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Published July 1, 2023 | Published + Supplemental Material
Journal Article Open

Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints


Background. Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use. Method. In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints. Results. After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07 to 0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impact. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints. Conclusion. Alcohol, nicotine, and cannabis use were linked to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links.

Additional Information

© 2023 Elsevier. This work was supported by grants from the National Institute of Mental Health (2P50MH094258), the Caltech Chen Neuroscience Institute, and the Caltech Merkin Institute (RA), by the Oscar M. Ruebhausen Fund at Yale Law School, the Rutgers Center of Alcohol & Substance Use Studies, the John Templeton Foundation and the Kay Family COVID-19 Rapid Response Research Awards at Chapman University. No other funding sources were declared. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. We thank the COVID-Dynamic team (https://coviddynamic.caltech.edu/investigators) for their contributions to the dataset used in this study, including specifically (alphabetical order): Yanting Han, Dehua Liang, Uri Maoz, and Tessa Rusch. We thank the COVID-Dynamic team (https://coviddynamic.caltech.edu/investigators) for their contributions to the dataset used in this study. CRediT authorship contribution statement. Santiago Papini: Conceptualization, Methodology, Software, Formal Analysis, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing. Teresa López-Castro: Conceptualization, Funding Acquisition, Project Administration, Writing - Review & Editing. Peggy Swarbrick: Conceptualization, Writing - Original Draft, Writing - Review & Editing. Lynn K. Paul: Data Curation, Investigation, Writing - Original Draft, Writing - Review & Editing. Damian Stanley: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Validation, Writing - Review & Editing. Alex Bauer: Conceptualization, Writing - Original Draft, Writing - Review & Editing. Denise A. Hien: Conceptualization, Funding Acquisition, Project Administration, Resources, Writing - Original Draft, Writing - Review & Editing. No conflict declared.

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Additional details

September 28, 2023
September 28, 2023