First principles of cost-effectiveness analysis in health.

Abstract

Cost-effectiveness analysis (CEA) can help individual medical care providers and health policy makers to determine the most beneficial uses of limited resources. It includes 1) defining the scope and target population of the program to be analyzed 2) computing the net monetary costs of the program 3) determining the net health effects 4) applying the appropriate decision rule and 5) delineating the possible effects of analytic imprecision through sensitivity analysis. Cost- effectiveness analysis is valuable in choosing among alternative programs of preventive and curative health. Examples show how CEA has been applied to identify the appropriate number of screening tests to be used in detecting colon cancer and to measure the effectiveness of mobile coronary care units in a community in extending lives. The cancer screening example indicated the importance of focusing on the incremental effects of additional expenditures; that is examining what is achieved by devoting additional resources to retests of each patient. This perspective compares marginal costs to marginal benefits or effectiveness. It shows that the dollars used for 1st 2nd and 3rd tests per patient are much more cost effective than those spent on subsequent tests. Another example--an analysis of mobile coronary care units--shows how their effects in extending life value judgments about the quality of impaired health and the discounting of future benefits are combined to obtain a cost-effectiveness ratio relating the resources expended to the benefits gained by this program. Results of CEA may be used in deciding how to allocate funds among programs according their cost-effectiveness ratios. Important difficulties encountered in cost- effectiveness analysis as well as in alternative decision-making methods are insufficient available data concerning costs and values and problems in quantifying and in incorporating the values of health care to consumers and society. Cost-effectiveness analysis responds to these difficulties through the use of sensitivity analysis and by providing an explicit framework for organizing information about program effectiveness and values. (authors modified)