Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model and a Bayesian network modelling approach to estimate a directed acyclic graph. Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both approaches pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to examine, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.
Keywords: Anxiety; COVID-19; Directed acyclic graph; GAD; Gaussian Graphical Model; Lockdown; Network Approach to Psychopathology; Pandemic; Psychopathology; Worry.
Copyright: © 2021 The Author(s).