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. 2024 Oct 26;14:25461. doi: 10.1038/s41598-024-75550-7

The interplay among burnout, and symptoms of depression, anxiety, and stress in Chinese clinical therapists

Mengyue Gu 1,2,3,#, Song Wang 1,2,3,#, Shujing Zhang 4, Suqi Song 1,3, Jingyang Gu 1,2,3, Yudong Shi 1,2,3, Wenzheng Li 1,3,5,6, Long Chen 1,3,5,6, Yan Liang 1,3, Yating Yang 1,2,3, Ling Zhang 1,2,3, Mengdie Li 1,2,3, Feng Jiang 7,8,9,10,✉, Huanzhong Liu 1,3,✉, Yi-lang Tang 4,11
PMCID: PMC11513086  PMID: 39462028

Abstract

Burnout, depression, anxiety, and stress negatively impact the well-being and retention of healthcare professionals. The interplay of these symptoms is understudied. Utilizing network analysis, this study examined the interrelationships among these symptom clusters in clinical therapists in China. An anonymous survey was conducted among clinical therapists from 41 tertiary psychiatric hospitals in China. Burnout was assessed using the Maslach Burnout Inventory-Human Service Survey (MBI-HSS), while symptoms of depression, anxiety, and stress were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21). Analyses were performed to identify central symptoms and bridge symptoms of this network. A total of 419 participants were included in this survey. The prevalence rate for burnout, depression, anxiety, and stress was 19.8%, 22.2%, 17.9%, and 8.6%, respectively. Network analysis indicated that stress symptoms had the highest expected influence values, closely followed by emotional exhaustion from MBI-HSS. Notably, emotional exhaustion emerged as the strongest bridge of expected influence. The stability of the expected influence and bridge expected influence was robust, with coefficients at 0.75. The study’s findings underscore the importance of recognizing the central symptoms and bridge symptoms, which could lead to more effective early detection and intervention for burnout, depression, anxiety, and stress among clinical therapists.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-75550-7.

Keywords: Network-analysis; Burnout; Depression; Anxiety; Stress; Clinical therapists, China

Subject terms: Psychology, Health care, Health occupations

Introduction

Over the past three decades, China has witnessed rapid economic and social transformations, more psychological stress and a myriad of mental health issues have been reported1,2. The 2019 China Mental Health Survey reported the lifetime prevalence of mental disorders in China was 16.6%, indicating that more than 200 million Chinese people would suffer from mental disorders in their lifetime when considering the vast population in this country3,4. Such high prevalence places a significant burden on the public health system in China.

China’s mental health system faces challenges characterized by inadequate and unevenly distributed resources to meet the growing demand for mental health services5. The overall ratio of psychiatrists and psychiatric nurses per bed stood at 0.16 and 0.345, significantly lagging behind developed countries and falling short compared to some nations with similar economic levels6,7. Mental health services in China operate through multidisciplinary teams8, requiring a holistic approach that integrates pharmaceutical interventions, psychological counseling, psychotherapy, and social rehabilitation9–11. Clinical therapists, as an emerging profession in China, play a crucial role in providing essential psychosocial interventions and mental health care. Of note, the education and training backgrounds of clinical therapists in China vary, encompassing doctors, nurses, psychologists, and social workers, as long as formal training and certification have been obtained12,13. A comprehensive assessment of mental health workforce requirements across 44 middle-income countries indicated a need for 10.2 psychosocial caregivers per 100,000 individuals6,14. Unfortunately, as of 2015, China had only 135,000 clinical therapists (9.85 per 100,000 people)6. Another survey research also underscored the need for more clinical therapists, even in specialized psychiatric hospitals, with only 1,005 therapists (one every 30 beds) nationwide5.

