Introduction
On December 31, 2019, the WHO warned of the first cases of pneumonia caused by a new coronavirus in the city of Wuhan [1]. As of September 1, 2021, the disease caused by this virus (the COVID-19) has infected more than 215 million people worldwide and caused 4.5 million deaths, thus being considered a global pandemic [2].
Large outbreaks such as the one caused by COVID-19 place healthcare workers (HCWs) in a position of particular vulnerability [Reference Salazar de Pablo, Vaquerizo-Serrano, Catalan, Arango, Moreno and Ferre3]. HCWs are not only one of the groups most at risk of being infected by COVID-19 [Reference Galanis, Vraka, Fragkou, Bilali and Kaitelidou4], but they are exposed to a huge workload [Reference Almaghrabi, Alfaraidi, Al Hebshi and Albaadani5], the absence of adequate protective equipment [Reference Rosenbaum6] and the extensive media coverage [Reference Hart, Chinn and Soroka7,Reference Lynch, Evans, Ice and Costa8]. Routine clinical practice has been significantly changed, and many professionals have been removed from their usual workplace and redirected to higher-risk frontline works while also having to adhere to continuously changing guidelines [Reference Houghton, Meskell, Delaney, Smalle, Glenton and Booth9].
The literature published during the SARS and MERS pandemics more than a decade ago suggests that HCWs present, due to all the above, an increased risk of suffering adverse mental health effects in pandemic situations, including anxiety, depression, and post-traumatic stress symptoms [Reference Lee, Kang, Cho, Kim and Park10–Reference Su, Lien, Yang, Su, Wang and Tsai14]. In line with these results, recently numerous scientific articles have been published on this subject. Most of these studies and reviews, however, focus on one or few mental health domains or offer results from very specific populations, either in terms of geographical origin (mainly from mainland China) or professional category and medical specialty [Reference Luo, Chua, Xiong, Ho and Ho15–18].
No updated meta-analyses analyze the effects of the COVID-19 pandemic on the different domains of mental health impact in HCWs worldwide, including depression, anxiety, burnout, acute stress, post-traumatic symptoms, and insomnia. Therefore, the aim of this study is to synthesize the available scientific evidence about the state of mental health of HCWs during the COVID-19 pandemic.
Methods
This study protocol was registered on PROSPERO (registration number: CRD42021247610). The study was conducted in accordance with “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) [Reference Moher, Liberati, Tetzlaff and Altman19], (Supplementary Table S1) and “Meta-analyses of Observational Studies in Epidemiology” (MOOSE) checklist [Reference Stroup, Berlin, Morton, Olkin, Williamson and Rennie20] (Supplementary Table S2), following “EQUATOR Reporting Guidelines” [Reference Altman, Simera, Hoey, Moher and Schulz21].
Search strategy and selection criteria
A systematic literature search was carried out by two independent researchers (C.A. and B.P.). Web of Science database (Clarivate Analytics) was searched, incorporating the Web of Science Core Collection, the BIOSIS Citation Index, the KCI-Korean Journal Database, MEDLINE®, the Russian Science Citation Index, and the SciELO Citation Index as well as Cochrane Central Register of Reviews, and Ovid/PsycINFO databases, from inception until March 1, 2021.
The following keywords were used: “CoV-19” OR “SARS-CoV-2” OR “2019 nCoV” OR “2019nCoV” OR “2019 novel coronavirus” OR “new coronavirus” OR “novel coronavirus” OR “SARS CoV-2” OR “Wuhan coronavirus” OR “COVID 19” OR “2019-nCoV” AND “professionals” OR “worker*” OR “doctor*” OR “nurse*” OR “occupation*” OR “employee*” OR “healthcare provider*” OR “healthcare worker*” OR “healthcare employee*” OR “personnel” OR “emergency worker” OR “paramedic*”.
Articles identified were first screened as abstracts, and after the exclusion of those which did not meet the inclusion criteria, the full texts of the remaining articles were assessed for eligibility and inclusion.
