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. 2020 Dec 3;281:91–98. doi: 10.1016/j.jad.2020.11.117

Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis

Tianchen Wu a, Xiaoqian Jia b,c, Huifeng Shi a, Jieqiong Niu a, Xiaohan Yin a, Jialei Xie a, Xiaoli Wang a,c,⁎
PMCID: PMC7710473  PMID: 33310451

Abstract

Background

The global COVID-19 pandemic has generated major mental and psychological health problems worldwide. We conducted a meta-analysis to assess the prevalence of depression, anxiety, distress, and insomnia during the COVID-19 pandemic.

Methods

We searched online biomedical databases (PubMed, Embase, Web of Science, Ovid, CNKI, and Wanfang Data) and preprint databases (SSRN, bioRxiv, and MedRxiv) for observational studies from January 1, 2020 to March 16, 2020 investigating the prevalence of mental health problems during the COVID-19 pandemic.

Results

We retrieved 821 citations from the biomedical databases and 53 citations from the preprint databases: 66 studies with 221,970 participants were included in our meta-analysis. The overall pooled prevalence of depression, anxiety, distress, and insomnia was 31.4%, 31.9%, 41.1% and 37.9%, respectively. Noninfectious chronic disease patients, quarantined persons, and COVID-19 patients had a higher risk of depression (Q=26.73, p<0.01) and anxiety (Q=21.86, p<0.01) than other populations. The general population and non-medical staff had a lower risk of distress than other populations (Q=461.21, p< 0.01). Physicians, nurses, and non-medical staff showed a higher prevalence of insomnia (Q=196.64, p<0.01) than other populations.

Limitations

All included studies were from the early phase of the global pandemic. Additional meta-analyses are needed to obtain more data in all phases of the pandemic.

Conclusions

The COVID-19 pandemic increases the mental health problems of the global population, particularly health care workers, noninfectious chronic disease patients, COVID-19 patients, and quarantined persons. Interventions for mental health are urgently needed for preventing mental health problems.

Keywords: COVID-19, Depression, Anxiety, Distress, Insomnia, Systemic review

1. Introduction

COVID-19 has spread rapidly worldwide (Zhu et al., 2020a). On January 30, 2020, the International Health Regulations (2005) Emergency Committee declared the COVID-19 outbreak a Public Health Emergency of International Concern (World Health Organization, 2020a). Subsequently, COVID-19 was characterized as a pandemic by the World Health Organization (WHO) on March 11, 2020 (World Health Organization, 2020c). According to the latest data from the WHO, as of July 15, 2020 over 13 million people have been infected in 217 countries and regions worldwide, with more than 572,000 deaths reported (World Health Organization, 2020b). Individual and government responses to this major public health emergency have affected the lives of hundreds of millions of people and changed people's ways of socializing, working, studying and living. Mental health problems such as anxiety, sleep disorders, depression, frustration, and stress-related disorders may be caused by worry about becoming infected, increasing work pressure, lifestyle changes and worsening living conditions (Pulvirenti et al., 2020; Zhang et al., 2020d).

The types and severity of mental health problems may vary among people with different health conditions and social roles, due in turn to differences in the risk of becoming infected and influence on lifestyle (Tan et al., 2020). Previous studies on the health impacts of outbreaks of infectious diseases, such as severe acute respiratory syndrome (SARS) (Lu et al., 2006; Wing and Leung, 2012), influenza caused by H1N1 (Matsuishi et al., 2012), Middle East respiratory syndrome (MERS) (Jeong et al., 2016), and Ebola virus (Kisely et al., 2020), suggested that mental health problems may occur among health care workers and other populations during major public health emergencies. There have been similar reports during the COVID-19 outbreak (González-Sanguino et al., 2020; Kong et al., 2020; Tang et al., 2020; Zhang et al., 2020b). The United Nations reported that the COVID-19 pandemic is not only compromising physical health, but is also increasing psychological suffering (United Nations, 2020). Therefore, the mental health status of populations during this pandemic is of great importance.

