Misinformation about COVID-19: Evidence for Differential Latent Profiles and a Strong Association with Trust in Science

Abstract

BackgroundThe global spread of coronavirus disease 2019 (COVID-19) has been mirrored by diffusion of misinformation and conspiracy theories about its origins (such as 5G cellular networks) and the motivations of preventive measures like vaccination, social distancing, and face masks (for example, as a political ploy). These beliefs have resulted in substantive, negative real-world outcomes but remain largely unstudied.MethodsThis was a cross-sectional, online survey (n=660). Participants were asked about the believability of five selected COVID-19 narratives, their political orientation, their religious commitment, and their trust in science (a 21-item scale), along with sociodemographic items. Data were assessed descriptively, then latent profile analysis was used to identify subgroups with similar believability profiles. Bivariate (ANOVA) analyses were run, then multivariable, multivariate logistic regression was used to identify factors associated with membership in specific COVID-19 narrative believability profiles.ResultsFor the full sample, believability of the narratives varied, from a low of 1.94 (SD=1.72) for the 5G narrative to a high of 4.56 (SD=1.64) for the zoonotic (scientific consensus) narrative. Four distinct belief profiles emerged, with the preponderance (70%) of the sample falling into Profile 1, which believed the scientifically accepted narrative (zoonotic origin) but not the misinformed or conspiratorial narratives. Other profiles did not disbelieve the zoonotic explanation, but rather believed additional misinformation to varying degrees. Controlling for sociodemographics, political orientation and religious commitment were marginally, and typically non-significantly, associated with COVID-19 belief profile membership. However, trust in science was a strong, significant predictor of profile membership, with lower trust being substantively associated with belonging to Profiles 2 through 4.ConclusionsBelief in misinformation or conspiratorial narratives may not be mutually exclusive from belief in the narrative reflecting scientific consensus; that is, profiles were distinguished not by belief in the zoonotic narrative, but rather by concomitant belief or disbelief in additional narratives. Additional, renewed dissemination of scientifically accepted narratives may not attenuate belief in misinformation. However, prophylaxis of COVID-19 misinformation might be achieved by taking concrete steps to improve trust in science and scientists, such as building understanding of the scientific process and supporting open science initiatives.

Trust in health information sources and its associations with COVID-19 disruptions to social relationships and health services among people living with HIV

“…Most notably, President Trump questioning public health science, widely promoting conspiracy theories and falsely claiming an anti-malaria drug can treat COVID-19 has been a concern for eroding public trust in health messaging [ 36 , 37 ]. Similar levels of COVID-19 inaccurate information have been reported in other studies [ 38 , 39 ], including studies that find people who rely more on social media and social networks for information are less well-informed [ 40 ]. Although our study is unable to draw any causal connection between inaccurate information and lack of trust in the US government or COVID-19 information sources, we also cannot rule out such a connection.…”

Racial/Ethnic, Sex, Sexual Orientation, and Socioeconomic Disparities in Suicidal Trajectories and Mental Health Treatment Among Adolescents Transitioning to Young Adulthood in the USA: A Population-Based Cohort Study

“…The Lo-Mendell-Rub adjusted likelihood ratio test (LMR-LRT; Lo et al 2001 ) was used to test further whether a proposed model with more classes significantly improved model fit relative to a model with fewer classes, indicated by a significant LMR-LRT value. While there is no consensus regarding the smallest size of the latent class (Bollen and Curran 2006 ; Curran et al 2010 ), classes with less than 1% of the population was considered substantially less parsimonious and thus not considered (Agley and Xiao 2020 ; Geary et al 2009 ; Rose et al 2017 ; Xiao et al 2019 ; Xiao et al 2020 ). LCGA analyses were conducted using Mplus version 8 (Muthén and Muthén 1998 –2017).…”