Inclusion criteria for the systematic review and meta-analysis were (a) individual studies with original data, (b) focusing on HCWs exposed to COVID-19 (defined as HCWs who have been working during COVID-19 pandemic tending to patients potentially infected with SARS-COV-2, but not necessarily limited toHCWs working in frontline units), (c) reporting meta-analyzable proportions about mental health outcomes included in at least one of the following categories: anxiety, depression, acute stress/distress, post-traumatic symptoms, burnout, and sleep disturbances, (d) using validated, structured, evaluation scales, (e) nonoverlapping samples (overlap was determined by looking at the inclusion dates, type of population and country in which the study was carried out, and the study with the largest sample was then selected), (f) sample size ≥50 participants, and (g) written in English. Exclusion criteria were (a) reviews, clinical cases, study protocols or qualitative studies, conferential proceedings, letters, and commentaries, (b) reporting outcomes on populations other than HCWs, including the general population, medical and nursing students, dentists, and podologists.
Data extraction
Three researchers (J.L.P., M.L., and J.H.) independently extracted data from all the included studies. The three databases were then cross-checked, and discrepancies were resolved through consensus under the supervision of a senior researcher (A.C.). A summary of selected variables included: first author and year of publication, country and city, HCW category involved, sample size, age (mean ± standard deviation [SD]), sex (% female), mental health domain studied, evaluation tool used, quality assessment (see below), and key findings.
Risk of bias (quality) assessment
Risk of bias was assessed using a modified version of the Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomized studies due to the heterogeneity expected in the included studies [Reference Wells, Shea, O’Connell, Peterson, Welch and Losos22] (Supplementary Methods S1).
Strategy for data synthesis
First, we provided a systematic synthesis (Supplementary Table S4) of the findings from the included studies structured around the selected six mental health outcomes: anxiety, depression, acute stress/distress, post-traumatic symptoms, burnout, and sleep disturbances. Second, we performed meta-analyses using, as primary effect size, the proportion (% and standard error [SE], when available) of mental health outcomes in HCWs exposed to COVID-19.
Meta-regressions were performed to determine the effect of the (a) sex, (b) age, and (c) NOS score on the mental health domains. Sensitivity analyses were conducted to estimate the association between the mental health domains and (a) continent of the study, (b) type of mental health worker (doctor, nurse, or multi-professional), and (c) used scale.
Heterogeneity among studies was assessed using the Q statistic, with the proportion of the total variability in effect size estimates evaluated using the I2 index (with an I2 > 50% representing significant heterogeneity) [Reference Lipsey MW and Wilson23]. Publication biases were assessed for the proportion of remission or recovery by inspecting funnel plots and assessing Egger’s test [Reference Egger, Davey Smith, Schneider and Minder24].
All analyzes were conducted using STATA version 17 [25]. The significance level was set at a p < 0.05, two-sided.
Results
The literature search yielded 15,459 citations through electronic database, which were screened for eligibility; 394 articles were assessed in full text, and 155 were excluded (reasons for exclusion are detailed in Supplementary Table S3). The final database for the systematic review and meta-analysis included 239 studies (Figure 1).
Figure 1. PRISMA 2009 flow diagram [Reference Tan, Wang, Yap, Chan, Ho and Hamid26].
One hundred and sixty studies (66.95%) focused on depression, 179 (74.90%) on anxiety, 57 (23.85%) on acute stress/distress, 55 (23.01%) on sleep problems, 39 (16.32%) on post-traumatic symptoms, and 24 (10.04%) on burnout. The full sample includes 271,319 HCWs, including articles with sample sizes ranging from 54 to 21,199 HCWs. The mean age of the sample was 36.08 years, ranging from 21 to 55.13 years (SD = 8.33). 66.99% were female. Studies included HCWs from 50 countries in five continents: 150 (62.76%) from Asia, 55 (23.01%) from Europe, 20 (8.37%) from America, 11 (4.60%) from Africa, and 2 (0.84%) from Oceania; there was also one multicontinental study [Reference Kroenke, Spitzer and Williams27].
