==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 33291511 10.3390/ijerph17239096 ijerph-17-09096 Article Investigating the Psychological Impact of COVID-19 among Healthcare Workers: A Meta-Analysis https://orcid.org/0000-0002-0722-0191Batra Kavita 12* https://orcid.org/0000-0002-9244-2177Singh Tejinder Pal 3 https://orcid.org/0000-0002-4624-2414Sharma Manoj 1 Batra Ravi 4 https://orcid.org/0000-0001-5250-6298Schvaneveldt Nena 5 1 Department of Environmental and Occupational Health, University of Nevada, Las Vegas, NV 89119, USA; manoj.sharma@unlv.edu 2 Office of Research, School of Medicine, University of Nevada, Las Vegas, NV 89102, USA 3 Department of Family and Preventive Medicine, Division of Public Health, School of Medicine, University of Utah, Salt Lake City, UT 84108, USA; tp.singh@utah.edu 4 Department of Information Technology and Testing Center of Excellence, Coforge, Atlanta, GA 30338, USA; ravi.batra@coforgetech.com 5 Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, UT 84112, USA; nena.schvaneveldt@utah.edu * Correspondence: Kavita.batra@unlv.edu 05 12 2020 12 2020 17 23 909626 9 2020 02 12 2020 © 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Previous meta-analyses were conducted during the initial phases of the COVID-19 pandemic, which utilized a smaller pool of data. The current meta-analysis aims to provide additional (and updated) evidence related to the psychological impact among healthcare workers. The search strategy was developed by a medical librarian and bibliographical databases, including Medline, Embase, CINAHL, PsycINFO, and Scopus were searched for studies examining the impact of the COVID-19 pandemic on the psychological health of healthcare workers. Articles were screened by three reviewers. Heterogeneity among studies was assessed by I2 statistic. The random-effects model was utilized to obtain the pooled prevalence. A subgroup analysis by region, gender, quality of study, assessment methods, healthcare profession, and exposure was performed. Publication bias was assessed by Funnel plot and Egger linear regression test. Sixty-five studies met the inclusion criteria and the total sample constituted 79,437 participants. The pooled prevalence of anxiety, depression, stress, post-traumatic stress syndrome, insomnia, psychological distress, and burnout was 34.4%, 31.8%, 40.3%, 11.4%, 27.8%, 46.1%, and 37.4% respectively. The subgroup analysis indicated higher anxiety and depression prevalence among females, nurses, and frontline responders than males, doctors, and second-line healthcare workers. This study highlights the need for designing a targeted intervention to improve resilience and foster post-traumatic growth among frontline responders. COVID-19SARS-COV-2psychologicalanxietydepressionstresspost-traumatic stress syndromeinsomniaburnoutfatigue ==== Body 1. Introduction In December 2019, a novel coronavirus originated in Wuhan (China), which was later identified by the International Committee on Taxonomy of Viruses (ICTV) as SARS-CoV-2 causing the disease COVID-19 [1]. The spread of the virus was rampant, with the cases spiraling up to nearly 148,838 in March, which led to COVID-19 being designated as a pandemic by the World Health Organization [WHO] on 11 March 2020 [2]. Undoubtedly, pandemics have a long-standing history of impacting physical and mental health across all population groups, of which healthcare workers (HCWs) bear a disproportionate burden [3]. During Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) outbreaks, a sizable proportion of the HCWs experienced anxiety, emotional distress, and post-traumatic stress disorder (PTSD, aka fatigue battle syndrome) [4,5,6]. These adverse psychological outcomes persisted until 1–3 years in the post-pandemic periods [6]. Following the aggressive course of transmission, the COVID-19 pandemic has taken a firm grip worldwide and has surpassed the historical outbreaks in generating extraordinary amounts of pressure and psychiatric morbidities among healthcare workers (HCWs) [3,6,7,8,9]. Physical and emotional exhaustion associated with managing large volumes of COVID-19 cases, shortage of personal protective equipment (PPE), risk of nosocomial infections, and fear of secondary transmission to family members, feelings of being rejected by others, and social isolation make frontline HCWs more vulnerable to the psychological corollaries of the COVID-19 pandemic [7,8,10]. Similar to previous outbreaks, the stress induced by this bio-disaster (COVID-19) has the potential to develop into PTSD [11,12,13]. In one Japan-based study, stress associated with deployment activities among the healthcare workforce was investigated [11]. The symptoms of PTSD were reported among Disaster Medical Assistance Team (DMAT) members, who were deployed to analyze, manage, and contain the transmission of COVID-19 on a Diamond Princess Cruise ship during the initial phases of the pandemic [11,12]. PTSD has already been cited as the main psychiatry disorder associated with disaster-related experiences or activities, especially among those being on the front lines of the battle against the pandemic [13]. In the wake of the COVID-19 pandemic, frontline responders are continuously working to meet the heavy healthcare demands and are exposed to higher levels of psychiatric morbidities. It is likely that these psychiatric issues will take a chronic course and will translate into PTSD in the repairing phases of the pandemic. Therefore, it is critical to assess the psychological impact among HCWs throughout the evolution of the COVID-19 pandemic to design early interventions to improve psychological outcomes. Several qualitative and quantitative studies have been conducted to explore all dimensions of the psychological spectrum among HCWs and provided valuable insights [11,13,14,15]. Previous meta-analyses investigated the psychological impact on healthcare populations during the early episodes of this bio-disaster and utilized a smaller pool of data [14,15]. One (single- arm) meta-analysis of observational studies included 13 studies [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] with a sample of 33,062 participants [14]. This study was based on a literature search on April 17, 2020. Another meta-analysis attempted to expand the evidence (by adding 12 more studies) [29,30,31,32,33,34,35,36,37,38,39,40] by comparing the psychological impact among the general population, healthcare workers, and patients with preexisting conditions [15]. This study included a total of 19 studies [16,17,18,19,20,21,29,30,31,32,33,34,35,36,37,38,39,40,41] (based on a search on May 25, 2020), and the overall prevalence of psychological indicators among healthcare workers with no further distinction based on exposure (i.e., frontline and second-line workers) were reported [15]. With the rapid evolution of pandemic and increased number of hospitalizations, frontline HCWs are experiencing unprecedented emotional and psychological challenges [15,33,34,35,36,37]. Along the course of the pandemic, new studies (n = 38) have rapidly been conducted in different parts of the world, [6,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81], which will provide additional insight into the existing literature. Additionally, it is imperative to make continuous efforts in collecting and synthesizing more data until the full picture of the psychological toll on healthcare workers emerges. Therefore, the purpose of this meta-analysis is to provide updated evidence (based on search prior to July 27, 2020, with 40 additional studies) across 24 countries to investigate the psychological impact of the COVID-19 pandemic on HCWs, with further stratification to the level of COVID-19 exposure. 2. Materials and Methods 2.1. Protocol Registration To conduct a meta-analysis on studies investigating the psychological impact of the COVID-19 pandemic among health care workers, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines [82]. This study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42020205824; https://www.crd.york.ac.uk/PROSPERO/). 2.2. Databases and Search Strategy Information retrieval was conducted by a medical librarian (N.S.) on July 27, 2020. Our primary database was MEDLINE. The MEDLINE strategy was developed, then peer-reviewed by colleagues prior to being translated to other databases. The full search strategy is available in Appendix A. We searched MEDLINE (Ovid) 1946–2020, Embase (embase.com) 1974–2020, CINAHL Complete (EBSCOhost) 1937–2020, PsycINFO (EBSCOhost) 1872–2020, and Scopus (scopus.org) 1970–2020. For preprints, we searched medRxiv (https://www.medrxiv.org/) and SSRN’s COVID-19 Research Topic (https://www.ssrn.com/index.cfm/en/). The references of included publications and previous reviews were also assessed to identify additional studies. 2.3. Eligibility Criteria Quantitative and observational studies based on original research examining the impact of the COVID-19 pandemic on the psychological health of HCWs were included. Studies which met the following criteria were included: (1) directly related to the context of the impact of the COVID-19 pandemic and reported any or a combination of the psychological outcomes, including anxiety, depression, stress, psychological distress, insomnia, and sleep disturbances; (2) non-interventional; (3) conducted on a healthcare population; (4) published in the English language; (5) published between December 1, 2019, to July 27, 2020; (6) used validated assessment methods or survey instruments to record the psychological outcomes; (7) available as full-texts. We excluded studies if they: (1) were irrelevant to the exposure (COVID-19) and the psychological outcomes; (2) were interventional; (3) were conducted on a general population; (4) reported insufficient data with unclear methods; (5) were not in the English language; (6) were conducted before December 1, 2019, and after July 27, 2020; (7) utilized qualitative or mixed methods; (8) did not use validated survey tools; (9) were case reports, reviews, editorials, duplicates, abstracts/poster-only records, animal studies, or biochemical studies. 