This Interpretation is based on work published in: <a target="_blank" rel="noopener noreferrer nofollow" href="https://doi.org/10.1098/rsos.201721">https://doi.org/10.1098/rsos.201721</a>&nbsp;The interviews and the survey responses combined help give us an overall view of the public’s information needs regarding their personal COVID-19 risk, as of July 2020.The majority of participants clearly showed a desire for quantitative information about COVID-19 (much of which was not, at the time of this survey, available). The comments from the primary care physicians suggested that they viewed patients as perhaps becoming used to being given simplified instructions rather than information on which to base their own decisions. However, the results from the surveys implied that the majority of the public would actually appreciate being given more detailed information on which to base their own risk decisions.The quantitative survey emphasized that there was a clear public concern about the virus and its potential consequences, and people’s perception of chances of themselves dying of the virus if they caught it were not highly correlated with the actual chances as calculated from a personalized risk calculator given the health information participants gave us, with only a weak relationship between the two.Some of this discrepancy seemed very likely to be due to the way that most people think about ‘risks’—as a subjective impression, not easily translated into a numerical concept [1]. Even when pushed to quantify their feeling of risk, most members of the public in our interviews described the risk in terms of a ‘persona’ that they had in their mind to represent that level of risk: a person who embodied the major risk factors that would exemplify that level of risk (such as age and relevant health attributes).Given that only a minority of people in the surveys wanted simply to be told that their risk fell into a simple category such as ‘low’ (as well as the practical and ethical difficulties of doing that given that an absolute risk and relative risk would need to be combined in some way), it would be useful to consider further ways in which to help people understand the likelihood figure of them dying from COVID-19 should they catch it.The other themes emerging from the interviews were useful in determining important endpoints to measure in future work developing a personalised communication tool: levels of perceived uncertainty, perceptions of the quality of the underlying evidence, trust (in the data itself and in the source of the data) and degree of worry associated with the results. Trustworthiness is a crucial dimension to consider during the design of any communication—something previously emphasized in the risk communications literature (e.g. [2]). In line with previous research (e.g. [3]), the interviews suggested that trustworthiness and relevance were enhanced by appropriate branding and making it clear that the results presented were based on research (ideally from a trustworthy source) and on relevant data. Previous experience (e.g. [3]) suggests that people are sensitive to cues of quality of evidence (such as sizes of datasets and relevance of the population on which the evidence is based), as well as assessing the information’s source.

Interpretation: what the public already know, want to know and need to know, about their personal risks from COVID-19

Main text: This Analysis is based on work published in: <a target="_blank" rel="noopener noreferrer nofollow" href="https://royalsocietypublishing.org/doi/10.1098/rsos.201721">https://royalsocietypublishing.org/doi/10.1098/rsos.201721</a>Data analysis was conducted by one member of the team (AL) and was descriptive in nature [1]. Analysis was done by populating a table per round of interviews with data from the partial transcriptions, whereby each question, or appropriate groups of questions, represented a row of the table, with each column representing a participant. Once the table had been populated, the researcher summarized each row of answers. In conjunction with this approach, there was some use of quantitative content analysis methods [2] so the data could more easily be used for making decisions regarding risk communication. If multiple participants made similar comments, these comments formed inductive ‘codes’, which were then weighted through the use of descriptive statistics. While undertaking the descriptive analysis, themes were informally identified within the data. As this was a purely