Introduction

Since December 2022, there had been a surge in Coronavirus Disease 2019 (COVID-19) cases in China following a swift relaxation of its stringent zero-COVID restrictions [1,2,3]. According to a key source at the Center of Disease Control and Prevention, approximately 80% of the population in China could have been infected with COVID-19 by the end of January 2023 [4]. The majority of the COVID-19 patients experienced minimal or mild symptoms, with only a few cases of severe respiratory distress or failure requiring ICU care during that time [5]. However, a substantial number of COVID-19 patients might have had post-COVID-19 related symptoms, which could persist for at least 2 months following the acute phase of infection [6, 7].

The post-COVID-19 condition, which is also known as long COVID-19, can include fatigue, pain, dizziness, sore throat, and so on [11]. Additionally, a prospective study of COVID-19 patients found that 22.34% of participants had suffered insomnia after 1 year follow-up [47]; CS-coefficient larger than 0.25 indicated nodes had moderate stability, while values greater than 0.5 indicated strong stability [48]. The network accuracy was assessed by bootstrapped 95% confidence intervals (CIs) through a non-parametric bootstrap** procedure [47], with a narrower CI having a more accurate network. Finally, we performed a bootstrapped difference test between the EI values and between weights of edge to identify whether the nodes and edges were different from each other [47]. Node stability and accuracy were examined using “bootnet” packages [18]. Data analyses were performed using the R program [49].

Results

Participants information

Altogether, 11760 mental health workers were invited to participate in this survey, of whom, 9858 met the inclusion criteria and were included in analyses. Basic demographic and clinical characteristics are shown in Table 1. The mean age of participants was 38.24 years (standard deviation = 8.302), and the majority of the participants were married (73.1%). The prevalence rate of depression and insomnia were 47.10% (95% confidence interval (CI) = 46.09–48.06%) and 36.2% (95%CI = 35.35–37.21%), respectively. The overall prevalence of suicidality was 7.8% (95%CI = 7.31–8.37%), while the prevalence of SI, SP, and SA were 6.5% (95%CI = 6.00–6.97%), 2.7% (95%CI = 2.40–3.04%), and 3.0% (95%CI = 2.64–3.31%), respectively.

Table 1 Basic demographic and clinical characteristics (N = 9858).
Full size table

Network structure and centrality

Figure 1 presents the network structure of the depression, insomnia, and suicidality model in MHPs who recovered from COVID-19 with a total of 15 nodes. The mean predictability of the nodes was 0.878, suggesting that 87.8% of nodes could be predicted by their neighboring nodes in the model. The top three central nodes that activated the whole network (Z score of expected influence) were “Distress caused by the sleep difficulties” (ISI7) (EI = 1.34), “Interference with daytime functioning” (ISI5) (EI = 1.08), and “Sleep dissatisfaction” (ISI4) (EI = 0.74). The EI values are presented in Table S1.

Fig. 1: The network structure of depression and insomnia with suicidality in MPHs who recovered from COVID-19.
figure 1

Left panel: Network structure of depression, insomnia and suicidality; Right panel: Centrality index of EI for each node.

Full size image

Figure 2 shows the edges across different clusters in the network and the rank of bridge EI values (1-step Bridge Expected Influence). Expected “Suicidality” (SU) (Bridge EI = 2.66) (which was regarded as a bridge symptom since it was a cluster composed of a single node), “Fatigue” (PHQ4) (Bridge EI = 1.98), “Distress caused by the sleep difficulties” (ISI7) (Bridge EI = 1.71) and “Motor Disturbances” (PHQ8) (Bridge EI = 1.67) were important bridge symptoms that linked the whole network.

Fig. 2: The bridge network structure of depression, insomnia, and suicidality in MPHs who recovered from COVID-19.
figure 2

Left panel: Network structure of depression, insomnia and suicidality; Right panel: Bridge centrality index of BEI for each node.

Full size image

Figure 3 shows the flow diagram of the network model, which indicates in descending order that “Guilt” (PHQ6) (Edge weight = 1.17), and “Sad Mood” (PHQ2) (Edge weight = 0.67) in the depression cluster were directly associated with suicidality with strong weights, while the association between guilt and suicidality was the strongest. In addition, “Distress caused by the sleep difficulties” (ISI7) (Edge weight = 0.31) in the insomnia cluster were directly associated with suicidality with weak weights. The edge-weighted values are presented in Table S2.

Fig. 3: Flow network of depression and insomnia with suicidality in MPHs who recovered from COVID-19.
figure 3

Green edges represent positive partial correlations.

Full size image

Network stability and accuracy

In terms of network stability shown in Fig. 4, an excellent level of stability was observed in both EI and bridge EI, with CS-coefficient being 0.75 and 0.67, respectively. Figures S1 and S2 display the Bootstrapped 95% CIs, indicating that the estimated EIs and bridge EI were reliable and stable. The bootstrap difference test in Figs. S3, S4 indicates that most edges and EIs were significantly different from others.

Fig. 4: Centrality and bridge centrality stability tests.
figure 4

Average correlation between the centrality indices with cases dropped and the original sample. The lines indicate the means and the areas indicate the range from 10% to 90% quantile.

Full size image

Discussion

To the best of our knowledge, this was the first study globally that examined the prevalence and network structure of depression, insomnia, and suicidality among MHPs who recovered from COVID-19 infection. Our findings found that the three most central symptoms in the network were in the insomnia cluster, whereas the three strongest associations with suicidality were observed in the depressive cluster.