Burnout, a psychological syndrome resulting from a prolonged response to chronic interpersonal stressors on the job15, predominantly affects people-oriented professions, such as human services, education, and healthcare. Healthcare professionals, in particular, tend to report moderate to high levels of burnout due to the ongoing empathy required in developing long-term therapeutic relationships with consumers16. Some studies suggested that mental health professionals may be more prone to burnout than other healthcare professionals17,18. For example, one Finnish study found that 89% of psychiatrists had experienced a clear threat of severe burnout19. Burnout is a significant negative predictor of the subjective well-being of medical workers20. Higher levels of burnout often correlate with more negative feelings about patients and a poorer quality of care21,22. Burnout may also pose the risk of hastening the attrition of professionals and contributing to a shortage of mental health professionals23,24. Over the past few years, the COVID-19 pandemic has had a significant impact on global public health and mental health systems. During that time, healthcare workers were under tremendous pressure, both physically and psychologically. A nationwide online survey in France conducted during the pandemic found that 55.2% of 10,087 healthcare workers reported burnout25–27. Many socio-demographic and occupational factors appear to be associated with burnout, such as high workloads, lack of job control, poor work-life balance, and loss of support28. Meanwhile, many studies have focused on the relationship between burnout and mental disorders, questions persist regarding the specific nature of the connection. When the construct of burnout was first proposed, some viewed it as synonymous with job stress, dissatisfaction, anxiety, anger, depression, or a combination of thereof29. Although subsequent research has demonstrated that burnout is indeed a distinct construct15,30, it remains closely linked to various mental disorders or symptoms. Healthcare professionals who experience burnout also show high levels of depression, anxiety, and stress, emphasizing the need to clarify the intricate relationships and interactions between burnout and mental disorders31,32.

As a burgeoning analytical tool, network analysis has been applied widely across various domains, revealing the interconnections within complex, multi-component systems33. Recently, this analytical approach has been utilized in the analysis of mental disorders, offering insights into the intricate relationships among psychological symptoms and fostering a nuanced understanding of mental disorders at the symptom level34,35. In network analysis, “nodes” and “edges” form the structure of the network graph, with each node representing a specific symptom and each edge denoting the partial correlation coefficient between two nodes after controlling for the influence of other nodes in the model36. The primary objective is to pinpoint a central node that is highly interconnected with others, thereby indicating its potential clinical significance in disease occurrence37. This identification facilitates the implementation of targeted intervention measures. Furthermore, network analysis holds promise in unraveling psychiatric comorbidity, shedding light on how particular symptoms of one disorder may correlate with an elevated risk of developing other disorders37. Referred to as “bridge symptoms,” these indicators of increased transmission risk between diseases can be discerned through network analysis38. Armed with this knowledge, clinicians can proactively target these bridge symptoms in their intervention or treatment, offering a more effective strategy for managing comorbidities39.

Although many studies have focused on the prevalence and correlates of depression, anxiety, stress, and burnout among healthcare professionals, a notable research gap exists in understanding the connections among these psychological symptoms remain unclear, particularly how burnout may contribute to or result from other mental health issues. Therefore, our study had three primary objectives: (a) examining the prevalence of burnout, depression, anxiety, and stress among clinical therapists in China; (b) utilizing network analysis methods to scrutinize the interplay of burnout and symptoms of depression, anxiety, and stress; (c) identifying central symptoms and bridge symptoms within the burnout-depression-anxiety-stress network. Through these objectives, we aim to provide valuable insights into the comprehensive understanding of psychological well-being among clinical therapists in China. Additionally, we seek to guide targeted interventions that enhance mental health support and mitigate professional burnout within this specific professional group.

Methods

Participants and study procedure

This study was a component of the National Hospital Performance Evaluation Survey (NHPES) 2021, sponsored by the National Health Care Commission of China (NHCC). Conducted between January 11 and March 15, 2021, the survey included 41 tertiary psychiatric hospitals across 28 provinces in China. Implemented through the official WeChat account of the National Health Commission, “Health China,” each WeChat user account was restricted to completing the survey once to prevent duplicate submissions. A total of 445 clinical therapists were invited to participate in the online survey, 419 (response rate of 94.2%) who completed questionnaires were included for analysis. A comparison of demographic characteristics between valid and invalid respondents showed no statistically significant differences in age (t=-0.414, P = 0.682), gender (χ2 = 0.963, P = 0.327), and marital status (χ2 = 5.075, P = 0.079). To ensure participant confidentiality and result reliability, the questionnaires were anonymous. The study protocol was reviewed and approved by the Ethics Committee of Anhui Medical University Hospital (approval number: 202002-kyxm-02). All participants provided their informed consent to participate in this study.

All procedures followed the relevant guidelines and regulations.

