Research paperNetwork analysis of comorbid posttraumatic stress disorder and depression in adolescents across COVID-19 epidemic and Typhoon Lekima
Introduction
Individuals exposed to traumatic events report many negative psychological outcomes (Ben-Ezra, 2015; Lewis et al., 2019; Li & Graham, 2017); posttraumatic stress disorder (PTSD) and depression are two common psychopathologies (Cenat, Smith, Morse, & Derivois, 2019; Goenjian et al., 2020; Nichter et al., 2020). For instance, a study of adolescents exposed to the Jiuzhaigou earthquake found a prevalence of PTSD and depression of 46.3% and 64.5%, respectively (Qi et al., 2020). Liu et al.’s (2020) study of young adults during the COVID-19 epidemic indicated that 43.3% and 31.8% participants reported depression and PTSD, respectively. Given the high prevalence of PTSD and depression following trauma, the possible comorbidity of PTSD and depression has attracted many researchers’ attention. There is growing evidence that PTSD is always comorbid with depression (Adams et al., 2019; Hernandez et al., 2020; Nichter et al., 2020). For example, the prevalence of comorbid PTSD and depression in adolescents exposed to natural disasters ranges from 3.3% (Cheng et al., 2015) to 49.7% (Tang, Lu, & Xu, 2018).
There are several potential explanations of the comorbidity between PTSD and depression (Stander, Thomsen, & Highfill-McRoy, 2014; Zhen, Zhou, & Wu, 2019). One is the “causality” hypothesis, which posits a causal relation between PTSD and depression (Stander et al., 2014). PTSD may predict depression (Ginzburg, Eindor, & Solomon, 2010) or depression may aggravate the severity of PTSD (Ying, Wu, & Lin, 2012). An alternative explanation is the “common factors” hypothesis, which suggests that PTSD and depressive symptoms share common factors (Angelakis & Nixon, 2015). A third explanation is the “confounding factors” hypothesis. This holds that PTSD and depression have some shared symptoms (e.g., sleep problems, diminished interest; Angelakis & Nixon, 2015).
Although these hypotheses may help us to understand the comorbidity between PTSD and depression, the “causality” and “common factors” hypotheses suggest that PTSD and depression are latent disorder entities, and that comorbidity arises from the association between these two latent variables and their shared factors. However, the latent variable model may not be the best psychometric perspective from which to conceptualize mental disorders (Borsboom, 2008), and models of latent mental disorders may not explain covariation between symptoms (Cramer et al., 2010). The “confounding factors” hypothesis acknowledges the symptom-level association between distinct disorders and suggests that the overlapping symptoms of PTSD and depression contribute to their comorbidity (Angelakis & Nixon, 2015). For example, there is evidence that PTSD symptoms such as poor sleep, irritability, and concentration difficulties overlap with counterpart symptoms in depression, such as irritability, concentration difficulties, and poor sleep (Elhai et al., 2011; Elhai et al., 2008; Spitzer, First, & Wakefield, 2007). These overlapping symptoms not only combine to form an underlying dimension (Grant et al., 2008), but also explain the dimensional communality between depression and the PTSD dysphoria factor (e.g., inability to recall important aspects of trauma, loss of interest, detachment, restricted affect, sense of foreshortened future, sleep disturbance, irritability, and concentration difficulties; Armour & Shevlin, 2009; Gootzeit & Markon, 2011; Simms, Watson, & Doebbeling, 2002). These studies have considered symptoms as indicators of latent dimensions; however, between-symptom associations are regarded as a byproduct of dimensional communality (Afzali et al., 2017; Borsboom, 2008), and thus the role of symptom-level associations in the comorbidity between PTSD and depression remains unclear (Afzali et al., 2017).
To address the shortcomings of these hypotheses, network analysis has been used as an alternative approach to examine the PTSD–depression comorbidity (Afzali et al., 2017). According to this approach, mental disorder comprises a system rather than separate entities (Borsboom & Cramer, 2013), and disorder is created by a system of causally interrelated symptoms (Borsboom & Cramer, 2013; Fried et al., 2017). Disorders thus arise when groups of symptoms actively maintain each other, leading to a cluster of psychopathological symptoms that becomes self-sustaining (Borsboom, 2017). These symptoms represent discrete entities with functional significance for other symptoms (Choi et al., 2017), and comorbidity reflects a set of direct relationships between symptoms of different disorders (Bekhuis et al., 2016; Borsboom et al., 2011; Cramer et al., 2010). These between-cluster symptom-level connections have been termed “bridge symptoms.” Such symptoms activate the co-occurrence of multiple disorders (Afzali et al., 2017). The use of network analysis to identify these bridge symptoms may clarify the pathways to comorbidity at the symptom level, identify the role of overlapping symptoms in explaining comorbidity, and elucidate the inequality of symptoms of disorders (Cramer et al., 2010).