The prevalence of depression and insomnia was high in MHPs who recovered from COVID-19, which is consistent with previous meta-analyses that found that the prevalence of depression among doctors during the COVID-19 pandemic was 20.5% [50], while the pool prevalence of depression in COVID-19 infected patients was 45% [51]. As for insomnia, a meta-analysis conducted during the COVID-19 pandemic showed that the prevalence of insomnia in healthcare workers and COVID-19 patients were 46.4% and 48.7%, respectively, which were roughly twice as high as that in the general population (26.0%) [52]. These findings appear to be consistent with the hypothesis of this study.

It was noted that the rapid lifting of the stringent zero-COVID-19 restrictions had put an unprecedented strain on China’s healthcare system [53]. COVID-19-infected MPHs faced dual pressure from both their work and illness recovery, thus experiencing a greater likelihood of depression or insomnia than the general population. Further, in that period, most MHPs were re-deployed to care for COVID-19 patients and faced with higher work pressure and longer work shifts, which could increase the risk of insomnia [54]. Moreover, during their own recovery from COVID-19, MHPs were often directly involved in treating COVID-19 patients, which increased their risk for secondary infection as well as depressive and insomnia symptoms [55].

“Distress caused by sleep difficulties” (ISI7), which reflects the level of worry caused by sleep problems, was the most central and important bridge symptom among MHPs who had recovered from COVID-19 inflection, thus suggesting it could more readily influence other symptoms within the depression-insomnia-suicidality network model [48]. As a fundamental aspect of anxiety [56], worry also plays a crucial role in maintaining insomnia and predicting depression [57]. This is consistent with the findings of another network analysis study in a community-dwelling population during the COVID-19 pandemic, in which anxiety symptoms were the most important bridge symptoms linking depression and insomnia [22]. One potential explanation is that distress caused by sleep problems is usually accompanied by a range of negative emotions in patients who recovered from an infection [58]. MHPs who had recovered from COVID-19 might worry about various issues such as patients’ safety, shortage of medicine, and the risk of infecting their families and friends, all of which could exacerbate depression [59]. Moreover, there was a direct connection between “Distress caused by sleep difficulties” (ISI7) and “Suicidality” (SU) in the flow network, indicating that interventions aimed at addressing worry and distress arising from sleep difficulties could be useful in reducing both depressive symptoms and suicidality in this subpopulation. For instance, a widely used approach is cognitive behavior therapy, which has good evidence in treating insomnia related worries [60].

Another insomnia symptom “interference with daytime functioning” (ISI5) (i.e., trouble with concentration, mood disturbances, or decreased ability to perform daily activities) was identified as a core symptom in the network model. For patients who recovered from COVID-19, problems with memory, concentration, or sleep were among the most common post-COVID-19 symptoms, even after an extended time following infection [65]. Moreover, self-reported sleep dissatisfaction were found to be significantly associated with suicidal ideation in the past year [66].

Depressive symptom “Fatigue” (PHQ4) was one of the top bridge symptoms within the depression-insomnia-suicidality network model. Previous research found that approximately one-third of people experienced fatigue after being diagnosed with COVID-19 [61]. These findings are consistent with studies in Filipino domestic workers and hospital clinicians, where fatigue was identified as the key symptom in the depression and anxiety network model [67,

Data availability

The data of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors are grateful to all participants and clinicians involved in this study. The study was supported by the National Science and Technology Major Project for Investigational New Drug (2018ZX09201-014), the Bei**g Hospitals Authority Clinical Medicine Development of Special Funding Support (XMLX202128), and the University of Macau (MYRG2019-00066-FHS; MYRG2022-00187-FHS).

Author information

Author notes

  1. These authors contributed equally: He-Li Sun, Pan Chen, Wei Bai, Ling Zhang, Yuan Feng.

  2. These authors jointly supervised this work: Chee H. Ng, Feng-Rong An,Yu-Tao **ang.

Authors and Affiliations

  1. Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China

    He-Li Sun, Pan Chen & Yu-Tao **ang

  2. Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China

    He-Li Sun, Pan Chen & Yu-Tao **ang

  3. Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China

    Wei Bai

  4. Bei**g Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Bei**g Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Bei**g, China

    Ling Zhang, Yuan Feng & Feng-Rong An

  5. School of Public Health, Southeast University, Nan**g, China

    Zhaohui Su

  6. School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China

    Teris Cheung

  7. Section of Psychiatry, University of Notre Dame Australia, Fremantle, WA, Australia

    Gabor S. Ungvari

  8. Division of Psychiatry, School of Medicine, University of Western Australia, Perth, WA, Australia

    Gabor S. Ungvari

  9. Department of Business Administration, Hong Kong Shue Yan University, Hong Kong, Hong Kong SAR, China

    **-Ling Cui

  10. Department of Psychiatry, The Melbourne Clinic and St Vincent’s Hospital, University of Melbourne, Richmond, VIC, Australia

    Chee H. Ng

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Contributions

Study design: Feng-Rong An, Ling Zhang, Yuan Feng, and Yu-Tao **ang. Data collection, analysis, and interpretation: He-Li Sun, Pan Chen, Wei Bai, Zhaohui Su, Teris Cheung, Gabor S. Ungvari, **-Ling Cui, and Feng-Rong An. Drafting of the manuscript: He-Li Sun and Yu-Tao **ang. Critical revision of the manuscript: Chee H. Ng. Approval of the final version for publication: all co-authors.

Corresponding authors

Correspondence to Chee H. Ng, Feng-Rong An or Yu-Tao **ang.

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The study protocol was approved by the Ethics Committee of the Bei**g Anding Hospital, China.

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Sun, HL., Chen, P., Bai, W. et al. Prevalence and network structure of depression, insomnia and suicidality among mental health professionals who recovered from COVID-19: a national survey in China. Transl Psychiatry 14, 227 (2024). https://doi.org/10.1038/s41398-024-02918-8

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  • DOI: https://doi.org/10.1038/s41398-024-02918-8

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