Using Decision Analytic Methods to Assess the Utility of Family History Tools Anupam Tyagi, PhD, Jill Morris, PhD Abstract: Family history may be a useful tool for identifying people at increased risk of disease and for developing targeted interventions for individuals at higher-than-average risk. This article addresses the issue of how to examine the utility of a family history tool for public health and preventive medicine. We propose the use of a decision analytic framework for the assessment of a family history tool and outline the major elements of a decision analytic approach, including analytic perspective, costs, outcome measurements, and data needed to assess the value of a family history tool. We describe the use of sensitivity analysis to address uncertainty in parameter values and imperfect information. To illustrate the use of decision analytic methods to assess the value of family history, we present an example analysis based on using family history of colorectal cancer to improve rates of colorectal cancer screening. (Am J Prev Med 2003;24(2):199 –207) © 2003 American Journal of Preventive Medicine Introduction F amily history (FH) of disease is a risk factor for most diseases of public health significance. 1 Al- though FH information is routinely collected in clinical settings, its systematic use in public health and preventive medicine is largely absent. Other papers in this issue attest to the usefulness of FH informa- tion. 2–9 This article addresses the use of decision analysis to quantify the value of FH information. Questions we consider are: (1) Of what use is FH information? and (2) How valuable is it? At a simple level, the answer to the first is that FH can be used to differentiate risk, motivate individuals to seek care or change behavior, and target interventions more ef- fectively. A simple answer to the second question is that the value of FH is the improvement it brings about in desirable health outcomes (taking into account the potential costs associated with obtaining and using FH information). We start by outlining the main components of a decision analytic approach and issues to consider when exploring the value of FH. We then present an illustration based on using FH of colorectal cancer (CRC) to improve rates of CRC screening. The Elements of Decision Analysis Decision analysis is a systematic method for making decisions when outcomes are uncertain. The basic building blocks of a decision analysis are (1) decisions, (2) outcomes, and (3) probabilities. A decision is a choice made by a person, group, or organization to select a course of action from among a set of mutually exclusive alternatives. The decision maker compares expected outcomes of available alternatives and chooses the best among them. This choice is repre- sented by a decision node, a square, with branches representing the choices in the decision-tree diagram (for example, see Figure 1). Because a decision is chosen and does not occur by chance, no probability is attached to it. For example, after receiving information that a person has FH of a disease, that person may decide (choose) to seek medical advice or choose not to do so. Outcomes are the chance events that occur in response to a decision. Outcomes can be intermediate or final. Intermediate outcomes are followed by more decisions or chance events. For example, if a person decides to seek medical care for hypertension, his or her physician may advise behavior modification alone or a combination of behavior modification and drug therapy. From the person’s perspective, this is a chance outcome; from a healthcare provider’s perspective, it is a decision. An outcome can be intermediate or final depending upon the context of the decision problem. For example, hypertension control may be the final outcome in a decision analysis focusing on hyperten- From the National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, (Tyagi); and the National Center for Environmen- tal Health, Centers for Disease Control and Prevention, Atlanta, Georgia, (Morris) This work was done while Tyagi was a Steven M. Teustch Preven- tion Effectiveness Fellow at the Cardiovascular Health Branch, Divi- sion of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC) and Morris was an Epidemic Intelli- gence Service Officer, Office of Genomics and Disease Prevention, National Center for Environmental Health, CDC. Address correspondence to: Anupam Tyagi, PhD, 1161 Greenbriar Circle, Decatur GA 30033. E-mail: TyagiAnupam@aol.com. 199 Am J Prev Med 2003;24(2) 0749-3797/03/$–see front matter © 2003 American Journal of Preventive Medicine • Published by Elsevier doi:10.1016/S0749-3797(02)00594-9