The rapid spread of COVID-19 worldwide multiplied by forced quarantine and nationwide lockdowns could lead to public panic (Bao et al., 2020; Dubey et al., 2020), and cause rumors and conspiracy theories to spread (Addis et al., 2020; Ali, 2020). For health care workers, the stress of dealing with patients could affect their mental health, reduce their ability to work, and even lead to a lack of clinical competency (Greenberg et al., 2020; Liu et al., 2020c). These effects could ultimately be worse than the direct consequences of the COVID-19 pandemic itself (Bao et al., 2020). Therefore, comprehensive understanding of the prevalence of mental health problems during the COVID-19 pandemic on different populations is needed so that policymakers can implement effective countermeasures. Although many studies have evaluated the prevalence of the aforementioned mental health problems, most recent studies have focused on between one and three different populations (Huang and Zhao, 2020; Kong et al., 2020; Tan et al., 2020); comparative studies of multiple populations are still lacking. We conducted a systematic review and meta-analysis using data pooled from seven populations to provide a comprehensive perspective of the prevalence mental health problems during the COVID-19 pandemic and identify high risk groups of mental health problems related to COVID-19 pandemic. Governments should include mental health care in national public health emergency response plans, and prioritize interventions for high-risk populations.

2. Methods

2.1. Search strategy and study selection

The search strategy was designed based on MeSH terms and free-text keywords (shown in online Supplementary Table S1), and citation retrieval was conducted by two authors independently. Biomedical databases (PubMed, Embase, Web of Science, Ovid, CNKI, and Wanfang Data) and preprint databases (SSRN, bioRxiv, and MedRxiv) were searched for studies published between January 1, 2020 to March 16, 2020, without language restrictions. Two authors independently screened the titles and abstracts, and reviewed the full text of articles meeting the following criteria: (1) cross-sectional study, cohort study, case-control study, or intervention study; (2) use of a validated measurement tool to assess mental or psychological health outcomes including depression, anxiety, distress, and insomnia in the general, or a specific, population since the COVID-19 pandemic; (3) use of standardized measurement tools; and (4) inclusion of point prevalence data for each outcome. We also included the control group or baseline survey of the intervention studies in the searched citations into the analysis, and both randomized and non-randomized intervention studies were considered. The protocol for this systematic review and meta-analysis was registered on PROSPERO (CRD42020178045) and can be accessed at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=178045

2.2. Data extraction

Two authors extracted the primary data independently using a standardized data extraction form; the data were then checked by a third author. The following data were extracted: title, first author names, country, date of survey, study participants, measurement tools, sample sizes, and number of positive cases for each symptom.

2.3. Assessment of risk of bias

The methodological quality of the included studies was assessed using Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), which assesses cross-sectional studies for risk of bias based on the design, participants, sample size, measurement tools, data collection methods, confounders, and statistical analyses (von et al., 2007). The risk of bias was classified as low risk (study reported sufficient data for quality assessment and fulfilled the criteria for the quality item), partial risk (study reported incomplete data for the quality item were reported or had an intermediate risk of bias), or high risk (study reported sufficient data for quality assessment but didn't fulfill the criteria for the quality item).

2.4. Mental health outcomes and subgroup comparisons

In this study, we focused on four mental health outcomes: depression, anxiety, distress, and insomnia. Several self-administered screening measurement tools were used to evaluate the four outcomes, each with its own threshold scores for a particular symptom. Articles that used a non-standard threshold to define a particular symptom were excluded.

We compared the mental health of different populations. The overall population was divided into seven groups: (1) general population; (2) noninfectious chronic disease patients, including patients with cancer, diabetes, or chronic kidney disease; (3) students, including university, college, and middle school students; (4) physicians and nurses working in clinical departments; (5) non-medical staff working in non-clinical departments, including technicians, administrators, clerical staff, and logistics staff; (6) quarantined persons; and (7) COVID-19 patients. Only one study (Hua et al., 2020) reported on the distress level of a group suspected as being infected by SARS CoV-2, so “suspected infection” was included as a further subgroup. We divided physicians and nurses into front-line (directly engaged in diagnosing, treating, or providing care to patients with elevated temperatures, or confirmed COVID-19) and second-line workers.

