Mental health is an important component of health, and the concept of “no health without mental health” as proposed by the World Health Organization (WHO) has become the consensus view. Mental health problems are currently a prominent challenge worldwide. General estimates suggest that 11–18% of the world’s population meets the criteria for a diagnosable psychiatric disorder at any given time (Wainberg et al., 2017). The drivers of poor mental health include multiple transitions affecting the global population, such as the increasing rates of some social determinants (pandemics, conflict, and displacement) and environmental threats (increased natural disasters associated with climate change) (Yokoyama et al., 2014; Patel et al., 2018). Alarmingly, in crisis situations, such as natural disasters or humanitarian emergencies, the prevalence rate of mental health disorders can reach nearly 22%, which is nearly double the general prevalence (Charlson et al., 2019).

Many studies have suggested that infectious disease epidemics, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and 2009 novel influenza A (H1N1), are associated with mental health problems among the general population (Cowling et al., 2010; Taha et al., 2014), healthcare workers (HCWs) (Bai et al., 2004; Lee et al., 2018), and patients (Lee et al., 2007; Wang et al., 2011).

At the end of December 2019, an outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, Hubei Province, China (Wang et al., 2020). In only a month, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a public health emergency by the WHO, and it was declared an epidemic in March 2020 (World Health Organization (WHO), 2020a). Due to the severe situation in the early stage of the epidemic, requiring a substantial amount of work in a short time, health systems were strained by the effort to contain the disease, which spread to over 200 countries throughout the world (World Health Organization (WHO), 2020b). In particular, healthcare workers, including public health workers, are the main persons involved in the screening and treatment of this disease, and they remain under great pressure.

The epidemic is still in a severe stage. As of 16 June 2021, there were about two hundred million confirmed cases of COVID-19, including 4,265,903 fatalities, reported to the WHO (World Health Organization (WHO), 2020c). Due to the limited effectiveness and insufficient supply of currently available vaccines, preventive measures, including controlling the sources of infection, impeding transmission, and protecting susceptible populations, are the most effective strategies to contain the spread of the disease (Li et al., 2020). Therefore, public health systems still face significant challenges in countries and regions where local epidemics remain severe. Public health systems vary among countries (Donaldson, 2008); China’s public health system comprises specialized institutions [e.g., the Centers for Disease Control and Prevention (CDC)] as well as primary healthcare institutes (PHIs, e.g., community health centers in urban areas and village clinics in rural areas) (World Health Organization (WHO), 2015). Public health workers are usually affiliated with the CDC or PHIs and are mainly responsible for various prevention and control tasks, including the development of technical instructions, epidemiological investigation of patients and close contacts, surveillance of high-risk populations, collection and examination of specimens, and collection and reporting of data. A high level of overcommitment easily leads to physical and mental stress and exhaustion in healthcare workers (Siegrist, 2008), which, in turn, leads to anxiety and depression. Individuals who are overcommitted wish to be recognized and respected (Siegrist and Li, 2016) and are especially sensitive to work pressure and negative emotions (Du Prel et al., 2018); thus, they face an increased chance of feeling misunderstood and wronged, among other troubles. During the epidemic, public health workers experienced rapid increases in work intensity and work pressure. During periods of intense work, they faced an increased risk of self-perceived work difficulties, which caused physical and mental stress and thus led to anxiety and depression. Overwork has taken a great toll on the physical and mental health of health care workers in particular. Therefore, studying the factors that influence the mental health of public health workers during the COVID-19 epidemic will provide reference data for other countries, protect the health of public health workers and help contain the epidemic.

Studies have shown that epidemics cause serious psychological distress among the general public and healthcare workers (Pappas et al., 2009). Several recent studies have shown that due to overwork, inadequate preparation, and emotional disturbances (such as fear of infection and concerns about family members) attributable to the COVID-19 epidemic, there is a high prevalence of mental health symptoms among medical professionals, including depression, insomnia, anxiety and posttraumatic stress disorder (PTSD) (Cai et al., 2020; Hou et al., 2020; Lai et al., 2020). However, most of the research on the factors influencing mental health has focused on doctors and nurses in hospital settings, rather than public health workers who are mainly affiliated with the CDC and PHIs.

Among the methodologies used to carry out such analyses, the classical methods predominated; such methods include descriptive statistics, multiple linear regressions, hierarchical linear regression, and multivariate logistic regression, among others. Most of these techniques do not allow descriptions of the complex, direct or conditional, and linear or non-linear relationships between the variables considered in the model (García-Herrero et al., 2017). In reality, these factors may not be independent of each other and may have complex network relationships. Bayesian networks (BNs), also called belief networks, combine graph theory and probability theory to explore relationships between variables and are easily interpretable by means of the resulting graph (Larrañaga and Moral, 2011). The model has no strict requirements regarding data distribution; therefore, it can incorporate all the data to reveal the influences of various factors on mental health and the relationships between them. BNs have been widely used in many fields, including social and behavioral science (Aghaabbasi et al., 2020), clinical science (Beaudequin et al., 2020), occupational health (García-Herrero et al., 2017). However, no study to date has used a BN model to investigate the psychological impacts of the COVID-19 epidemic on populations.

Therefore, the aims of the current study were to investigate the mental health status of public health workers during the outbreak and the influences on their mental health by using a BN model and to provide reference data for the development of targeted interventions for this population to improve their mental health during the COVID-19 epidemic. These goals are important for controlling the COVID-19 epidemic and improving public health workers’ long-term health. Moreover, the findings from this investigation will be critical in addressing future outbreaks of emerging infectious diseases.