2.4. Selection Process Results were first exported into an EndNote library for deduplication and then imported to Rayyan for screening. Three reviewers (K.B., T.P.S., and R.B.) performed screening independently and did not know each other’s decisions. All articles first underwent title screening, and then advanced to abstract screening, if deemed relevant. Then, the full texts of the selected abstracts were assessed to determine the eligibility of articles for inclusion. If an article was not included, the reason for exclusion was listed (Figure 1, PRISMA). If there were any disagreements, then the senior investigator (M.S.) evaluated the article, and a consensus was achieved through discussion. 2.5. Data Extraction Full texts of eligible articles were obtained for data abstraction. Three independent reviewers (K.B., T.P.S., and R.B.) abstracted all studies for potential inclusion using a customized data abstraction form. Inconsistencies between the three reviewers were adjudicated by a fourth, independent reviewer (M.S.). The data elements included information about the author with year, study title, study location, gender proportion, categories of healthcare occupations (if available), sample size, assessment methods with the cut-off scores, and the prevalence of anxiety, depression, stress, psychological distress, insomnia, and impaired sleep quality. Data related to each study were verified for accuracy; any discrepancies were resolved through a discussion between the reviewers. We attempted to contact corresponding authors of included records to obtain additional information when there was uncertainty about study characteristics and data points. 2.6. Assessment of Bias Risk The quality assessment was performed by utilizing the National Institutes of Health (NIH) quality assessment tool (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools) to assess the quality of the studies. Two reviewers (K.B. and T.P.S.) independently evaluated the risk of bias and quality of the studies and rated them according to the tool’s dictionary and guidelines. After assessing all the study components, the overall rating was determined using the criteria listed in the tool. Based on the number of “yes” answers, a rating of good (7–9), medium (4–6), or poor (≤3) was assigned to each study (Appendix B). Disagreements related to quality scores were resolved through a discussion among the reviewers, and a consensus was achieved upon the final judgement offered by the senior investigator (M.S.). The rating of two reviewers was compared, and the inter-rater agreement was calculated using Microsoft Excel. 2.7. Statistical Analysis The overall prevalence and 95% confidence intervals of psychological outcomes were pooled using the Comprehensive Meta-Analysis Package (CMA version 3.0, Englewood, NJ, USA). The primary effect measure was the proportions of events, such as anxiety, depression, stress, psychological distress, insomnia, and impaired sleep quality. Due to methodologic variations and sample diversity across studies, the random-effects model was used to extract the pooled estimate [83]. Heterogeneity was assessed by the I2 statistic, which measures the percentage of variance resulting from true differences in the effect sizes rather than the sampling error. Substantial heterogeneity [84] was defined as I2 > 50%. Subgroup analysis (by potential sources of heterogeneity) was conducted according to the categorical moderating variables: country (China vs. other countries), gender, continent (Asia vs. other continents), quality of the study (good/medium), assessment methods, health care profession (doctors vs. nurses), type of exposure (high risk or frontline vs. low risk or second-line), and severity of psychological symptoms (mild/moderate/severe). Sensitivity analysis was conducted to identify studies which may severely affect the pooled prevalence. Funnel plot and Egger linear regression test was used to assess publication bias [85]. Significant level was set as two-sided and p < 0.05. 3. Results 3.1. Study Screening Our systematic search yielded 7255 potentially relevant papers (Figure 1), out of which 2768 duplicate studies were removed. The titles of the remaining 4487 records were screened, and 4052 studies were excluded because the studies were conducted on non-healthcare population (n = 1347), had a different outcome of interest (n = 1256), different study designs, including case series, reviews, perspectives or opinions, and interventional studies (n = 1013), and were irrelevant to the study’s objective (n = 436). This resulted in 435 papers, which were advanced to abstract screening. A total of 321 papers were excluded after abstract screening because they were qualitative or mixed studies (n = 68), studies on a general population (n = 73), studies without prevalence data (n = 96), serological studies (10), or they were published as editorials, posters, or reviews (n = 74). Full-text screening of the remaining 114 papers generated 65 articles, which were included in the final review. Forty-nine articles were excluded in the final step because of following reasons: they were abstracts-only or in other languages (n = 6), used non-validated questionnaires (n = 16), had an unclear methodology or of low quality (n = 10), or had outcomes reported as means instead of the number of cases/or proportions (n = 17) (Figure 1). 3.2. Study Quality Twenty-nine studies were of good quality [16,17,18,19,24,25,26,27,28,31,34,39,41,42,44,46,53,54,57,59,60,61,63,64,66,68,71,79,80] (score range 7–9) and thirty-six studies were of medium quality [6,20,21,23,27,29,30,32,33,35,36,37,38,43,45,47,48,49,50,51,52,55,56,58,62,65,67,69,70,72,73,74,76,77,78,81] (score range: 4–6). The quality scores of the included study evaluation as assessed using the National Institutes of Health quality assessment tool are summarized (Table A2 in Appendix B). The ratings of two reviewers (K.B. and T.P.S.) were averaged, and the inter-rater agreement was 84.5. 3.3. Study Characteristics After deduplication and screening, 65 studies (Table A3 in Appendix C) [6,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81] with a total of 79,437 participants were included in the analysis. Among them, 51 were from Asia (31 from China, 4 from India, 1 from Singapore, 1 from India and Singapore, 3 from Iran, two from Pakistan, 2 from Jordan, 1 from Bahrain, 1 from Hong Kong, 1 from Israel, 1 from Nepal, 1 from Oman, 1 from Saudi Arabia, and 1 from South Korea), 10 were from Europe (3 from Italy, 4 from Turkey, 1 from Switzerland, 1 from Serbia, and 1 from Ireland), 2 were from South America (1 from Argentina, Brazil, Chile and Mexico, and one from Brazil alone), and 2 were from North America. All the studies were cross-sectional and reported at least one psychological outcome (if not all) among HCWs in the context of the COVID-19 pandemic. The median number of individuals per study was 582 (range: 37 to 11,118) with a median male proportion of 25% (range: 0 to 96%) and a median response rate of 20.0% (range: 10.2% to 100%). Nearly 3/4th of the sample was female (n = 57,244; 72%). In terms of occupation distribution, nurses constituted nearly 45.7% (n = 36,315), followed by physicians or doctors, forming 1/4th (n = 19,287) of the entire sample. Remaining professions include allied health staff, laboratory specialists, anesthetist technicians and general technicians, physical therapists, pharmacists, dental professionals, etc. 3.4. Meta-Analysis 3.4.1. Anxiety Prevalence The pooled prevalence of anxiety in 46 studies with a sample size 51,596 was 34.4% (Table 1, Figure 2). The pooled prevalence of anxiety among good quality studies (n = 22) was 31.2% compared to 38.1% among medium quality studies (n = 24) (Table 1). The pooled prevalence of anxiety in the continent of Asia across 34 studies was 32.7% compared to 39.3% found in studies among other continents. Twenty-two studies were conducted in China and had a pooled prevalence of 28.5% as opposed to the 40.4% (Table 1) pooled prevalence across studies (n = 24) conducted in other countries. The Generalized Anxiety Disorder survey questionnaire was used across 19 studies and a pooled prevalence of 36.8% was found (Table 1). Gender data were available in seven studies with a pooled prevalence of 46.9% for females and 44.2% for males. In groups by healthcare professions, the pooled prevalence was higher in nurses compared to doctors (39.3% vs. 32.5%). Anxiety by exposure with a pooled prevalence of 39.8% among frontline HCWs compared to 27.1% prevalence among second-line HCWs. Levels of anxiety with the highest pooled prevalence of 60.3% related to mild symptoms, followed by a 26.0% prevalence of moderate symptoms and a prevalence of only 14.3% for severe symptoms (Table 1). 3.4.2. Depression Prevalence The pooled prevalence of depression in 46 studies with a sample size 53,164 was 31.8% (Table 2, Figure 3). The pooled prevalence of depression among good quality studies was 35.1%, compared to 28.6% among medium quality studies (Table 2). The pooled prevalence of depression in the continent of Asia was 30.8% compared to 35.0% found in other continents. Twenty-three studies were conducted in China and had a pooled prevalence of 33.2% as opposed to the 30.4% pooled prevalence across studies conducted in other countries (n = 23). The Patient Health Questionnaire (PHQ) was used across 25 studies and a pooled prevalence of 29.7% was found. Gender data were available in seven studies, with a pooled prevalence of 43.4% for females and 40.9% for males. In the healthcare profession groups, examined in nine studies, the pooled prevalence of depression was higher in nurses compared to doctors (42.4% vs. 39.1%). Depression by exposure was reported in six studies with a pooled prevalence of 23.6% among frontline healthcare workers compared to 19.6% prevalence among second-line healthcare workers. Levels of depression were reported in 17 studies, with the highest pooled prevalence of 57.6% related to mild symptoms, followed by a 27.9% prevalence of moderate and only 10.4% of severe symptoms (Table 2). 