COVID-19 and its related pandemic is having a serious impact on the mental health of many individuals (Huang & Zhao, 2020; Pfefferbaum & North, 2020). A systematic review of 19 studies concluded associations with anxiety, stress, posttraumatic stress disorder, depression, and psychological distress (Xiong J, Lipsitz O, Nasri F, et al., 2020; see also the review by Salari et al., 2020). This has also been noticed in Australia (Dawel et al, 2020), where the current study was conducted. In addition to these traditionally recognized psychopathologies, the pandemic has caused many people to develop anxiety and fear of being infected with the COVID virus (McKay, Yang, Elhai, Asmundson, 2020), leading them to respond negatively to coronavirus related thoughts or information (Lee, Mathis, Jobe, & Pappalardo, 2020). This reaction has been referred to as COVID anxiety or “coronaphobia” (Arora et al., 2020). Related to this, Lee (2020) developed the Covid Anxiety Scale (CAS) in order to measure anxiety-related physiological (somatic) symptoms that are aroused by information and thoughts related to COVID-19. This measure has been validated multiple times and is now considered a valid measure for screening COVID 19 related anxiety (Nikčević & Spada, 2020). Indeed, our examination of Google Scholar indicated a total of 614 citations as of 21 September 2021, however the measure is still relatively new and thus requires examination. In the current study, we used a novel technique called network analysis (Epskamp, Borsboom, & Fried, 2018) to examine the structure of the CAS anxiety symptoms, and how these symptoms are related uniquely with distress (combination of depression, anxiety, stress) and alcohol use.
COVID Anxiety: Description, Impact and Measurement
Arora et al. (2020) has noted that while coronaphobia is like other phobias (i.e., characterized by fear of a specific stimuli, dysfunctional and distorted thoughts, and avoidance responses), it is unique in that the fear is not only limited to public places/situations/objects, but also appears when coming in physical contact with humans. According to Arora et al. (2020), COVID anxiety has three primary interacting components: physiological (i.e., triggering of the fight or flight response when exposed to antecedent event, accompanied by worry that palpitations, tremors, difficulty in breathing, dizziness, change in appetite, and sleep ); cognitive (fear of virus casing preoccupation with threat provoking cognitions); and behavioral (preventive responses such as avoidance).
Evidence indicates that individuals prone to higher levels of COVID anxiety have a higher risk of developing severe mental health problems (Arora et al., 2020), including depression, stress, anxiety (Xiong et al., 2020; see also the review by Salari et al., 2020), and alcohol use (Gasteiger et al., 2021; Stanton et al., 2020), and that these may last well beyond the course of the pandemic (Nikčevića & Spada, 2020).
To date, several other measures have also been developed for measuring COVID-19 anxiety (and fear). They include the Fear of COVID-19 Scale (FCV-19S; Ahorsu et al., 2020), the COVID Stress Scales (CSS; Taylor et al., 2020), Fear of the Coronavirus Questionnaire (FCQ; Mertens et al., 2020), and the Covid Anxiety Scale (CAS; Lee, 2020a). A recent study showed that the FCV-19S, the FCQ and the FCQ scales/subscales measure different aspects of COVID anxiety and fear, thereby indicating that COVID anxiety is heterogeneous. Given these findings, it can be argued that for a clear understanding of COVID anxiety, researchers need to identify what aspect of COVID anxiety their study is focusing on, and then consider this when interpreting their findings (Mertens et al., 2020).
The CAS is a 5-item self-report measure of anxiety-related physiologically (somatic) symptoms that are aroused by information and thoughts related to COVID-19. The five items of the CAS, all loading on a single factor, are (1) I felt dizzy, lightheaded, or faint, when I read or listened to news about the coronavirus (dizziness); (2) I had trouble falling or staying asleep because I was thinking about the coronavirus (sleep disturbances); (3) I felt paralyzed or frozen when I thought about or was exposed to information about the coronavirus (tonic immobility); (4) I lost interest in eating when I thought about or was exposed to information about the coronavirus (appetite loss); and (5) I felt nauseous or had stomach problems when I thought about or was exposed to information about the coronavirus (abdominal distress). According to Lee (2020b), the dizziness and tonic immobility symptoms in the CAS capture the physiological reactions of elevated fear to corona virus related stimuli. The sleep disturbances and appetite loss symptoms capture the physical effects of excessive worry about the coronavirus. The abdominal distress symptom captures fear and anxiety, result from a fearful reaction or the physical effect of excessive worry, or both.
