Bayesian Mixed Models Approach to Exploring Resilience: Impact of Stress on Subjective Health and Affects Over Time During the COVID‐19 Pandemic

BACKGROUND: Profound stressors such as the COVID-19 pandemic have highlighted the importance of understanding resilience mechanisms and approaches for quantifying them in longitudinal studies. METHODS: We used Bayesian mixed models to analyze resilience dynamics with ordinal dependent variables: subjective physical and mental health, and fear, sadness, and anger. The models included fixed effects for individual stressors and random intercepts for participants, applied to the Gutenberg-COVID-19 cohort study. RESULTS: There were 206,912 responses from 7386 participants (mean age 55.09 years, 51.52% women) over one year (Oct 29, 2020 - Oct 25, 2021). Social stressors, such as loss of social contacts, had stronger negative associations with health and negative affects than work-related stress. Subjective health and emotions declined during lockdowns but quickly recovered afterward. CONCLUSION: Our longitudinal study design and mixed-model analysis highlight the role of social stress and encourage further research into protective factors like social support and positive reappraisal.