The associations between burnout with depression and anxiety among healthcare workers: A network analysis

doi:10.21203/rs.3.rs-4082872/v1

Healthcare professionals encounter notably elevated levels of work-related stress in comparison to individuals employed in other sectors. Such work stress frequently contributes to the development of burnout, depression, and anxiety among healthcare workers. However, there exists a lack of consensus in research regarding the associations between burnout with depression and anxiety in this population, impeding endeavors aimed at enhancing their mental well-being. The present study constructed a comprehensive network model that encompasses job burnout, depression, and anxiety, provided a clear depiction of the interrelationship among burnout, depression, and anxiety in symptomatic level. The findings indicate that "Psychomotor Problems", "Irritability", and "Collapse" function as bridge symptoms connecting burnout, depression, and anxiety network. The result recommended that interventions prioritize the alleviation of the "Irritability" symptom in individuals with anxiety, as this has the potential to reduce the risk of developing depression. Furthermore, the study constructed a network using principal components analysis which revealed that burnout symptoms cannot be distinguished from depressive and anxiety symptoms.

depression

anxiety

HCWs

burnout

network analysis

Healthcare workers (HCWs) face a high risk of work-related stress particularly in China where they encounter notably elevated levels of psychological exhaustion in comparison to enterprise employees. This can be attributed to enduring heavy workloads, prolonged overtime hours, restricted prospects for professional growth, and an imbalance between work and personal life. (Sun et al., 2023). The most prevalent psychological outcomes of work stress for HCWs encompass burnout, depression, and anxiety(Kuehnl et al., 2019). A comprehensive study encompassing 85 countries reveals that the overall prevalence rates of severe burnout, depression, and anxiety among HCWs are 51%, 30.2%, and 46.5%, respectively.(Azoulay et al., 2020).

Significantly, a consensus has not yet been reached in research findings regarding the associations between burnout and symptoms of depression and anxiety, thereby impeding the effectiveness of interventions for mental health in healthcare workers (HCWs). Previous studies have indicated a close relationship between burnout, depression, and anxiety (Cummings et al., 2014; Maske et al., 2016; Tavella et al., 2023). Additionally, burnout, depression, and anxiety are defined based on symptomatology. However, at the symptomatic level, burnout exhibits significant overlap with both depression and anxiety(Schaufeli and Enzmann, 2020). This situation hampers the early identification and precise intervention for depression, anxiety, and burnout among HCWs. Therefore, it is crucial to uncover the relationship between symptoms of burnout, depression, and anxiety in order to develop precise clinical therapies and interventions.

Several studies have indicated that burnout syndrome may be better understood as a mood condition rather than a separate entity (Bianchi et al., 2015). Previous research has shown that approximately 90% of individuals exhibiting burnout symptoms also meet the diagnostic criteria for depression(Bianchi et al., 2014), and 59% are additionally diagnosed with an anxiety disorder (Maske et al., 2014).Furthermore, Verkuilen et al. employed network analysis to demonstrate that burnout symptoms and depression symptoms exhibit a community structure at the symptomatic level (Verkuilen et al., 2021). The available evidence suggests burnout may be related to subtypes of anxiety or depression. Nonetheless, it is crucial to acknowledge that Verkuilen et al's identification of communities within the network was solely based on the spatial arrangement of nodes (i.e., symptoms). However, it is important to recognize that the visual representation of networks can sometimes lead to misinterpretation(Jones et al., 2018). Consequently, it is imperative to employ suitable plotting methodologies to ascertain whether burnout syndrome warrants recognition as a distinct entity.

In contrast, some studies argue that burnout is a distinct construct related to depression and anxiety (Koutsimani et al., 2019). Furthermore, a qualitative inquiry revealed that burnout, as perceived by individuals experiencing it, is distinct from mood disorders and typically stems from work-related factors(Tavella and Parker, 2020). Additionally, an extensive longitudinal study spanning eight years suggested that burnout had its own unique structure(Tóth‐Király et al., 2021).