Upon its construction, number of validation studies have examined the CAS, finding good psychometric properties. The initial CAS development and validation study supported a unidimensional structure, with high reliability (α = .93). Scores on the CAS correlated in theoretically meaningful ways with coronavirus diagnosis, functional impairment, coping through substance use and religion, hopelessness, and suicidal ideation. ROC analysis indicated 90% sensitivity and 85% specificity for detection and classification with a cutoff point ≥9 (Lee, 2020a). Based on a subsequent study (Lee, 2020b), the cutoff score was reduced to ≥5. To date numerous other studies have provided additional support for the psychometric properties of the CAS (e.g., Evren et al., 2020; Lee et al., 2020; Skalski et al., 2020). For example, Lee et al. (2020) found that dysfunctional scores on the CAS were associated with coronavirus infection, generalized anxiety, depression, functional impairment, perceived lack of social support, and suicidal ideation. For a full list of research studies that have used the CAS, the reader is referred to https://sites.google.com/cnu.edu/coronavirusanxietyproject/home.
Overall, therefore the CAS has sound psychometric properties. Although COVID anxiety is a heterogeneous construct, the CAS with its five somatic items, is essentially a unidimensional, tapping physiologically and somatic anxiety and fear. As ready noted, despite it brevity and undimensionality the CAS is considered a valid measure for screening COVID 19 anxiety and has been used widely in COVID anxiety research. Therefore it can be argued that the CAS is ideally suited for examining the somatic-related anxiety symptoms of COVID anxiety. Focusing on this group of symptoms is important as there is now some evidence that moderate to high levels of COVID-19 anxiety is associated with more somatic symptoms, even after controlling for generalized anxiety disorder (GAD), preexisting health problems, age, gender, and income.
A Novel Approach for Examining the Psychometric properties of the CAS
To date, the psychometric properties of CAS have largely been examined from a latent variable perspective. In this perspective, it is assumed that there is a latent (unobservable) construct (which is the disorder/problem in question) that causes a range of observable responses (that are the symptoms of the latent disorder/problem). This is a reflective view of psychopathology. Seen in the context of COVID anxiety, the reflective view suggests that the COVID anxiety symptoms are responses arising from an assumed underlying latent COVID anxiety construct. This means that the COVID anxiety symptoms are interchangeable and equally reflective of latent COVID anxiety. Also, the COVID anxiety symptoms are considered to have nothing in common after controlling for the latent construct (an assumption referred to as local independence).
Although the latent variable approach (like that captured in a CFA) is currently the most dominant approach for understanding psychopathologies and related syndromes, a newly developed perspective, called the network approach, has a different view of psychopathologies. In the network framework, symptoms are understood as a causal system, interacting with each other in meaningful ways, resulting in the disorder or the syndrome (Borsboom & Cramer, 2013). A network model can be tested empirically using ‘network analysis’ (Borsboom & Cramer, 2013; Boschloo et al., 2015). Network analysis is an exploratory approach that provides visual and quantitative information about symptoms that are “core” or “central” (important) to the overall network of symptoms, and the strength of connections between symptoms (Borsboom & Cramer, 2013; Fried et al., 2015). As noted by Epskamp and others (Epskamp & Fried, 2018; Epskamp et al., 2017), such a network can identify unique interactions between variables that cannot be identified using multiple regression analysis, and when the network analysis is exploratory it is advantageous over structural equation modelling (SEM), because there are no equivalent undirected models possible in SEM.
Clinical Importance of Network Analysis of the COVID Anxiety Symptoms
Results from network analysis of the symptoms of a disorder/syndrome can have important implications for theory, assessment and diagnosis, treatment and prevention. Traditionally, the theoretical importance of a symptom is viewed in terms of its severity which is ascertained in terms of its mean score. However, in network models, centrality, that is different from mean score, defines the importance of a symptom. Indeed, the mean levels of symptoms can change without changes in their centrality in the network (Yang et al., 2016). Thus, different conclusions about what are core symptoms in a disorder/syndrome could be arrived at when looking at symptom centrality and symptom severity (Mullarkey et al., 2019).
In relation to treatment, as symptoms for a disorder/syndrome identified as central in a network are considered most influential in producing or maintaining the disorder/syndrome, intervening on these symptoms can be expected to maximize the impact of intervention. In this respect, and given its network characteristic, focusing on the central symptoms could potentially have a downstream effect in improving other network symptoms. Specific to COVID anxiety, as note by Ramos-Vera (2021), network analysis allows clinicians to identify and understand more accurately the most important components in the dysfunctional dynamics of COVID anxiety, and consequently, findings from network analysis, can contribute more effectively to detection and intervention of the negative effects of COVID-19.
Also, related to treatment, an expended network model that includes the COVID anxiety symptoms with other psychopathologies will increasing our understanding of the development and maintenance of other comorbid psychopathologies, which in turn would have major implications for preventing and treating these comorbidities.
