Roach and Marrero achieved over time through successful therapeutic interventions. Putting risk information in context While probability-based information regarding personal CVD risk is a fundamental component of the overall CVD risk message, many patients do not reliably understand and interpret numerical probability statistics (probabilities, percent risk, risk ratios, etc). The understanding of numerical probability of risk is greatly facilitated when the information is presented in a context that makes it relevant to the individual patient (Rothman et al 1999). Several approaches have been successful in providing this all-important context. Assigning a specific level of risk to an event (eg, high, intermediate, low) imparts a meaning to the stated probability that is readily understandable regardless of mathematical ability (Rothman et al 1999). For example, an individual with a 10-year risk of a CVD event of greater than 20% is considered to be at high risk for the event (NCEP 2001). While “20%” could be variably interpreted by patients to reflect different degrees of risk, when they are informed that this represents “high risk”, a clear message is sent regarding the seriousness of the risk and the need to act accordingly. Comparing the patient’s personal risk to “average” risk has been shown to be effective in communicating quantitative risk information (Rothman et al 1999). In the case of CVD risk, individual risk could be compared with that of an age- and gender-matched individual without diabetes or other CVD risk factors. For example, a 56 year- old white man with a 10-year history of DM, a systolic blood pressure of 163 mmHg, a total cholesterol of 205, and an HDL cholesterol of 35 has a 35% 10-year risk of having a CVD event according to the UKPDS Risk Engine. Using the Framingham-based risk-calculator available at http://rover2.nhlbi.nih.gov/guidelines/cholesterol/, a man of the same age without diabetes, hypertension, or hyper- lipidemia has a 10-year CVD risk of 10%, less than one- third the risk of the individual with diabetes. This clearly sends the message that the individual is at greatly increased risk compared with a similar person without risk factors. Finally, the provision of an “action standard”, ie, the level at which action is required or recommended by health experts, has also been shown to facilitate the communication of risk information (Avis et al 1989; Gerrard et al 1999; Lipkus and Hollands 1999; Rothman et al 1999). When the individual’s blood glucose, blood pressure, and lipid levels are placed alongside recommended treatment targets, this serves to illustrate “how abnormal” his or her clinical parameters are compared with those associated with minimal risk. These aspects of the risk message can be most effectively presented using simple and colorful graphical presentations designed to make the information easily understood. Bar charts are well suited for depicting percentages and proportions, eg, comparing the patient’s risk with average risk and for depicting the patient’s clinical parameters in comparison to recommended target ranges as described above (Lipkus and Hollands 1999). Bar-chart presentations can be created on pre-printed forms that contain a grid onto which patients’ clinical information and risk status can be depicted alongside normal ranges, treatment goals, and risk estimates for similar healthy persons. The illustration of absolute risk has been accomplished using figures that display probabilities in familiar terms (Lipkus and Hollands 1999). For example, the patient with a 10-year CVD risk of 35% might be shown a picture of a chart showing 35 Xs and 65 dots, in which the Xs represent the proportion of individuals with the same level of CVD risk. Pre-printed illustrations of 15% risk, 20% risk, etc can be prepared and patient’s can be provided with the graphic that most closely reflects their level of risk. “How can I reduce my risk?” The final critical element in the risk communication sequence is to provide patients with information about how they can work with their healthcare providers to reduce their risk (Gerrard et al 1999). The understanding of individual risk and what can be done to decrease risk may present a unique “teachable moment” in the therapeutic relationship in which the patient is receptive to the adoption of behavioral and therapeutic interventions. As suggested by the “stages of change” model (Ruggiero and Prochaska 1993), providers should act on the patient’s willingness to accept some therapeutic changes while respecting decisions not to adopt others. For example, if the patient is willing to begin treatment with a cholesterol-lowering medication to address hyperlipidemia, but is still contemplating the initiation of insulin therapy, the patient should not be pressured to make both changes simultaneously. If patients are given the opportunity to participate in decisions that affect their healthcare, including the asking of questions and the expression of concerns, they may be more likely to remain compliant with prescribed therapies and to report side effects before discontinuing medications. 304 Vascular Health and Risk Management 2005:1(4)