Measurements

Demographic and work-related characteristics

Demographic characteristics used in this study encompassed various factors: age, sex (male or female), marital status (married, single, and others), annual income, and educational level. Work-related factors were also collected, including professional title, administrative position, working hours per week, and involvement in frontline work related to COVID-19. The selection of these demographic and work-related factors aligns with previous studies40–42.

Depression, anxiety, and stress

The Depression Anxiety and Stress Scale (DASS) is a self-report tool designed to assess emotional states related to depression, anxiety, and stress. It has gained recognition from researchers in various countries43–45. The DASS-21 has three components with seven questions each: depression (Q3, Q5, Q10, Q13, Q16, Q17, Q21), anxiety (Q2, Q4, Q7, Q9, Q15, Q19, Q20), and stress (Q1, Q6, Q8, Q11, Q12, Q14, Q18). Participants rate each item on a scale from 0 (not at all) to 3 (very much or most of the time). The score of each item is the sum of the answers multiplied by 2, with the highest score for each component being 42. Participants are categorized as having “clinically meaningful” symptoms if their scores are ≥ 10 for depression, ≥ 8 for anxiety, and ≥ 15 for stress, respectively. The scale exhibits high internal consistency for the current sample with a Cronbach’s alpha of 0.936. The Cronbach’s alpha values for the individual dimensions of depression, anxiety, and stress were 0.908, 0.794, and 0.847, respectively.

Burnout

The Maslach Burnout Inventory-Human Service Survey (MBI-HSS)46 was employed to assess professional burnout. Comprising 22 items, the scale utilizes a 7-point rating system ranging from 0 (never) to 6 (every day), indicating the frequency of experienced symptoms. The instrument measures three dimensions: emotional exhaustion, depersonalization, and personal accomplishment. Scores for emotional exhaustion and depersonalization are in the positive direction, while scores for personal accomplishment are in the negative direction. Individuals scoring ≥ 27 points for emotional exhaustion or ≥ 10 points for depersonalization were considered to be experiencing ‘burnout’47,48. The Chinese version of the MBI-HSS has been widely used and has shown good reliability and validity49–51. For the current sample, the scale demonstrates a Cronbach’s alpha of 0.793, and the Cronbach’s alpha values for the emotional exhaustion, depersonalization, and personal accomplishment dimensions were 0.894, 0.751, and 0.901, respectively.

Data analysis

Statistical analysis

All statistical analyses were conducted using R (version 4.3.2)52. Descriptive statistics were applied to variables, encompassing demographic characteristics, work-related information, and other psychological symptoms. The Kolmogorov-Smirnov test was15 employed to assess the normal distribution of continuous variables. Continuous variables adhering to normal distribution were presented as mean ± SD, while those deviating from normal distribution were reported as Median (IQR). Categorical variables were expressed in frequencies and percentages. A two-tailed test was employed, and a significance level of P < 0.05 was considered statistically significant.

Network estimation

The R programming language was employed for network analysis, where each node in the model represented the three dimensions of burnout, and symptoms of depression, anxiety, and stress, respectively. The connections between nodes were considered edges53. The variables in the network were estimated using “EBICglasso,” an extension of graphical Lasso (glasso) that utilizes the Empirical Bayesian Information Criterion (EBIC) for selecting regularization parameters in graphical Lasso54,55. The estimation and visualization of network models were conducted using the R package qgraph (version 1.9.8)53. Nodes with higher expected influence in network models are considered more pivotal. Thicker edges in the visualization represent stronger associations between two nodes, where blue edges indicate positive associations and red edges indicate negative associations. The magnitude and number of the edges depict the magnitude of the correlation. The R package networktools (version 1.4.2) and its bridge function were employed to determine the bridge’s expected influence for identifying symptoms that act as bridges. Higher bridge expected influence values indicate an increased risk of transmission between communities than those with lower values39. The 80th percentile of the bridge’s expected influence values served as the cutoff point for identifying bridge symptoms56. Additionally, the R package mgm (version 1.2–14) facilitated the calculation of predictability for each node, expressed as the area of the annular region surrounding each node57. Predictability values indicate the extent to which a node is interconnected with its neighbors, nodes with high predictability were thought to be easily influenced by the adjacent nodes.

Centrality and Stability

To assess the accuracy and stability of the observed network model, we utilized the R package bootnet (version 1.5.6)54 with 1000 bootstrap samples per node. The evaluation of centrality indices, including expected influence and bridge expected influence, was conducted using the correlation stability (CS)-coefficients. Values exceeding 0.5 indicate strong stability.

