Conjoint analysis of preferences for cardiac risk assessment in primary care.
OBJECTIVE
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.
Sassi
Franco
F
Department of Social Policy, The London School of Economics and Political Science, UK. f.sassi@lse.ac.uk
McDaid
David
D
Ricciardi
Walter
W
D000328:Adult; D001291:Attitude of Health Personnel; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000191:economics; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D006305:Health Status Indicators; D006801:Humans; D007407:Interviews as Topic; D007558:Italy; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010821:Physicians, Family / Q000523:psychology; D011320:Primary Health Care / Q000191:economics*; D011379:Prognosis; D018570:Risk Assessment / Q000191:economics*; D013673:Technology Assessment, Biomedical; D006113:United Kingdom