iHeard STL: Development and first year findings from a local surveillance and rapid response system for addressing COVID-19 and other health misinformation

Abstract

Background The U.S. Surgeon General and others have emphasized a critical need to address COVID-19 misinformation to protect public health. In St. Louis, MO, we created iHeard STL, a community-level misinformation surveillance and response system. This paper reports methods and findings from its first year of operation. Methods We assembled a panel of over 200 community members who answered brief, weekly mobile phone surveys to share information they heard in the last seven days. Based on their responses, we prioritized misinformation threats. Weekly surveillance data, misinformation priorities, and accurate responses to each misinformation threat were shared on a public dashboard and sent to community organizations in weekly alerts. We used logistic regression to estimate odds ratios (ORs) for associations between panel member characteristics and misinformation exposure and belief. Results In the first year, 214 panel members were enrolled. Weekly survey response rates were high (mean = 88.3% ± 6%). Exposure to a sample of COVID-19 misinformation items did not differ significantly by panel member age category or gender; however, African American panel members had significantly higher reported odds of exposure and belief/uncertain belief in some misinformation items (ORs from 3.4 to 17.1) compared to white panel members. Conclusions Our first-year experience suggests that this systematic, community-based approach to assessing and addressing misinformation is feasible, sustainable, and a promising strategy for responding to the threat of health misinformation. In addition, further studies are needed to understand whether structural factors such as medical mistrust underly the observed racial differences in exposure and belief.