3.4.3. Stress Prevalence The pooled prevalence of stress in 17 studies with a sample size of 16,235 was 40.3% (Table 3, Figure 4). The pooled prevalence of stress among good quality studies (n = 9) was 37.3% compared to 45.7% among medium quality studies (n = 8). The pooled prevalence of stress in the continent of Asia across 14 studies was 41.3% compared to 38.8% found in other continents. Seven studies were conducted in China and had a pooled prevalence of 44.2% as opposed to the 37.1% pooled prevalence in studies conducted in other countries (n = 10). The Perceived Stress Scale (PSS) was used across eight studies and a pooled prevalence of 61.4% was found. Levels of stress were reported in six studies, with the highest pooled prevalence related to moderate symptoms of 52.3%, followed by a 25.8% prevalence of mild and 18.9% of severe symptoms (Table 3). For stress, subgroup analyses by gender, healthcare occupations, and risk exposure were not conducted due to inadequate data. 3.4.4. Prevalence of Insomnia and Impaired Sleep Quality Under the random effects model, the overall prevalence of insomnia in a sample size of 18,546 was 27.8% (95% CI: 21.4–35.3, I2 = 98.1%; p < 0.001; Figure A1 in Appendix C across 11 studies [16,17,18,25,26,32,44,63,64,76,79]. The insomnia severity index (ISI) was used across eight studies [17,18,26,32,44,63,64,79] and a pooled prevalence of 62.8 (95% CI: 44.8–77.9; I2 = 98.2%; p value < 0.001) was found. In groups by healthcare professions, three studies were included [16,18,63]. The pooled prevalence of insomnia was slightly higher in nurses compared to doctors (42.4% vs. 39.1%). The quality of sleep was assessed in five studies [26,32,58,66,70]. The overall prevalence of impaired sleep quality in a sample size of 2443 was 64.3% (95% CI:55.0–72.7, I2 = 93.1%; p < 0.001; Figure A2 in Appendix C). Subgroup analysis for insomnia by gender and exposure was not conducted due to inadequate data. 3.4.5. Other Psychological Indicators Post-traumatic stress disorder (PTSD) was assessed in six studies [21,24,34,64,71,77]. Under the random effects model, the overall prevalence of PTSD in a sample size of 3676 was 11.4% (95% CI: 3.6–30.9; I2 = 99.2% p < 0.001; Figure A3 in Appendix C). Psychological distress was reported in 12 studies [16,23,28,29,37,38,49,51,53,56,57,60]. Under the random effects model, the overall prevalence of psychological distress in a sample size of 30,963 was 46.1% (95% CI: 36.0–56.6; I2 = 99.6%; p < 0.001; Figure A4 in Appendix C). Burnout was assessed in three studies [33,49,57] and the overall prevalence in a sample size of 2487 was 37.4% (95% CI: 14.8–67.2; I2 = 98.6%; p < 0.001; Figure A5 in Appendix C). 3.4.6. Publication Bias The publication bias was assessed with Egger’s test indices. As indicated by the p values for the prevalence of anxiety (Egger test: p = 0.15), depression (p = 0.90), stress (p = 0.69), insomnia (p = 0.01), impaired sleep quality (p = 0.22), PTSD (p = 0.22), psychological distress (p = 0.45), and burnout (p = 0.47) (Figure A6, Figure A7, Figure A8, Figure A9, Figure A10, Figure A11, Figure A12, Figure A13 in Appendix C), the publication bias was insignificant for all the psychological outcomes, except insomnia. 4. Discussion This pooled analysis included a large data sample of studies (n = 65) with 79,437 participants, compared to previous meta-analyses that included 13–19 studies [14,15]. Notably, to our knowledge, this study is the largest to evaluate the psychological impact of COVID-19 among HCWs. Moreover, we extended the existing evidence by including other psychological outcomes of stress, psychological distress, burnout, and impaired sleep quality. The findings suggest that the overall prevalence of anxiety, depression, stress, insomnia was 34.4%, 31.8%, 40.3%, and 27.8%, respectively. Compared to previous meta-analyses [14,15], we report a higher prevalence for anxiety (34.4% vs. 23.2%–26.0%), depression (31.8% vs. 22.8%–25.0%), and PTSD (11.4% vs. 3%). The higher prevalence of anxiety, depression, and PTSD may be explained by the pervading climate of uncertainty generated with the advancing pandemic, limited signs of a workable vaccine, increased workload, lack of social support, and an intense fear of family transmission [86,87,88]. According to a recent Chinese report of 14,825 healthcare workers, depressive symptoms and PTSD were more common among HCWs with lower levels of social support and longer daily working hours (>12 h/day) [88]. We found a lower prevalence of insomnia compared to a previous analysis [14] (27.8% vs. 34.4%) despite having a greater number of studies in the current analysis (11 vs. 5). This may be due to the variance in the cut-off values (>14 vs. >8) of the ISI used by the recent studies, which were included in this updated meta-analysis [32,44,63,64]. Moreover, the direction of the etiological relationships between psychological morbidities and insomnia remains complex; for instance, anxiety disorders precede insomnia in nearly 70% of the cases [89]. Therefore, it is likely that significant changes in sleep architecture among HCWs will occur later and will be observed by prospective studies. In concordance with previous studies, our subgroup analysis by gender revealed that females had a higher prevalence of anxiety and depression compared to males [14,15,27,28,89,90,91,92]. The current meta-analysis found higher levels of anxiety and depression among nurses compared to doctors, which may be because nurses have closer and prolonged contact with patients compared to doctors [3,93]. We investigated the prevalence of anxiety and depression by risk groups and found higher levels of anxiety and depression (as expected) among frontline responders as compared to the second-line workers. All of these results may be partly confounded by the fact that majority of the frontline workers’ group constitutes nurses, who are responsible for providing direct care to the COVID-19 patients and for collecting sputum specimen for virus detection, and tend to be female [3,27,28,94,95,96,97]. This repeats a finding from the SARS outbreak that nurses reported higher anxiety, depression, behavioral problems, and moral injuries (related to death and ethical dilemmas) [3,5,7,14,27,28,92,93,94,95]. These intersecting factors make clear that it is imperative to develop interventions for the most vulnerable population: frontline nurses, who tend to be female and work long hours. Our study reported a higher prevalence of anxiety in other countries compared to China (40.4% vs. 28.5%). The reasons for this may be complex and could be influenced by how other countries are directing their medical resources towards the containment efforts or in treating infected patients rather than providing psychological services. The higher prevalence of the psychosocial impact of COVID-19 on health care workers found in this study draws more careful attention to educational and policy interventions for this subgroup. Educational and behavioral interventions emphasizing hardiness, social support, positive thinking, a sense of coherence, and others have been advocated in the literature [96,97,98]. The role of self-care is also highlighted by some researchers [98,99,100]. Positive traumatic growth (a positive approach to the management of complex traumas) among health care workers has gained special attention, and interventions to build this have been suggested [96,99,100]. Some strategies, such as mindfulness interventions, can be instituted quickly to promote healthcare workers’ mental health [97,98,99]. Organizational support is vital at the policy level and may need sufficient lead time to be enacted [97,98,99]. Work-based interventions, such as curtailing hours of work, having buddy support systems, having listening sessions between administration and health care functionaries, increasing coverage of tele counseling through employee assistance programs, having mental health consultants available to staff through telehealth, and other such measures, can go a long way in reducing the adverse psychosocial impact caused by COVID-19 [98,99]. 4.1. Quality of Evidence Compared to the previous reviews and meta-analyses, the current meta-analysis provides the most extensive evidence with a much bigger sample size of 79,437 participants. Although all studies included in our meta-analysis were cross-sectional, they were of high and medium quality. We performed subgroup analyses to account for the potential sources of heterogeneity and to identify additional vulnerabilities. Additionally, we investigated the potential for publication bias across all studies. 4.2. Study Limitations There are a few limitations that merit discussion. First is the presence of heterogeneity across studies in terms of the survey tool and cut-off scores. Additionally, threshold criteria for defining levels of outcomes varied across studies; for example, some studies reported results as mild, moderate, moderate-severe, and severe, while others reported outcomes as mild, moderate, and severe. This may affect our subgroup analysis by severity. Second, data provided by the studies included in this meta-analysis depend on the self-reported psychological outcomes as recorded through assessment tools. Thus, there may be uncertainty related to the actual psychological illness or diagnosis. Third, sampling bias may exist (although lower than the previous meta-analysis), because nearly 48% (31/65) of the studies were conducted in China. This may also limit the generalizability of the results. Fourth, all studies included in this meta-analysis were cross-sectional, which only provided a snapshot of the existing situation with no exploration of longitudinal aspects. Last, we expect to have a language bias in the study because only studies published in the English language were included. 4.3. Research and Clinical Implications The findings of this meta-analysis highlight the need to develop psychological interventions to promote the post-traumatic growth among HCWs. Higher prevalence estimates of stress provided by this study have important implications for developing early interventions to prevent PTSD, which may be higher in the repairing phases of the pandemic. Mental health and well-being interventions, such as education on coping techniques, online wellness activities, fostering post-traumatic growth, and opening channels for assistance in early signs of PTSS (Posttraumatic Stress Syndrome) before they manifest to PTSD, are essential. 