Existing Network-based Psychometric Data for the CAS and for COVID Anxiety in General
To date, as far as we were able to establish, there have been only one study that has examined the network structure of COVID anxiety symptoms in the CAS (Ramos-Vera, 2021). The network was examined in terms of regularized partial correlation coefficients for ratings provided by a Peruvian community sample. The results indicate that appetite loss (symptom #4) was most central (indicating that it has greatest influence in the network) and featured a strong connection (called edge weight) with dizziness (item # 1). Additionally, sleep disturbances (item #2) and tonic immobility (item # 3); and appetite loss (item # 4), and abdominal distress (item # 5) were also strongly connected with each other indicating a stronger influence upon each other than they would have with less strongly connected symptoms.
Two other studies have used network analysis to examine COVID-19 related anxiety and fear (Mertens et al., 2021; Taylor et al., 2020). Mertens et al. (2021) applied network analysis concurrently to the scales/subscales of the FCV-19S, CSS, FCQ, and a measure related to COVID-19 worries. They reported four clusters: fear of health-related consequences, fear of supplies shortages and xenophobia, fear about socio-economic consequences, and symptoms of fear (e.g., compulsions, nightmares). The most central cluster was that related to fear of health. Taylor et al. (2020) applied network analysis on CSS variables together with other variables related to COVID-19 (e.g., avoidance, self-protective behaviors, stockpiling and panic buying; use of personal protective equipment, and belief in COVID-19-related conspiracy theories). The CSS variables were (1) worry about the dangerousness of COVID-19 (2) worry about the socioeconomic consequences of COVID-19 (3) xenophobic fears that foreigners are spreading Covoid-19, (4) traumatic stress symptoms associated with direct or vicarious traumatic exposure to COVID-19 and (5) COVID-19-related compulsive checking and reassurance-seeking. They reported three major clusters, with a cluster related toworries about the dangerousness of COVID-19 being most central, followed by the cluster related to the belief that the COVID-19 threat is exaggerated. The third cluster was related to compulsive checking, reassurance-seeking, and self-protective behaviors. Taken together, while these studies proved valuable information on the heterogeneous nature of anxiety and fear related to COVID-19, they offer no specific network information on the CAS.
Limitations of Existing Network-Based Data on the CAS
Although there is some network analysis data on the COVID anxiety symptoms, as presented in the CAS, the findings are limited. Firstly, there is only one study (Ramos-Vera, 2021). As network analysis is based on classical test theory, the findings from such studies are largely sample dependent. Thus is need for replication studies. Secondly the only existing study that has used network analysis to examine the CAS (Ramos-Vera,2021), did not examine and report the accuracy and stability of the findings for centrality and edge weights (connections between symptoms). This is a limitation as network analysis experts have recommended that a network must also be evaluated for its accuracy and stability (Epskamp et al., 2018). Thirdly, the Ramos-Vera (2017) study did not examine or illustrate how the network model would be used to examine how COVID anxiety contributes to the development and maintenance of other comorbid psychopathologies, which in turn would have major implications for understanding, preventing, and treating these comorbidities. For example, as already noted, pandemic-related psychological anxiety and distress are related to elevated levels of depression, stress, anxiety or distress (Xiong et al., 2020; see also the review by Salari et al., 2020), and alcohol use (Gasteiger et al., 2021; Stanton et al., 2020). From a network perspective, the inclusion of such distress and alcohol use together with the CAS symptoms in the same network model will reveal the specific CAS symptom or symptoms that are central to the development and maintenance of these comorbidities, and therefore identify important targets of intervention. Fourthly, to date there is no network data for COVID anxiety symptoms in a Western community. Given these limitations, there is clearly need for more network analysis studies using Western samples and examining network findings for accuracy and stability, and also relationships for CAS symptoms with potential comorbidities.
Aims of the Present Study
Given the limitations in existing network data on the CAS, and the positive clinical contributions that network analysis can offer, the major aim in the current study was to use network analysis, with regularized partial correlation, to examine the network structure of the five COVID anxiety symptoms in the CAS (dizziness, sleep disturbances, tonic immobility, appetite loss, and abdominal distress) in a large Western (Australian) community sample. In the current study, we produced a network graph, displaying the topology of the symptom network, comprising the five CAS somatic symptoms. We then evaluated statistically (using both edge width and centrality) the respective influence of the symptoms in the network; and the robustness and stability of the network findings. A secondary aim of the study was to compute an expended network model that included the COVID anxiety symptoms with distress and alcohol usage to ascertain the major associations of the COVID anxiety symptom with distress and alcohol usage.