. Patients’ preferences for the management of non-metastatic prostate cancer: discrete choice experiment.  BMJ. 2004;  328 382
• 86 Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care.  BMJ. 2000;  320 1530-1533
• 87 Hensel-Börner S. Validität computergestützter hybrider Conjoint-Analysen. Gabler. Wiesbaden; 2000
• 88 Gerard K, Shanahan M, Louviere J. Using stated preference discrete choice modelling to inform health care decision-making: A pilot study of breast screening participation.  Applied Economics. 2003;  35 1073
• 89 Ryan M, Wordsworth S. Sensitivity of Willingnes to Pay Estimates to the Level of Attributes in Discrete Choice Experiments.  Scottish Journal of Political Economy. 2000;  47 504
• 90 Mark T L, Swait J. Using stated preference and revealed preference modeling to evaluate prescribing decisions.  Health Econ. 2004;  13 563-573
• 91 Gunther O H, Kurstein B, Riedel-Heller S G et al. The role of monetary and nonmonetary incentives on the choice of practice establishment: a stated preference study of young physicians in Germany.  Health Serv Res. 2010;  45 212-229
• 92 Ryan M, Watson V. Comparing welfare estimates from payment card contingent valuation and discrete choice experiments.  Health Econ. 2009;  18 389-401
• 93 Coast J, Salisbury C, Berker de D et al. Preferences for aspects of a dermatology consultation.  Br J Dermatol. 2006;  155 387-392
• 94 Coast J, Flynn T, Sutton E et al. Investigating Choice Experiments for Preferences of Older People (ICEPOP): evaluative spaces in health economics.  J Health Serv Res Policy. 2008;  13 Suppl 3 31-37
• 95 Elrod T, Louviere J J, Davey K S. An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models.  Journal of Marketing Research (JMR). 1992;  29 368-377
• 96 Flynn T N, Louviere J J, Peters T J et al. Best-worst scaling: What it can do for health care research and how to do it.  J Health Econ. 2007;  26 171-189
• 97 Coast J, Horrocks S. Developing attributes and levels for discrete choice experiments using qualitative methods.  J Health Serv Res Policy. 2007;  12 25-30
• 98 Grewal I, Lewis J, Flynn T et al. Developing attributes for a generic quality of life measure for older people: preferences or capabilities?.  Soc Sci Med. 2006;  62 1891-1901
• 99 Swancutt D R, Greenfield S M, Wilson S. Women’s colposcopy experience and preferences: a mixed methods study.  BMC Womens Health. 2008;  8 2
• 100 Cunningham C E, Deal K, Rimas H et al. Modeling the information preferences of parents of children with mental health problems: a discrete choice conjoint experiment.  J Abnorm Child Psychol. 2008;  36 1123-1138
• 101 Kievit W, Hulst van L, Riel van P et al. Factors that influence rheumatologists’ decisions to escalate care in rheumatoid arthritis: results from a choice-based conjoint analysis.  Arthritis Care Res (Hoboken). 2010;  62 842-847
• 102 Haughney J, Partridge M R, Vogelmeier C et al. Exacerbations of COPD: quantifying the patient’s perspective using discrete choice modelling.  Eur Respir J. 2005;  26 623-629
• 103 Himme A. Conjoint-Analysen. In Albers S, Klapper D, Konradt U, (Hrsg.) Methodik der empirischen Forschung.. 3rd ed Wiesbaden: Gabler; 2009: 283-299
• 104 Baumgartner B, Steiner W J. Hierarchisch bayesianische Methoden bei der Conjointanalyse. In Baier D, Brusch M, (Hrsg.) Conjointanalyse.. Berlin Heidelberg: Springer; 2009: 147-159
• 105 Regier D A, Ryan M, Phimister E et al. Bayesian and classical estimation of mixed logit: An application to genetic testing.  J Health Econ. 2009;  28 598-610
• 106 Regier D A, Friedman J M, Makela N et al. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children.  Clin Genet. 2009;  75 514-521
• 107 Hole A R. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment.  J Health Econ. 2008;  27 1078-1094
• 108 Gerard K, Currie G. Using discrete choice experiments in health economics. In Jones A, (Hrsg.) The Elgar Companion to Health Economics.. Bodmin, Cornwall: MPG Books; 2006: 405-414
• 109 Nayaradou M, Berchi C, Dejardin O et al. Eliciting population preferences for mass colorectal cancer screening organization.  Med Decis Making. 2010;  30 224-233
• 110 Chuck A, Adamowicz W, Jacobs P et al. The Willingness to Pay for Reducing Pain and Pain-Related Disability.  Value Health. 2009;  12 498-506