Results

Demographic, work-related characteristics and symptoms

Table 1 presents the demographic characteristics of the participants. The mean age was 34 years (SD = 7.5), and more than three-quarters (77.6%) were females. More than three-fifths (62.5%) were married. The median annual income for the group stood at 90,000 RMB (equivalent to US $13,846.2). Among the participants, 176 (42%) held a master’s degree or above. The majority (58.5%) had junior or lower professional titles, and approximately one-fifth had actively participated in frontline work related to COVID-19. The median weekly working hours were 40. The prevalence rate for burnout, depression, anxiety, and stress was 19.8%, 22.2%, 17.9%, and 8.6%, respectively.

Table 1.

Sample characteristics.

Variables Categories Total sample
(n = 419) a
Age 34 ± 7.5
Sex (%) Male 94 (22.4)
Female 325 (77.6)
Marital status (%) Single 140 (33.4)
Married 262 (62.5)
Others 17 (4.1)
Annual income (Ten thousand RMBs) b 9 (7,12)
Educational level (%) Junior 243 (58)
Senior 176 (42)
Professional title (%) Junior or below 245 (58.5)
Mid-level 149 (35.6)
Senior or above 25 (6)
Working hours per week 40 (40,45)
Participate in the frontline work of COVID-19 (%) Yes 86 (20.5)
No 333 (79.5)
Burnoutc Emotional exhaustion score 11 (6,20)
Depersonalization score 4 (1,7)
Personal accomplishment score 37 (28,42)
Burnout symptom(%) 83 (19.8)
Depressiond Score 2 (0,8)
Symptom(%) 93 (22.2)
Anxietyd Score 2 (0,6)
Symptom(%) 75 (17.9)
Stressd Score 4 (0,10)
Symptom(%) 36 (8.6)
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aMean±SD; n (%); Median (IQR).

bUS dollar to RMB (renminbi) ratio: 1 US dollar ≈ 6.5 RMB.

cMBI-HSS.

dDASS-21.

Network structure

The left panel of Fig. 1 illustrates the network of burnout, depression, anxiety, and stress symptoms. We observed that 10 of the 15 possible edges (66.7%) were non-zero, indicating substantial connections between symptoms (Table S1). The mean weight of the edges was 0.179. The top three most robust edges identified in the model were distributed within their respective communities. This distribution emphasizes the prominence of connections within each psychological problem cluster. The strongest edge was observed between emotional exhaustion and depersonalization in the burnout community. Other notable firm edges within the DASS community include anxiety-stress and depression-stress. Stress symptoms within the DASS community exhibited the highest predictability, followed by depression symptoms. Personal accomplishment among the burnout community was identified as the least predictable. The right panel of Fig. 1 depicts the expected influence of the entire network structure, with stress symptoms in the DASS community and emotional exhaustion in the burnout community having the highest expected influence values. Regarding bridge symptoms, emotional exhaustion in the burnout community exhibited the most vital bridge expected influence, as shown in Fig. 2.

Fig. 1.

Fig. 1

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The network structure of burnout, and symptoms of depression, anxiety, and stress in clinical therapists. Note: Nodes represent variables, and edges define partial correlations between the variables. The color of the edge indicates the direction of the correlation (red = negative, blue = positive). The magnitude and number of the edges depict the magnitude of the correlation. The area in the rings around the nodes indicates predictability (the upper bound of the variance of a given node explained by the remaining nodes in the network). The right panel of the figure shows the strength and EI (expected influence) value (after the standardized z-score) of each node in the network.

Fig. 2.

Fig. 2

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The network structure of burnout, and symptoms of depression, anxiety, and stress showing bridge symptoms in clinical therapists. Note: EE in burnout symptoms exhibits the strongest BEI (bridge expected influence) value. The right panel of the figure shows the BEI (after standardized z-score) of each node in the network.

Network stability

Figure 3 illustrates stability coefficients for expected influence and bridge expected influence, both equaling 0.75, suggesting that 75% of participants could be dropped from analyses without significantly changing the network structure. Bootstrap stability tests on expectancy effects revealed significant distinctions for central symptoms compared to other nodes and edge weights (Figures S1 and S2). This implies that specific symptoms play a more crucial role in influencing the network structure, and their impact is consistent across different samples obtained through bootstrapping. These findings contribute to the overall validity and reliability of the observed network model.

