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. 2021 Jan 14;107:110244. doi: 10.1016/j.pnpbp.2021.110244

Prevalence of anxiety in health care professionals during the COVID-19 pandemic: A rapid systematic review (on published articles in Medline) with meta-analysis

Javier Santabárbara a,b,i,1, Juan Bueno-Notivol c,1, Darren M Lipnicki d, Beatriz Olaya b,e,⁎, María Pérez-Moreno f, Patricia Gracia-García b,c,h, Nahia Idoiaga-Mondragon g, Naiara Ozamiz-Etxebarria g
PMCID: PMC9188432  PMID: 33453320

Abstract

During the COVID-19, healthcare workers are exposed to a higher risk of mental health problems, especially anxiety symptoms. The current work aims at contributing to an update of anxiety prevalence in this population by conducting a rapid systematic review and meta-analysis. Medline and Pubmed were searched for studies on the prevalence of anxiety in health care workers published from December 1, 2019 to September 15, 2020. In total, 71 studies were included in this study. The pooled prevalence of anxiety in healthcare workers was 25% (95% CI: 21%–29%), 27% in nurses (95% CI: 20%–34%), 17% in medical doctors (95% CI: 12%–22%) and 43% in frontline healthcare workers (95% CI: 25%–62%). Our results suggest that healthcare workers are experiencing significant levels of anxiety during the COVID-19 pandemic, especially those on the frontline and nurses. However, international longitudinal studies are needed to fully understand the impact of the pandemic on healthcare workers’ mental health, especially those working at the frontline.

Keywords: Health care workers, Anxiety, Professional categories, COVID-19

1. Introduction

The COVID-19 outbreak has led to an increment of psychological distress levels in the general population. Some contributing risk factors for this increment are the unpredictable nature of the disease, home isolation and confinement, a lack of clarity from leaders regarding the seriousness of the risk, or the emotional contagion between individuals (Huremović, 2019). The psychological impact has been reported to be especially high in healthcare workers (HCW), who face additional group-specific stressors (Cheng and Li Ping Wah-Pun Sin, 2020; C. Zhang et al., 2020b). Very intense work-related stressors include long working hours, strict instructions and safety measures, a permanent need for concentration and vigilance, reduced social contact, and the performance of tasks which they may not have been prepared for (Vieta et al., 2020).

This emotional distress experienced by HCW during the COVID-19 pandemic has been significantly associated with depression, stress and anxiety (Elbay et al., 2020; Lai et al., 2020), with anxiety being frequently observed in HCW (Garcia-Iglesias et al., 2020). Recent studies have shown that frontline HCW may be experiencing the highest levels of anxiety (Buselli et al., 2020), because they are usually responsible for the care of patients with COVID-19, and more mentally overwhelmed by the lack of specific treatment guidelines or adequate support (Liu et al., 2020b). A previous study reported that nurses with a higher level of stress were more likely to develop anxiety (Mo et al., 2020) and HCW women seem also to be at higher risk for anxiety (Babore et al., 2020).

Three systematic reviews and meta-analyses have reported prevalence rates of anxiety among HCW during the COVID-19 pandemic. The first, based on 13 cross-sectional studies, reported an overall prevalence of 23.2% (Pappa et al., 2020). The second meta-analysis included seven studies from China and reported an increased risk of anxiety among HCW (OR=1.32, 95%CI=1.09–1.6) compared with other professionals (da Silva and Neto, 2021). The third study found that the prevalence of anxiety and depression was similar among HCW and the general population (33%), but higher among patients with pre-existing conditions and COVID-19 infection (55%) (Luo et al., 2020). These systematic reviews and meta-analyses were conducted in April/May 2020. Since then, there has been a growing number of studies analyzing the prevalence of anxiety in HCW during the COVID-19 pandemic. Thus, the current meta-analysis aims to update the evidence on the prevalence of anxiety in HCW during the COVID-19 pandemic. Furthermore, and due to the evidence that suggests that the psychological impact of the COVID-19 pandemic might differ across different professional categories, we also investigated the prevalence of anxiety separately for professional groups (i.e., medical doctors, nurses, and frontline HCW).

2. Materials and methods

This study was conducted in accordance with the PRISMA guidelines for reporting systematic reviews and meta-analysis (Moher et al., 2009) (Table S1).

