This Problem is based on work published in: https://doi.org/10.1098/rsos.201721
When a new threat emerges, such as that presented by the SARS-CoV-2 virus, it is very difficult for individuals to assess the risk it poses to them personally: how likely they are to be affected by it, and how severely. These two aspects are important components of a person’s ‘risk perception’—a subjective feeling—which also incorporates emotional components such as worry, and drives choices that people make in their day-to-day lives. The emotional components of risk perception are affected by feelings of control, knowledge about the risk and other aspects, which can cumulatively make a risk be perceived as a ‘dread risk’ [1,2]. Risk perceptions are key drivers of behaviour. High risk perceptions can lead people to engage in protective behaviours [3–5], but can also lead to worry, anxiety and behaviours whose harms may be greater than their benefits [6–8]. Excessively low perceptions can lead to inadequate protective behaviours, which has both individual and societal consequences. Providing information that can influence people’s perception of a risk, then, has to be done with care and be based on an understanding of what effect it is likely to have.
As countries accumulate more data on mortality and hospitalization rates from COVID-19, as well as the proportion of people who suffer long-term effects, it is possible to produce increasingly personalized risk calculators (e.g. [9]). The issue, then, is how to communicate this potentially highly emotional information, which may challenge people’s prior perceptions about the risk, triggering identity-protective cognition (e.g. [10]), to diverse audiences—and what effect different presentations of such a risk are likely to have.
Risks from COVID-19 fall into one of the more difficult areas to communicate for several reasons. The thought of the disease can provoke strong emotion (or ‘dread’ [1]), which is known to affect risk perceptions [11–14]. For many people, the probability of dying from COVID-19 is low (less than 0.1% for most), making the numbers difficult to comprehend [15–17]. However, there is very wide variation between people meaning that it is difficult to represent the range of risks on a single, linear scale. It is well known that even relatively subtle changes in methods of communication can have profound effects on audiences’ perceptions of risks and their behaviours (e.g. [18–20]) and hence careful empirical work alongside qualitative work with the intended audience is key to designing effective communication messages.
One of the first decisions for a communicator is the aim of the communication. Communication messages lie on a continuum from a purely persuasive design (e.g. many public health messages) where the desired outcome is behavioural change; to purely informative (e.g. informed consent processes) where the outcome of interest is objective comprehension only. Some authors have described ‘risk communication’ as concentrating on informing and ‘crisis communication’ as concentrating on behaviour change (e.g. [21]).
Many trying to communicate the risk from COVID-19 might be aiming for some level of behavioural change (e.g. people adopting either more or fewer actions to mitigate the risks from the disease), which might mean, for instance, placing an individual into a risk band (e.g. ‘high risk’) with tailored behavioural advice. Others may be aiming to be as neutral as possible, allowing individual interpretation of the risk, which will naturally vary between individuals.
The first approach communicates less information to the audience and requires less from them, so may be preferred by some, while others may find its persuasive intent less trustworthy. The second approach avoids some practical difficulties. For example, the same additional absolute risk of death would present a very different prospect to, say, a 9-year-old and a 90-year-old because of their different background levels of risk, making automated categorization of risks difficult. Simply presenting the absolute risks and allowing the audience to interpret them avoids that difficulty, but makes the communication element more challenging.
Slovic P, Fischoff B, Lichtenstein S. 1981 Perceived risk: psychological factors and social implications. Proc. R. Soc. Lond. A 376, 17–34. (doi:10.1098/rspa.1981.0073)
Fischhoff B, Slovic P, Lichtenstein S, Read S, Combs B. 1978 How safe is safe enough?
A psychometric study of attitudes
towards technological risks and benefits. Policy Sci. 9, 127–152. (doi:10.1007/ BF00143739)Leppin A, Aro AR. 2009 Risk perceptions related to SARS and avian influenza: theoretical foundations of current empirical research.
