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Little has been done to examine socioeconomic differences in the trajectory of psychological well-being during a disaster. This paper investigates educational differences in the changes in psychological well-being during the COVID-19 pandemic and explores the mechanisms of the differences. The data were obtained from a unique two-wave panel survey conducted during the peak and waning phases of the pandemic in Hubei province, the epicenter of the pandemic in China. At the peak of the pandemic, individuals with different levels of education reported high and similar levels of psychological distress. As the pandemic subsided, the psychological well-being of people of all educational levels rebounded, but the recovery was greater and faster for those with tertiary education. Mediation analysis shows that neighborhood social support and evaluation of the performance of grassroots government partially explained the advantage of the more highly educated. This study reveals how social differentiation is translated into health disparities when a disaster strikes. It may also inform public policy by identifying segments of the population at heightened risk of psychological distress during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Objectives: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.Design: We explored numerous psychopathology, sociodemographic, and virus exposure-related variables associated with anxiety and perceived threat of death regarding COVID-19.Method: We recruited 908 adults from Eastern China for a cross-sectional web survey, from 24 February to 15 March 2020, when social distancing was heavily enforced in China. We used several machine learning algorithms to train our statistical model of predictor variables in modeling COVID-19-related anxiety, and perceived threat of death, separately. We trained the model using many simulated replications on a random subset of participants, and subsequently externally tested on the remaining subset of participants.Results: Shrinkage machine learning algorithms performed best, indicating that stress and rumination were the most important variables in modeling COVID-19-related anxiety severity. Health anxiety was the most potent predictor of perceived threat of death from COVID-19.Conclusions: Results are discussed in the context of research on anxiety and fear from prior virus outbreaks, and from theory on outbreak-related emotional vulnerability. Implications regarding COVID-19-related anxiety are also discussed. [ABSTRACT FROM AUTHOR]