2.5. Data Synthesis and Statistical Analyses

Freeman-Tukey double arcsine transformation was used to stabilize the variance of the raw data (Freeman and Tukey, 1950). The I2 statistic was used to assess the statistical heterogeneity (Higgins et al., 2003). We used DerSimonian-Laird random-effects meta-analysis to pool the raw data for each outcome, and the Clopper-Pearson interval to calculate the confidence interval (CI) for each variable (DerSimonian and Laird, 1986). Egger regression and funnel plots were used to assess publication bias (Egger et al., 1997). A series of random-effects meta-regression models was performed to determine the effects of between-group difference of the following variables: participants, published language, gender, and measurement tools. Sensitivity analyses was conducted to evaluate the impact of methodological quality of included studies on pooled results and to explore potential sources of heterogeneity. We used the Cochran's Q test to compare the different populations. All statistical tests were two-tailed and p <s0.05 was considered statistically significant. The analyses were performed using R software (version 3.6.3; R Development Core Team, Vienna, Austria) and the ‘meta’ package (version 4.11-0).

3. Results

3.1. Studies selection

We retrieved 821 citations from the biomedical databases and 53 from the preprint databases, up to April 2020. 803 citations were excluded due to duplicate publication, review articles, did not focus on mental or psychological health, did not evaluate depression, anxiety, distress, or insomnia, or did not report point prevalence for the outcomes of interest. We excluded another five studies (Chen et al., 2020a; Hong et al., 2020; Nguyen et al., 2020; Qian et al., 2020; and Zhang and Ma, 2020) that used a non-standard threshold to define a particular symptom. Ultimately, we included 66 studies with a total of 221,970 participants. Figure 1 shows the study selection process.

Figure 1.

Figure 1

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PRISMA Flowchart of citations selection

3.2. Characteristics of included studies

The characteristics of included studies are showed in online Supplementary Table S2. Depression data were reported by 48 studies with 125,121 participants drawn from seven populations, anxiety data were reported by 53 studies with 128,855 participants drawn from seven populations, distress data were reported by 10 studies with 67,950 participants drawn from five populations, and insomnia data were reported by 8 studies with 23,022 participants drawn from four populations. Of the included studies, 95.5% were cross-sectional, and most were online surveys; two were interventional studies (Huang and Ke, 2020; Yang et al., 2020b) and one was a qualitative interview (Cao et al., 2020b). Most of the studies (62 of 66) were for the Chinese population, although there was one each from Iran (Moghanibashi-Mansourieh, 2020), Jordan (Naser et al., 2020), Singapore (Tan et al., 2020), and India (Kazmi, 2020). All included surveys were conducted between January 1, 2020, and April 1, 2020.

3.3. Risk of bias in the included studies

The risk of bias in the included studies are shown in Figure S1 and S2. Of the 66 studies, none were judged as high risk on all risk of bias measures; 61 reported clear definitions and measurement methods for all four outcomes, 13 did not report participant recruitment dates, 32 reported using consecutive or random sampling, and 27 reported the number of individuals enrolled at each stage of the study. All 66 studies reported the characteristics of the study participants.

3.4. Prevalence of depression

The point prevalence of depression ranged from 3.1% to 87.3% among 48 studies and seven populations; the overall pooled prevalence was 31.4% (95% CI: 27.3–35.5%) and a high degree of heterogeneity was found (I2 = 99.6%), as shown in Table 1 and online Supplementary Figure S3. The point prevalence of depression showed a significant difference among the seven populations (Q = 26.73, p <0.01). In subgroup analyses, the prevalence of depression was in the following (descending order): noninfectious chronic disease patients (44.8%, 95% CI: 36.4–53.2%), COVID-19 patients (41.7%, 95% CI: 26.3–57.9%), quarantined persons (38.8%, 95% CI: 19.7–59.9%), students (34.8%, 95% CI: 16.4–55.9%), the general population (31.5%, 95% CI: 24.2–39.2%), physicians and nurses (31.0%, 95% CI: 24.7–37.5%), and non-medical staff (14.1%, 95% CI: 7.4–22.4%). We compared the medical personnel (physicians and nurses) between front- and second-line, as shown in Table 2 and online Supplementary Figure S7. The prevalence of depression was not significantly different (Q = 0.02, p = 0.88) between front- and second-line medical personnel.