5. Conclusions This article represents, to our knowledge, the most extensive meta-analysis to assess the psychological impact of COVID-19 among HCWs. This meta-analysis provides additional evidence to the higher psychological impact among HCWs, particularly those who are female, nurses, and frontline responders. Furthermore, this adds a valuable insight to the existing meta-analysis findings by highlighting a significant difference in the psychological impact across frontline and second-line workers. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author Contributions Conceptualization, K.B.; Formal analysis, K.B. and R.B.; Investigation, K.B., T.P.S., M.S., and R.B.; Search strategy, N.S.; Methodology, K.B., T.P.S., M.S., and R.B.; Software, R.B.; Validation, K.B. and R.B.; Visualization, K.B. and R.B.; Writing—original draft, K.B., R.B., T.P.S., M.S., and N.S.; Writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest. Appendix A ijerph-17-09096-t0A1_Table A1Table A1 Database search strategies for psychological impact of COVID-19 among healthcare workers (search date: 27 July 2020). Database: Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily <1946 to 27 July 2020> Search Strategy: -------------------------------------------------------------------------------------------------------------------(2019nCoV or 2019-nCoV or coronavirus or coronavirinae or (corona adj3 (virinae or virus)) or “Corona virinae19” or “Corona virinae2019” or “corona virus19” or “corona virus2019” or Coronavirinae19 or Coronavirinae2019 or coronavirus19 or coronavirus2019 or covid19 or COVID-19 or SARS-CoV-2 or “Severe Acute Respiratory Syndrome Corona virus 2” or “Severe Acute Respiratory Syndrome Coronavirus 2”).ti,ab,kw. [covid-19 keywords] (46270) coronavirus/or Coronavirus Infections/[covid-19 MeSH] (19104) or/1–2 [covid-19 set] (48733) mental health/or mental fatigue/or Affective Symptoms/or psychological distress/[Mental health MeSH] (53257) (emotional disturbanc* or affective symptom* or Alexithymia* or ((mental or psychological) adj3 (fatigue or health or status or distress or well-being)) or psychosocial).ti,ab,kw. [mental health keywords] (283768) or/4–5 [mental health set] (305532) Stress, Psychological/or occupational stress/or compassion fatigue/or burnout, psychological/or burnout, professional/[stress MeSH] (131108) (stress* or “adaptation syndrome” or (caregiver adj4 (burden or fatigue)) or “compassion fatigue” or “reality shock” or “social defeat”).ti,ab,kw. [stress keywords] (842732) or/7–8 [stress set] (897231) Depression/or anhedonia/[depression MeSH] (119688) (depression or depressed or anhedonia or dysphoria or dysthymia or melancholia or sadness).ti,ab,kw. [depression keywords] (404119) or/10–11 [depression set] (436174) anxiety/or catastrophization/[anxiety MeSH] (81955) (anxiety or Catastrophiz* or hypervigilan* or nervousness).ti,ab,kw. [anxiety keywords] (195877) or/13–14 [anxiety set] (218337) “Sleep Initiation and Maintenance Disorders”/[insomnia MeSH] (13134) (drowsiness or dyssomnia * or hypersomnia * or insomnia * or parasomnia * The asterisk (“*”) used in the search string serves as a truncation operator. Appendix B ijerph-17-09096-t0A2_Table A2Table A2 Methodological quality assessment of included studies using the National Institutes of Health (NIH) tool. Author/Year Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Final Quality Score Rating Aksoy and Kocak et al., 2020 [81] Y Y NR N N Y N N Y NA Y NA NA N 5 Medium Alshekaili et al., 2020 [44] Y Y Y Y N Y Y Y Y NA Y NA NA Y 8 Good Amerio et al., 2020 [45] Y Y N N N Y Y Y Y NA N NA NA N 6 Medium Amin et al., 2020 [37] Y Y Y N N Y N N Y NA Y NA NA N 5 Medium An et al., 2020 [46] Y Y NR Y Y Y Y Y Y NA Y NA NA N 9 Good Cai et al., 2020 [79] Y Y NR Y Y Y Y Y Y NA Y NA NA N 9 Good Caliskan et al., 2020 [47] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Cao et al., 2020 [33] Y Y Y N N Y N N Y NA Y NA NA N 6 Medium Chatterjee et al., 2020 [48] Y Y NR N N Y N Y Y NA Y NA NA N 6 Medium Chen et al., 2020 [30] Y Y Y N N Y N N NR NA Y NA NA N 5 Medium Chew et al., 2020 [34] Y Y Y N Y Y Y N Y NA Y NA NA N 8 Good Chung and Yeung et al., 2020 [78] Y Y NR N N Y N N Y NA Y NA NA N 5 Medium Civantos et al., 2020 [49] Y Y N N N Y Y N Y NA Y NA NA N 6 Medium Consolo et al., 2020 [35] Y Y N N N Y Y N Y NA Y NA NA N 6 Medium Corbett et al., 2020 [50] Y Y N N N Y Y N Y NA Y NA NA N 6 Medium Dai et al., 2020 [51] Y Y Y N N Y Y N Y NA N NA NA N 6 Medium Dal’Bosco et al., 2020 [52] Y Y N N N Y Y N Y NA Y NA NA N 6 Medium Dong et al., 2020 [53] Y Y NR Y Y Y Y N Y NA Y NA NA N 8 Good Du et al., 2020 [20] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Elbay et al., 2020 [54] Y Y NR Y Y Y Y N Y NA Y NA NA N 8 Good Guiroy et al., 2020 [36] Y Y N N N Y Y N Y NA Y NA NA N 6 Medium Guo et al., 2020 [23] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Hassannia et al., 2020 [55] Y Y Y N N Y Y N Y NA N NA NA N 6 Medium Hawari et al., 2020 [56] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Hu et al., 2020 [57] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Huang and Zhao et al., 2020 [25] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Huang et al., 2020 [24] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Jahrami et al., 2020 [58] Y Y Y N N Y N N Y NA Y NA NA N 6 Medium Kang et al., 2020 [39] Y Y NR Y Y Y Y Y Y NA Y NA NA N 9 Good Kaveh et al., 2020 [43] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Khanna et al., 2020 [59] Y Y NR Y Y Y Y N Y NA Y NA NA N 8 Good Koksal et al., 2020 [80] Y Y NR Y Y Y Y N Y NA Y NA NA N 8 Good Lai et al., 2020 [16] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Li et al., 2020 [31] Y Y Y Y N Y Y N Y NA Y NA NA N 8 Good Liu et al., 2020 [27] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Liu et al., 2020 [28] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Lu et al., 2020 [19] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Nair et al., 2020 [60] Y Y NR Y N Y Y N Y NA Y NA NA N 7 Good Naser et al., 2020 [61] Y Y NR Y N Y Y Y Y NA Y NA NA N 8 Good Podder et al., 2020 [62] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Qi et al., 2020 [26] Y Y Y Y Y Y N N Y NA Y NA NA N 8 Good Que et al., 2020 [63] Y Y NR Y Y Y Y Y Y NA Y NA NA N 9 Good Rossi et al., 2020 [64] Y Y NR Y N Y Y Y Y NA Y NA NA Y 9 Good Salman et al., 2020 [65] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Shacham et al., 2020 [38] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Shechter et al., 2020 [66] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Stojanov et al., 2020 [67] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Sun et al., 2020 [77] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Tan et al., 2020 [21] Y Y Y N N Y Y N Y NA Y NA NA N 6 Medium Temsah et al., 2020 [6] Y Y Y N N Y Y N N NA Y NA NA N 6 Medium Teng et al., 2020 [68] Y Y N Y Y Y N Y Y NA Y NA NA Y 9 Good Thapa et al., 2020 [69] Y Y NR N N Y N N Y NA Y NA NA N 5 Medium Tu et al., 2020 [70] Y Y Y N N Y Y N N NA Y NA NA N 6 Medium Wang et al., 2020 [32] Y Y Y Y N Y Y N Y NA Y NA NA N 8 Good Wang et al., 2020 [71] Y Y Y N N Y Y N N NA Y NA NA N 6 Medium Weilenmann et al., 2020 [72] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Xiao et al., 2020 [73] Y Y NR N N Y N Y Y NA Y NA NA N 6 Medium Yang et al., 2020 [74] Y Y Y N N Y N N Y NA Y NA NA N 6 Medium Yin et al., 2020 [75] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Zhan et al., 2020 [76] Y Y Y N N Y Y N N NA Y NA NA N 6 Medium Zhang et al., 2020 [17] Y Y NR Y Y Y Y Y Y NA Y NA NA N 9 Good Zhang et al., 2020 [18] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Zhang et al., 2020 [29] Y Y NR N N Y Y N Y NA Y NA NA N 6 Medium Zhu et al., 2020 [41] Y Y Y Y Y Y Y N Y NA Y NA NA N 9 Good Zhu et al., 2020 [42] Y Y Y Y N Y Y N Y NA Y NA NA N 8 Good Y: Yes, N: No, NR: Not reported, NA: Not applicable. (Q1. Was the research question or objective in this paper clearly stated? Q2. Was the study population clearly specified and defined? Q3. Was the participation rate of eligible persons at least 50%? Q4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? Q5. Was a sample size justification, power description, or variance and effect estimates provided? Q6. For the analyses in this paper, were the exposure(s) of interest measured prior to the outcome(s) being measured? Q7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? Q8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (e.g., categories of exposure, or exposure measured as continuous variable)? Q9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Q10. Was the exposure(s) assessed more than once over time? Q11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Q12. Were the outcome assessors blinded to the exposure status of participants? Q13. Was loss to follow-up (response rate) after baseline 20% or less? Q14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? Rating—Good, Medium or Poor), Good = (7–9 yes); Medium = (4–6 yes).) Appendix C ijerph-17-09096-t0A3_Table A3Table A3 Characteristics of studies meeting the search inclusion criteria. Author/Year Sample Size Country Health Care Workers Male (%) Survey Tool Cut-Off Outcomes (%) (n) Physician (%) Nurses (%) Depression Anxiety Insomnia Stress PTSD a Distress Aksoy and Kocak et al., 2020 [81] 758 Turkey 0.0 100.0 7.0 STAI y NA ae NA 36.3 (275) NA NA NA NA Alshekaili et al., 2020 [44] 1139 Oman 33.7 39.4 20.0 DASS t -21(D aa) DASS t -21(A ab) DASS t -21(S ac) ISI d ≥10 ≥8 ≥16 ≥14 32.3 (368) 34.1 (388) 18.5 (211) 23.8 (271) NA NA Amerio et al., 2020 [45] 131 Italy NA NA 51.9 PHQ c -9 ≥10 39.3 (30) NA NA NA NA NA Amin et al., 2020 [37] 250 Pakistan 49.2 30.4 63.6 PGWBI l ≥30 NA NA NA NA NA 72.4 (181) An et al., 2020 [46] 1103 China 0.0 100.0 9.2 PHQ c -9 ≥5 43.6 (481) NA NA NA NA NA Cai et al., 2020 [79] 2346 China NA NA 29.9 PHQ c -9 BAI m ISI d ≥10 ≥15 ≥9 12.23 (287) 11.6 (271) 38.4 (902) NA NA NA Caliskan et al., 2020 [47] 290 Turkey 100.0 NA 61.7 HADS k -D HADS k -A ≥7 ≥10 62.1 (180) 35.5 (103) NA NA NA NA Cao et al., 2020 [33] 37 China 16.0 19.0 21.6 PHQ c -9 ≥10 18.9 (7) NA NA NA NA NA Chatterjee et al., 2020 [48] 152 India NA NA 78.3 DASS t -21 NA 34.9 (53) 39.5 (60) NA 32.9 (50) NA NA Chen et al., 2020 [30] 105 China NA NA 9.5 SAS r SDS z ≥50 ≥50 29.5 (31) 18.1 (19) NA NA NA NA Chew et al., 2020 [34] 906 Singapore and India NA NA NA DASS t -21(D aa) DASS t -21(A ab) DASS t -21(S ac) IES i -R >9 >7 >14 ≥24 21.4 (96) 31.5 (142) NA 10.3 (47) 14.8 (67) NA Chung and Yeung et al., 2020 [78] 69 Hong Kong 4.4 34.8 NA PHQ c -9 ≥10 49.3 (34) NA NA NA NA NA Civantos et al., 2020 [49] 349 USA 52.7 NA 60.7 GAD b -7 PHQ c -2 IES i ≥10 ≥3 ≥27 10.6 (37) 47.9 (167) NA NA NA 60.2 (210) Consolo et al., 2020 [35] 356 Italy NA NA 60.4 GAD b -7 ≥5 NA 57.0 (204) NA NA NA NA Corbett et al., 2020 [50] 240 Dublin Ireland 15.0 36.25 9.2 GAD b -7 PHQ c -9 ≥10 ≥10 20.3 (49) 21.0 (51) NA NA NA NA Dai et al., 2020 [51] 4357 China 32.6 53.8 23.5 GHQ e -12 ≥3 NA NA NA NA NA 39.1 (1704) Dal’Bosco et al., 2020 [52] 88 South America NA NA 10.2 HAD k ≥3 25.0 (22) 48.9 (43) NA NA NA NA Dong et al., 2020 [53] 4618 China 24.6 62.7 16.3 HEI q ≥8 NA NA NA NA NA 24.2 (1118) Du et al., 2020 [20] 134 China 35.1 41.0 39.6 BDI o -II BAI m PSS j ≥14 ≥8 ≥14 12.7 (17) 20.1 (28) NA 59.0 (79) NA NA Elbay et al., 2020 [54] 442 Turkey NA NA 43.2 DASS t -21(D aa) DASS t -21(A ab) DASS t -21(S ac) >9 >7 >14 64.7 (286) 51.6 (224) NA 41.2 (182) NA NA Guiroy et al., 2020 [36] 204 Latin America ad 100.0 NA 96.6 PHQ c -9 ≥10 100 (204) NA NA NA NA NA Guo et al., 2020 [23] 11,118 China 30.28 53.07 25.2 SAS r SDS z ≥50 ≥50 31.5 (3497) 17.5 (1940) NA NA NA 40.7 (4530) Hassannia et al., 2020 [55] 487 Iran 26.08 21.56 NA HADS k -D aa HADS k -A ab ≥8 ≥8 48.3 (235) 62.8 (306) NA NA NA NA Hawari et al., 2020 [56] 1006 Jordan 13.02 63.02 44.7 K6 ag ≥11 NA NA NA NA NA 96.5 (971) Hu et al., 2020 [57] 2101 China NA 100.00 12.4 SAS r ZSDS v ≥50 ≥60 42.0 (878) 40.0 (833) NA NA NA 41.5 (835) Huang and Zhao et al., 2020 [25] 2250 China NA NA NA GAD b -7 CES-D s PSQI p ≥9 ≥28 ≥7 19.8 (446) 35.6 (802) 23.6 (531) NA NA NA Huang et al., 2020 [24] 230 China 30.4 69.6 18.7 SAS r PTSD-SS ≥50 ≥50 NA 23.0 (53) NA NA 27.4 (63) NA Jahrami et al., 2020 [58] 257 Bahrain 31.1 46.3 30.0 PSQI p PSS j ≥5 ≥14 NA NA NA 100.0 (257) NA NA Kang et al., 2020 [39] 994 China 18.4 81.6 14.5 PHQ c -9 GAD b -7 ISI d IES i -R ≥5 ≥5 ≥8 ≥9 63.0 (627) NA NA NA NA NA Kaveh et al., 2020 [43] 1038 Iran 20.6 63.3 12.4 BAI m ≥7 NA 100.0 (1038) NA NA NA NA Khanna et al., 2020 [59] 2355 India NA NA 56.6 PHQ c -9 ≥4 32.6 (765) NA NA NA NA NA Koksal et al., 2020 [80] 702 Turkey NA 48.3 30.0 HADS k -D aa HADS k -A ab ≥7 ≥10 36.9 (259) 57.5 (404) NA NA NA NA Lai et al., 2020 [16] 1257 China 39.2 60.8 23.3 PHQ c -9 GAD b -7 ISI d IES i -R ≥5 ≥5 ≥8 ≥9 50.4 (634) 44.6 (560) 34 (427) NA NA 71.5 (899) Li et al., 2020 [31] 4369 China 13.3 77.4 0.0 IES i –R PHQ c -9 GAD b -7 ≥33 ≥10 ≥8 14.2 (621) 25.2 (1101) NA 31.6 (1382) NA NA Liu et al., 2020 [27] 512 China NA NA 15.4 SAS r ≥50 NA 12.5 (64) NA NA NA NA Liu et al., 2020 [28] 4679 China 39.6 60.4 17.7 SAS r SDS z SRQ x -20 ≥50 ≥50 ≥7 34.6 (1619) 16.0 (749) NA NA NA 15.9 (744) Lu et al., 2020 [19] 2299 China 88.8 NA 22.4 HADS k -D aa HADS k -A ab ≥7 ≥7 11.7 (268) 24.7 (569) NA NA NA NA Nair et al., 2020 [60] 586 India NA NA 53.1 CPDI w PSS j ≥28 ≥13 NA NA NA 100.0 (586) NA 52.0 (304) Naser et al., 2020 [61] 1163 Jordan 48.2 13.0 43.9 GAD b -7 PHQ c -9 ≥4 ≥5 78.0 (907) 71.0 (823) NA NA NA NA Podder et al., 2020 [62] 384 India NA NA 55.5 PSS j -10 ≥13 NA NA NA 100 (384) NA NA Qi et al., 2020 [26] 1306 China NA NA 19.6 PSQI p AIS f >7 >6 NA NA 45.5 (594) NA NA NA Que et al., 2020 [63] 2285 China 37.6 9.1 30.9 GAD b -7 PHQ c ISI d ≥10 ≥10 ≥15 44.4 (1014) 46.0 (1052) 28.8 (657) NA NA NA Rossi et al., 2020 [64] 1379 Italy 37.64 34.23 22.8 GPS PHQ c -9 GAD b -7 ISI d -7 PSS j -10 ≥3 ≥15 ≥15 ≥22 NA 24.73 (341) 19.8 (273) 8.3 (114) 21.9 (302) 49.4 (681) NA Salman et al., 2020 [65] 398 Pakistan 51.5 33.4 46.0 GAD b -7 PHQ c -9 ≥10 ≥10 21.9 (87) 21.4 (85) NA NA NA NA Shacham et al., 2020 [38] 338 Israel NA NA 41.4 K6 ag ≥19 NA NA NA NA NA 11.5 (39) Shechter et al., 2020 [66] 657 USA 28.8 47.6 21.8 PC-PTSD u PHQ c -2 GAD b -2 ≥3 ≥3 ≥3 48.0 (313) 33.0 (215) NA 57.0 (371) NA NA Stojanov et al., 2020 [67] 201 Serbia NA 100.0 34.3 GAD b -7 ZSDS v ≥5 ≥50 30.8(62) 48.2 (97) NA NA NA NA Sun et al.,2020 [77] 320 China NA NA NA PCL af -5 ≥33 NA NA NA NA 4.4 (14) NA Tan et al., 2020 [21] 470 Singapore 28.7 34.3 31.7 DASS t -21(D aa) DASS t -21(A ab) DASS t -21(S ac) IES i -R >9 >7 ≥14 ≥24 8.9 (42) 14.5 (68) NA 6.6 (31) 7.7 (36) NA Temsah et al., 2020 [6] 582 Saudi Arabian 18.6 62.4 24.9 GAD b -7 ≥5 NA 100.0 (582) NA NA NA NA Teng et al., 2020 [68] 398 China NA NA 24.1 PHQ c -9 SAS r ≥5 ≥50 35.9 (143) 14.1 (56) NA NA NA NA Thapa et al., 2020 [69] 100 Nepal 9.0 62.0 22.0 SAS r ≥45 NA 34.0 (34) NA NA NA NA Tu et al., 2020 [70] 100 China NA 100.0 0.0 GAD b -7 PHQ c -9 PSQI p ≥4 ≥4 ≥7 46.0 (46) 40.0 (40) NA NA NA NA Wang et al., 2020 [71] 1045 China 14.3 74.0 14.2 HADS k -D aa HADS k -A ab PSS j -14 ISI d ≥8 ≥8 ≥14 ≥14 53.0 (554) 67.8 (708) 10.4 (109) 21.0 (223) NA NA Weilenmann et al., 2020 [72] 1410 Switzerland 60.8 39.2 33.8 GAD b -7 PHQ c -9 ≥10 ≥10 20.7 (292) 25.9 (365) NA NA NA NA Xiao et al., 2020 [73] 958 China 39.5 37.5 32.8 HADS k -D aa HADS k -A ab ≥8 ≥8 57.3 (549) 54.2 (518) NA NA NA NA Yang et al., 2020 [74] 65 South Korea NA NA 52.3 GAD b -7 PHQ c -9 ≥5 ≥10 18.5 (12) 32.3 (21) NA NA NA NA Yin et al., 2020 [75] 371 China 18.1 71.2 38.5 PCL af -5 ≥33 NA NA NA NA 3.8 (14) NA Zhan et al., 2020 [76] 1794 China NA 100.0 3.0 AIS f FS g 14 CPSS h ≥6 ≥7 ≥25 NA NA 52.8 (948) 44.0 (789) NA NA Zhang et al., 2020 [17] 2182 China 31.2 11.3 35.8 ISI d SCL n -90-R PHQ c -2 GAD b -2 >8 ≥2 ≥3 ≥3 10.6 (232) 10.4 (228) 33.9 (739) NA NA NA Zhang et al., 2020 [18] 1563 China 29.0 62.9 17.3 ISI d PHQ c -9 GAD b -7 IES i -R ≥8 ≥5 ≥5 ≥9 50.7 (792) 44.7 (699) 36.1 (564) 73.4 (1147) NA NA Zhang et al., 2020 [29] 304 Iran NA NA NA PHQ c -4 K6 ag NA NA 20.6 (63) 28.0 (85) NA NA NA 20.1 (61) Zhu et al., 2020 [41] 5062 China 19.8 67.5 15.0 IES i -R PHQ c 9 GAD b -7 ≥33 ≥10 ≥8 13.5 (681) 24.1 (1218) NA 29.8 (1509) NA NA Zhu et al., 2020 [42] 165 China 47.9 52.1 17.0 SAS r SDS z ≥50 ≥50 44.2 (73) 20.0 (33) NA NA NA NA a PTSD: Post-traumatic stress syndrome, b GAD: Generalized anxiety disorder scale, c PHQ: Patient health questionnaire, d ISI: Insomnia severity index, e GHQ: General health questionnaire, f AIS: Athens insomnia scale, g FS: Fatigue scale, h CPSS: Chinese perceived stress scale, i IES: Impact of event scale, j PSS: Perceived stress scale, k HAD: Hospital anxiety depression scale, l PGWBI: Psychological General Well-Being Index, m BAI: Beck anxiety Inventory, n SCL-19-R: Symptom checklist, o BDI: Beck depression inventory, p PSQI: Pittsburgh sleep quality index, q HEI: Huaxi emotional distress index, r SAS: Self-rating anxiety scale, s CES-D: The center for epidemiology scale for depression, t DASS: Depression, anxiety, stress scales, u PC-PTSD: Primary care PTSD Screen, v Zsds: Zung self-rating depression scale, w CPDI: COVID-19 Peri traumatic distress index, x SRQ: Self reporting questionnaire, y STAI: State-trait anxiety inventory, z SDS: Self-rating depression scale, aa D: Depression, ab A: Anxiety, ac S: Stress, ad Latin America: Countries included Brazil, Argentina, Chile, and Mexico, ae NA: Not applicable, af PCL: Post-traumatic stress disorder checklist, ag K6—Kessler Phycological Distress Scale. Figure A1 Forest plot for the studies on the prevalence of insomnia among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled insomnia using a random effects DerSimonian-Laird method was 27.8% (95% CI: 21.4–35.3). Figure A2 Forest plot for the studies on the prevalence of impaired sleep quality among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled impaired sleep quality using a random effects DerSimonian-Laird method was 64.3% (95% CI: 55.0–72.7). Figure A3 Forest plot for the studies on the prevalence of Post-traumatic stress syndrome (PTSD) among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled PTSD using a random effects DerSimonian-Laird method was 11.4% (95% CI: 3.6–30.9). Figure A4 Forest plot for the studies on the prevalence of psychological distress among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled psychological distress using a random effects DerSimonian-Laird method was 46.1% (95% CI: 36.0–56.6). Figure A5 Forest plot for the studies on the prevalence of burnout among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIz). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled burnout using a random effects DerSimonian-Laird method was 37.4% (95% CI: 14.8–67.2). Figure A6 Funnel plot for studies on the prevalence of anxiety (Egger test: P = 0.15; Begg test: P = 0.90). The vertical solid line represents the summary effect estimates. Figure A7 Funnel plot for studies on the prevalence of depression (Egger test: P = 0.90; Begg test: P = 0.64). The vertical solid line represents the summary effect estimates. Figure A8 Funnel plot for studies on the prevalence of stress (Egger test: P = 0.69; Begg test: P = 0.86). The vertical solid line represents the summary effect estimates. Figure A9 Funnel plot for studies on the prevalence of insomnia (Egger test: P = 0.01; Begg test: P = 0.03). The vertical solid line represents the summary effect estimates. Figure A10 Funnel plot for studies on the prevalence of impaired sleep quality (Egger test: P = 0.22; Begg test: P = 0.22). The vertical solid line represents the summary effect estimates. Figure A11 Funnel plot for studies on the prevalence of PTSD (Egger test: P = 0.22; Begg test: P = 0.90). The vertical solid line represents the summary effect estimates. Figure A12 Funnel plot for studies on the prevalence of psychological distress (Egger test: P = 0.45; Begg test: P = 0.73). The vertical solid line represents the summary effect estimates. Figure A13 Funnel plot for studies on the prevalence of burnout (Egger test: P = 0.47; Begg test: P = 0.60). The vertical solid line represents the summary effect estimates. Figure 1 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flow diagram detailing the disposition of screened, included, and excluded records. Figure 2 Forest plot for the studies on the prevalence of anxiety among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CIs of the overall population. The overall