Fig. 3.

Fig. 3

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The stability and accuracy of the network. Note: Both expected influence and bridge expected influence values indicated an excellent level of stability (both CS-coefficients equaled 0.75), suggesting that 75% of participants could be dropped from analyses without significantly changing the network structure.

Discussion

This study utilized network analysis to explore the interrelationships among burnout and mental health symptoms including depression, anxiety, and stress, among clinical therapists in China. The results showed nearly one-fifth of the participants experienced burnout, and symptoms of depression and anxiety, and nearly 9% experienced stress. Specifically, the prevalence rates of burnout, depression, anxiety, and stress among clinical therapists were 19.8%, 22.2%, 17.9%, and 8.6%, respectively. While these rates are relatively high and concerning, the prevalence of burnout and anxiety in clinical therapists was notably lower than that of other mental health professionals (including psychiatrists and nurses) (burnout: 27.27–38.4%, anxiety: 24.1-24.36%)23,40,58. Meanwhile, the prevalence of depression and stress symptoms aligns with findings from other studies of mental health professionals in China (depression: 17.24–26.7%, stress: 7.28–11.6%)23,40,58. This distinction may be attributed to the current structure of China’s mental health system and social development level. In most clinical settings, psychiatrists and nurses still assume the primary therapeutic roles. Clinical therapists, despite their potential advantages in addressing negative emotions, face limited involvement in therapeutic alliances due to their scarcity and the high cost of psychotherapy services59–61.

Consistent with previous research, this study enhances our understanding of the interconnections among burnout, depression, anxiety, and stress through an examination of their network structure and centralities15,32,62–64. This study included two communities: one contained the three dimensions of burnout (emotional exhaustion, depersonalization, and personal accomplishment), and the other comprised symptoms of depression, anxiety, and stress on the DASS scale. Robust connections were predominantly found within specific psychological symptom communities, aligning with prior research on network structures65,66.

The strongest edges in the network were found between the emotional exhaustion and depersonalization dimensions in the burnout community. This aligns with the three-dimensional model structure theory of burnout, where emotional exhaustion reflects excessive exhaustion of personal emotional and physical resources, depersonalization indicates an adverse reaction to work, and personal accomplishment relates to self-evaluation of work achievements46. The significance of this model is to contextualize individual stress within the social context of the workplace and link it to individuals’ perceptions of self and others. However, these three dimensions are not independent. Previous research suggests that while emotional exhaustion reflects the personal stress dimension of burnout, it fails to capture critical aspects of people’s relationships with their work. Emotional exhaustion is not a simple experience—instead, it prompts people to emotionally and cognitively distance themselves from their work. Therefore, depersonalization may directly respond to emotional exhaustion15,67,68. However, some studies have indicated that personal accomplishment might be a distinct construct, unassociated with the other two dimensions of burnout: emotional exhaustion or depersonalization. Instead, it aligns more closely with different factors like engagement, motivation, and fulfillment69,70.

Stress symptoms had the highest expected influence value in the network analysis, indicating that stress plays the most critical role in maintaining the entire symptom network. At the same time, stress has the highest node predictability, which also emphasizes the potential significance of stress symptoms in intervention strategies. Interventions targeted at reducing stress symptoms may potentially alleviate other symptoms and mitigate the severity of interconnected manifestations in the burnout-depression-anxiety-stress network. Other previous studies have focused more on symptoms of depression and anxiety, ignoring the significance of stress32,62,63. Smith et al. proposed that ongoing chronic stress can hinder the body’s immune function and adversely affect overall mental health71. The two significant sources of stress for clinical therapists are the clientele they serve and the workplace environment72. Clinical therapists work with patients who often have psychological trauma. Throughout the therapeutic process, patients may present a range of challenging demands, all of which can impose significant pressure on clinical therapists. In this process, clinical therapists may also experience compassion fatigue, secondary traumatic stress, and vicarious trauma. Additionally, patients may project their emotions onto clinical therapists, leading to negative transference, where therapists become the targets of anger and hostility and may even experience violent incidents72–74. Based on these two factors, several recommendations can be considered: First, clinical therapists should develop robust coping skills via professional supervision, psychological health support, and team guidance while providing psychological services to patients (such as cognitive restructuring, conflict resolution, and various forms of counseling or therapy, intervening in emotional burdens during the therapeutic process). Moreover, various forms of social support (from colleagues and family) are also crucial. Lastly, and most importantly, ensuring the safety of the clinical therapist’s work environment to prevent occurrences of violence is imperative72,75.