Table 1.

Prevalence of mental health problems in difference populations.

Mental health outcomes No. of studies Positive cases Simple size Pooled prevalence (%) Heterogeneity I2 (%) Q statistic P value
Depression
Overall depression 64 42,876 125,121 31.4 (27.3–35.5) 99.6
General population 17 25,115 69,697 31.5 (24.2–39.2) 99.8 26.73 <0.01
Noninfectious chronic disease patients 2 72 161 44.8 (36.4–53.2) 16.0
Students 7 3,697 10,128 34.8 (16.4–55.9) 99.8
Physicians and nurses 23 13,360 41,889 31.0 (24.7–37.5) 99.5
Other medical staff 6 296 2,471 14.1 (7.4–22.4) 95.4
Quarantined persons 5 169 367 38.8 (19.7–59.9) 93.6
COVID-19 patients 4 167 480 41.7 (26.3–57.9) 90.3
Anxiety
Overall anxiety 69 41,498 128,855 31.9 (27.9–36.0) 99.6
General population 18 21,310 57,898 29.8 (21.5–38.8) 99.8 21.86 <0.01
Noninfectious chronic disease patients 3 99 185 55.4 (37.8–72.3) 82.7
Students 8 4,978 17,257 28.2 (16.8–41.2) 99.7
Physicians and nurses 23 14,312 50,143 29.0 (23.6–34.7) 99.4
Other medical staff 7 378 2,521 19.9 (12.4–28.6) 94.8
Quarantined persons 5 235 367 57.9 (34.4–79.7) 94.9
COVID-19 patients 5 186 484 42.3 (27.3–58.1) 91.5
Distress
Overall distress 14 23,147 67,950 41.1 (32.6–49.8) 99.6
General population 4 19,263 56,528 31.1 (25.8–36.8) 97.2 461.68 <0.01
Noninfectious chronic disease patients 2 110 232 49.1 (36.0–62.3) 73.5
Physicians and nurses 5 3,325 10,165 41.2 (19.8–64.5) 99.8
Other medical staff 2 194 769 17.3 (1.3–45.4) 98.1
Suspected infection 1 255 256 99.6 (98.3–100.0) -
Insomnia
Overall insomnia 11 6,430 23,022 37.9 (29.9–46.2) 99.3
General population 1 1,317 7,236 18.2 (17.3–19.1) - 196.64 <0.01
Students 1 79 1,031 7.7 (6.1–9.4) -
Physicians and nurses 7 4,606 13,375 47.3 (38.8–55.8) 98.7
Other medical staff 2 428 1,380 31.8 (27.2–36.5) 37.5
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Table 2.

Prevalence of mental health problems between front-line and second-line medical workers

Mental health outcomes No. of studies Positive cases Simple size Pooled prevalence (%) Heterogeneity I2 (%) Q statistic P value
Depression
Overall depression 13 9,412 29,866 32.9 (26.9–39.2) 99.2
Front-line physicians and nurses 7 3,628 10,466 33.3 (23.8–43.5) 99.0 0.02 0.88
Second-line physicians and nurses 6 5,784 19,400 32.4 (24.0–41.3) 99.3
Anxiety
Overall anxiety 12 7,205 29,829 27.5 (21.9–33.4) 99.2
Front-line physicians and nurses 6 2,874 10,429 28.8 (20.7–37.6) 98.9 0.18 0.67
Second-line physicians and nurses 6 43,310 19,400 26.2 (18.4–34.8) 99.4
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3.5. Prevalence of anxiety

The point prevalence of anxiety ranged from 6.3% to 87.5% in the 53 studies with 7 populations. The pooled prevalence of anxiety was 31.9% (95% CI: 27.9–36.0%) with and a high degree of heterogeneity was found (I2 = 99.6%), as shown in Table 1 and online Supplementary Figure S4. The prevalence of anxiety was significantly different among the seven populations (Q = 21.86, p <0.01). In subgroup comparisons, quarantined persons (57.9%, 95% CI: 34.4–79.7%) showed the highest prevalence of anxiety, followed in descending order by noninfectious chronic disease patients (55.4%, 95% CI: 37.8–72.3%), COVID-19 patients (42.3%, 95% CI: 27.3–58.1%), the general population (29.8%, 95% CI: 21.5–38.8%), physicians and nurses (29.0%, 95% CI: 23.6–34.7%), students (28.2%, 95% CI: 16.8–41.2%), and non-medical staff (19.9%, 95% CI: 12.4–28.6%). We also compared the physicians and nurses between front- and second-line, as shown in Table 2 and online Supplementary Figure S8. The prevalence of anxiety didn't show significant difference (Q = 0.18, p = 0.67) between front- and second-line medical personnel.

