The Drug Facts Box: Improving the communication of prescription drug information

Abstract

Communication about prescription drugs ought to be a paragon of public science communication. Unfortunately, it is not. Consumers see $4 billion of direct-to-consumer advertising annually, which typically fails to present data about how well drugs work. The professional label—the Food and Drug Administration's (FDA) mechanism to get physicians information needed for appropriate prescribing—may also fail to present benefit data. FDA labeling guidance, in fact, suggests that industry omit benefit data for new drugs in an existing class and for drugs approved on the basis of unfamiliar outcomes (such as depression rating scales). The medical literature is also problematic: there is selective reporting of favorable trials, favorable outcomes within trials, and “spinning” unfavorable results to maximize benefit and minimize harm. In contrast, publicly available FDA reviews always include the phase 3 trial data on benefit and harm, which are the basis of drug approval. However, these reviews are practically inaccessible: lengthy, poorly organized, and weakly summarized. To improve accessibility, we developed the Drug Facts Box: a one-page summary of benefit and harm data for each indication of a drug. A series of studies—including national randomized trials—demonstrates that most consumers understand the Drug Facts Box and that it improves decision-making. Despite calls from their own Risk Communication Advisory Committee and Congress (in the Affordable Care Act) to consider implementing boxes, the FDA announced it needs at least 3–5 y more to make a decision. Given its potential public health impact, physicians and the public should not have to wait that long for better drug information.

The sciences of science communication

“…For example, predicting a drug's risks and benefits for any patient requires behavioral science research, extrapolating from the controlled world of clinical trials to the real world in which people sometimes forget to take their meds and overlook the signs of side effects (6). Indeed, every science-related decision has a human element, arising from the people who design, manufacture, inspect, deploy, monitor, maintain, and finance the technologies involved.…”

“…If people need those estimates, then someone needs to run the numbers for them. Similarly, people cannot be expected to understand the uncertainty in a field, unless scientists summarize it for them (6,11,16,27,57,58).…”

“…For example, in the context of medical decisions (6,7,11), such analyses ask when it is enough to describe a procedure's typical effects and when decision makers need to know the distribution of outcomes (e.g., "most patients get mild relief; for some, though, it changes their lives"). Such analyses can also ask when uncertainties matter (e.g., "because this is a new drug, it will take time for problems to emerge") and how precise to be (e.g., will patients make similar choices if told that "most people get good results" and "researchers are 90% certain that between 30% and 60% of patients who complete the treatment will be disease-free a year later") (12,(14)(15)(16).…”

“…Messages should begin with the most valuable fact and then proceed as long as the benefits of learning more outweigh its costs-or recipients reach their absorptive capacity (13). Although one can formalize such analyses (7,11,12), just asking the question should reduce the risk of assuming that the facts that matter to scientists also matter to their audiences (2,6,8,9,11).…”

“…That can happen, for example, when the same communication tries to meet both the public's right to know, which requires saying everything, and its need to know, which requires saying what matters. Full disclosure is sometimes used in hopes of hiding inconvenient facts in plain sight, by burying them in irrelevant ones (6,7,10).…”