Jon D Elhai1,2,3, Haibo Yang1, Dean McKay4, Gordon J G Asmundson5, Christian Montag6,7. 1. Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, People's Republic of China. 2. Department of Psychology, University of Toledo, Toledo, OH, USA. 3. Department of Psychiatry, University of Toledo, Toledo, OH, USA. 4. Department of Psychology, Fordham University, Bronx, NY, USA. 5. Anxiety and illness Behaviors Laboratory, Department of Psychology, University of Regina, Regina, Canada. 6. Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany. 7. neuSCAN Laboratory, Clinical Hospital of the Chengdu Brain Science Institute and Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
Abstract
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.
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.