qualitative analysis, there was no need to assign descriptive statistics to each theme. A formal thematic analysis [3] was not carried out as time was limited and this was not considered a primary output of the research. Once all the analyses for each round had taken place, it was discussed with two other team members (LF and GL). These two other team members had either partially transcribed the interviews or had carried out their own rapid descriptive analysis using the same populated table. Within these discussions, the researchers identified and resolved any discrepancies between their impressions of the data, as well as adding any missing points to the existing analyses. The alterations and additions made were minimal.In a descriptive analysis (purely qualitative, with no descriptive statistics), we extracted emerging themes from the interviews.Emerging theme: infantalization vs empowermentOne of the themes identified within the interviews with primary care physicians was that of infantilization and empowerment. Some physicians spoke strongly about how they felt the public in the UK were being ‘infantilized’ by clinicians who implied that ‘every risk can be managed away’ and by the government for not being honest about the risks that some socializing can bring. They also recounted how patients ask them what they should do, wanting an authority instead of ‘the risk of thinking for themselves’. However, they also felt it was important that patients made their own decision about certain risks, as only the patient’s values can inform that. Additionally, one physician felt such risk information could empower patients to ‘have those conversations with the people who are making the decisions’, for example, employers.Emerging theme: conflict between politics and medical scienceAnother theme recognized within the primary care physician interviews was related to conflicting medical advice whereby the participants spoke of experiencing contention between the advice given by the government, which they viewed as political, and the advice they wanted to give, which they viewed as clinical.Emerging theme: trustIn interviews, we specifically asked the participants if they would trust an outcome from an online tool that showed them a personalised risk of dying from COVID-19, and what would make them trust it more. Sub-themes (which were unprompted) related to trust were identified:Trust related to the dataParticipants questioned whether the data were collected in a rigorous way, how accurate the data were and whether it was being ‘tampered with’ once collected.Trust related to the source 11Some participants identified the University of Cambridge logo displayed on materials relating to this study; knowing that the source of the tool was a university institution that undertook research gave them trust in the outcome.Trust related to the methods behind the resultsSuggestions that the risk information was produced through careful research (indicating the high quality of underlying evidence) affected the participants’ perceptions. One said: ‘It’s important that people know it’s not been plucked out the air, like they think it might be, but that it’s actually based on data’.Trust related to the mediumReferring to increasing incidences of online scams and other malicious online activity, one participant commented: ‘People are careful these days of anything online’, going on to suggest that the inclusion of institutional logos was particularly important in gaining people’s trust when communicating online.Emerging theme: uncertaintyUncertainty was identified as a theme within interviews, with many participants commenting that the risk score itself was inherently uncertain. This did not seem to affect their feelings of how useful a tool would be, nor their trust in the guidance it might provide. Some participants indicated that the range around the risk score presented in the mock-ups was superfluous, possibly because they instinctively acknowledged the uncertainty of the result. Some also felt that the data which could be used to calculate the risk score was uncertain. This uncertainty of the data, and whether it was accurate and reliable, did seem to affect trust. These findings were also broadly similar within the primary care physicians, though unlike the public, uncertainty about the risk score itself was attributed to applying population-level data to individuals.Emerging theme: worryThroughout the general public interviews, numerous participants spoke about how the tool may worry them or others. Many primary care physicians also commented on how the tool could have a negative effect on the mental well-being of patients. By contrast, one primary care physician detailed how a tool like this would empower them to talk openly with their patients who were unnecessarily concerned, which for some had resulted in deteriorating mental health.