Bayesian Analysis of Quality of Life and PPE (Personal Protective Equipment) Use During the Coronavirus (COVID-19) Pandemic

Presented by:
Natalia Iannucci, Hannah Snell, Dianne Caravela, and Elaine Ye (Smith College)
Abstract:

Concerned with the documented psychological effects of the COVID-19 pandemic, this study examines factors associated with quality of life and PPE use during the pandemic. After controlling for demographics, we created a Bayesian multiple regression model to examine the associated factors of quality of life; we also utilized Bayesian LASSO techniques to create a model predicting PPE use. As hypothesized, annual income, being employed, being in long-term relationships, and mindfulness were all positively associated with quality of life scores during the pandemic; whereas self isolation was negatively related to psychological well-being. We found that the variables with the highest predictive power for PPE use were age, education, marital status, religion, intolerance of uncertainty, and avoidance of public settings. These results suggest that it is critical to keep in mind that financial difficulties and social isolation have an impact on mental health when planning pandemic responses. For individuals, our results stress the importance of maintaining social relationships as well as the potential benefit of adopting mindfulness practices. Additionally, understanding the key predictors of PPE usage can better drive education about PPE as well as the allocation of PPE-related resources.