Contrasting Objective and Perceived Risk: Predicting COVID-19 Health Behaviors in a Nationally Representative US Sample

Background Individuals confronting health threats may display an optimistic bias such that judgments of their risk for illness or death are unrealistically positive given their objective circumstances.Purpose We explored optimistic bias for health risks using k-means clustering in the context of COVID-19. We identified risk profiles using subjective and objective indicators of severity and susceptibility risk for COVID-19.Methods Between 3/18/2020-4/18/2020, a national probability sample of 6,514 U.S. residents reported both their subjective risk perceptions (e.g., perceived likelihood of illness or death) and objective risk indices (e.g., age, weight, pre-existing conditions) of COVID-19-related susceptibility and severity, alongside other pandemic-related experiences. Six months later, a subsample (N = 5,661) completed a follow-up survey with questions about their frequency of engagement in recommended health protective behaviors (social distancing, mask wearing, risk behaviors, vaccination intentions).Results The k-means clustering procedure identified five risk profiles in the Wave 1 sample; two of these demonstrated aspects of optimistic bias, representing almost 44% of the sample. In OLS regression models predicting health protective behavior adoption at Wave 2, clusters representing individuals with high perceived severity risk were most likely to report engagement in social distancing, but many individuals who were objectively at high risk for illness and death did not report engaging in self-protective behaviors.Conclusions Objective risk of disease severity only inconsistently predicted health protective behavior. Risk profiles may help identify groups that need more targeted interventions to increase their support for public health policy and health enhancing recommendations more broadly. As we move into an endemic stage of the COVID-19 pandemic, understanding engagement in health behaviors to curb the spread of disease remains critically important to manage COVID-19 and other health threats. However, peoples' perceptions about their risk of getting sick and having severe outcomes if they do fall ill are subject to bias. We studied a nationally representative probability sample of over 6,500 U.S. residents who completed surveys immediately after the COVID-19 pandemic began and approximately 6 months later. We used a computer processing (i.e., machine learning) approach to categorize participants based on both their actual risk factors for COVID-19 and their subjective understanding of that risk. Our analysis identified groups of individuals whose subjective perceptions of risk did not align with their actual risk characteristics. Specifically, almost 44% of our sample demonstrated an optimistic bias: they did not report higher risk of death from COVID-19 despite having one or more well-known risk factors for poor disease outcomes (e.g., older age, obesity). Six months later, membership in these risk groups prospectively predicted engagement in health protective and risky behaviors, as well as vaccine intentions, demonstrating how early risk perceptions may influence health behaviors over time.