Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis

Since COVID-19 broke out in Wuhan of China in December 2019, the World Health Organization (WHO) on March 11, 2020 categorized the disease as a worldwide epidemic as global rapidly spreading [1, 2]. People hit by SARS-CoV-2 were more prone to develop acute respiratory distress syndrome (ARDS) or even multiple system organ failure. Studies reported approximately 5–10% of patients diagnosed with COVID-19 were admitted to Intensive Care Units (ICU) for critical care due to the high mortality [3, 4].

With the use of vaccines and the implementation of active epidemic prevention measures in China, the nationwide lockdown policy was ended. China entered the late stage of the COVID-19 pandemic in April 2020, and the China National Health Commission (CNHC) announced to lift most of the restrictions implemented for “Zero-COVID” policy (restricting mass gatherings, maintaining social distancing, and staying at home) on December 7, 2022 [5]. The pandemic of China came to a peak stage again in a short time and the number of COVID-19 patients in the ICUs was increasing rapidly due to high-speed spreading. ICU, unlike other parts of the hospital, are areas where complex and state-of-the-art devices are used and special treatment and care is delivered. Nurses in ICU are the backbones of effective health systems during this pandemic [6]. ICU nurses were confronted with difficult conditions, such as substantial workload, prolonged work hours and considerable risk of infection, which led to serious mental distress [7, 8] and resulted in an increasing risk of psychiatric health problems, such as depression and burnout [9, 10].

Many studies reported that nurses working in ICU showed high risk of depression during the pandemic of COVID-19 [11‐13]. A cross-sectional survey demonstrated over 40% of ICU nurses suffered from moderate to severe symptoms of depression during COVID-19 [14]. One systematic and meta-analysis including 20,617 healthcare workers proved that the prevalence of burnout in ICU nurses achieved 45% [15]. Burnout and depression of nurses had adverse effects on health of patients, and even threatened the safety of patient (i.e., medical administration errors, injury even death) [16‐18], which was a global healthcare concern [19]. Besides, the intention to leave work in ICU nurses was rising during COVID-19. The WHO predicted that there would be a deficit of around 7.6 million nurses globally by 2030 before COVID-19 [20]. This deficit seemed likely to be greater as recent research had indicated that about 20% nurses had a thoughtful consideration of quitting for adverse mental health outcomes. Between them, ICU nurses had the highest of nearly 27% [21], which would be detrimental to the sustainable development of the nursing profession. Therefore, efforts should be made to improve the burnout and depression for this important group of care providers.

WHO in 2022 announced the implement of 7th of the International Classification of Disease [22], in which burnout is defined as a syndrome caused by chronic occupational stress that has not been managed successfully, and has resulted in feeling of energy exhaustion, negativism related to one’s work and lack of achievement. Nurse engaging in ICU are particular prone to suffer burnout due to exposure to pain, trauma, dying and closed environments, not necessary within pandemic [23]. Depression is a leading cause of disability and contributes greatly to global burden of disease [24]. People suffered from depression are characterized by persisted sadness, diminished pleasure or interest, even feeling of excessive guilt, hopelessness. Severe depression patients will think of self-harm or suicide [22]. WHO in 2022 has listed depression as one of high-risk factors leading to disability and the major contributor to suicide [24]. Depression not only negatively impact well-being of ICU nurses, but also extract toll on the health industry, with a severe adverse effect on healthcare quality [25, 26].

The relation between burnout and depression has received a great deal of attention in recent years [27, 28]. Burnout was identified as one of strongest predictor of depressive symptoms [29, 30]. Meanwhile, depression symptoms contributed to the development of burnout [31]. But more importantly, burnout often co-occurred with depression [32]. A system review and meta-analysis reported that over 50% employers with burnout had depression [33]. A survey of healthcare workers in Macao and China found that depression was associated with all subscales of burnout after controlling for the strong effects of demographic factors [34]. 16.5% of psychiatric with depressive symptoms had high rate of burnout [35]. These data suggest there exist strong associations between burnout and depression symptoms. But how the symptoms of the two variables are associated still remain unclear.

Altogether, the previous studies have explored the interactions between burnout and depression at the syndrome level using traditional correlational methods, which included path analysis and multiple regression analysis in general. But those methods were based on the assumption of linear relationship, and couldn’t describe the complex non-linear contact between burnout and depression symptoms [36]. In order to overcome the issue, network analysis (NA) was applied to quantify the correlations between burnout and depression symptoms from the perspective of mathematical and display it intuitively. It wasn’t just on the basis of assumptions but a data-driven approach about causality between multiple variables [37]. In the theory of NA, mental syndromes and disorders were induced by the direct interactions between their corresponding symptoms, which included nodes representing observed variables (e.g., 15 nodes of burnout and 10 nodes of depression) and edges representing the associations between nodes. Therefore, exploring the accurate interactions was critical to elaborate psychopathological mechanisms and develop targeted intervention policies. Furthermore, NA could also provide centrality and predictability indices of each node, which helped researchers to identify and quantify to what extent burnout may transmit positive/negative influence to depression [38]. Since central symptoms in a network model closely connected with other symptoms, and they might active other symptoms. Thus, central symptoms with higher ranking score might become the target of treatment interventions, as they had a significant impact on the network. NA provided a new way to understand human psychological phenomena, and had been applied to the research of social psychology, clinical psychology, psychiatry and other fields [39, 40].

Several studies have explored the symptom level interactions between burnout and depression symptoms using NA among different groups of people. Network structure among educational professions demonstrated that suicidal thoughts was only associated with other symptoms of depression, but not with those of burnout [41]. Another study showed that the symptoms of “feel down-hearted” and “no hope for future” were target interventions to relieve mental disorders of pharmacists [42]. However, it was uncertain whether these findings could be generalized to ICU nurses. Therefore, the current study applied the NA to further examine the interrelationship between burnout and depression symptom of ICUs nurses in order to implement effective and targeted interventions to prevent or reduce the occurrence of burnout and depression. The aims of the current study were two-fold: (1) to explore potential pathways linking between burnout and depression symptoms; (2) to use bridge expected influence to identify the most influential symptoms within the burnout-depression network.