For example, at the organizational level, hospitals should deploy security personnel to ensure the personal safety of clinical therapists while ensuring a quiet and comfortable treatment environment. At the same time, an emergency behavior team should be established to regularly simulate the occurrence of emergencies, train the emergency team’s ability to quickly handle emergencies, and avoid the occurrence of adverse events76.

Based on the bridge expected influence value, the emotional exhaustion dimension within the burnout community is the bridge symptom between burnout and the depression-anxiety-stress community. Bridge symptoms in different psychiatric disorder groups increase the risk of transmission between psychiatric syndromes, leading to psychiatric comorbidity39. Therefore, to reduce the comorbidity of burnout and psychiatric symptoms, the emotional exhaustion dimension deserves special attention. Emotional exhaustion is also a personal stress dimension and a core symptom of burnout15,67. In addition to the above suggestions for reducing clinical therapist stress, there are also some potential measures: altering work patterns (for instance, reducing work hours, taking more breaks, avoiding overtime, and achieving a balance between work and personal life). Furthermore, clinical therapists should be encouraged to engage in a variety of professional activities, such as part-time teaching, writing, and expanding clinical practice to cater to different clients. Moreover, cultivating a more diversified lifestyle such as involvement in extracurricular activities like hobbies and other personal interests is advisable72–74.

Limitations

A few limitations of the study need to be acknowledged. First, the research was conducted exclusively among clinical therapists practicing in urban tertiary psychiatric hospitals, potentially limiting the generalizability of the findings to therapists in other diverse settings, such as rural or remote areas. Second, the cross-sectional design of this study constrains the capacity to make causal inferences or comprehend the developmental progression of diverse symptoms over time. Third, the constraints imposed by the COVID-19 pandemic only permitted self-report symptom assessment, introducing the possibility of response bias associated with meeting expectations and social desirability.

Conclusion

Regardless of the limitations, the current research marks the first research delving into the experiences of Chinese clinical therapists. The findings underscore the importance of screening for and addressing the manifestations of burnout, depression, anxiety, and stress symptoms among clinical therapists. Furthermore, our study found that stress symptoms and emotional exhaustion occupy a prominent place in the network. At the same time, emotional exhaustion, as the only bridge symptom, provides the means and direction of intervention for the comorbidity of clinical therapist burnout and mental health symptoms.

Acknowledgments.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (295KB, docx)

Acknowledgements

Thanks to all the psychiatrists who participated in the survey and the staff in charge of the questionnaire.

Author contributions

Huanzhong Liu, Feng Jiang, and Yi-lang Tang conceived the study design. Jingyang Gu and Yudong Shi collected the data; Mengyue Gu and Song Wang conducted data analysis; Wenzheng Li, Long Chen, and Yan Liang interpreted the data and accessed the data. Suqi Song, Yating Yang, Ling Zhang, and Mengdie Li verified the data. Mengyue Gu wrote the first draft of the manuscript; Shujing Zhang and Yi-lang Tang critically revised the manuscript. All authors contributed to the paper and approved the submitted version.

Funding

The study was supported by the National Clinical Key Specialty Project Foundation (CN) and the Beijing Medical and Health Foundation (grant no. MH180924).

Data availability

The datasets analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The Chaohu Hospital of Anhui Medical University reviewed and approved the studies involving human participants.

Consent to participate

The participants provided their written informed consent to participate in this study.

Consent to Publish

All participants were provided with participant information before they started the survey and consented to the publication of the results of this study.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mengyue Gu and Song Wang contributed equally to this work.

Contributor Information

Feng Jiang, Email: fengjiang@sjtu.edu.cn.

Huanzhong Liu, Email: huanzhongliu@ahmu.edu.cn.

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Supplementary Materials

Supplementary Material 1 (295KB, docx)

Data Availability Statement

The datasets analysed during the current study are not publicly available due to privacy concerns but are available from the corresponding author on reasonable request.

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