&nbsp;&nbsp;1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sandelowski M. 2000 Whatever happened to qualitative description? Res. Nurs. Health 23, 334–340. (doi:10.1002/1098-240X(200008)23: 4&lt;334::AID-NUR9&gt;<a target="_blank" rel="noopener noreferrer nofollow" href="http://3.0.CO">3.0.CO</a>;2-G)2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Neuendorf KA, Kumar A. 2015 Content analysis. Int. Encycl. Polit. Commun. 1, 1–10.3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Braun V, Clarke V. 2006 Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101. (doi:10.1191/1478088706qp063oa)&nbsp;&nbsp;

Analysis of interviews: what the public already know, want to know and need to know, about their personal risks from COVID-19

Results of interviews: finding out what the public already know, want to know and need to know, about their personal risks from COVID-19 Authors: Alice C. E. Lawrence 0000-0002-8771-4844 Leila Finikarides 000-0002-0414-3207 Giulia Luoni 0000-0003-3582-5953Funding: This work was funded by the Winton Centre for Risk &amp; Evidence Communication at the University of Cambridge, which is financed by a donation from the David &amp; Claudia Harding Foundation.Main text: These Results are based on work published in: https://doi.org/10.1098/rsos.201721This study was approved by the Psychological Research Ethics Committee at the University of Cambridge (PRE.2020.070).All stages of the study were carried out between the 3 June 2020 and 23 July 2020.Qualitative interviews were carried out via video conferencing due to the pandemic. Recruitment initially used convenience and snowball sampling techniques. Participants from previous studies, and individuals who were otherwise known to the study team were contacted. They were asked to share an invitation to participate with family and friends. This led to a list of around 85 participants for whom we had basic demographic data (age, ethnicity and sometimes health status). Purposive sampling from this participant list led to interviews with a broad demographic range of individuals in each round of testing.Prior to interviews, participants were given relevant information about the study so they could determine whether they wanted to take part. If participants had any questions pertaining to the study, these were answered by the study team. Informed consent was taken at the beginning of each interview via an online survey platform. Participants received a £10 store voucher as payment for taking part.The discovery phase of the qualitative work consisted of one round of interviews each with the general public (n = 6) and primary care physicians (n = 7). The alpha phase consisted of four rounds of interviews (R1: n = 6, R2a: n = 4, R2b: n = 4, R3: n = 8) with the general public only. Five interviewers carried out semi-structured interviews using video call technology. Interviews took on average 1 h to complete (range 0.5–2.25 h). Calls were recorded and partially transcribed by two members of the team (AL and LF).Characteristics of participants in the qualitative interviews<table><tbody><tr><td colspan="1" rowspan="1">Phase</td><td colspan="1" rowspan="1">Number of participants</td><td colspan="1" rowspan="1">Ages</td><td colspan="1" rowspan="1">Genders</td><td colspan="1" rowspan="1">Ethnicities</td><td colspan="1" rowspan="1">Experience with COVID</td></tr><tr><td colspan="1" rowspan="1">Discovery - public</td><td colspan="1" rowspan="1">6</td><td colspan="1" rowspan="1">22-73</td><td colspan="1" rowspan="1">2 male, 4 female</td><td colspan="1" rowspan="1">5 white, 1 BAME</td><td colspan="1" rowspan="1">1 had a relative die from COVID-19, and knew others who had it (P4)</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">Alpha round 1</td><td colspan="1" rowspan="1">6</td><td colspan="1" rowspan="1">34-76</td><td colspan="1" rowspan="1">4 male, 2 female</td><td colspan="1" rowspan="1">5 white, 1 BAME</td><td colspan="1" rowspan="1">1 knew someone who had it (P10)</td></tr><tr><td colspan="1" rowspan="1">Alpha round 2 (a &amp; b)</td><td colspan="1" rowspan="1">8</td><td colspan="1" rowspan="1">19-87</td><td colspan="1" rowspan="1">4 male, 4 