There has been a notable dearth of research exploring the association between burnout and symptoms of depression and anxiety, despite the fact that the definitions of burnout, depression, and anxiety are predicated on symptomatology. In recent times, the network analysis approach has emerged as a viable means of investigating the interrelationships among these symptoms. Network theories posit that symptoms are outcomes of their mutual interactions, rather than mere manifestations of a solitary underlying pathological condition(Borsboom, 2017). The comprehension of these interconnections among symptoms is instrumental in identifying treatment targets that yield optimal outcomes and predicting comorbidity between burnout, depression, and anxiety. Therefore, it is imperative to consolidate these psychological variables, encompassing job burnout, depression symptoms, and anxiety symptoms, within an integrated model to examine their interrelationships.

The primary objective of the current study is to offer a comprehensive and lucid portrayal of the correlations between burnout and depression/anxiety among healthcare workers (HCWs) at the symptomatic level. This investigation seeks to develop targeted interventions and enhance the mental well-being of HCWs. In summary, we construct a network model encompassing job burnout, depression, and anxiety to address the following research questions:

In a network of job burnout, depression, and anxiety symptoms which symptom nodes are the most central?

Whether burnout symptoms can be distinguished from depression and anxiety symptoms?

Whether stress conditions affect the network structure of job burnout, depression, and anxiety symptoms?

2.1. Study design and sample collection

Cross-sectional online surveys were conducted from March 23-31, 2022, when the SARS-CoV-2 variant Omicron had just emerged in some areas in China. Due to the risk of contagion of Omicron, an online questionnaire was designed using the online survey software Questionnaire Star (https://www.wjx.cn). The questionnaire link between HCWs was established on the platform through WeChat. All questions in the questionnaire were made mandatory to ensure no missing data. Online informed consent was obtained.

2.2. Measures

2.2.1. Symptoms network

Depressive and anxiety symptoms were assessed by the Chinese version of the Patient Health Questionnaire (PHQ-8) and Generalized Anxiety Disorder Scale (GAD-7) respectively. PHQ-8 comprises 8 items that comply with the 5th Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) (Kroenke, Spitzer, Williams, & Löwe, 2010), which has been widely used in primary care and general populations. The Chinese version of the PHQ-8 has been demonstrated to have good reliability and validity (Zhao, Li, Xiao, & Du, 2013). In this study, the Cronbach alpha of PHQ-8 was 0.91.

GAD-7 contains seven items corresponding to the DSM-IV (Spitzer et al., 2006). The Chinese version of the GAD-7 has been demonstrated to be appropriate and psychometric (Yu et al., 2018). In the current study, the Cronbach alpha was 0.95.

2.2.2. Job Burnout

Job burnout was assessed by the 15-item Chinese Burnout Inventory (CBI) (Li et al., 2005). The CBI contains three dimensions: emotional exhaustion (EE) (5 items), depersonalization (DP) (4 items), and personal accomplishment (PA) (6 items). In the current study, the Cronbach alpha values were:a_total=0.81_; a_EE=0.94_;a_DP=0.91_;a_PA=0.92.

2.3. Analyzes

2.3.1 Network generation

We estimated network models using the qgraph package in R. The EBICglasso (Epskamp et al., 2012) model was chosen. Within the network, each node represented an item of PHQ-8, GAD-7, and CBI. The edges between two nodes represented the unique association between two nodes after controlling for the rest variables in the network. The thickness of an edge indicated the strength of the association, with thicker edges implying a more substantial association. Network plot layouts were generated using multidimensional scaling (MDS), nodes that are close together are closely related, and nodes that are far apart are less closely related. The stress-1 value provides a helpful estimate of how interpretable the distances are (A low-stress value means that the distances are highly interpretable). In the current study, we generated two networks, symptoms network and symptom/burnout network. The symptom network model included only the symptoms of depression and anxiety. The symptom/burnout network model included the symptoms of depression, anxiety, and burnout items. In the network, nodes that are close together are similar in terms of zero-order correlations, and nodes that share a thick edge are similar in terms of regularized partial correlations.