3.6. Prevalence of distress

In the 10 studies with five populations, the point prevalence of distress ranged from 6.4% to 99.6% and there was a high degree of heterogeneity (I2 = 99.6%). The overall prevalence of distress was 41.1% (95% CI: 32.6–49.8%), as shown in Table 1 and online Supplementary Figure S5. The prevalence of distress was significantly different among the five populations (Q = 461.21, p <0.01). Patients suspected of infection had the highest point prevalence of distress (99.6%, 95% CI: 98.3–100.0%), followed by noninfectious chronic disease patients (49.1%, 95% CI: 36.0–62.3%), physicians and nurses (41.5%, 95% CI: 19.4–65.4%), the general population (31.1%, 95% CI: 25.8–36.8%), and non-medical staff (17.3%, 95% CI: 1.3–45.4%).

3.7. Prevalence of insomnia

The point prevalence of insomnia was reported by the eight studies with four populations; the overall pooled prevalence was 37.9% (95%CI: 29.9–46.2%) and there was a high degree of heterogeneity (I2 = 99.3%), as shown in Table 1 and online Supplementary Figure S6. Subgroup analyses showed a significant difference among the four populations (Q = 196.64, p <0.01). Physicians and nurses had the highest rate of insomnia, at 47.3% (95% CI: 38.8–55.8%), followed by non-medical staff, at 31.8% (95% CI: 27.2–36.5%). The general population (18.2%, 95% CI: 17.3–19.1%) and students (7.7%, 95% CI: 6.1–9.4%) showed the lowest rates of insomnia.

3.8. Meta-regression and sensitivity analyses

The results of the meta-regression for each outcome (depression, anxiety, distress, and insomnia) are showed in online Supplementary Table S3, S4, S5, and S6, and the results of the sensitivity analyses for each outcome are presented in online Supplementary Table S7 and S8. Meta-regression showed no significant effects of published language and gender on pooled prevalence of depression and anxiety, while the effect on distress and insomnia were significant. We found significant effects of three measurement tools (GHQ-9, SDS, and WHO-5) in depression, and four measurement tools in distress (GHQ-12, IES, K6, PSS-10) on pooled results. Sensitivity analyses showed that in the methodological quality assessment, when item 1 (key elements of study design presented), item 3 (eligibility criteria, and methods of selection of participants provided), or item 6 (numbers of individuals at each stage of study reported) was assessed as partial or high risk, it would affect the pooled results. We used funnel plots to visualize and Egger regression to test the file-drawer effects for each of the outcomes. The funnel plots and the results of Egger regression are presented in online Supplementary Figure S9, S10, S11, and S12. We didn't find significant publication biases for the four outcomes.

4. Discussion

This meta-analysis of 66 studies (221,970 participants) was conducted to analyze the prevalence of mental health problems (depression, anxiety, distress, and insomnia) during the COVID-19 pandemic in different populations. Mental health problems were common in the different populations. The overall pooled prevalence of depression, anxiety, distress, and insomnia were 31.4% (95%CI: 27.3–35.5%), 31.9% (95% CI: 27.9–36.0%), 41.1% (95% CI: 32.6–49.8%) and 37.9% (95% CI: 29.9–46.2%), respectively. Subgroup comparisons showed that noninfectious chronic disease patients, quarantined persons, patients suspected of infection, and physicians and nurses had the highest prevalence of depression, anxiety, distress and insomnia, while non-medical staff had the lowest prevalence of depression, anxiety and distress. We did not find significant differences in the prevalence of depression and anxiety between front- and second-line medical personnel (physicians and nurses).

