Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Abstract

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.

Figure 1

Graphical Gaussian Model Constructed via the Graphical LASSO. Note: The thickness of an edge reflects the magnitude of the association (the thickest edge representing a value of .32). The blue rings around the nodes indicate the proportion of explained variance in that node by all other nodes. Nervous = Feeling nervous, anxious, or on edge (item 1); Stop_Worry = Not being able to stop or control worrying (item 2); Worrying = Worrying too much about different things (item 3); Tr_Relaxing = Trouble relaxing (item 4); Restless = Being so restless that it is hard to sit still (item 5); Irritab = Becoming easily annoyed or irritable (item 6); Afraid = Feeling afraid as if something awful might happen (item 7).

Figure 2

Expected Influence Estimates of the Graphical Gaussian Model Constructed via the Graphical LASSO. Note: Nervous = Feeling nervous, anxious, or on edge (item 1); Stop_Worry = Not being able to stop or control worrying (item 2); Worrying = Worrying too much about different things (item 3); Tr_Relaxing = Trouble relaxing (item 4); Restless = Being so restless that it is hard to sit still (item 5); Irritab = Becoming easily annoyed or irritable (item 6); Afraid = Feeling afraid as if something awful might happen (item 7).

Figure 3

Directed acyclic graphs (DAGs). Note: Panel A: Arrow thickness denotes the importance of that arrow to the overall network model fit. Greater thickness reflects larger contribution to the model fit. Panel B: Arrow thickness indicates directional probability. Greater thickness reflects larger proportions of the bootstrapped networks wherein the arrow pointed in that direction. Nervous = Feeling nervous, anxious, or on edge (item 1); Stop_Worry = Not being able to stop or control worrying (item 2); Worrying = Worrying too much about different things (item 3); Tr_Relaxing = Trouble relaxing (item 4); Restless = Being so restless that it is hard to sit still (item 5); Irritab = Becoming easily annoyed or irritable (item 6); Afraid = Feeling afraid as if something awful might happen (item 7).