2.3.2 Item selection and Network stability

Items in the network may represent the same symptom, then it may artificially create or obscure relationships in the network that are not representative of how these symptoms might otherwise be related (Fried & Cramer, 2016). To test this possibility, we used “goldbricker” function in the networktools package (Jones, 2018; Levinson et al., 2018). We used 0.25 as our significant proportion for inclusion and .01 as our p-values for determining statistical significance (Hittner, May, & Silver, 2003).

Case-dropping bootstrapping in bootnet package (Epskamp et al., 2018) was used to test the stability of the network. We created plots displaying confidence intervals for edges and centrality values.

2.3.3 Principal Components Analysis

We used principal components analysis（PCA）to explore whether burnout symptoms can be distinguished from depression and anxiety symptoms. PCA can extract meaningful dimensions on which to plot nodes, which specifically applies to psychometric networks(Jones et al., 2018). The graphed Euclidean distance between nodes is not meaningful and the X distance and the Y distance are each meaningful and represent the difference between nodes on each extracted principal component. Put differently, if burnout is a distinct entity, EE, DP, and PA symptoms should capture a dimension of the network, and depressive/anxiety symptoms should capture another dimension.

2.3.4 Comparison network

We compared the symptom and burnout network based on stressful situations (Hospitals with COVID-19 cases vs Hospitals with no COVID-19 cases) via the R package ‘Network Comparison tool’(Van Borkulo et al., 2022), to determine whether stress conditions affect the network structure.

3.1. Demographic characteristics of the sample

A total of 1002 HCWs were recruited from Shanghai and Beijing, of whom 10 HCWs were excluded from the study, including 7 HCWs whose time of their answers to the questionnaire was less than 80 seconds, and 3 HCWs who gave abnormal answers. Finally, 992 HCWs (84.0% female; M_age=32.89, SD_age=7.49) were included for analysis. The mean score on PHQ-8, GAD-7 and CBI was M_PHQ=6.25(SD_PHQ=5.12), M_GAD=4.59(SD_GAD=4.71), M_EE=11.59(SD_EE=7.65), M_DP=7.38(SD_DP=5.86), M_PA=25.32(SD_PA=8.78), respectively. “Low Energy” was the depressive symptom reported with the highest frequency (Table S1). Among anxiety symptoms, “Irritability” was the most common symptom (Table S2).

3.2 Item selection and Network stability

We use goldbricker function to analyze the dependent correlation. Results revealed three pairs of items that were potentially overlapping, suggesting that they were similar constructs. We combined items that evidenced overlap. Item 3(Sleep Problems) and item 5(Poor Appetite) were combined (this node is hereafter referred to as “Somatization”). Items 9(Feeling Nervous) and 11(Worrying) were combined (this node is hereafter referred to as “Worried Feelings”). On the CBI, item 19(Good at job) and item 20(Worthwhile Work) were combined. Finally, a total of 27 symptoms (items) were included in the network (see Table 1).

Table 1 Item selection of network

Item number	Symptoms
Depressive symptoms
1	Anhedonia
2	Feeling Sad
3	Somatization (Sleep Problems and Poor Appetite)
4	Low Energy
5	Feeling like a Failure
6	Concentration Problems
7	Psychomotor Problems
Anxiety symptoms
8	Controlling Worries
9	Worried Feelings (Feeling Nervous and Worrying)
10	Trouble Relaxing
11	Restlessness
12	Irritability
13	Feeling Afraid
PA symptoms
14	Problem-Solving
15	Contribution
16	Good at Job
17	Worthwhile Work
18	Effectively
DP symptoms
19	Uninterested
20	Less Enthusiastic
21	Doubt Meaning
22	Indifferent to Contribution
EE symptoms
23	Exhausted
24	Tired Out
25	Weary
26	Stress
27	Collapse

