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Communicating personalized risks from COVID-19: guidelines from an empirical study.

ABSTRACT: As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative 'persona' who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other 'personas' illustrating people who would face risks of a wide range of different levels. These 'personas' should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels.

SUBMITTER: Freeman ALJ

PROVIDER: S-EPMC8059635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to "Google it." As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people's deviant behaviors toward public health safety measures.ObjectiveThe goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people's behavior toward public health measures.MethodsThis infodemiology study used Google Trends' worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models.ResultsThe PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords "biological weapon," "virus hoax," "common cold," "COVID-19 hoax," and "China virus"), conspiracy theory 1 (ConspTheory1; keyword "5G" or "@5G"), and conspiracy theory 2 (ConspTheory2; keyword "ingest bleach"). These principal components explained 84.85% of the variability. The principal components represent two measurements of public health safety guidelines-public health measures 1 (PubHealthMes1; keywords "social distancing," "wash hands," "isolation," and "quarantine") and public health measures 2 (PubHealthMes2; keyword "wear mask")-which explained 84.7% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword "@5G") was identified as a predictor of people's behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords "COVID-19," "hoax," "virus hoax," "common cold," and more) and ConspTheory2 (keyword "ingest bleach") with PubHealthMes1 (keywords "social distancing," "hand wash," "isolation," and more) were r=0.83 and r=-0.11, respectively, neither was statistically significant (P=.27 and P=.13, respectively).ConclusionsSeveral studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19.

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Communicating personalized risks from COVID-19: guidelines from an empirical study.

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