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Conjoint analysis of preferences for cardiac risk assessment in primary care

Published online by Cambridge University Press:  26 April 2005

Franco Sassi ,
David McDaid  and
Walter Ricciardi
Franco Sassi
Affiliation:
The London School of Economics and Political Science
David McDaid
Affiliation:
The London School of Economics and Political Science
Walter Ricciardi
Affiliation:
Catholic University of the Sacred Heart
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Abstract

Objectives: Many evaluations underestimate the utility associated with diagnostic interventions by failing to capture the nonclinical value of diagnostic information. This is a cause of bias in resource allocation decisions. A study was undertaken to investigate preferences for the assessment of cardiac risk, testing the suitability of conjoint analysis, a multiattribute preference elicitation method, in the field of clinical diagnosis.

Methods: Two conjoint analysis models focusing on selected characteristics of cardiac risk assessment in asymptomatic patients 40–50 years of age were applied to elicit preferences for cardiac risk assessment from samples of general practitioners and the general public in the United Kingdom and Italy. Both models were based on rankings of alternative scenarios, and the results were analyzed using multivariate analysis of variance and an ordered probit model.

Results: In both countries, members of the public attached at least three times more importance to prognostic value (relative to clinical value) than did general practitioners. Significantly different patterns were found in the two countries with regard to other characteristics of the assessment. Variation within samples was partly associated with personal characteristics.

Conclusions: Only a fraction of the value of cardiac risk assessment to individuals and physicians in this study was linked to health outcomes. The study confirmed the appropriateness and validity of conjoint analysis in the assessment of preferences for diagnostic interventions. A wider use of this technique might significantly strengthen the existing evidence-base for diagnostic interventions, leading to a more efficient use of health-care resources.

Keywords

Decision support techniquesDiagnosisPrimary health careCardiovascular disease
Type
GENERAL ESSAYS
Information
International Journal of Technology Assessment in Health Care , Volume 21 , Issue 2 , April 2005 , pp. 211 - 218
Copyright
© 2005 Cambridge University Press