Depression
Depression prevalence was reported in 160 studies, including a total sample of 210,762 participants. Multiple evaluation scales were used, including Patient Health Questionnaire-9 (PHQ-9) [Reference Kroenke, Spitzer and Williams28], PHQ-2 [Reference Kroenke, Spitzer, Williams and Lowe29], PHQ-4 [Reference Zung30], Zung Self-Rating Depression Scale (SDS) [Reference Snaith31], Hospital Anxiety and Depression Scale (HADS) [Reference Beck, Ward and Mendelson32], Beck Depression Inventory (BDI) [Reference Radloff33], Center for Epidemiologic Studies Depression Scale (CES-D) [Reference Henry and Crawford34], and Depression, Anxiety, and Stress Scale-21 (DASS-21) [Reference Spitzer, Kroenke, Williams and Lowe35]. The pooled prevalence of depression was 0.33 (95% confidence intervals [CI] 0.28–0.38). Prevalence varied widely depending on the scale used, from 0.53 with PHQ-2/4 to 0.26 with CES-D. Detailed results of depression and the other mental health domains are displayed in Table 1. Sensitivity analyses and meta-regressions revealed no statistically significant differences regarding age, gender, NOS score, or continent.
Table 1. Prevalence of mental health impacts across each of the domains and scales studied.
Abbreviations: AIS, Athens Insomnia Scale; BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; CAS, Coronavirus Anxiety Scale; CBI, Copenhagen Burnout Inventory; CES-D, Center for Epidemiologic Studies Depression Scale; DASS-21, Depression, Anxiety, and Stress Scale-21; GAD-7, Generalized Anxiety Disorder-7; HADS, Hospital Anxiety and Depression Scale; IES-R, Impact of Event Scale—Revised; ISI, Insomnia Severity Index; MBI-DP, Maslach Burnout Inventory—depersonalization; MBI-EE, Maslach Burnout Inventory—emotional exhaustion; MBI-RPA, Maslach Burnout Inventory—reduced personal accomplishment; Mini-Z, Mini-Z Burnout Survey; n.a., not applicable; PC-PTSD, Primary Care—Post Traumatic Stress Disorder Scale; PCL-C, Post Traumatic Stress Disorder Checklist—Civilian Version; PHQ, Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality Index; SAS, Zung Self-Rating Anxiety Scale; SASRQ, Stanford Acute Stress Reaction Questionnaire; SDS, Zung Self-Rating Depression Scale; SQS, Sleep Quality Scale; STAI-S, State-–Trait Anxiety Inventory—State Subscale.
Anxiety
Anxiety prevalence was reported in 179 studies, including a total sample of 206,513 participants. Multiple evaluation scales were used, including Generalized Anxiety Disorder-7 (GAD-7) [Reference Zung36], DASS-21 [Reference Spitzer, Kroenke, Williams and Lowe35], HADS [Reference Beck, Ward and Mendelson32], Zung Self-Rating Anxiety Scale (SAS) [Reference Beck, Epstein, Brown and Steer37], Beck Anxiety Inventory (BAI) [Reference Spielberger38], State–Trait Anxiety Inventory—State Subscale (STAI-S) [Reference Lee39], PHQ-4 [Reference Zung30], and Coronavirus Anxiety Scale (CAS) [Reference Cardena, Koopman, Classen, Waelde and Spiegel40]. The pooled prevalence of anxiety was 0.42 (95% CI 0.35–0.48). Again, prevalence varied substantially depending on the scale used, from 0.34 with BAI to 0.68 with STAI-S. Sensitivity analyses and meta-regressions did not show statistically significant differences regarding age, gender, NOS score, or continent.