4.1. The COVID-19 pandemic increased the prevalence of mental health problems

Higher than normal levels of depression, anxiety, distress and insomnia have been reported since the outbreak of COVID-19. Previous studies found that 6.6% (95% CI: 5.9%–7.3%) of the general population of the USA were affected by major depressive disorder (Kessler et al., 2003), while 10.8% (95% CI: 9.1%–12.5%) and 14.7% (95% CI: 12.7%–16.6%) of the 3,001 adults selected at random from the general population of Sweden underwent depression and anxiety in 2009, respectively (Johansson et al., 2013). Elsewhere, 6.2% (95% CI: 3.4%–10.4%) of the adult indigenous population of Panama reported serious psychological distress under normal circumstances (Walker et al., 2019). All of these rates were much lower than those of all of the populations included in our analysis. Similarly, before the outbreak of COVID-19, the prevalence of insomnia in the general population of Turkey was 12.2% (95%CI: 11.2%–13.1%) (Benbir et al., 2015), while a meta-analysis reported a prevalence of insomnia among the general population of China of 15.0% (95% CI: 12.1%–18.5%) (Cao et al., 2017). Although a relatively high prevalence of insomnia was reported among the adult population of the USA, at 27.3% (Olfson et al., 2018), this did not exceed the prevalence of 47.3% and 31.8% for non-medical staff and physicians/nurses, respectively, in this study.

Therefore, a higher prevalence of mental health problems has resulted from COVID-19, as seen previously after other novel disease epidemics and natural disasters. All major emergencies, not just COVID-19, undoubtedly create mental health problems. Studies of past outbreaks showed that about 35% of SARS survivors in Hong Kong, and 31.2% of persons quarantined due to SARS in Toronto reported anxiety and/or depressive symptoms (Cheng et al., 2004; Hawryluck et al., 2004). Moreover, a meta-analysis of 27 studies revealed that the rates of psychological distress (overall standardized mean difference = 0.63, 95% CI: 0.27–0.98, p = 0.005) and psychiatric disorders (overall odds ratio = 1.84, 95% CI: 1.43–2.38, p < 0.001) increased after natural disasters (Beaglehole et al., 2018). With the continued spread of COVID-19, more attention should be paid to its potentially harmful effects on the mental health of the entire population (Cui et al., 2020). Physical activity was beneficial to mental health (Lavie et al., 2016) and immunity (Laddu et al., 2020). Encouraging and helping individuals be physically active during the COVID-19 pandemic could reduce the risk of mental health problems and achieve optimal immune support (Hall et al., 2020).

4.2. Focus on the high-risk groups

The patients with noninfectious chronic diseases in this study had the highest prevalence of depression, and high rates of anxiety and distress, which may be associated with local restrictions on movements (Brooks et al., 2020), inconvenient routine screening and medical treatments, and high economic costs (Egede and Zheng, 2003). Although no relevant study was found pertaining to previous epidemics, almost all cancer survivors’ comorbid chronic diseases were reportedly associated with anxiety (Yan et al., 2019), and type 2 diabetes patients were more likely to have major depressive disorder compared with the general population (odds ratio = 1.73, 95% CI:1.38–2.16) (Wang et al., 2019), even in the absence of infectious disease. This suggests that medical monitoring and treatments for patients with chronic diseases should be preoptimized during the COVID-19 pandemic to reduce the incidence of mental health problems. Other groups with serious mental health problems were those infected, or suspected to be infected with SARS-CoV-2 (Rogers et al., 2020), including COVID-19 patients, quarantined persons, and suspected cases. This may be due to fear of the consequences of infection (Xiang et al., 2020) or worry about being stigmatized or discriminated because of COVID-19 (Siu, 2008). Longer quarantine duration and infection fears could serve as stressors leading to serious emotional and psychological distress (Brooks et al., 2020). The prevalence of anxiety in quarantined persons was reported to be higher than in the general population, and even in confirmed patients (Chen et al., 2004; Jin et al., 2003), which may be related to the unknown outcomes (Li et al., 2020b). It is worth noting that SARS survivors continued to show higher levels of depression, anxiety, and posttraumatic symptoms 1 year after the outbreak, so the long-term adverse mental health effects of infectious diseases should not be ignored (Lee et al., 2007). In another study, almost all of 256 suspected COVID-19 patients underwent distress (Cao et al., 2020b), the high rate of distress for suspected cases warrants further investigation. The prevalence of depression and anxiety in students were close to those of the general population, but were higher compared with normal conditions; this may be due to the implementation of distance learning, which reduced the opportunity for socializing (Lewnard and Lo, 2020; Main et al., 2011).

