In this piece, Kavita and her co-authors explore the questions: Why does language matter and why is it so important in medicine and healthcare? How has language and messaging influenced pandemic outcomes in the UK?
Mind your language: Language and outcomes in the COVID-19 pandemic, and lessons for the future
Language matters in medicine and healthcare. What we say and how we say it influences how patients and the public respond to health threats.[1-3] A key determinant of our responses to these threats is the language we use to convey our beliefs and understanding regarding the causes of the threat, its duration and consequences, and the ability of the individual and treatment to manage the threat. This raises questions about how language has shaped the COVID-19 response in the UK [4, 5]. We consider three examples of how language may have informed the behaviour of the public, as well as the decisions of politicians and policy-makers.
The first concerns the language surrounding the disease itself. Leaders of several countries, including the UK, Brazil and USA, initially drew parallels between COVID-19 and ‘flu’. Did this shape the reactions of politicians and policy-makers? It would appear that familiarity with influenza led some countries to underestimate the consequences of COVID-19 for public health. In the UK, despite growing evidence of a public health crisis elsewhere in Europe, the first quarter of 2020 was characterised by unrestricted travel, continuation of large-scale public events, failure to invest in contact tracing and mass testing and the delayed decision to initiate a UK wide lockdown. Regarding COVID-19 as no more serious than influenza, and transmitted in the same way, resulted in a failure to act in a manner that was timely and proportionate to the health threat. This may have contributed to the excess deaths (about 37,000 deaths in the first wave of infections in the UK), a figure that could have been halved if the lockdown had been initiated even one week earlier.[6]
The second concerns the language used to promote risk-reduction behaviours. The UK response to COVID-19 was initially dominated by language (‘stay at home’) and policies (lockdown) that ascribed ‘agency’ to the virus – that is, the virus was the entity determining the risk of disease. Accordingly, ‘stay at home’ and lockdown aimed to sequester the public away from the virus causing the disease. Such ‘agency assignment framing’ has been effective in heightening perceptions of threat in the context of other viruses;[7, 8] and was probably critical in promoting engagement with lockdown. However, this continued framing of the virus as the ‘active agent’ may have undermined efforts to encourage the public to engage in risk reduction behaviours as lockdowns eased. Put another way, if lockdowns control the risk of disease, then the easing of lockdowns also signal that the risk of disease has retreated. Yet, every time lockdown restrictions have eased, ‘agency’ for disease reduction has not shifted away from the virus itself to the public and their behaviours. Counterintuitively, the language used has often promoted behaviours likely to increase the risk of COVID-19 infection (‘Eat Out to Help Out’) and the public then vilified for the consequent increase in infection.[9]
This failure to shift ‘agency’ for infection risk to the public, against a backdrop of high levels of community transmission,[10, 11] likely contributed to escalating rates of infection after the UKs first two lockdowns. Indeed, the risk of history repeating itself is now heightened by language around ‘inevitable 3rd waves’, ‘living with the virus’, ‘acceptable deaths’, and vaccines being the route by which we ‘reclaim our lives’. All of these ascribe very little agency to the public in preventing and containing COVID-19 infection.
The final example relates to the language used to describe variants. At the time of writing, the UK is emerging from its third lockdown in response to a second wave of COVID-19 infections attributed to the so-called ‘Kent’ variant (SARS CoV-2 B.1.1.7) which has been associated with higher transmissibility. While B.1.1.7 played an important role in the acceleration of the second wave, virus transmission is influenced by the virus, the host and the behaviours of the host. Thus, the second wave was unlikely to have been due to the virus alone. Cases of infection were rising even before B.1.1.7 became dominant. The behaviours of the communities in which it was seeded and the public health policies overseeing these communities will have played a role.