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References

Albert MA, Danielson E, Rifai N, Ridker PM, 2001 PRINCE Investigators. Effect of statin therapy on C-reactive protein levels: The pravastatin inflammation/CRP evaluation (PRINCE): A randomized trial and cohort study. JAMA. 286: 64– 70.Google Scholar
Albert MA, Glynn RJ, Ridker PM. 2003 Plasma concentration of C-reactive protein and the calculated Framingham Coronary Heart Disease Risk Score. Circulation. 108: 161– 165.Google Scholar
Aristides M, Chen J, Schulz M, et al. 2002 Conjoint analysis of a new chemotherapy: Willingness to pay and preference for the features of raltitrexed versus standard therapy in advanced colorectal cancer. Pharmacoeconomics 20: 775– 784.Google Scholar
Bassuk SS, Rifai N, Ridker PM. 2004 High-sensitivity C-reactive protein. Curr Probl Cardiol. 29: 439– 493.Google Scholar
Berwick DM, Weinstein MC. 1985 What do patients value? Willingness to pay for ultrasound in normal pregnancy. Med Care. 23: 881– 893.Google Scholar
Bishop AJ, Marteau TM, Armstrong D, et al. 2004 Women and health care professionals' preferences for Down's Syndrome screening tests: A conjoint analysis study. BJOG. 111: 775– 779.Google Scholar
Blake GJ, Ridker PM, Kuntz KM. 2002 Projected life-expectancy gains with statin therapy for individuals with elevated C-reactive protein levels. J Am Coll Cardiol. 40: 49– 55.Google Scholar
Bryan S, Buxton M, Sheldon R, Grant A. 1998 Magnetic resonance imaging for the investigation of knee injuries: An investigation of preferences. Health Econ. 7: 595– 603.Google Scholar
Cao JJ, Thach C, Manolio TA, et al. 2003 C-reactive protein, carotid intima-media thickness, and incidence of ischemic stroke in the elderly: The Cardiovascular Health Study. Circulation. 108: 166– 170.Google Scholar
Carroll NV, Gagnon JP. 1984 Consumer demand for patient-oriented pharmacy services. Am J Public Health. 74: 609– 611.Google Scholar
Cunningham MA, Gaeth GJ, Juang C, Chakraborty G. 1999 Using choice-based conjoint to determine the relative importance of dental benefit plan attributes. J Dent Educ. 63: 391– 399.Google Scholar
Farquhar PH. A survey of multiattribute utility theory and applications. In: Starr MK, Zeleny M, eds. 1977. Studies in management science. Vol. 6. Multiple criteria decision making. Amsterdam: North-Holland;
Farrar S, Ryan M. 1999 Response-ordering effects: A methodological issue in conjoint analysis. Health Econ. 8: 75– 79.Google Scholar
Fraenkel L, Bodardus S, Wittnik DR, Wittink DR. 2001 Understanding patient preferences for the treatment of lupus nephritis with adaptive conjoint analysis. Med Care. 39: 1203– 1216.Google Scholar
Graf MA, Tanner DD, Swinyard WR. 1993 Optimizing the delivery of patient and physician satisfaction: Conjoint analysis approach. Health Care Manage Rev. 18: 34– 43.Google Scholar
Holtgrave DR, Weber EU. 1993 Dimensions of risk perception for financial and health risks. Risk Anal. 13: 553– 558.Google Scholar
Keeney RL, Raiffa H. 1976. Decisions with multiple objectives. New York: John Wiley;
Luce DR, Tukey JW. 1964 Simultaneous conjoint measurement: A new type of fundamental measurement. J Math Psychol. 1: 1.Google Scholar
Magat WA, Viscusi WK, Huber J. 1988 Paired comparison and contingent valuation approaches to morbidity risk valuation. J Environ Econ Manage. 15: 395– 411.Google Scholar
Mushlin AI, Mooney C, Grow V, Phelps CE. 1994 The value of diagnostic information to patients with suspected multiple sclerosis. Arch Neurol. 51: 67– 72.Google Scholar
Nickerson CAE, McClelland GH, Petersen DM. 1991 Measuring contraceptive values: An alternative approach. J Behav Med. 14: 241– 266.Google Scholar
Phillips KA, Maddala T, Johnson FR. 2002 Measuring preferences for health care interventions using conjoint analysis: An application to HIV testing. Health Serv Res. 37: 1681– 1705.Google Scholar
Propper C. 1995 The disutility of time spent on the United Kingdom's National Health Service waiting list. J Hum Resources. 30: 677– 700.Google Scholar
Ratcliffe J. 2000 The use of conjoint analysis to elicit willingness to pay values. Proceed with caution? Int J Technol Assess Health Care. 16: 270– 290.Google Scholar
Ratcliffe J, Buxton M. 1999 Patients' preferences regarding the process and outcomes of life-saving technology. Int J Technol Assess Health Care. 15: 340– 351.Google Scholar
Ridker PM. 2003 High-sensitivity C-reactive protein and cardiovascular risk: Rationale for screening and primary prevention. Am J Cardiol. 92: 17K– 22K.Google Scholar
Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. 1997 Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N Engl J Med. 336: 973– 979.Google Scholar
Rohde LE, Hennekens CH, Ridker PM. 1999 Survey of C-reactive protein and cardiovascular risk factors in apparently healthy men. Am J Cardiol. 84: 1018– 1022.Google Scholar
Rosko MD, Walker LR, McKenna W, DeVita M. 1983 Measuring consumer preferences for ambulatory medical care arrangements. J Med Syst. 7: 545– 554.Google Scholar
Ryan M. 1999 Using conjoint analysis to take account of patient preferences and go beyond health outcomes: An application to in vitro fertilisation. Soc Sci Med. 48: 535– 546.Google Scholar
Ryan M, Hughes J. 1997 Using conjoint analysis to assess women's preferences for miscarriage management. Health Econ. 6: 261– 273.Google Scholar
Ryan M, McIntosh E, Shackley P. 1998 Methodological issues in the application of conjoint analysis in health care. Health Econ. 7: 373– 378.Google Scholar
Singh J, Cuttler L, Shin M, et al. 1998 Medical decision-making and the patient. Med Care. 36: AS31– AS45.Google Scholar
Slothuus Skjoldborg U, Gyrd-Hansen D. 2003 Conjoint analysis. The cost variable: An Achilles' heel? Health Econ. 12: 479– 491.Google Scholar
Swallow S, Opaluch J, Weaver T. 1992 Siting noxious facilities: An approach that integrates technical, economic and political consideration. Land Econ. 68: 283– 301.Google Scholar
Telser H, Zweifel P. 2002 Measuring willingness-to-pay for risk reduction: An application of conjoint analysis. Health Econ. 11: 129– 139.Google Scholar
Torrance GW, Boyle MH, Horwood SP. 1982 Applications of multi-attribute utility theory to measure social preferences for health states. Oper Res. 30: 1043– 1069.Google Scholar
van der Pol M, Cairns J. 1998 Establishing patients preferences for blood transfusion support: An application of conjoint analysis. J Health Serv Res Policy. 3: 70– 76.Google Scholar
Wanless D. 2004. Securing good health for the whole population. Norwich: HMSO;
Wardman M. 1988 A comparison of revealed preference and stated preference models. J Transport Econ Policy. 22: 71– 91.Google Scholar
Wigton RS, Hoellerich VL, Patil KD. 1986 How physicians use clinical information in diagnosing pulmonary embolism: An application of conjoint analysis. Med Decis Making. 6: 2– 11.Google Scholar