Study population and setting
The authors conducted an online survey of 1,522 Americans between May 6 and 13, 2020 to assess individual perceptions of risk related to COVID-19. The authors recruited respondents based on previously selected quotas for demographic variables (sex, age, household income, geographic region, and race). Respondents exhibiting misunderstandings of ratios and proportions were screened out. Respondents were asked to consider 1,000 people similar to themselves (based on age, zip code, sex, income, race, etc.), and to estimate the number of people who would contract COVID-19 in the next nine weeks, and how many would be hospitalized or die; they were then asked how these risks differed by age, sex, and race. Respondents were also asked to estimate the risk of non-COVID health issues (hospitalization, death, heart attack or cancer) amongst people that were similar to themselves. Participants were also asked a range of questions about their attitudes and personal behaviors related to COVID-19.
Summary of Main Findings
Compared to older participants, younger respondents (18-34 years) estimated higher risks for infection, hospitalization, and death due to COVID-19 for people similar to themselves and for those older than themselves. The authors performed a similar analysis using an international dataset and found similar results, with older respondents estimating lower risks of contracting, hospitalization, and death from COVID-19 compared to those who were younger. Individuals who were more likely to estimate greater COVID-19 risks were also likely to estimate greater non-COVID-19 health risks (e.g. risk of heart attack, cancer). Finally, individuals who perceived higher COVID-19 related risks for themselves were less likely to go out and attend routine doctor’s appointments and fill prescriptions, suggesting that individual risk perception correlated with individual behavior. On average, those that perceived greater risks for others were more likely to prefer stricter or longer lockdown measures, suggesting that individual beliefs about average risks inform policy preferences.
The authors recruited and collected data from a large, diverse sample of the US population. The authors observed broadly similar results in a publicly available global database. This suggests that the observed age-related differences in risk perception may not be specific to the US.
The authors did not report the number of potential participants who were screened out due to incorrect answers or were ineligible due to demographic quotas. When participants were asked to consider a group of people similar to themselves, it is not clear which social identifiers participants used. Based on the data analysis, the authors assumed that age is a primary social identifier. However, given that participants were asked to consider multiple social identifiers at once (race, age, sex, etc.), it is unclear if analysis by a single variable (i.e. age) is justified. Recruitment via social media platforms or phone may bias the sample in ways not captured by basic demographic variables. Furthermore, very little detail was provided on how the data were analyzed, making the statistical methodology hard to assess. Two potentially important but unmeasured determinants of risk perception that may be correlated with age (therefore potential confounders of the observed association) are political views/affiliations and personal knowledge of someone who had COVID-19.
How COVID-19 risk perceptions vary by subpopulation has not been widely researched, but could inform how social and behavioral change interventions are adapted for different demographic groups in the US.
This review was posted on: 3 September 2020