This appears plausible since the emergence of the variant coincided with restrictions being eased and school re-openings in the UK. Thus, while ascribing of ‘agency’ to the virus probably facilitated early compliance with the lockdown, in the future it will increasingly be the ‘agency’ of the public and the clarity of the guidance they receivethat will be crucial in controlling and containing the virus in the UK as lockdown is eased. This is clearly and painfully being demonstrated in India where the language used by many already assumes that the current unprecedented surge in infections is due to increased transmissibility of the B.1.617.2 variant. But could it also be due to the behaviours of an unsuspecting public encouraged to engage in religious, sporting and political events without adequate mitigation?
The variation between countries in their public health response to, and experiences of the COVID-19 pandemic has been striking.[12] In the UK, the public health response has been found wanting: culminating in 2020 in one of the worst excess death rates in the world and the deepest economic recession in the G7.[13]. Perhaps it is time to examine how language may be used to catalyse more effective changes in both policy and the public in the future.
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References
[1] Petrie KJ, MacKrill K, Derksen C, Dalbeth N. An illness by any other name: The effect of renaming gout on illness and treatment perceptions. Health Psychol. 2018;37:37-41.
[2] Dalbeth N, Petrie KJ. It’s time to change the name of gout. In: The BMJ Opinion, editor.2018.
[3] Leventhal H, Phillips LA, Burns E. The Common-Sense Model of Self-Regulation (CSM): a dynamic framework for understanding illness self-management. J Behav Med. 2016;39:935-46.
[4] Ghio D, Lawes-Wickwar S, Tang MY, Epton T, Howlett N, Jenkinson E, et al. What influences people’s responses to public health messages for managing risks and preventing infectious diseases? A rapid systematic review of the evidence and recommendations. PsyArXiv Preprints. 2020.
[5] Barocas J, Gandhi M. Harm reduction principles can help us restore trust in public health messaging on covid-19. In: The BMJ Opinion, editor.2020.
[6] https://www.imperial.ac.uk/news/211673/covid-19-england-analysis-first-waves/ [Accessed 09/04/2021].
[7] McGlone MS, Bell RA, Zaitchik ST, McGlynn J, 3rd. Don’t let the flu catch you: agency assignment in printed educational materials about the H1N1 influenza virus. J Health Commun. 2013;18:740-56.
[8] McGlone MS, Stephens KK, Rodriguez SA, Fernandez ME. Persuasive texts for prompting action: Agency assignment in HPV vaccination reminders. Vaccine. 2017;35:4295-7.
[9] Jia R, Ayling K, Chalder T, Massey A, Broadbent E, Morling JR, et al. Young people, mental health and COVID-19 infection: the canaries we put in the coal mine. Public Health. 2020;189:158-61.
[10] Han E, Tan MMJ, Turk E, Sridhar D, Leung GM, Shibuya K, et al. Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe. Lancet. 2020;396:1525-34.
[11] https://www.bbc.co.uk/news/uk-52858392 [Accessed 09/04/2021].
[12] Balmford B, Annan JD, Hargreaves JC, Altoè M, Bateman IJ. Cross-Country Comparisons of Covid-19: Policy, Politics and the Price of Life. Environmental and Resource Economics. 2020;76:525-51.
[13]https://www.ons.gov.uk/economy/grossdomesticproductgdp/articles/internationalcomparisonsofgdpdu
About the authors
Professor Kavita Vedhara FAcSS, Professor of Health Psychology University of Nottingham, UK
Professor Keith Petrie, Professor in Psychological Medicine, University of Auckland, New Zealand
Dr Deepti Gurdasani, Senior Lecturer in Machine Learning, Queen Mary University of London, UK
Dr Joanne R. Morling, Clinical Associate Professor in Public Health, University of Nottingham, UK
Professor Jonathan K. Ball, Professor of Molecular Virology, University of Nottingham, UK
The perspectives expressed in these commentary pieces represent the independent views of the authors, and as such they do not represent the views of the Academy or its Campaign for Social Science.
This article may be republished provided you place the following statement and link at the top of the article:
This article was originally commissioned and published by the Campaign for Social Science as part of its Covid-19 programme.