Background: Various types of framing can influence risk perceptions, which may have an impact on treatment decisions and adherence. One way of framing is the use of verbal terms in communicating the probabilities of treatment effects. We systematically reviewed the comparative effects of words versus numbers in communicating the probability of adverse effects to consumers in written health information.
Methods: Nine electronic databases were searched up to November 2012. Teams of two reviewers independently assessed studies.
Inclusion criteria: randomised controlled trials; verbal versus numerical presentation; context: written consumer health information.
Results: Ten trials were included. Participants perceived probabilities presented in verbal terms as higher than in numeric terms: commonly used verbal descriptors systematically led to an overestimation of the absolute risk of adverse effects (Range of means: 3% - 54%). Numbers also led to an overestimation of probabilities, but the overestimation was smaller (2% - 20%). The difference in means ranged from 3.8% to 45.9%, with all but one comparison showing significant results. Use of numbers increased satisfaction with the information (MD: 0.48 [CI: 0.32 to 0.63], p < 0.00001, I2 = 0%) and likelihood of medication use (MD for very common side effects: 1.45 [CI: 0.78 to 2.11], p = 0.0001, I2 = 68%; MD for common side effects: 0.90 [CI: 0.61 to 1.19], p < 0.00001, I2 = 1%; MD for rare side effects: 0.39 [0.02 to 0.76], p = 0.04, I2 = not applicable). Outcomes were measured on a 6-point Likert scale, suggesting small to moderate effects.
Conclusions: Verbal descriptors including "common", "uncommon" and "rare" lead to an overestimation of the probability of adverse effects compared to numerical information, if used as previously suggested by the European Commission. Numbers result in more accurate estimates and increase satisfaction and likelihood of medication use. Our review suggests that providers of consumer health information should quantify treatment effects numerically. Future research should focus on the impact of personal and contextual factors, use representative samples or be conducted in real life settings, measure behavioral outcomes and address whether benefit information can be described verbally.