Abstract
Abstract According to the literature, comorbidity rates observed on emotional disorders are linked to how the main diagnostic classification systems have traditionally defined these disorders. This paper aims to analyze the structure of symptoms evaluated with the Inventory of Depression and Anxiety Symptoms-II (IDAS-II) with network analysis. A mixed sample (n = 2021) of 1692 community adults and 329 patients was used. 14.79% (n = 299) of the sample met the diagnostic criteria for at least one DSM-5 mental disorder and 5.29% (n = 107) had diagnostic comorbidity. The sample was randomly divided into two sub-samples: estimation sample (n = 1010) and replication sample (n = 1011). The detection of community structures was carried out on estimation sample using the walktrap algorithm. Four local inference measures were estimated: Strength, one-step Expected Influence, two-step Expected Influence, and node predictability. Exploratory graphic analysis of modularity yielded an optimal solution of two communities on estimation sample: first linked to symptoms of depression and anxiety and second grouping symptoms of bipolar disorder and obsessive – compulsive disorder. Mania, Panic, Claustrophobia, and Low Well-Being Bridge emerged as bridge symptoms, connecting the two substructures. Networks estimated on replication subsamples did not differ significantly in structure. Dysphoria, Traumatic Intrusions and Checking and Ordering were the symptoms with greatest number of connections with rest of the network. Results sheds light on specific links between emotional disorder symptoms and provides useful information for the development of transdiagnostic interventions by identifying the influential symptoms within the internalizing spectrum.
Highlights
Depression and anxiety affect 4.4% and 3.6% of the world’s population respectively, having serious consequences for health, quality of life and severe negative clinical outcomes such as suicidal behavior (World Health Organization, 2017)
Previous studies have identified neurobiological factors that explain the complexity of suicide (Orsolini et al, 2020) and provide advances in treatments (De Berardis et al, 2018), suicidal behavior still remains a real challenge associated with mental disorders comorbidity (Quevedo et al, 2020)
The community adults were recruited in two phases of data collection: a) Wave 1, 620 adults selected by means of non-probability sampling in the province of Huelva (Spain); and b) Wave 2, 1,072 adults recruited by stratified random sampling, proportionally represented in the Spanish population according to age group, sex, and geographic area
Summary
Depression and anxiety affect 4.4% and 3.6% of the world’s population respectively, having serious consequences for health, quality of life and severe negative clinical outcomes such as suicidal behavior (World Health Organization, 2017). According to empirical literature (e.g., Skodol, 2012), high rates of co-morbidity are linked to the way in which diagnostic classification systems have traditionally defined disorders. These systems, which use polythetic criteria, establish categorical classifications according to an observed set of symptoms, resulting in patients with different symptoms receiving the same diagnosis (Skodol, 2012). The translation of this conceptualization into the applied field has allowed for the development of unified intervention protocols, yielding more effective and cheaper interventions (Barlow et al, 2017). Whilst these are promising findings, the transdiagnostic approach still requires a better understanding of the relationships between symptoms
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