Network analysis of comorbidity of multiple disorders is a recent technique (Afzali et al., 2017; Cramer et al., 2010; McElroy et al., 2018a, b). Only a few studies have examined the comorbidity of PTSD and depression using network analysis. Boschloo et al. (2015) found 11 symptom-level associations between PTSD and depression, including four overlapping symptom associations that contribute to their comorbidity. Afzali et al. (2017) found four overlapping symptoms and five non-overlapping symptoms that had substantial bridge roles and that contributed to the comorbidity of PTSD and depression. However, these studies overlook possible variation in findings arising from differences in traumatic events, limiting our understanding of the replicability and generalizability of the findings. In fact, the newly developing field of network analysis of disorders currently faces a replicability crisis (Epskamp, Borsboom, & Fried, 2018; Fried et al., 2018). How to avoid this crisis is an important issue (Fried & Cramer, 2017) that is particularly relevant given the recent focus on replicability in psychology (Tackett et al., 2017). In addition to testing and reporting the precision of statistical parameters derived from network models, it is important to empirically test whether network structures generalize across different trauma types (Fried et al., 2018). This study, for the first time, assesses the comorbidity network of PTSD and depression across different trauma types.
Because of the universality of natural disasters in China (Ting, 2007), there are always reports of PTSD and depression following natural disasters (Guo et al., 2017; Zhou & Wu, 2019). Thus, one focus of this study was PTSD and depression following a natural disaster. Particularly, with the development of global warming, extreme weathers may become more frequent and terrible. Another focus was PTSD and depression during the 2019 novel coronavirus pneumonia (COVID-19) epidemic. Although whether panic caused by COVID-19 can meet the DSM-5 Criteria A has been sharply criticized by Asmundson and Taylor (2021), however, opinions dispute. Especially, Kalin (2021) figures out that “COVID-19 pandemic represents the perfect storm of stressors and traumatic events”, for it makes people face loss of life, friends and loved ones. Thus, COVID-19 can be considered as a traumatic event that has caused many mental health problems (Qiu et al., 2020), particularly PTSD and depression (Fekih-Romdhane et al., 2020; Peng et al., 2020). Therefore, it is very meaningful to focus on the two trauma types and negative psychological effects caused by the them. Extant studies suggest that differences in the trauma types were associated with the variation in PTSD symptoms structures (Benfer et al., 2018; Esterwood & Saeed, 2020). For example, studies found that individuals who experienced major public health crisis events (e.g., MERS, Ebola, COVID-19) were more likely to experience insomnia, anger, and extreme fear (Esterwood & Saeed, 2020; Jeong et al., 2016; Morganstein & Ursano, 2020), but those who exposed to natural disasters were more likely to report physiological cue reactivity, flashbacks (Liang, Li, Zhou, & Liu, 2020), avoid thoughts and feelings about the trauma, and feeling irritable. Because PTSD and depression comorbidity was partially explained by the relation between PTSD symptoms and depression (Armour & Shevlin, 2009; Gootzeit & Markon, 2011; Simms et al., 2002), thus the variation in PTSD symptoms structures may be more likely to related to the difference of comorbidity of PTSD and depression in symptoms. Nevertheless, studies to date have been limited in their methodology such that they are not able to speak to the differences of network of symptoms between the disorders. To fill this gap, we used network analysis to assess the comorbidity of PTSD and depression following one natural disaster and the COVID-19 outbreak, and proposed that the characteristics of the comorbidity networks of PTSD and depression would differentiate between these two traumatic events.
Section snippets
Participants and procedures
Participants were recruited following two traumatic events (the super typhoon Lekima and the COVID-19 outbreak). One set of participants was recruited 3 months after Typhoon Lekima, which occurred in China in August 2019. We focused on Wenling city in Zhejiang province, as this city was severely affected by the typhoon. We first contacted the Zhejiang Research Institute of Education Science to inform them of the study's aims and methods of investigation, and then contacted the Wenling Education
Network analysis
Robustness tests were first used to assess the stability and accuracy of the two networks (see Appendices S1, S2, and S3). The results showed that the CS coefficients of EI and bEI were, respectively, 0.59 and 0.59 for the PTSD–depression network in adolescents following the COVID-19 outbreak, and 0.75 and 0.75 for the PTSD–depression network in adolescents following Typhoon Lekima. We also found that some edges in the two networks had no overlapping CIs with other edges in the same networks.
Discussion
To our knowledge, this is the first study to examine and compare PTSD and depression comorbidity networks across natural disasters (e.g., Typhoon Lekima) and major public health crises (e.g., COVID-19 epidemic). The results indicate that the comorbidity of PTSD and depression may be attributable to their shared bridge symptoms; however, some bridge symptoms differed across the two networks. Furthermore, the findings indicate that the global and local connectivity of the PTSD–depression network
Funding
This study was supported by the General Project for National Social Science Fund of China (Grant No. 20BSH167).
CRediT authorship contribution statement
Junjun Qi: Data curation, Formal analysis, Methodology, Writing – original draft. Rui Sun: Formal analysis, Methodology. Xiao Zhou: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors have no conflicts of interest to declare.
Acknowledgment
We would like to thank our students for helping us to carry out the survey and interviews. We would also like to thank all of students for participating in this study.
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