female</td><td colspan="1" rowspan="1">7 white, 1 BAME</td><td colspan="1" rowspan="1">1 had symptoms, assumed COVID-19 (P19) 5 knew people who had it (P13; P16; P17; P19; P20)3 knew people who died from it (P14; P18; P20)</td></tr><tr><td colspan="1" rowspan="1">Alpha round 3</td><td colspan="1" rowspan="1">8</td><td colspan="1" rowspan="1">23-71</td><td colspan="1" rowspan="1">4 male, 4 female</td><td colspan="1" rowspan="1">7 white, 1 BAME</td><td colspan="1" rowspan="1">1 has symptoms, assumes COVID-19, was awaiting test results (P23)3 knew people who had it (P24; P27; P28) 2 knew people who died from it (P26; P22)</td></tr><tr><td colspan="1" rowspan="1">Discovery - primary care physicians</td><td colspan="1" rowspan="1">7</td><td colspan="1" rowspan="1">35-65</td><td colspan="1" rowspan="1">2 male, 5 female</td><td colspan="1" rowspan="1">5 white, 2 BAME</td><td colspan="1" rowspan="1">6 out of 7 GPs had patients who were suspected of having it/did have it (1 GP wasn't practicing throughout the pandemic). They came across it frequently during the peak, but less so by the time of the interviews.</td></tr></tbody></table>Full qualitative data is not publicly available in order to help protect the anonymity of the participants.When asked how they felt about a tool in which they would see a risk of death, and whether it would be useful to them, participants said they would use the tool for decision-making (e.g. relating to going to work, school, social events, travel) (11 out of 22; 50%) or out of curiosity (‘...it would just be interesting to know if it was something you could beat, or if it would beat you.’) (7 out of 22; 32%), but recognized others might not want to as it may be anxiety-inducing (6 out of 22; 27%). All participants felt comfortable seeing it in the context of the study.Other information that participants mentioned being useful included: how infectious an individual with COVID-19 is; what the treatment would be if you did catch it; how distance affects infection rate; likelihood of hospitalization; likelihood of long-term effects; likelihood of catching it and how much each suggested piece of mitigation advice reduces that; likelihood of transmitting it (and again, effectiveness of preventative measures); and an idea of the likely severity of symptoms if you get it.During interviews, when asked to quantify risks from COVID-19, participants rarely used numbers without being prompted, and instead naturally described the characteristics of people they would consider ‘high’ or ‘low’ risk (11 out of 22; 50%), for example, ‘the older you get the more at risk you are’ or, low-risk individuals are ‘in good health’. However, when pushed to quantify the risk, participants gave estimates as seen in Table 1.Table 1: Participants’ numerical interpretations of ‘high’ and ‘low’ risk of dying from COVID-19 during interviews<table><tbody><tr><td colspan="1" rowspan="1">Participant number</td><td colspan="1" rowspan="1">‘Low risk’ estimate</td><td colspan="1" rowspan="1">‘High risk’ estimate</td></tr><tr><td colspan="1" rowspan="1">11</td><td colspan="1" rowspan="1">1/10</td><td colspan="1" rowspan="1">10/10</td></tr><tr><td colspan="1" rowspan="1">7</td><td colspan="1" rowspan="1">1/10</td><td colspan="1" rowspan="1">8/10</td></tr><tr><td colspan="1" rowspan="1">16</td><td colspan="1" rowspan="1">-</td><td colspan="1" rowspan="1">90%</td></tr><tr><td colspan="1" rowspan="1">13</td><td colspan="1" rowspan="1">&lt;50%</td><td colspan="1" rowspan="1">&gt;90%</td></tr><tr><td colspan="1" rowspan="1">20</td><td colspan="1" rowspan="1">1%</td><td colspan="1" rowspan="1">50%</td></tr><tr><td colspan="1" rowspan="1">18</td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">100%</td></tr><tr><td colspan="1" rowspan="1">21</td><td colspan="1" rowspan="1">&lt;20%</td><td colspan="1" rowspan="1">-</td></tr><tr><td colspan="1" rowspan="1">22</td><td colspan="1" rowspan="1">&lt;3/10</td><td colspan="1" rowspan="1">7/10</td></tr><tr><td colspan="1" rowspan="1">23</td><td colspan="1" rowspan="1">2/10</td><td colspan="1" rowspan="1">8/10</td></tr><tr><td colspan="1" rowspan="1">24</td><td colspan="1" rowspan="1">&lt;1%</td><td colspan="1" rowspan="1">70%</td></tr><tr><td colspan="1" rowspan="1">25</td><td colspan="1" rowspan="1">&lt;40%</td><td colspan="1" rowspan="1">&gt;70%</td></tr></tbody></table>

This study was approved by the Psychological Research Ethics Committee at the University of Cambridge (PRE.2020.070). 

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