Int. J. Behav. Med. 16, 7–29. (doi:10.1007/ s12529-008-9002-8)Bish A, Michie S. 2010 Demographic and attitudinal determinants of protective behaviours during a pandemic: a review. Br. J. Health Psychol. 15, 797–824. (doi:10. 1348/135910710X485826)
Dryhurst S, Schneider CR, Kerr J, Freeman ALJ, Recchia G, van der Bles AM, Spiegelhalter D, van der Linden S. 2020 Risk perceptions of COVID-19 around the world. J. Risk Res. 23, 1466–4461. (doi:10.1080/13669877.2020. 1758193)
Jones JH, Salathé M. 2009 Early assessment of anxiety and behavioral response to novel swine- origin influenza A(H1N1). PLoS ONE 4, 2–9. (doi:10.1371/journal.pone.0008032)
Takebayashi Y, Lyamzina Y, Suzuki Y, Murakami M. 2017 Risk perception and anxiety
regarding radiation after the 2011 Fukushima nuclear power plant accident: a systematic qualitative review. Int. J. Environ. Res. Public Health 14, 1306. (doi:10.3390/ijerph14111306)Olagoke AA, Olagoke OO, Hughes AM. 2020 Exposure to coronavirus news on mainstream media: the role of risk perceptions and depression. Br. J. Health Psychol. 25, 865–874. (doi:10.1111/bjhp.12427)
Coggon D, Croft P, Cullinan P, Williams A. 2020 Assessment of workers’ personal vulnerability to covid-19 using ‘covid-age.’ Occup. Med. (Chic Ill) 70, 461–464. (doi:10.1093/occmed/kqaa150)
Kahan DM, Braman D, Gastil J, Slovic P, Mertz CK. 2007 Culture and identity-protective cognition. J. Empir. Leg. Stud. 4, 465–505. (doi:10.1111/j.1740-1461.2007.00097.x)
Finucane ML, Alhakami ALI, Slovic P, Johnson SM. 2000 The affect heuristic in judgments of risks and benefits. J. Behav. Decis. Mak. 13, 1–17. (doi:10.1002/(SICI)1099-0771(200001/ 03)13:1<1::AID-BDM333>3.0.CO;2-S)
Slovic P, Finucane ML, Peters E, MacGregor DG. 2007 The affect heuristic. Eur. J. Oper. Res. 177, 1333–1352. (doi:10.1016/j.ejor.2005.04.006)
Smith RD. 2006 Responding to global infectious disease outbreaks: lessons from SARS on the role of risk perception, communication and management. Soc. Sci. Med. 63, 3113–3123. (doi:10.1016/j.socscimed.2006.08.004)
Abrams EM, Greenhawt M. 2020 Risk communication during COVID-19. J. Allergy Clin. Immunol. Pract. 8, 1791–1794. (doi:10.1016/j. jaip.2020.04.012)
Halpern DF, Blackman S, Salzman B. 1989 Using statistical risk information to assess oral contraceptive safety. Appl. Cogn. Psychol. 3, 251–260. (doi:10.1002/acp.2350030305)
Camerer CF, Kunreuther H. 1989 Decision processes for low probability events: policy implications. J. Policy Anal. Manag. 8, 565–592. (doi:10.2307/3325045)
Lipkus IM. 2007 Numeric, verbal, and visual formats of conveying health risks: suggesting best practices and future recommendations. Med. Decis. Mak. 27, 696–713. (doi:10.1177/ 0272989X07307271)
Fischhoff B. 2012 Communicating risks and benefits: an evidence-based user’s guide. 8 March. Government Printing Office.
Lipkus IM, Hollands JG. 1999 The visual communication of risk. J. Natl Cancer Inst. Monogr. 27701, 149–163. (doi:10.1093/ oxfordjournals.jncimonographs.a024191)
Ancker JS, Senathrajah Y, Kukafka R, Starren JB. 2006 Design features of graphs in health risk communication: a systematic review. J. Am. Med. Inform. Assoc. 13, 608–619. (doi:10.1197/ jamia.M2115)