Our results suggest that physicians and nurses experienced significant mental health problems in association with COVID-19, particularly insomnia. As front-line medical personnel, physicians and nurses experienced the fear associated with a novel infectious disease and close contact with infected patients, and had to work under extreme pressure to diagnose, treat, and care for COVID-19 patients; such conditions would put them at high risk of developing mental health problems (Greenberg et al., 2020; Lu et al., 2020a). The non-medical staff in our analysis had the lowest prevalence of mental health problems, except for insomnia, which may be due to not being in direct contact with COVID-19 or other febrile patients. No significant differences in mental health problems between front- and second-line medical personnel were seen; both had a high prevalence of depression and anxiety, which may be due to the stress associated with providing intense treatment, and working toward prevention and control, over long periods (Xiao et al., 2020). Effective interventions should be implemented to safeguard the mental and physical wellbeing of health care workers (Wu et al., 2020a).

4.3. Strengths and limitations

There were several strengths to this study. First, to the best of our knowledge, this is the first systematic review and meta-analysis of four different mental health problems related to COVID-19; moreover, it involved multiple populations, including patients with noninfectious chronic diseases and students. Second, we used a comprehensive search strategy to identify relevant studies, and ultimately analyzed 66 studies, 24 studies from preprint databases. Finally, English and Chinese articles were both included in this meta-analysis. As the first country affected by COVID-19, the population of China was surveyed at an early stage regarding mental health problems. Therefore, Chinese studies should be included in any analysis seeking to comprehensively address this issue.

There were also some limitations to our study. Although we searched for all relevant literature from January 1, 2020 to March 16, 2020, most published studies in the early phase of the global pandemic were conducted for the Chinese population; therefore, additional meta-analyses are needed to obtain more data on the mental health of different populations, during all phases of the pandemic. Also, most research has focused on depression and anxiety; distress and insomnia have not been studied comprehensively, which limited our analyses of these symptoms. Only one study focused on suspected COVID-19 patients, who are vulnerable to mental health problems due to the pressure of being physically unwell and the uncertainty of the outcome; their mental health status merits attention. In addition, as COVID-19 is still a worldwide pandemic, it is not yet possible to assess its long-term impact on mental health. A further limitation was that significant heterogeneity existed among the studies of noninfectious chronic disease patients, which may be due to the use of different measurement tools. Therefore, the results regarding noninfectious chronic disease patients should be interpreted carefully.

5. Conclusions

In conclusion, the worldwide COVID-19 pandemic has imposed a huge mental health burden on society, particularly health care workers, COVID-19 patients and quarantined persons, and noninfectious chronic disease patients; these populations all showed a high risk of mental health problems in this meta-analysis. We recommend that preventative interventions for mental health problems as part of the global response to the pandemic.

Declaration of Competing Interest

The authors declare no conflict of interest.

Acknowledgments

Contributors

Dr. Tianchen Wu designed this study and registered on PROSPERO, searched the online databases and assessed the risk of bias of included studies, carried out the initial statistical analysis, drafted the initial manuscript, reviewed and revised the manuscript. Dr. Xiaoqian Jia designed this study and registered on PROSPERO, drafted the initial manuscript, reviewed and revised the manuscript. Dr. Huifeng Shi searched the online databases, assessed the risk of bias of included studies, reviewed and revised the manuscript. Ms. Jieqiong Niu searched the online databases and evaluated all searched citations and extracted the primary data. Mr. Xiaohan Yin searched the online databases, evaluated all searched citations. Mr. Jialei Xie searched the online databases and extracted the primary data. Prof. Xiaoli Wang designed this study and registered on PROSPERO, reviewed and revised the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgements

None

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.11.117.

Appendix. Supplementary materials

mmc1.pdf (302.1KB, pdf)

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