Acute stress
Acute stress prevalence was reported in 57 studies, including a total sample of 48,042 participants. Multiple evaluation scales were used, including Stanford Acute Stress Reaction Questionnaire (SASRQ) [Reference Cohen, Kamarck and Mermelstein41], DASS-21 [Reference Spitzer, Kroenke, Williams and Lowe35], and Perceived Stress Scale (PSS) [Reference Morin, Belleville, Belanger and Ivers42]. The pooled prevalence of acute stress was 0.40 (95% CI 0.32–0.47). Prevalence varied from 0.26 as measured with DASS-21 to 0.62 with PSS. Again, sensitivity analyses and meta-regressions revealed no statistically significant differences regarding age, gender, NOS score, continent, or professional category.
Insomnia
Insomnia prevalence was reported in 55 studies, including a total sample of 37,068 participants. Multiple evaluation scales were used, including Insomnia Severity Index (ISI) [Reference Yi, Shin and Shin43], Sleep Quality Scale (SQS) [Reference Soldatos, Dikeos and Paparrigopoulos44], Athens Insomnia Scale (AIS) [Reference Buysse, Reynolds, Monk, Berman and Kupfer45], and Pittsburgh Sleep Quality Index (PSQI) [Reference Weiss, Marmar, Wilson and Keane46]. The pooled prevalence of insomnia was 0.42 (95% CI 0.36–0.48). Sensitivity analyses and meta-regressions revealed no statistically significant differences regarding age, gender, NOS score, or continent.
Post-traumatic symptoms
Relevant post-traumatic symptoms prevalence was reported in 39 studies, including a total sample of 58,995 participants. Multiple evaluation scales were used, including Impact of Event Scale—Revised (IES-R) [47], Post Traumatic Stress Disorder Checklist—Civilian Version (PCL-C) [Reference Prins, Ouimette and Kimerling48], and Primary Care—Post Traumatic Stress Disorder Scale (PC-PTSD) [Reference Dolan, Mohr, Lempa, Joos, Fihn and Nelson49]. The pooled prevalence of post-traumatic symptoms was 0.32 (95% CI 0.26–0.37). Prevalence varied from 0.20 with PCL-C to 0.38 with IES-R. No statistical statistically significant differences regarding age, gender, or NOS score were found in meta-regressions. Sensitivity analyses found a statistically significant lower prevalence of post-traumatic symptoms in Asia (0.29; 95% CI 0.18–0.34) compared to North America (0.41; 95% CI 0.34–0.48).
Burnout
Burnout prevalence was reported in 25 studies, including a total sample of 30,873 participants. Three scales were used to evaluate it: Mini-Z Burnout Survey (Mini-Z) [Reference Kristensen, Borritz, Villadsen and Christensen50], Copenhagen Burnout Inventory (CBI) [Reference Maslach, Jackson, Leiter, Zalaquett and Wood51], and Maslach Burnout Inventory (MBI) [Reference Kim, Park, Kim, Park, Jhon and Kim52]. MBI has three measurable domains: emotional exhaustion (MBI-EE), depersonalization (MBI-DP), and reduced personal accomplishment (MBI-RPA). Scoring positively to any of these areas implies a relevant level of professional burnout. The pooled prevalence of burnout symptoms was 0.37 (95% CI 0.31–0.42). Prevalence varied from 0.22 with Mini-Z to 0.53 with CBI. In MBI, emotional exhaustion was the most deteriorated area among the sample. Sensitivity analyses and meta-regressions revealed no statistically significant differences regarding age, gender, NOS score, or continent.
Quality assessment and meta-regressions
According to NOS Scale, the mean quality of the included studies was 5.12 ± 0.80 and ranged from three to seven. Scores for each individual article are available in Supplementary Table S4.
Discussion
This meta-analysis has identified, for the first time on a large scale and at a global level, the prevalence of mental health symptoms in several domains in the HCWs group. HCW exposed to COVID-19 were found to have a significant prevalence rate of anxiety, depression, acute stress, insomnia, post-traumatic symptoms and burnout.