The present results show that during the COVID-19 epidemic, mental health problems were prominent among public health workers, with 49.2% and 45.7% suffering from anxiety and depression, respectively. These high prevalence rates were similar to the rates reported in a previous study, which found that the prevalence of anxiety and depression among 1,563 medical staff were 44.7% and 50.7%, respectively, using the same measurements and cutoff points (Liu et al., 2020). The mental health of public health workers during the COVID-19 epidemic is of great concern.

Before PSM, there were demographic differences between the groups with different health conditions (anxiety and non-anxiety, depression and non-depression). Research variables related to COVID-19 (e.g., knowledge mastery, perceived troubles at work, perceived tension) may be affected by demographic factors (e.g., age, sex, and professional title). Thus, it is difficult to accurately assess the influence of COVID-19-related variables on mental health status. For reasons involving time and cost, most of the current studies on mental health among various populations during the epidemic are retrospective studies, which cannot be randomized to control for confounding factors to reduce their interference with outcome effect estimates. Therefore, by using PSM to control for confounding factors, the relationships between COVID-19-related variables and the mental health of public health workers could be thoroughly explored.

Univariate analysis after PSM showed that public health workers in the anxiety and depression group were more likely to work 9–15 h and sleep an average of 5–6 h per day than to work 8 or fewer hours and sleep more than 6 h per day. Working long hours potentially reduces the sleep time of public health workers (Marjanovic et al., 2007; Afonso et al., 2017). Those who reported insufficient sleep had adverse mental health effects, similar to the findings in other studies (Simon et al., 2020). During the most severe period of the epidemic in China, a study noted that medical workers had insufficient time to rest (Chen et al., 2020). Public health workers experienced psychological distress were more likely to encounter 6–10 difficulties at work than to encounter five or less, such as poor communication (Naushad et al., 2019) and shortages of medical protective equipment (Guangming Online, 2020; World Health Organization (WHO), 2020d). Moreover, public health workers suffering from anxiety and depression tended to be the effort-reward unbalance. The reason may be that they are not widely understood or respected by the public (Hellewell et al., 2020). During the epidemic, the prevention and control work they perform, such as isolating close contacts (at home or in a designated hotel) and conducting home inspections, is often met with non-cooperation and even opposition. This conflict not only hinders prevention efforts but also has a negative impact on the mental health of public health workers. Time-consuming paperwork and data analyses, especially in emergency situations, may further increase people’s physical and mental health burdens (World Health Organization and International Labour Organisation (WHO and ILO), 2000).

To avoid potentially false associations between the research variables and the dependent variable in the univariate analysis, we performed multivariate logistic regression analysis. The results showed that the impacts of type of work, number of difficulties in COVID-19 control and prevention, average sleep time per day (hours), average work time per day (hours), and work/life balance on mental health were offset. This may be related to the insufficient sample size or the collinearity of independent variables.

Research has shown that overcommitment is a risk factor for declining mental health (Hinsch et al., 2019). If public health workers overexert themselves at work and fail to obtain a reward, their frustration at work increases, potentially leading to anxiety and depression. Public health workers in anxious and depressed states had increased scores for perceived distress; these states were mainly due to their work not being understood, feeling wronged and unfairly treated at work, and regular work pressure. These factors may be related to poor social support, high labor intensity, and high personal costs among public health workers. In the fight against COVID-19, public health workers have been under tremendous pressure and are overworked and exhausted from long hours of intense work, while worrying about themselves and their families contracting the infection (Kang et al., 2020). Persistent internal physical and mental tension may manifest as psychotic symptoms in many public health workers during the epidemic.

Given that a multivariate logistic regression analysis usually considers each influencing factor under the assumption of independence and reveals only the independent factors influencing mental health, a BN model was used to construct a network diagram and conditional probability table to further describe how each factor was interrelated and affected the occurrence of anxiety and depression. The time at current work intensity, perceived troubles at work, and perceived tension were directly related to anxiety and depression, consistent with the results of the multivariate logistic regression analysis.

Mental health issues affect the attention, understanding, and decision-making abilities of public health workers, thereby hindering the fight against COVID-19 and affecting their overall health. Therefore, various interventions should be established to protect the mental health of public health workers. For example, the unit should reasonably schedule working hours, recruit volunteers with medical backgrounds and provide adequate training, thereby distributing the burden of basic public health work and relieving work-related pressure on public health workers. Moreover, social support and public awareness of the importance of public health work should be increased through media campaigns. The difficulties encountered by public health workers should be reduced by the provision of adequate protective equipment, the simplification of reporting data and the introduction of a reasonable and fair system of rewards and punishments. Finally, timely psychological interventions targeting public health workers should be implemented to alleviate stress during the epidemic.

This study has several limitations. First, our study was a cross-sectional study that extracted data from only one point in time. Changes in the mental health status of public health workers in different periods during the epidemic should be investigated. Second, to prevent the potential spread of COVID-19, an online questionnaire was administered. Therefore, there was non-response bias in the study. Third, all the measures relied on self-reported questionnaires, which are dependent on individuals’ subjective accounts and are vulnerable to reporting bias.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

The studies involving human participants were reviewed and approved by the Chongqing Medical University. Written informed consent for participation was not required for this study in accordance with the National Legislation and the Institutional Requirements.

JG and HZ were responsible for conceiving and designing the study. XJ and QZ collected the data. XP and YP conducted statistical analyses and drafted the manuscript. XP and DD commented and revised the manuscript. All authors contributed, reviewed, and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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