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While patients often receive risk information, exactly what constitutes being "informed" about health risks is often unclear. Patients have specific needs, such as avoiding being surprised by a possible outcome and making complex risk trade-off decisions. Yet all risk information is not equally informative for those needs. In this article, I present a taxonomy of seven risk concepts that vary in their inherent precision and evaluability. Congruent with the "less is more" concept, I argue that risk communications should use formats that are tailored to message recipients' specific informational needs. Simpler formats can be used when patients only need to order risks, while more complex numerical probability statements will be necessary when patients need to assess differences in risk magnitude and put those differences into meaningful context. Selecting need-congruent formats when designing communications about risks to patients is a novel approach that may better support patients' health care decision making.

AbstractIn close, disputed elections, outcomes can depend on determinations of voter intent for ballots that have been filled out improperly. We surveyed 899 adult Minnesotans during a time when the state's U.S. Senate election was still disputed and presented them with ambiguous ballots similar to ballots under dispute in the same election. We randomized participants to three experimental groups, across which we varied the names on the ballot. We found that participants' judgments of voter intent were strongly biased by their voting preferences (p < .002 in all four ballots).

While cancer screening guidelines increasingly recommend incorporating life expectancy estimates to inform screening decisions for older adults, little is known about how this happens in practice. This review summarizes current knowledge about primary care clinician and older adult (65+) perspectives about use of life expectancy to guide cancer screening decisions. Clinicians report operational barriers, uncertainty, and hesitation around use of life expectancy in screening decisions. They recognize it may help them more accurately weigh benefits and harms but are unsure how to estimate life expectancy for individual patients. Older adults face conceptual barriers and are generally unconvinced of the benefits of considering their life expectancy when making screening decisions. Life expectancy will always be a difficult topic for clinicians and patients, but there are advantages to incorporating it in cancer screening decisions. We highlight key takeaways from both clinician and older adult perspectives to guide future research.

Reports of randomized clinical trials often use survival curves to summarize clinical outcomes over time and graphically demonstrate evidence of treatment effectiveness. Survival curves can also be used in patient communications to display how health risks accumulate over time. In a randomized survey experiment, administered online, we tested whether people viewing survival curves appropriately adjust their risk perceptions to account for the duration shown. Internet users (N= 864) were recruited from a demographically balanced U.S. panel. Participants read about a hypothetical disease and then viewed one of four survival graphs that displayed mortality risks with and without treatment. Survival graphs showed either a visually large or visually small difference between treatments and were labeled to represent either 5‐year or 15‐year risk statistics. Participants then provided ratings of disease seriousness, as well as treatment effectiveness for each possible treatment. Variations in ratings corresponded more with visual dissimilarity than with changes in the statistical risk exhibited, with participants perceiving somewhat greater disease seriousness and significant differences in treatment effectiveness in large visual difference graphs. We conclude that when people interpret survival curves, they often fail to sufficiently account for the timeframe represented and perceive more risk and larger differences when identical risks are displayed over longer periods of time. We recommend that all presentations of survival graphics, whether to patients, physicians, or scientists, emphasize duration information (e.g., in the title) and remind readers that attending to graph axis labels is the only way to pierce these visual illusions.

Many health‐related decisions require choosing between two options, each with risks and benefits. When presented with such tradeoffs, people often make choices that fail to align with scientific evidence or with their own values. This study tested whether risk communication and values clarification methods could help parents and guardians make evidence‐based, values‐congruent decisions about children's influenza vaccinations. In 2013–2014 we conducted an online 2×2 factorial experiment in which a diverse sample of U.S. parents and guardians (n = 407) were randomly assigned to view either standard information about influenza vaccines or risk communication using absolute and incremental risk formats. Participants were then either presented or not presented with an interactive values clarification interface with constrained sliders and dynamic visual feedback. Participants randomized to the risk communication condition combined with the values clarification interface were more likely to indicate intentions to vaccinate (β = 2.10, t(399) = 2.63, p < 0.01). The effect was particularly notable among participants who had previously demonstrated less interest in having their children vaccinated against influenza (β = –2.14, t(399) = –2.06, p < 0.05). When assessing vaccination status reported by participants who agreed to participate in a follow‐up study six months later (n = 116), vaccination intentions significantly predicted vaccination status (OR = 1.66, 95%CI (1.13, 2.44), p < 0.05) and rates of informed choice (OR = 1.51, 95%CI (1.07, 2.13), p < 0.012), although there were no direct effects of experimental factors on vaccination rates. Qualitative analysis suggested that logistical barriers impeded immunization rates. Risk communication and values clarification methods may contribute to increased vaccination intentions, which may, in turn, predict vaccination status if logistical barriers are also addressed.

The arrival of the COVID-19 vaccine has been accompanied by increased discussion of vaccine hesitancy. However, it is unclear if there are shared patterns between general vaccine hesitancy and COVID-19 vaccine rejection, or if these are two different concepts. This study characterized rejection of a hypothetical COVID-19 vaccine, and compared patterns of association between general vaccine hesitancy and COVID-19 vaccine rejection. The survey was conducted online March 20-22, 2020. Participants answered questions on vaccine hesitancy and responded if they would accept the vaccine given different safety and effectiveness profiles. We assessed differences in COVID-19 rejection and general vaccine hesitancy through logistic regressions. Among 713 participants, 33.0% were vaccine hesitant, and 18.4% would reject a COVID-19 vaccine. Acceptance varied by effectiveness profile: 10.2% would reject a 95% effective COVID-19 vaccine, but 32.4% would reject a 50% effective vaccine. Those vaccine hesitant were significantly more likely to reject COVID-19 vaccination [odds ratio (OR): 5.56, 95% confidence interval (CI): 3.39, 9.11]. In multivariable logistic regression models, there were similar patterns for vaccine hesitancy and COVID-19 vaccine rejection by gender, race/ethnicity, family income, and political affiliation. But the direction of association flipped by urbanicity (P=0.0146, with rural dwellers less likely to be COVID-19 vaccine rejecters but more likely to be vaccine hesitant in general), and age (P=0.0037, with fewer pronounced differences across age for COVID-19 vaccine rejection, but a gradient of stronger vaccine hesitancy in general among younger ages). During the COVID-19 epidemic's early phase, patterns of vaccine hesitancy and COVID-19 vaccine rejection were relatively similar. A significant minority would reject a COVID-19 vaccine, especially one with less-than-ideal effectiveness. Preparations for introducing the COVID-19 vaccine should anticipate substantial hesitation and target concerns, especially ...

AbstractMany people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence‐based guidance, however, is complicated by inconsistencies in research terminology and researcher goals. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk‐benefit trade‐off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

AbstractPerceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.