Background: One of the promises of cost-effective analysis is that it can demonstrate how to maximize health benefits attainable within a specific limited budget. Many people argue, however, that when there are budget limitations, the use of cost-effectiveness analysis leads to health care policies that are inequitable.
Methods: We asked prospective jurors, medical ethicists, and experts in medical decision making to choose between two screening tests for a population at low risk for colon cancer. One test was more cost effective than the other but because of budget constraints was too expensive to be given to everyone in the population. With the use of the more effective test for only half the population, 1100 lives could be saved at the same cost as that of saving 1000 lives with the use of the less effective test for the entire population.
Results: Fifty-six percent of the prospective jurors, 53 percent of the medical ethicists, and 41 percent of the experts in medical decision making recommended offering the less effective screening test to everyone, even though 100 more lives would have been saved by offering the more expensive test to only a portion of the population. Most of the study participants justified this recommendation on the basis of equity. A smaller number stated either that it was not politically feasible to offer a test to only half the population or that the additional benefit of the more expensive test (100 more lives saved) was too small to justify offering it to only a portion of the public.
Conclusions: People place greater importance on equity than is reflected by cost-effectiveness analysis. Even many experts in medical decision making -- those often responsible for conducting cost-effectiveness analyses -- expressed discomfort with some of its implications. Basing health care priorities on cost effectiveness may not be possible without incorporating explicit considerations of equity into cost-effectiveness analyses or the process used to develop health care policies on the basis of such analyses.