2.1. Search strategy

Two researchers (JBN and MPM) searched for cross-sectional studies reporting the prevalence of anxiety published from December 1, 2019 through September 15, 2020, using MEDLINE via PubMed. The search strategy was: (covid OR covid-19 OR coronavirus OR "corona virus" OR SARSCoV-2 OR "Coronavirus"[Mesh] OR "severe acute respiratory syndrome coronavirus 2"[Supplementary Concept] OR "COVID-19"[Supplementary Concept] OR "Coronavirus Infections/epidemiology"[Mesh] OR "Coronavirus Infections/prevention and control"[Mesh] OR "Coronavirus Infections/psychology"[Mesh] OR "Coronavirus Infections/statistics and numerical data"[Mesh]) AND (anxiety OR anxiety symptoms OR anxiety disorders OR anxious OR "Trauma and Stressor Related Disorders"[Mesh] OR "Anxiety"[Mesh] OR "Anxiety Disorders"[Mesh] OR "Anxiety/epidemiology"[Mesh] OR "Anxiety/statistics and numerical data"[Mesh] OR depression OR depressive OR "Depression"[Mesh] OR "Depressive Disorder"[Mesh] OR "Depression/statistics and numerical data"[Mesh]) AND (“health care workers” OR “medical staff” OR “health care professionals” OR “health care workers” OR “health workers” OR “health professionals” OR “health personnel” OR "Health Personnel"[Mesh]). No language restriction was made. References from selected articles were inspected to detect additional potential studies. Any disagreement was resolved by consensus among two more reviewers (JS and BO).

2.2. Selection criteria

Studies were included if they: (1) reported cross-sectional data on the prevalence of anxiety during the COVID-19 pandemic; (2) were focused on samples of HCW; and (3) described the methods used to assess or diagnose anxiety. We excluded abstracts without the full text available and review articles.

A pre-designed data extraction form was used to extract the following information: country, sample size, prevalent rates of anxiety, proportion of women, average age, instruments used to assess anxiety, response rate and sampling methods.

2.3. Methodological quality assessment

Articles were assessed by two independent reviewers (JBN and MPM) for methodological validity before inclusion in the review using the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017). Quality was evaluated with a score of zero or one for each of nine criteria: 1) Was the sample frame appropriate to address the target population?; 2) Were study participants recruited in an appropriate way?; 3) Was the sample size adequate?; 4) Were the study subjects and setting described in detail?; 5) Was data analysis conducted with sufficient coverage of the identified sample?; 6) Were valid methods used for the identification of the condition?; 7) Was the condition measured in a standard, reliable way for all participants?; 8) Was there appropriate statistical analysis?; 9) Was the response rate adequate, and if not, was the low response rate managed appropriately?

Disagreements between the reviewers were resolved through discussions, or by consulting two more reviewers (JS and BO).

2.4. Data extraction and statistical analysis

A generic inverse variance method with a random effect model was used to estimate the pooled prevalence (DerSimonian and Laird, 1986). Random-effects model attempts to generalize findings beyond the included studies by assuming that the selected studies are random samples from a larger population (Cheung et al., 2012).

The Hedges Q statistic was reported to check heterogeneity across studies, with statistical significance set at p < 0.10. The I 2 statistic and 95% confidence interval was also used to quantify heterogeneity (von Hippel, 2015). I 2 values between 25% and 50% are considered as low, 50%–75% as moderate, and 75% or more as high (Higgins et al., 2003). Heterogeneity of effects between studies occurs when differences in results for the same exposure-disease association cannot be fully explained by sampling variation. Sources of heterogeneity can include differences in study design or demographic characteristics. We performed meta-regression and subgroup analyses (Thompson and Higgins, 2002) to explore sources of heterogeneity expected in meta-analyses of observational studies (Egger et al., 1998). We also conducted a sensitivity analysis to determine the influence of each individual study on the overall result by omitting studies one by one. Publication bias was determined through visual inspection of a funnel plot and also with the Egger's test (Egger et al., 1997) (p value < 0.05 indicates publication bias) since funnel plots were found to be an inaccurate method for assessing publication bias in meta-analyses of proportion studies (Hunter et al., 2014).

Statistical analyses were conducted by JS and run with STATA statistical software (version 10.0; College Station, TX, USA) and R (R Core Team, 2019).