Edge values were estimated with narrow confidence intervals for the symptom network (Fig. S1) as well as the network of symptoms/burnout (Fig. S2). Case-dropping bootstrap indicated that the majority of edges were stable. The Mean weight of edges was 0.08 on the symptom network, and 0.04 on the symptom and burnout network. For both networks, the CS coefficients were at the tested ceiling of 0.75, indicating that at least 75% of the sample could be dropped before the correlation with original centrality values dropped below r=0.7.

3.3 Network of symptom in HCWs

The network layout using MDS was presented in Figure 1 with 0.11 stress-1 value. As shown in Figure 1(A) depressive and anxiety symptoms correlated positively with each other. The strongest edges were observed between: “Anhedonia” and “Feeling Sad” (weight=0.33); “Controlling Worries” and “Worried Feelings” (weight=0.33); “Worried Feelings” and “Trouble Relaxing” (weight=0.30); “Restlessness” and “Feeling Afraid” (weight=0.31). As shown in Figure 1(B), “Motor Problem” and “Irritability” had the highest expected influence values.

3.3 Network of symptom/burnout in HCWs

Network layout using MDS was presented in Figure 2 (A) with 0.04 stress-1 value. We reduced the size of the node to ensure that node labels do not overlap (FigS3).

Fig. 2(B) shows bridge expected influence values with confidence intervals. Of the symptoms in the symptom community, “Motor Problem” and “Irritability” demonstrated the most robust bridge expected influence value, indicating that “Motor Problem” and “Irritability” were most highly associated with the community of burnout items. Of the items in the burnout community, “Collapse” had the most robust bridge expected influence value, indicating that “Collapse” was the item most highly associated with the community of symptoms.

3.4 Principal Components Analysis

As shown in Fig 3, EE, DP, depression, and anxiety symptoms likely capture a dimension of the symptom and burnout network. DP and EE symptoms, especially EE symptoms cannot be distinguished from depressive and anxiety symptoms.

3.5 Comparison network

Figures S4 and S5 showed the network of each subgroup. The network comparison test indicated that the overall network structure was invariant (M=0.23, p=0.10), There was no difference in the global strength. (global strength in Hospitals with covid-19 cases:13.50; Hospitals with no covid-19 cases:13.06; S=0.44, p= 0.17). The most notable change in centrality was observed for “Feeling like a Failure” and “Worthwhile Work”. The expected influence of both two symptoms was decreased in stress situations (Table S3). FigS4 and S5 indicated that EE symptoms were closer in distance to the symptoms in the symptom community in stress situations.

Network analysis was employed to demonstrate the connections between burnout and depression/anxiety among healthcare workers (HCWs) at the level of symptoms. Notably, "Psychomotor Problems" and "Irritability" exhibited the highest bridge centrality values within the symptom network. Similarly, within the symptom/burnout network, "Psychomotor Problems," "Irritability," and "Collapse" displayed the strongest bridge centrality values. Remarkably, these same symptoms emerged as the most influential bridges within the symptom/burnout network in both hospitals with COVID-19 cases and hospitals without COVID-19 cases. Additionally, we constructed a network using principal components analysis which revealed that burnout symptoms cannot be distinguished from depressive and anxiety symptoms.