Thirty three percentage of the HCWs exposed to COVID-19 presented depressive symptomatology. This prevalence is higher than that reported in the general population during the pandemic, between 20.9% [Reference Ettman, Abdalla, Cohen, Sampson, Vivier and Galea53] and 27.8% [Reference Pashazadeh Kan, Raoofi, Rafiei, Khani, Hosseinifard and Tajik54]. The prevalence of anxiety in HCWs reported by this meta-analysis, 42%, is also much higher than that detected in the general population, 27.3% [Reference Dutta, Sharma, Torres-Castro, Pachori and Mishra55]. These results are consistent with previous findings in the literature; both Dutta et al. [Reference Saragih, Tonapa, Saragih, Advani, Batubara and Suarilah56] and Saragih et al. [Reference Salari, Khazaie, Hosseinian-Far, Khaledi-Paveh, Kazeminia and Mohammadi57] reported very similar data to those found in this study. These authors reported a total prevalence of depression of 0.32 and 0.37 and anxiety of 0.33 and 0.40, respectively. Another recent meta-analysis [Reference Liu, Zhu, Zhang, Zhang, Zhang and Liu58] reported a lower prevalence, 0.24 for depression and 0.26 for anxiety. However, this meta-analysis [Reference Liu, Zhu, Zhang, Zhang, Zhang and Liu58] included only 29 articles published in the initial months of the pandemic. All of this suggests that a progressive worsening in the mental health of HCWs may have occurred as the COVID-19 pandemic dragged on.
Insomnia was found to have a prevalence of 42%, higher than the 18–31% prevalence identified in other meta-analyses studying the general population for the same period [59–61]. This difference between the samples may be caused at least in part by the long and strenuous work shifts that characterize the duties of HCWs, which worsen insomnia and sleep quality [Reference Shehata, Mohamed Nour, Abdelrahim Badr and Khalifa62,Reference Al Maqbali, Al Sinani and Al-Lenjawi63].
As for acute stress, a prevalence of 40% was found in the sample included in our meta-analysis. While these results are similar to those previously reported in another recent meta-analysis [Reference Norhayati, Che Yusof and Azman64], the prevalence of relevant post-traumatic stress symptoms in our sample (32%) was unexpectedly high, more than doubling the 15% prevalence previously reported [Reference Krishnamoorthy, Nagarajan, Saya and Menon65,Reference Tromans66]. This may be due to several reasons. Firstly, post-traumatic stress symptoms, as per definition, take time to appear, so it is reasonable to expect an increase in its prevalence as months go by. Furthermore, the previous meta-analysis included a lower number of studies, including mostly samples from Asia, limiting the generalization of its results to a global sample. General population samples also report a significantly lower prevalence of post-traumatic symptoms during the same time frame [Reference Adriaenssens, De Gucht and Maes67].
Finally, our study also analyzed the prevalence of burnout in HCWs exposed to the COVID-19 pandemic, a mental health domain little studied in previous meta-analyses. The sample included in our meta-analysis presented a 37% burnout prevalence. This is consistent with data reported by a previous study [Reference Salazar de Pablo, Vaquerizo-Serrano, Catalan, Arango, Moreno and Ferre3]. Burnout was already a relevant problem in HCWs before the COVID-19 pandemic, heavily related to a decrease in occupational well-being [Reference Jimenez-Labaig, Pacheco-Barcia, Cebria, Galvez, Obispo and Paez68], so an increase in burnout prevalence is a growingly concerning phenomenon. During the pandemic, burnout has been especially high among young professionals due to increased workload, the loss of formational activities, and the perceived lack of proper supervision [Reference Lacobucci69].