3. Results

3.1. Identification and selection of articles

Figure 1 shows the flowchart of the literature search strategy and study selection process. Initially, 354 potential records were identified, from which 1 duplicate was removed, 168 were excluded after screening the titles and abstracts for not meeting the inclusion criteria and 2 of them were then excluded because full text was not available. After reading the remaining 184 articles in full, we finally included 71 in our meta-analysis (Almater et al., 2020(Apisarnthanarak et al., 2020; Ayhan Başer et al., 2020; Badahdah et al., 2020; Cai et al., 2020; Chen et al., 2020b, Chen et al., 2020c, Chen et al., 2020d; Cheng et al., 2020b; Chew et al., 2020; Civantos et al., 2020; Consolo et al., 2020; Dal’Bosco et al., 2020; Di Tella et al., 2020; Dosil Santamaría et al., 2020; Elbay et al., 2020; Elhadi et al., 2020; Gallopeni et al., 2020; Giusti et al., 2020; Gupta et al., 2020a; Gupta et al., 2020b; Huang et al., 2020a; Huang et al., 2020b; Huang and Zhao, 2020; Kannampallil et al., 2020; Keubo et al., 2020; Koksal et al., 2020; Lai et al., 2020; Li et al., 2020b; Li et al., 2020b; Liang et al., 2020; Lin et al., 2020; Liu et al., 2020a, Liu et al., 2020b; Lu et al., 2020; Luceño-Moreno et al., 2020; Magnavita et al., 2020; Mahendran et al., 2020; Naser et al., 2020; Ning et al., 2020; Pouralizadeh et al., 2020; Prasad et al., 2020; Que et al., 2020; Şahin et al., 2020; Salopek-Žiha et al., 2020; Sandesh et al., 2020; Si et al., 2020; Skoda et al., 2020; Stojanov et al., 2020; Suryavanshi et al., 2020; Temsah et al., 2020; Teng et al., 2020; Teo et al., 2020; Tu et al., 2020; Vanni et al., 2020; Wang et al., 2020a, Wang et al., 2020b, Wang et al., 2020c, Wang et al., 2020d; Wańkowicz et al., 2020; Xiao et al., 2020; Xiaoming et al., 2020; Xiong et al., 2020b; Yang et al., 2020a, Yang et al., 2020b; Yáñez et al., 2020; Zhang et al., 2020b; Zhang et al., 2020b; Zhou et al., 2020; Zhu et al., 2020a, Zhu et al., 2020b).

Figure 1.

Figure 1

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Flowchart of the study selection.

3.2. Characteristics of the included studies

Table 1 shows the characteristics of those studies (n = 59) that reported prevalence rates of anxiety in HCW (without distinction of the type of workers); Table 2 displays characteristics of studies reporting data from nurses (n = 17); Table 3 for medical doctors (n = 13), and Table 4 for frontline HCW (n = 13).

Table 1.

Characteristics of the studies included in the meta-analysis based on samples of healthcare workers.