The findings of this study indicate that there are distinct relationships between symptoms of depression and anxiety and burnout. Notably, the symptoms of "Psychomotor Problems" and "Irritability" exhibited the strongest associations with burnout symptoms. Conversely, the symptoms of "Anhedonia", "Somatization", and "Worried Feeling" displayed only weak associations with burnout symptoms. These findings align with previous research that has demonstrated a correlation between individuals with extensive work experience and heightened psychomotor problems(Machado et al., 2018), as well as increased incidences of irritability (Zheng et al., 2018). This result could explain why burnout can predict depression(Liu et al., 2021) and anxiety(Zhou et al., 2018), as well as why comorbidities between burnout and depression/anxiety occur in some individuals but not in others. According to the network theory of comorbidity(Cramer et al., 2010), symptoms that increase the risk of contagion to other disorders are “bridge symptoms”, In our study “Psychomotor Problems”, “Irritability” and “Collapse” serve as “bridge symptoms”. Therefore, targeting interventions towards addressing these specific symptoms may help prevent comorbidities. Additionally, it is worth mentioning that the presence of both “Psychomotor Problems” and “Irritability” is consistently observed together, whether within the same syndrome or among different syndromes' comorbidity (Afzali et al., 2017; Manzo et al., 2017; White et al., 1995). This comorbidity likely to reflects a shared underlying mechanism.

Moreover, we observed that in “bridge symptoms”, “Irritability” was the most common symptom in anxiety. However, “Psychomotor Problems” was the least common in depression. This phenomenon may explain why individuals diagnosed with anxiety disorder tend to develop into depressive disorder later(Cho et al., 2022; Starr and Davila, 2012), however, the reverse has poorly reported. In other words, a majority of anxiety patients with “Irritability” symptom consequently increase the risk of developing depressive disorders, while only a minority of depression patients with “Psychomotor Problems” symptom consequently very few depression patients may develop anxiety disorders. This finding will further enhance our understanding of the mechanisms underlying the comorbidity and distinction between depression and anxiety. Nevertheless, it is crucial to acknowledge that our analysis should be regarded as a hypothesis-generating endeavor and necessitates further empirical validation in subsequent research.

We also wondered if burnout symptoms can be distinguished from symptoms of depression and anxiety. However, Principal Components Analysis fails to support this perspective as Burnout symptoms neither capture a dimension alone nor are distinguished from symptoms o depression and anxiety. According to another perspective, burnout develops over time and may eventually lead to mental disability(Christina Maslach, 1996). Consequently, burnout nodes(symptoms) should be more closer to nodes(symptoms) of depression and anxiety in stressful situations. However, the network comparison test indicated that the overall network structure remained consistent between stress situations and usual situations. In summary, our results do not support the notion of burnout as an independent syndrome.

Together, our findings provide a new perspective for understanding the association between burnout and mental disability, which have several implications for hospital administrators and HCWs. First, extra attention needs to be paid to assessing the mental well-being of this at-risk group in terms of "Psychomotor Problems," "Irritability," and "Collapse" symptoms. Secondly, when developing burnout-based interventions, depressive and anxiety symptoms should also be taken into consideration. Thirdly, our research findings indicate that it would be beneficial for interventions to prioritize the mitigation of the "Irritability" symptom in individuals with anxiety, as this may effectively decrease the likelihood of developing depression.

Conflict of interest statement

All authors disclosed no relevant relationships.

Funding statement

This work was supported by Shanghai Natural Science Foundation under Grant [19ZR1469500]; Beijing Municipal Science &Technology Commission under Grant [Z181100001718041].

Data availability statement

Data available on request from the corresponding author.

Author contributions

Qi-Qi Ge and Ji-Feng Feng were co-first authors who were responsible for interpreting the data and drafting the manuscript. Wei Wang and Yun-E Liu were co-corresponding authors who had led the whole study and revised the manuscript. Yan-Jun Liu and Yi-Lin Wu participated in the questionnaire prepared. Ting Hu and Xiao-Na Zhou contributed to collection of data. All authors read and approved the final manuscript.

Ethical approval statement

The study protocol was approved by the Ethics Committee of the Navy Military Medical University (Shanghai, China) in accordance with the principles stated in the Declaration of Helsinki.

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No competing interests reported.

The associations between burnout with depression and anxiety among healthcare workers: A network analysis

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

3. Results

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1