pooled anxiety using a random effects DerSimonian-Laird method was 34.4% (95% CI: 29.5–39.7). Figure 3 Forest plot for the studies on the prevalence of depression among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CI of the overall population. The overall pooled depression using a random effects DerSimonian-Laird method was 31.8% (95% CI: 26.8–37.2). Figure 4 Forest plot for the studies on the prevalence of stress among healthcare workers. The squares and horizontal lines correspond to the study-specific event (anxiety) rates and 95% confidence intervals (CIs). The diamond represents the pooled prevalence and 95% CIs of the overall population. The overall pooled stress using a random effects DerSimonian-Laird method was 40.3% (95% CI 31.4–50.0). ijerph-17-09096-t001_Table 1Table 1 Subgroup analyses of anxiety across different categories. Overall Number of Studies Proportion (%) 95% CI I2 p Value References Anxiety prevalence 46 34.4% 29.5–39.7 99.1% <0.0001 [6,16,17,18,19,20,21,23,24,25,27,28,29,30,31,32,34,35,41,42,43,44,45,47,48,49,50,52,54,55,57,61,63,64,65,66,67,68,69,70,71,72,73,74,79,80,81] Subgroup Analysis Categories Subgroups Number of Studies Proportion (%) 95% CI I2 p Value References Quality Good 22 31.2% 24.5–38.7 99.2% <0.0001 [16,17,18,19,24,25,28,31,32,34,41,42,44,54,57,61,63,64,66,68,79,80] Medium 24 38.1% 30.7–46.0 99.0% <0.0001 [6,20,21,23,27,29,30,35,43,47,48,49,50,52,55,65,67,69,70,71,72,73,74,81] Continents Asia 34 32.7% 27.1–38.8 99.2% <0.0001 [6,16,17,18,19,20,21,23,24,25,27,28,29,30,31,32,34,41,42,43,44,48,55,57,61,63,65,68,69,70,71,73,74,79] Other 12 39.3% 29.6–49.9 97.8% <0.0001 [35,47,49,50,52,54,64,66,67,72,80,81] Countries China 22 28.5% 22.5–35.4 99.3% <0.0001 [16,17,18,19,20,23,24,25,27,28,30,31,32,41,42,57,63,68,70,71,73,79] Other 24 40.4% 33.2–48.0 98.4% <0.0001 [6,21,29,34,35,43,44,47,48,49,50,52,54,55,61,64,65,66,67,69,72,74,80,81] Assessment GAD 19 36.8% 29.1–45.2 99.1% <0.0001 [6,16,17,18,25,31,35,41,49,50,61,63,64,65,66,67,70,72,74] SAS 9 24.6% 16.1–35.6 99.7% <0.0001 [23,24,27,28,32,42,57,68,69] Other 18 37.1% 29.1–45.9 99.0% <0.0001 [19,20,21,29,30,34,43,44,45,47,48,52,54,55,71,73,79,80,81] Gender Female 7 46.9% 38.6–55.3 84.6% <0.0001 [16,47,48,49,73,74,80] Male 7 44.2% 36.3–52.5 93.2% <0.0001 [16,47,48,49,73,74,80] Healthcare Professions Nurses 8 39.3% 27.5–52.6 98.9% <0.0001 [16,23,41,42,55,63,66,73] Doctors 8 32.5% 21.9–45.2 98.9% <0.0001 [16,23,41,42,55,63,66,73] Healthcare Workers Frontline 5 39.8% 24.1–58.0 98.6% <0.0001 [16,23,43,44,79] Second-line 5 27.1% 15.1–43.7 99.0% <0.0001 [16,23,43,44,79] Level of Anxiety Mild 18 60.3% 53.8–66.4 94.8% <0.0001 [6,16,23,24,27,28,30,34,35,43,48,49,54,55,57,61,65,68] Moderate 18 26.0% 21.4–31.3 95.4% <0.0001 [6,16,23,24,27,28,30,34,35,43,48,49,54,55,57,61,65,68] Severe 18 14.3 11.2–18.1 97.1% <0.0001 [6,16,23,24,27,28,30,34,35,43,48,49,54,55,57,61,65,68] CI = Confidence Interval; GAD = Generalized Anxiety Disorder; SAS = Self-rating Anxiety Scale; Good quality score = 7–9; Medium Quality score = 4–6; I2 statistic indicates the heterogeneity. ijerph-17-09096-t002_Table 2Table 2 Subgroup analyses of depression across different categories. Overall Number of Studies Proportion (%) 95% CI I2 p Value References Depression prevalence 46 31.8% 26.8–37.2 99.2% <0.001 [16,17,18,19,20,21,23,25,27,29,30,31,32,33,34,36,39,41,42,44,45,46,47,48,49,50,52,54,55,57,59,61,63,64,65,66,67,68,70,71,72,73,74,78,79,80] Subgroup Analysis Categories Subgroups Number of Studies Proportion (%) 95% CI I2 p Value References Quality Good 24 35.1% 27.6–43.5 99.5% <0.001 [16,17,18,19,25,27,31,32,34,39,41,42,44,46,54,57,59,61,63,64,66,68,79,80] Medium 22 28.6% 21.6–36.7 97.9% <0.001 [20,21,23,29,30,33,36,45,47,48,49,50,52,55,65,67,70,71,72,73,74,78] Continents Asia 34 30.8% 25.1–37.1 99.4% <0.001 [16,17,18,19,20,21,23,25,27,29,30,31,32,33,34,39,41,42,44,46,48,55,57,59,61,63,65,68,70,71,73,74,78,79] Other 12 35.0% 24.9–46.7 98.1% <0.001 [36,45,47,49,50,52,54,64,66,67,72,80] Countries China 23 33.2% 26.0–41.3 99.4% <0.001 [16,17,18,19,20,23,25,27,30,31,32,33,39,41,42,46,57,63,70,71,73,79] Other 23 30.4% 23.6–38.3 98.7% <0.001 [21,29,34,36,44,45,47,48,49,50,52,54,55,59,61,64,65,66,67,72,74,78,80] Assessment PHQ 25 29.7% 23.1–37.2 99.4% <0.001 [16,17,18,29,31,33,36,39,41,45,46,49,50,59,61,63,64,65,66,68,70,72,74,78,79] Other 21 34.7% 26.8–43.5 98.9% <0.001 [19,20,21,23,25,27,30,32,34,42,44,47,48,52,54,55,57,67,71,73,80] Gender Female 7 43.4% 33.6–53.9 95.8% <0.001 [16,47,48,59,73,74,80] Male 7 40.9% 31.4–51.5 95.5% <0.001 [16,47,48,59,73,74,80] Healthcare Professions Nurses 9 42.4% 30.4–55.4 99.0% <0.001 [16,23,33,41,42,55,63,66,73] Doctors 9 39.1% 27.3–52.2 98.4% <0.001 [16,23,33,41,42,55,63,66,73] Healthcare Workers Frontline 6 23.6% 14.1–36.7 99.1% <0.001 [16,17,21,23,44,79] Second-line 6 19.6% 11.5–31.5 98.8% <0.001 [16,17,21,23,44,79] Level of Depression Mild 17 57.6% 50.0–64.8 97.8% <0.001 [16,23,27,30,34,36,39,46,48,54,57,59,61,68,70,71,78] Moderate 17 27.9% 22.1–34.6 97.9% <0.001 [16,23,27,30,34,36,39,46,48,54,57,59,61,68,70,71,78] Severe 17 10.4% 7.0–14.0 97.8% <0.001 [16,23,27,30,34,36,39,46,48,54,57,59,61,68,70,71,78] CI = Confidence Interval; PHQ = Patients Health Questionnaire; Good quality score = 7–9; Medium Quality score = 4–6; I2 statistic indicates the heterogeneity. ijerph-17-09096-t003_Table 3Table 3 Subgroup analyses of stress across different categories. Overall Number of Studies Proportion (%) 95% CI I2 p Value References Stress prevalence 17 40.3% 31.4–50.0 99.1% <0.001 [18,20,21,31,32,34,41,44,48,54,58,60,62,64,66,73,76] Subgroup Analysis Categories Subgroups Number of Studies Proportion (%) 95% CI I2 p Value References Quality Good 9 37.3% 25.6–50.7 99.4% <0.001 [18,31,34,41,44,54,60,64,66] Medium 8 45.7% 31.2–61.1 98.4% <0.001 [20,21,32,48,58,62,73,76] Continents Asia 14 41.3% 30.9–52.6 99.2% <0.001 [18,20,21,31,32,34,41,44,48,58,60,62,73,76] Other 3 38.8% 20.6–60.8 99.1% <0.001 [54,64,66] Countries China 7 44.2% 30.9–58.4 99.5% <0.001 [18,20,31,32,41,73,76] Other 10 37.1% 25.4–50.5 98.6% <0.001 [21,34,44,48,54,58,60,62,64,66] Survey Instrument PSS 8 61.4% 45.1–75.6 98.6% <0.001 [20,32,58,60,62,64,73,76] DASS 5 17.5% 9.4–30.3 98.5% <0.001 [21,34,44,48,54] Other 4 47.8% 29.3–66.8 99.7% <0.001 [18,31,41,66] Level of Stress Mild 6 25.8% 16.8–37.6 91.2% <0.001 [34,48,54,58,60,62] Moderate 6 52.3% 38.7–65.5 95.8% <0.001 [34,48,54,58,60,62] Severe 6 18.9% 11.9–28.9 93.4% <0.001 [34,48,54,58,60,62] CI = Confidence Interval; PSS = Perceived Stress Scale; DASS = Depression Anxiety Stress Scale; Good quality score = 7–9; Medium Quality score = 4–6; I2 statistic indicates the heterogeneity. ==== Refs References 1. Shereen M.A. Khan S. Kazmi A. Bashir N. Siddique R. COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses J. Adv. Res. 2020 24 91 98 10.1016/j.jare.2020.03.005 32257431 2. Huang C. Wang Y. Li X. Ren L. Zhao J. Hu Y. Zhang L. Fan G. Xu J. Gu X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China Lancet 2020 395 497 506 10.1016/S0140-6736(20)30183-5 31986264 3. Liu X. Kakade M. Fuller C.J. Fan B. Fang Y. Kong J. Guan Z. Wu P. Depression after exposure to stressful events: Lessons learned from the severe acute respiratory syndrome epidemic Compr. Psychiatry 2012 53 15 23 10.1016/j.comppsych.2011.02.003 21489421 4. Bai Y.-M. Lin C.-C. Lin C.-Y. Chao-Cheng L. Chue C.-M. Chou P. Survey of Stress Reactions Among Health Care Workers Involved With the SARS Outbreak Psychiatr. Serv. 2004 55 1055 1057 10.1176/appi.ps.55.9.1055 15345768 5. Nickell L.A. Crighton E.J. Tracy C.S. Al-Enazy H. Bolaji Y. Hanjrah S. Hussain A. Makhlouf S. Upshur R.E.G. Psychosocial effects of SARS on hospital staff: Survey of a large tertiary care institution Can. Med. Assoc. J. 2004 170 793 798 10.1503/cmaj.1031077 14993174 6. Temsah M.-H. Alsohime F. AlAmro N. Al-Eyadhy A. Al-Hasan K. Jamal A. Al-Maglouth I. Aljamaan F. Al Amri M. Barry M.A. The psychological impact of COVID-19 pandemic on health care workers in a MERS-CoV endemic country J. Infect. Public Health 2020 13 877 882 10.1016/j.jiph.2020.05.021 32505461 7. Maunder R. Hunter J. Vincent L. Bennett J. Peladeau N. Leszcz M. Sadavoy J. Verhaeghe L.M. Steinberg R. Mazzulli T. The immediate psychological and occupational impact of the 2003 SARS outbreak in a teaching hospital Can. Med. Assoc. J. 2003 168 1245 1251 12743065 8. Maunder R.G. Lancee W.J. Rourke S. Hunter J.J. Goldbloom D. Balderson K. Petryshen P. Steinberg R. Wasylenki D. Koh D. Factors Associated With the Psychological Impact of Severe Acute Respiratory Syndrome on Nurses and Other Hospital Workers in Toronto Psychosom. Med. 2004 66 938 942 10.1097/01.psy.0000145673.84698.18 15564361 9. Talevi D. Socci V. Carai M. Carnaghi G. Faleri S. Trebbi E. Di Bernardo A. Capelli F. Pacitti F. Mental health outcomes of the CoViD-19 pandemic Riv Psichiatr. 2020 55 137 144 32489190 10. Stawicki S.P. Jeanmonod R. Miller A.C. Paladino L. Gaieski D.F. Yaffee A.Q. De Wulf A. Grover J. Papadimos T.J. Bloem C. The 2019–2020 novel coronavirus (severe acute respiratory syndrome coronavirus 2) pandemic: A joint American college of academic international medicine-world academic council of emergency medicine multidisciplinary COVID-19 working group consensus paper J. Glob. Infect. Dis. 2020 12 47 93 10.4103/jgid.jgid_86_20 32773996 11. Jimi H. Hashimoto G. Challenges of COVID-19 outbreak on the cruise ship diamond princess docked at Yokohama, Japan: A real-world story Glob. Health Med. 2020 2 63 65 10.35772/ghm.2020.01038 12. Asaoka H. Koido Y. Kawashima Y. Ikeda M. Miyamoto Y. Nishi D. Post-traumatic stress symptoms among medical rescue workers exposed to COVID-19 in Japan Psychiatry Clin. Neurosci. 2020 74 503 505 10.1111/pcn.13092 32691955 13. Chong M.-Y. Wang W.-C. Hsieh W.-C. Lee C.-Y. Chiu N.-M. Yeh W.-C. Huang O.-L. Wen J.-K. Chen C.-L. Psychological impact of severe acute respiratory syndrome on health workers in a tertiary hospital Br. J. Psychiatry 2004 185 127 133 10.1192/bjp.185.2.127 15286063 14. Pappa S. Ntella V. Giannakas T. Giannakoulis V.G. Papoutsi E. Katsaounou P. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: A systematic review and meta-analysis Brain Behav. Immun. 2020 88 901 907 10.1016/j.bbi.2020.05.026 32437915 15. Luo M. Guo L. Yu M. Jiang W. Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public—A systematic review and meta-analysis Psychiatry Res. 2020 291 113190 10.1016/j.psychres.2020.113190 32563745 16. Lai J. Ma S. Wang Y. Cai Z. Hu J. Wei N. Wu J. Du H. Chen T. Li R. Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019 JAMA Netw. Open 2020 3 e203976 10.1001/jamanetworkopen.2020.3976 32202646 17. Zhang W.-R. Wang K. Yin L. Zhao W.-F. Xue Q. Peng M. Min B.-Q. Tian Q. Leng H.-X. Du J.-L. Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China Psychother. Psychosom. 2020 89 242 250 10.1159/000507639 32272480 18. Zhang C. Yang L. Liu S. Ma S. Wang Y. Cai Z. Du H. Li R. Kang L. Su M. Survey of Insomnia and Related Social Psychological Factors Among Medical Staff Involved in the 2019 Novel Coronavirus Disease Outbreak Front. Psychiatry 2020 11 306 10.3389/fpsyt.2020.00306 32346373 19. Lu W. Wang H. Lin Y. Li L. Psychological status of medical workforce during the COVID-19 pandemic: A cross-sectional study Psychiatry Res. 2020 288 112936 10.1016/j.psychres.2020.112936 32276196 20. Du J. Dong L. Wang T. Yuan C. Fu R. Zhang L. Liu B. Zhang M. Yin Y. Qin J. Psychological symptoms among frontline healthcare workers during COVID-19 outbreak in Wuhan Gen. Hosp. Psychiatry 2020 10.1016/j.genhosppsych.2020.03.011 21. Tan B.Y. Chew N.W. Lee G.K. Jing M. Goh Y. Yeo L.L. Zhang K. Chin H.-K. Ahmad A. Khan F.A. Psychological Impact of the COVID-19 Pandemic on Health Care Workers in Singapore Ann. Intern. Med. 2020 173 317 320 10.7326/M20-1083 32251513 22. Du J. Dong L. Wang T. Yuan C. Fu R. Zhang L. Liu B. Zhang M. Yin Y. Qin J. COVID-19 in Wuhan: Immediate psychological impact on 5062 health workers MedRxiv 2020 2 20025338 23. Guo J. Liao L. Wang B. Li X. Guo L. Tong Z. Guan Q. Zhou M. Wu Y. Zhang J. Psychological Effects of COVID-19 on Hospital Staff: A National Cross-Sectional Survey of China Mainland SSRN Electron. J. 2020 10.2139/ssrn.3550050 24. Huang J.Z. Han M.F. Luo T.D. Ren A.K. Zhou X.P. Mental health survey of 230 medical staff in a tertiary infectious disease hospital for COVID-19 Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 2020 38 E001 25. Huang Y. Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: A web-based cross-sectional survey Psychiatry Res. 2020 288 112954 10.1016/j.psychres.2020.112954 32325383 26. Qi J. Xu J. Li B.-Z. Huang J.-S. Yang Y. Zhang Z.-T. Yao D.-A. Liu Q.-H. Jia M. Gong D.-K. The evaluation of sleep disturbances for Chinese frontline medical workers under the outbreak of COVID-19 Sleep Med. 2020 72 10.1016/j.sleep.2020.05.023 27. Liu C.-Y. Yang Y.-Z. Deng R. Xu X. Dou Q.-L. Zhang W.-W. Cheng A.S.K. The prevalence and influencing factors in anxiety in medical workers fighting COVID-19 in China: A cross-sectional survey Epidemiol. Infect. 2020 148 1 17 10.1017/S0007485320000413 28. Liu Z. Han B. Jiang R. Huang Y. Ma C. Wen J. Zhang T. Wang Y. Chen H. Ma Y. Mental health status of phisicians and nurses during COVID-19 epidemic in China Lancet 2020 10.2139/ssrn.3551329 29. Zhang S.X. Liu J. Jahanshahi A.A. Nawaser K. Yousefi A. Li J. Sun S. At the height of the storm: Healthcare staff’s health conditions and job satisfaction and their associated predictors during the epidemic peak of COVID-19 Brain Behav. Immun. 2020 87 144 146 10.1016/j.bbi.2020.05.010 32387345 30. Chen Y. Zhou H. Zhou Y. Zhou F. Prevalence of self-reported depression and anxiety among pediatric medical staff members during the COVID-19 outbreak in Guiyang, China Psychiatry Res. 2020 288 113005 10.1016/j.psychres.2020.113005 32315886 31. Li G. Miao J. Wang H. Xu S. Sun W. Fan Y. Zhang C. Zhu S. Zhu Z. Wang W. Psychological impact on women health workers involved in COVID-19 outbreak in Wuhan: A cross-sectional study J. Neurol. Neurosurg. Psychiatry 2020 91 895 897 10.1136/jnnp-2020-323134 32366684 32. Wang S. Xie L. Xu Y. Yu S. Yao B. Xiang D. Sleep disturbances among medical workers during the outbreak of COVID-2019 Occup. Med. 2020 70 364 369 10.1093/occmed/kqaa074 32372077 33. Cao J. Wei J. Zhu H. Duan Y. Geng W. Hong X. Jiang J. Zhao X. Zhu B. A Study of Basic Needs and Psychological Wellbeing of Medical Workers in the Fever Clinic of a Tertiary General Hospital in Beijing during the COVID-19 Outbreak Psychother. Psychosom. 2020 89 252 254 10.1159/000507453 32224612 34. Chew N.W. Lee G.K. Tan B.Y. Jing M. Goh Y. Ngiam N.J. Yeo L.L. Ahmad A. Khan F.A. Shanmugam G.N. A multinational, multicentre study on the psychological outcomes and associated physical symptoms amongst healthcare workers during COVID-19 outbreak Brain Behav. Immun. 2020 88 559 565 10.1016/j.bbi.2020.04.049 32330593 35. Consolo U. Bellini P. Bencivenni D. Iani C. Checchi V. Epidemiological Aspects and Psychological Reactions to COVID-19 of Dental Practitioners in the Northern Italy Districts of Modena and Reggio Emilia Int. J. Environ. Res. Public Health 2020 17 3459 10.3390/ijerph17103459 32429193 36. Guiroy A. Gagliardi M. Coombes N. Landriel F. Zanardi C. Willhuber G.C. Guyot J.P. Valacco M. COVID-19 Impact Among Spine Surgeons in Latin America Glob. Spine J. 2020 10.1177/2192568220928032 37. Amin S. The psychology of coronavirus fear: Are healthcare professionals suffering from corona-phobia? Int. J. Health Manag. 2020 13 249 256 10.1080/20479700.2020.1765119 38. Shacham M. Hamama-Raz Y. Kolerman R. Mijiritsky O. Ben-Ezra M. Mijiritsky E. COVID-19 Factors and Psychological Factors Associated with Elevated Psychological Distress among Dentists and Dental Hygienists in Israel Int. J. Environ. Res. Public Health 2020 17 2900 10.3390/ijerph17082900 39. Kang L. Ma S. Chen M. Yang J. Wang Y. Li R. Yao L. Bai H. Cai Z. Yang B.X. Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study Brain Behav. Immun. 2020 87 11 17 10.1016/j.bbi.2020.03.028 32240764 40. Cai W. Lian B. Song X. Hou T. Deng G. Li H. A cross-sectional study on mental health among health care workers during the outbreak of Corona Virus Disease 2019 Asian J. Psychiatry 2020 51 102111 10.1016/j.ajp.2020.102111 41. Zhu Z. Xu S. Wang H. Liu Z. Wu J. Li G. Miao J. Zhang C. Yang Y. Sun W. COVID-19 in Wuhan: Sociodemographic characteristics and hospital support measures associated with the immediate psychological impact on healthcare workers EClinicalMedicine 2020 24 100443 10.1016/j.eclinm.2020.100443 32766545 42. Zhu J. Sun L. Zhang L. Wang H. Fan A. Yang B. Li W. Xiao S. Prevalence and influencing factors of anxiety and depression symptoms in the first-line medical staff fighting against COVID-19 in Gansu Front. Psychiatry. 2020 11 1 6 10.3389/fpsyt.2020.00386 32116830 43. Kaveh M. Davari-Tanha F. Varaei S. Shirali E. Shokouhi N. Nazemi P. Ghajarzadeh M. Feizabad E. Ashraf M.A. Anxiety levels among Iranian health care workers during the COVID-19 surge: A cross-sectional study medRxiv 2020 10.1101/2020.05.02.20089045 44. Alshekaili M. Hassan W. Al-Said N. Alsulimani F. Jayapal S.K. Al-Mawali A. Chan M.F. Mahadevan S. Al-Adawi S. Factors Associated with Mental Health Outcomes in Oman During COVID-19: Frontline vs Non-Frontline Healthcare Workers SSRN Electron. J. 2020 10.2139/ssrn.3624265 45. Amerio A. Bianchi D. Santi F. Costantini L. Odone A. Signorelli C. Costanza A. Serafini G. Amore M. Aguglia A. Covid-19 pandemic impact on mental health: A web-based cross-sectional survey on a sample of Italian general practitioners Acta bio-Med. Atenei Parm. 2020 91 83 88 46. An Y. Yang Y. Wang A. Li Y. Zhang Q. Cheung T. Ungvari G.S. Qin M.-Z. An F.-R. Xiang Y.-T. Prevalence of depression and its impact on quality of life among frontline nurses in emergency departments during the COVID-19 outbreak J. Affect. Disord. 2020 276 312 315 10.1016/j.jad.2020.06.047 32871661 47. Çalişkan F. Dost B. The Evaluation of Knowledge, Attitudes, Depression and Anxiety Levels among Emergency Physicians during the COVID-19 Pandemic Signa Vitae A J. Intensiv. Care Emerg. Med. 2020 16 163 10.22514/sv.2020.16.0022 48. Bhattacharyya R. Chatterjee S.S. Bhattacharyya S. Gupta S. Das S. Banerjee B.B. Attitude, practice, behavior, and mental health impact of COVID-19 on doctors Indian J. Psychiatry 2020 62 257 265 10.4103/psychiatry.IndianJPsychiatry_333_20 32773868 49. Ba A.M.C. Ba Y.B. Chang C. Prasad A. Chorath K. Poonia S.K. Jenks C.M. Bur A.M. Thakkar P. Graboyes E.M. Mental health among otolaryngology resident and attending physicians during the COVID -19 pandemic: National study Head Neck 2020 42 1597 1609 10.1002/hed.26292 32496637 50. Corbett G.A. Milne S.J. Mohan S. Reagu S. Farrell T. Lindow S.W. Hehir M.P. O’Connell M.P. Anxiety and depression scores in maternity healthcare workers during the Covid-19 pandemic Int. J. Gynecol. Obstet. 2020 151 297 298 10.1002/ijgo.13279 51. Hakami Z. Khanagar S.B. Vishwanathaiah S. Hakami A. Bokhari A.M. Jabali A.H. Alasmari D. AlDrees A.M. Psychological impact of the coronavirus disease 2019 (COVID-19) pandemic on dental students: A nationwide study J. Dent. Educ. 2020 10.1002/jdd.12470 33128397 52. Dal’Bosco E.B. Floriano L.S.M. Skupien S.V. Arcaro G. Martins A.R. Anselmo A.C.C. Mental health of nursing in coping with COVID-19 at a regional university hospital Rev. Bras. Enferm. 2020 73 e20200434 10.1590/0034-7167-2020-0434 32667576 53. Dong Z.-Q. Ma J. Hao Y.-N. Shen X.-L. Liu F. Gao Y. Zhang L. The social psychological impact of the COVID-19 pandemic on medical staff in China: A cross-sectional study Eur. Psychiatry 2020 63 1 22 10.1192/j.eurpsy.2020.59 32372731 54. Elbay R.Y. Kurtulmuş A. Arpacıoğlu S. Karadere E. Depression, anxiety, stress levels of physicians and associated factors in Covid-19 pandemics Psychiatry Res. 2020 290 113130 10.1016/j.psychres.2020.113130 32497969 55. Taghizadeh F. Hassannia L. Moosazedeh M. Zarghami M. Taghizadeh H. Dooki A.F. Navae R.A. Hedayatizadeh-Omran A. Anxiety and Depression in Health Workers and General Population During COVID-19 Epidemic in IRAN: A Web-Based Cross-Sectional Study medRxiv 2020 10.1101/2020.05.05.20089292 56. Hawari F.I. Obeidat N.A. Dodin Y.I. Albtoosh A.S. Manasrah R.M. Alaqeel I.O. Mansour A.H. The inevitability of Covid-19 related distress among healthcare workers: Findings from a low caseload country under lockdown MedRxiv 2020 10.1101/2020.06.14.20130724 57. Hu D. Kong Y. Li W. Han Q. Zhang X. Zhu L.X. Wan S.W. Liu Z. Shen Q. Yang J. Frontline nurses’ burnout, anxiety, depression, and fear statuses and their associated factors during the COVID-19 outbreak in Wuhan, China: A large-scale cross-sectional study EClinicalMedicine 2020 24 100424 10.1016/j.eclinm.2020.100424 32766539 58. Jahrami H. Bahammam A.S. AlGahtani H. Ebrahim A. Faris M. Aleid K. Saif Z. Haji E. Dhahi A. Marzooq H. The examination of sleep quality for frontline healthcare workers during the outbreak of COVID-19 Sleep Breath. 