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Genetic testing in health care can provide information to help with disease prediction, diagnosis, prognosis, and treatment. Assessing the clinical utility of genetic testing requires a process to value and weight different outcomes. This article discusses the relative merits of different economic measures and methods to inform recommendations relative to genetic testing for risk of disease, including cost-effectiveness analysis and cost-benefit analysis. Cost-effectiveness analyses refer to analyses that calculate the incremental cost per unit of health outcomes, such as deaths prevented or life-years saved because of some intervention. Cost-effectiveness analyses that use preference-based measures of health state utility such as quality-adjusted life-years to define outcomes are referred to as cost-utility analyses. Cost-effectiveness analyses presume that health policy decision makers seek to maximize health subject to resource constraints. Cost-benefit analyses can incorporate monetary estimates of willingness-to-pay for genetic testing, including the perceived value of information independent of health outcomes. These estimates can be derived from contingent valuation or discrete choice experiments. Because important outcomes of genetic testing do not fit easily within traditional measures of health, cost-effectiveness analyses do not necessarily capture the full range of outcomes of genetic testing that are important to decision makers and consumers. We recommend that health policy decision makers consider the value to consumers of information and other nonhealth attributes of genetic testing strategies.



Genet Med

Publication Date





648 - 654


Costs and Cost Analysis, Genetic Testing, Health Care Costs, Humans, Models, Econometric