These results may have several clinical implications. First, our study confirms that HCWs are an especially vulnerable population during the COVID-19 pandemic, being more prone to mental health impairment than the general population. These findings suggest that the deterioration in the mental health of HCWs is not due to measures of general confinement, social distance and concern about the pandemic, but to the particularities of the health professions and their conditions during the pandemic. These challenges include the lack of protective equipment [Reference Lucchini, Lozzo and Bambi70], increased workload and strenuous work shifts [Reference Hossain and Clatty71], but also ethical challenges and moral distress [Reference Drewett, Gibney and Ko72,Reference Dedeilia, Sotiropoulos, Hanrahan, Janga, Dedeilias and Sideris73]. In addition to this, residents and fellows have seen their training deprioritized while also increasing their responsibility and workload [Reference Kang, Li, Hu, Chen, Yang and Yang74]. Institutions should provide their professionals with proper formation, coping tools and strategies to alleviate the effects of the pandemic on their mental health. Preventive approaches should also be improved for HCWs facing these challenges, including the implementation of screening instruments to identify professionals with mental health symptomatology [Reference Salazar de Pablo, Vaquerizo-Serrano, Catalan, Arango, Moreno and Ferre3].
The meta-regression results reveal fewer symptoms in the post-traumatic domain in Asia than in other continents. Asia—and especially China, where most of the articles from this continent have been published—was initially the continent most affected by the pandemic [Reference Xie, Stone, Zheng and Zhang75], so these results may seem paradoxical. However, some studies have detected that proximity to the pandemic’s epicenter is inversely correlated with levels of distress, in a phenomenon known as the “Psychological Typhoon Eye” effect [Reference Gilbert, Lieberman, Morewedge and Wilson76]. Previous research shows that deep emotional feelings, such as those related to health emergencies, decrease more quickly than less deep feelings since they activate internal psychological mechanisms designed to mitigate them [Reference Zhang, Ma, Li and Xin77]. Coping efficacy has been identified as a mediating factor between both events [Reference Hook and Rose Markus78], which could, in turn, be stimulated by cultural factors related to collectivist cultures [Reference Jung, Di Santo, Clifford, Prosperi-Porta, Skanes and Hung79].
This study has several strengths. To the best of the authors’ knowledge, this is the largest meta-analysis published to date evaluating mental health outcomes in HCWs exposed to COVID-19. In addition, it evaluates domains of mental health less studied in previous meta-analyses, such as burnout or post-traumatic symptoms. Studies from more than 50 countries on 5 continents have been included, so its results are highly generalizable.
On the other hand, this study also has several limitations, mainly the considerable heterogeneity in the outcomes evaluated. Some authors have used different scales and cut-off points in the different domains of mental health. The exposure levels and the length of exposition duration of the HCWs included in the studied samples have not been analyzed due to the lack of data. Meta-regressions have been carried out to assess the impact of gender, professional category, and geographic origin on heterogeneity. COVID-19 pandemic has stimulated the publication of many studies in a short time, some of them of limited quality [80]. It is necessary to improve the design of the studies and standardize the methods and populations evaluated. Also, further studies should be conducted to determine in-depth the factors associated with mental health problems in HCWs during the pandemic.
In conclusion, HCWs worldwide exposed to COVID-19 were found to have a significant prevalence of concerning symptoms in a wide range of mental health domains. The effects of COVID-19 on HCWs’ mental health should not be underestimated. Further studies should be carried out to follow its evolution during the pandemic, and effective measures should be implemented to prevent and alleviate mental health deterioration in HCWs.
Supplementary Materials
To view supplementary material for this article, please visit http://doi.org/10.1192/j.eurpsy.2022.1.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author/first author, A.C./C.A., upon request.
Author Contributions
Conceptualization: C.A., B.P., L.A., G.S.P., and A.C.; Data curation: C.A., B.P., J.L.P., J.H., J.B., and G.M.; Formal analysis: C.A. and M.L.; Investigation: B.P., J.L.P., M.L., J.H., J.B., G.M., L.A., O.E., and M.F.; Methodology: B.P., J.L.P., M.L., J.H., J.B., G.M., L.A., O.E., G.S.P., and M.F.; Project administration: B.P.; Supervision: A.C. and M.A.G.-T.; Writing—original draft: C.A. and A.C.; Writing—review and editing: C.A., M.F., A.C., and M.A.G.-T.
Financial Support
This research received funding from the OSI Bilbao Basurto Research Commission.
Conflict of Interest
The authors declare no potential conflicts of interest.
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