2020 1 9 10.1007/s11325-020-02135-9 31240543 59. Khanna R.C. Honavar S.G. Metla A.L. Bhattacharya A. Maulik P.K. Psychological impact of COVID-19 on ophthalmologists-in-training and practising ophthalmologists in India Indian J. Ophthalmol. 2020 68 994 998 10.4103/ijo.IJO_1458_20 32461412 60. Nair A.K.R. Chellaswamy K.S. Kattula D. Thavarajah R. Mohandoss A.A. Perceived stress and psychological distress among indian endodontists during COVID-19 pandemic lockdown MedRxiv 2020 10.1101/2020.05.06.20092601 61. Naser A.Y. Dahmash E.Z. Al-Rousan R. Alwafi H. Alrawashdeh H.M. Ghoul I. Abidine A. Bokhary M.A. Al-Hadithi H.T. Ali D. Mental health status of the general population, healthcare professionals, and university students during 2019 coronavirus disease outbreak in Jordan: A cross-sectional study Brain Behav. 2020 10 e01730 10.1002/brb3.1730 32578943 62. Podder I. Agarwal K. Datta S. Comparative analysis of perceived stress in dermatologists and other physicians during national lock-down and COVID -19 pandemic with exploration of possible risk factors: A web-based cross-sectional study from Eastern India Dermatol. Ther. 2020 33 10.1111/dth.13788 32506785 63. Que J. Shi L. Deng J. Liu J. Zhang L. Wu S. Gong Y. Huang W. Yuan K. Yan W. Psychological impact of the COVID-19 pandemic on healthcare workers: A cross-sectional study in China Gen. Psychiatry 2020 33 e100259 10.1136/gpsych-2020-100259 32596640 64. Rossi R. Socci V. Pacitti F. Di Lorenzo G. Di Marco A. Siracusano A. Rossi A. Mental Health Outcomes Among Frontline and Second-Line Health Care Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in Italy JAMA Netw. Open 2020 3 e2010185 10.1001/jamanetworkopen.2020.10185 32463467 65. Salman M. Raza M.H. Mustafa Z.U. Khan T.M. Asif N. Tahir H. Shehzadi N. Hussain K. The psychological effects of COVID-19 on frontline healthcare workers and how they are coping: A web-based, cross-sectional study from Pakistan medRxiv 2020 10.1101/2020.06.03.20119867 66. Shechter A. Diaz F. Moise N. Anstey D.E. Ye S. Agarwal S. Birk J.L. Brodie D. Cannone D.E. Chang B. Psychological distress, coping behaviors, and preferences for support among New York healthcare workers during the COVID-19 pandemic Gen. Hosp. Psychiatry 2020 66 1 8 10.1016/j.genhosppsych.2020.06.007 32590254 67. Stojanov J. Malobabic M. Stanojevic G. Stevic M. Milosevic V. Stojanov A. Quality of sleep and health-related quality of life among health care professionals treating patients with coronavirus disease-19 Int. J. Soc. Psychiatry 2020 1 7 10.1177/0020764020942800 68. Teng Z. Wei Z. Qiu Y. Tan Y. Chen J. Tang H. Wu H. Wu R. Huang J. Psychological status and fatigue of frontline staff two months after the COVID-19 pandemic outbreak in China: A cross-sectional study J. Affect. Disord. 2020 275 247 252 10.1016/j.jad.2020.06.032 32734915 69. Thapa L. Ghimire A. Ghimire S. Sharma N. Shrestha S. Devkota M. Bhattarai S. Maharjan A. Lohani S. Phuyal S. Predictors of Anxiety Regarding The COVID-19 Pandemic Among Health-care Workers in a Hospital Not Assigned to Manage COVID-19 Patients in Nepal medRxiv 2020 10.1101/2020.07.08.20148866 70. Tu Z.-H. He J.-W. Zhou N. Sleep quality and mood symptoms in conscripted frontline nurse in Wuhan, China during COVID-19 outbreak Medicine 2020 99 e20769 10.1097/MD.0000000000020769 32590755 71. Wang H. Huang D. Huang H. Zhang J. Guo L. Liu Y. Ma H. Geng Q. The psychological impact of COVID-19 pandemic on medical staff in Guangdong, China: A cross-sectional study Psychol. Med. 2020 1 9 10.1017/S0033291720002561 32624037 72. Weilenmann S. Ernst J. Petry H. Sazpinar O. Pfaltz M.C. Gehrke S. Paolercio F. von Kaenel R. Spiller T.R. Health care workers mental health during the first weeks of the SARS-CoV-2 pandemic in Switzerland: A cross-sectional study medRxiv 2020 10.1101/2020.05.04.20088625 73. Xiao X. Zhu X. Fu S. Hu Y. Li X. Xiao J. Psychological impact of healthcare workers in China during COVID-19 pneumonia epidemic: A multi-center cross-sectional survey investigation J. Affect. Disord. 2020 274 405 410 10.1016/j.jad.2020.05.081 32663970 74. Yang S. Kwak S.G. Ko E.J. Chang M.C. The Mental Health Burden of the COVID-19 Pandemic on Physical Therapists Int. J. Environ. Res. Public Heal. 2020 17 3723 10.3390/ijerph17103723 32466164 75. Yin Q. Sun Z. Liu T. Ni X. Deng X. Jia Y. Shang Z. Zhou Y. Liu W. Posttraumatic stress symptoms of health care workers during the corona virus disease 2019 Clin. Psychol. Psychother. 2020 27 384 395 10.1002/cpp.2477 32415733 76. Zhan Y. Liu Y. Liu H. Li M. Shen Y. Gui L. Zhang J. Luo Z. Tao X. Yu J. Factors associated with insomnia among Chinese front-line nurses fighting against COVID-19 in Wuhan: A cross-sectional survey J. Nurs. Manag. 2020 28 1525 1535 10.1111/jonm.13094 32657449 77. Sun L. Sun Z. Wu L. Zhu Z. Zhang F. Shang Z. Jia Y. Gu J. Zhou Y. Wang Y. Prevalence and Risk Factors of Acute Posttraumatic Stress Symptoms during the COVID-19 Outbreak in Wuhan, China medRxiv 2020 10.1101/2020.03.06.20032425 78. Chung J.P. Yeung W.-S. Staff Mental Health Self-Assessment During the COVID-19 Outbreak East Asian Arch. Psychiatry 2020 30 34 10.12809/eaap2014 32229646 79. Cai Q. Feng H. Huang J. Wang M. Wang Q. Lu X. Xie Y. Wang X. Liu Z. Hou B. The mental health of frontline and non-frontline medical workers during the coronavirus disease 2019 (COVID-19) outbreak in China: A case-control study J. Affect. Disord. 2020 275 210 215 10.1016/j.jad.2020.06.031 32734910 80. Koksal E. Dost B. Terzi Ö. Ustun Y.B. Özdin S. Bilgin S. Evaluation of Depression and Anxiety Levels and Related Factors Among Operating Theater Workers During the Novel Coronavirus (COVID-19) Pandemic J. PeriAnesthesia Nurs. 2020 10.1016/j.jopan.2020.06.017 81. Aksoy Y.E. Koçak V. Psychological effects of nurses and midwives due to COVID-19 outbreak: The case of Turkey Arch. Psychiatr. Nurs. 2020 34 427 433 10.1016/j.apnu.2020.07.011 33032769 82. Liberati A. Altman D.G. Tetzlaff J. Mulrow C. Gøtzsche P.C. Ioannidis J.P.A. Clarke M. Devereaux P.J. Kleijnen J. Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration BMJ 2009 339 b2700 10.1136/bmj.b2700 19622552 83. DerSimonian R. Laird N. Meta-analysis in clinical trials Control. Clin. Trials 1986 7 177 188 10.1016/0197-2456(86)90046-2 3802833 84. Higgins J.P. Green S. Cochrane Handbook for Systematic Reviews of Interventions John Wiley & Sons Hoboken, NJ, USA 2011 85. Egger M. Smith G.D. Schneider M. Minder C. Bias in meta-analysis detected by a simple, graphical test BMJ 1997 315 629 634 10.1136/bmj.315.7109.629 9310563 86. Adams J.G. Walls R.M. Supporting the Health Care Workforce during the COVID-19 Global Epidemic JAMA 2020 323 1439 10.1001/jama.2020.3972 32163102 87. Bai Y. Yao L. Wei T. Tian F. Jin D.-Y. Chen L. Wang M. Presumed Asymptomatic Carrier Transmission of COVID-19 JAMA 2020 323 1406 1407 10.1001/jama.2020.2565 32083643 88. Song X. Fu W. Liu X. Luo Z. Wang R. Zhou N. Yan S. Lv C. Mental health status of medical staff in emergency departments during the Coronavirus disease 2019 epidemic in China Brain Behav. Immun. 2020 88 60 65 10.1016/j.bbi.2020.06.002 32512134 89. Johnson E.O. Roth T. Breslau N. The association of insomnia with anxiety disorders and depression: Exploration of the direction of risk J. Psychiatr. Res. 2006 40 700 708 10.1016/j.jpsychires.2006.07.008 16978649 90. Qiu J. Shen B. Zhao M. Wang Z. Xie B. Xu Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations Gen. Psychiatry 2020 33 e100213 10.1136/gpsych-2020-100213 91. Sriharan A. Ratnapalan S. Tricco A.C. Lupea D. Ayala A.P. Pang H. Lee D. Stress, burnout and depression in women in health care during COVID-19 Pandemic: Rapid Scoping Review medRxiv 2020 10.1101/2020.07.13.20151183 92. Maeng L.Y. Milad M.R. Sex differences in anxiety disorders: Interactions between fear, stress, and gonadal hormones Horm. Behav. 2015 76 106 117 10.1016/j.yhbeh.2015.04.002 25888456 93. Chan S. Nurses Fighting Against Severe Acute Respiratory Syndrome (SARS) in Hong Kong J. Nurs. Sch. 2003 35 209 10.1111/j.1547-5069.2003.00209.x 14562486 94. Wong T.W. Yau J.K. Chan C.L. Kwong R.S. Ho S.M. Lau C. Lau F.L. Lit C.H. The psychological impact of severe acute respiratory syndrome outbreak on healthcare workers in emergency departments and how they cope Eur. J. Emerg. Med. 2005 12 13 18 10.1097/00063110-200502000-00005 15674079 95. Liu Q. Luo D. Haase J.E. Guo Q. Wang X.Q. Liu S. Xia L. Liu Z. Yang J. Yang B.X. The experiences of health-care providers during the COVID-19 crisis in China: A qualitative study Lancet Glob. Health 2020 8 e790 e798 10.1016/S2214-109X(20)30204-7 32573443 96. Cui P.P. Wang P.P. Wang K. Ping Z. Wang P. Chen C. Post-traumatic growth and influencing factors among frontline nurses fighting against COVID-19 Occup. Environ. Med. 2020 10.1136/oemed-2020-106540 97. Kanekar A. Sharma M. COVID-19 and Mental Well-Being: Guidance on the Application of Behavioral and Positive Well-Being Strategies Healthcare 2020 8 336 10.3390/healthcare8030336 32932613 98. Heath C. Sommerfield A. Von Ungern-Sternberg B.S. Resilience strategies to manage psychological distress among healthcare workers during the COVID-19 pandemic: A narrative review Anaesthesia 2020 75 1364 1371 10.1111/anae.15180 32534465 99. DePierro J. Katz C.L. Marin D. Feder A. Bevilacqua L. Sharma V. Hurtado A. Ripp J. Lim S. Charney D. Mount Sinai’s Center for Stress, Resilience and Personal Growth as a model for responding to the impact of COVID-19 on health care workers Psychiatry Res. 2020 293 113426 10.1016/j.psychres.2020.113426 32861094 100. Greenberg N. Cooke J. Sullivan E. Tracy D.K. Mental health plan for workers of the London Nightingale Hospital: Following the evidence to support staff BMJ Mil. Health 2020 10.1136/bmjmilitary-2020-001624