LAUSR

Practically, every intervention decision that is made in the primary health care setting requires the application of a health benefit versus risk assessment. While there are many expert guidelines available that can help health practitioners with the decision-making process of, for example, cardiovascular disease and diabetes management, it is important that the supporting data used to develop these guidelines is understood as well. The effectiveness and safety of an intervention, preferably evaluated in a randomized clinical trial, is generally expressed in relative terms, such as the epidemiological measure “relative risk.” Absolute measures of risk, which predict the intervention’s benefit for an individual patient, are not always assessed or included in guidelines and therefore not commonly used by health practitioners to develop appropriate health care plans.1 It is recognized that most people do not have the required health care literacy and numeracy skills to understand health risks discussions in scientific research and to use this information to make fully informed decisions about their medical care or treatment.2 It is therefore the task of health practitioners to effectively and correctly communicate benefits and risks of proposed interventions with their patients, preferably with the help of decision aids such as brochures, videos, or webbased tools.3 Consequently, a health practitioner struggling to understand basic statistical concepts might miscommunicate risk, which may affect a patient’s ability to provide informed consent.2 This could ultimately result in patients making a decision that is incongruent with their preferences and values.1 This article offers a deeper understanding of some of the epidemiological measures used to report the safety and effectiveness of health interventions. The following explains what relative and the absolute risk measures are, and how they can be calculated, interpretated and applied. Further insight is provided with the help of 3 relevant examples.