Author (Publication year) Population Country Mean age (SD) % Females (n) Sample size (n) Response rate (%) Sampling method Anxiety assessment Diagnostic Criteria Prevalence
 Quality assessment
% n
Apisarnthanarak et al. (2020) HCW Thailand 32 (23-62) 59.00% (95) 160 NR NR GAD-7 ≥10 19.38% 31 6
Ayhan Başer et al. (2020) HCW Turkey NR NR 426 98% NR BAI ≥16 24.41% 104 7
Badahdah et al. (2020) HCW Oman 37.67 (7.68) 80.30% (407) 509 NR NR GAD-7 ≥10 25.93% 132 7
Chen et al. (2020b) HCW China 36.54 (8.57) 68.63% (619) 902 NR Convenience GAD-7 ≥10 16.63% 150 7
Chen et al. (2020c) HCW Ecuador NR 34.52% (87) 252 62.84% Convenience GAD-7 ≥10 28.17% 71 7
Chen et al., 2020d) Pediatric HCW China 32.6 (6.5) 90.48% (95) 105 84.68% NR SAS ≥50 18.10% 19 7
Cheng etal. (2020b) Pediatric HCW China NR 82.4% (440) 534 NR Convenience SAS ≥50 14.00% 75 7
Chew et al. (2020) HCW India, Singapore 29 (NR) 64.3% (583) 906 90.60% NR DASS-21 ≥10 8.72% 79 8
Consolo et al. (2020) Dental Workers Italy NR 39.6% (141) 356 40.00% Convenience GAD-7 ≥10 23.88% 85 7
Di Tella et al. (2020) HCW Italy 42.9 (11.2) 72.4% (105) 145 NR Convenience STAI-S ≥41 71.03% 103 6
Dosil Santamaría et al. (2020) HCW Spain 42.8 (10.2) 80.29% (338) 421 NR Snowball DASS-21 ≥10 28.74% 121 7
Elbay et al. (2020) HCW Turkey 36.05 (8.69) 56.8% (251) 442 NR Convenience DASS-21 ≥10 35.29% 156 7
Elhadi et al. (2020) HCW Libya 33.3 (7.4) 51.94% (387) 745 93.13% Convenience HADS >10 46.71% 348 8
Gallopeni et al. (2020) HCW Kosovo 39 (10.37) 61.32% (363) 592 NR NR HADS >10 44.59% 264 7
Giusti et al. (2020) HCW Italy 44.6 (13.5) 62.42% (206) 330 41.25% Convenience STAI-S ≥40 71.20% 235 7
Gupta et al. (2020a) HCW Nepal 29.5 (6.1) 52.67% (79) 150 NR Snowball GAD-7 ≥10 10.00% 15 6
Gupta et al. (2020b) HCW India NR 36.12% (406) 1124 79.45% Quota HADS >7 37.19% 418 8
Huang and Zhao (2020) HCW China NR NR 2250 85.30% Convenience GAD-7 ≥9 35.64% 802 7
Huang et al. (2020a) HCW China NR 81.30% (187) 230 93.50% Cluster SAS ≥50 23.04% 53 8
Huang et al. (2020b) HCW China NR 58.79% (214) 364 96.55% Cluster SAS ≥50 23.35% 85 9
Kannampallil et al. (2020) HCW USA NR 54.96% (216) 393 28.58% NR DASS-21 ≥8 18.58% 73 6
Keubo et al. (2020) HCW Cameroon NR 54.45% (159) 292 NR Snowball HADS >10 42.12% 123 6
Koksal et al. (2020) HCW Turkey 35.6 (8.5) 70.1% (492) 702 NR NR HADS >10 57.55% 404 7
Lai et al. (2020) HCW China NR 76.69% (964) 1257 68.69% Convenience GAD-7 ≥10 12.25% 154 8
Li et al. (2020a) HCW China NR 100% (4369) 4369 82.17% Convenience GAD-7 ≥8 25.20% 1101 8
Liang et al. (2020) HCW China NR 81.31% (731) 899 NR Convenience GAD-7 ≥10 14.24% 128 7
Lin et al. (2020) HCW China NR NR 2316 NR Convenience GAD-7 >5 41.11% 952 6
Liu et al. (2020b) HCW China NR 84.57% (433) 512 85.33% Convenience SAS ≥50 12.50% 64 8
Liu et al. (2020a) Pediatric HCW China NR 85.52% (1737) 2031 NR Convenience DASS-21 ≥10 12.21% 248 7
Lu et al. (2020) HCW China NR 77.64% (1785) 2299 94.88% NR HAMA ≥7 24.75% 569 8
Magnavita et al. (2020) HCW Italy NR 70.10% (417) 595 73.46% Convenience GADS ≥5 16.64% 99 8
Mahendran et al. (2020) Dental Workers China NR 72.5% (87) 120 96.00% Convenience GAD-7 ≥10 32.50% 39 7
Naser et al. (2020) HCW Jordan NR 56.1% (653) 1163 NR Convenience GAD-7 ≥10 32.76% 381 7
Ning et al. (2020) HCW China NR 72.88% (446) 612 NR Snowball SAS ≥50 16.34% 100 7
Prasad et al. (2020) Nonphysician HCW USA NR 90.8% (315) 347 NR Convenience GAD-7 ≥10 31.70% 110 7
Que et al. (2020) HCW China 31.06 (6.99) 69.06% (1578) 2285 NR Convenience GAD-7 ≥10 11.60% 265 7
Şahin et al. (2020) HCW Turkey NR 66.03% (620) 939 NR Convenience GAD-7 ≥10 18.96% 178 7
Salopek-Žiha et al. (2020) HCW Croatia NR NR 124 NR Convenience DASS-21 ≥10 16.94% 21 5
Si et al. (2020) HCW China NR 70.68% (610) 863 76.00% Convenience DASS-21 ≥10 7.88% 68 8
Skoda et al. (2020) HCW Germany NR 75.99% (1690) 2224 NR Convenience GAD-7 ≥10 9.49% 211 6
Stojanov et al. (2020) HCW Serbia 40.5 (8.37) 66.17% (133) 201 NR NR GAD-7 ≥10 25.37% 51 6
Suryavanshi et al. (2020) HCW India NR 51.27% (101) 197 20.40% Snowball GAD-7 ≥9 28.43% 56 6
Temsah et al. (2020) HCW Saudi Arabia NR 75.09% (437) 582 71.76% Convenience GAD-7 ≥10 31.79% 185 8
Teng et al. (2020) HCW China NR NR 338 NR Snowball SAS ≥50 13.31% 45 6
Teo et al. (2020) Laboratory HCW Singapore 34 (NR) 73.77% (90/122) 103 84.43% NR GAD-7 ≥10 24.27% 25 7
Vanni et al. (2020) HCW Italy 47 (10.37) 65.22% (30) 46 90.20% Convenience DASS-21 ≥10 28.26% 13 7
Wang et al. (2020a) HCW China NR 85.84% (897) 1045 73.18% Convenience HADS >10 20.00% 209 7
Wang et al. (2020d) HCW China 37 (NR) 77.37% (212) 274 NR Convenience GAD-7 ≥10 13.87% 38 6
Wang et al. (2020c) Pediatric HCW China 33.75 (8.41) 90.24% (111) 123 52.44% Convenience SAS ≥50 7.32% 9 7
Wang et al. (2020b) HCW China 33.5 (8.89) 64.52% (1291) 2001 72.06% Convenience HADS >7 22.59% 452 8
Wańkowicz et al. (2020) HCW Poland 40.25 (5.25) 52.15% (230) 441 NR NR GAD-7 >5 64.40% 284 7
Xiao et al. (2020) HCW China NR 67.22% (644) 958 NR Convenience HADS >7 54.07% 518 6
Xiaoming et al. (2020) HCW China 33.25 (8.26) 77.93% (6874) 8817 90.62% Convenience GAD-7 ≥10 5.09% 449 8
Yang et al. (2020a) Physical Therapists South Korea NR 47.69% (31) 65 89.04% Convenience GAD-7 >5 32.31% 21 7
Yang et al. (2020b) HCW China NR NR 449 91.08% Convenience SAS ≥40 (raw) 29.18% 131 7
Yáñez et al. (2020) HCW Peru NR 64.03% (194) 303 75.75% NR GAD-7 ≥10 21.78% 66 7
Zhang et al. (2020a) HCW China NR 82.73% (1293) 1563 80.32% Convenience GAD-7 ≥10 12.92% 202 8
Zhang et al. (2020b) HCW Bolivia, Ecuador, Peru 38.9 (10.1) 67.98% (484) 712 59.2% Cluster GAD-7 ≥10 23.03% 164 9
Zhu et al. (2020b) HCW China NR 85.03% (4304) 5062 77.07% Convenience GAD-7 ≥8 24.06% 1218 8
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Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). HCW = Healthcare workers; NR =not reported; BAI = Beck Anxiety Inventory; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale; STAI-S = State-trait Anxiety Scale.

Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). HCW = Healthcare workers; NR =not reported; BAI = Beck Anxiety Inventory; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale; STAI-S = State-trait Anxiety Scale.

Table 2.

Characteristics of the studies included in the meta-analysis based on samples of nurses.

Author (Publication year) Population Country Mean age (SD) % Females (n) Sample size (n) Response rate (%) Sampling method Anxiety assessment Diagnostic Criteria Prevalence
 Quality assessment
% n
Dal’Bosco et al. (2020) Nurses Brazil NR 79% (89.8) 88 18.49% Convenience HADS >7 48.90% 43 6
Gupta et al. (2020b) Nurses India NR NR 207 79.45% Quota HADS >7 49.76% 103 6
Huang et al. (2020a) Nurses China NR NR 160 93.50% Cluster SAS ≥50 26.88% 43 7
Keubo et al. (2020) Nurses Cameroon NR NR 168 NR Snowball HADS >10 44.64% 75 5
Lai et al. (2020) Nurses China NR 90.84% (694) 764 68.69% Convenience GAD-7 ≥10 12.70% 97 8
Li et al. (2020b) Frontline Nurses China NR 77.27% (136) 176 NR Convenience HAMA ≥7 77.27% 136 6
Liu et al. (2020a) Nurses China NR NR 1173 NR Convenience DASS-21 ≥10 12.87% 151 6
Ning et al. (2020) Nurses China NR 97.97% (289) 295 NR Snowball SAS ≥50 20.34% 60 6
Pouralizadeh et al. (2020) Nurses Iran 36.34 (8.74) 95.2% (420) 441 NR NR GAD-7 ≥10 38.78% 171 7
Prasad et al. (2020) Nurses USA NR 93.1% (231) 248 NR Convenience GAD-7 ≥10 34.27% 85 6
Que et al. (2020) Nurses China 35.94 (8.17) 97.75% (195) 208 NR Convenience GAD-7 ≥10 14.90% 31 6
Şahin et al. (2020) Nurses Turkey NR NR 254 NR Convenience GAD-7 ≥10 21.65% 55 5
Skoda et al. (2020) Nurses Germany NR 86.83% (1511) 1511 NR Convenience GAD-7 ≥10 11.38% 172 6
Tu et al. (2020) Frontline Nurses China 34.44 (5.85) 100% (100) 100 100% Cluster GAD-7 ≥10 7.00% 7 8
Wang et al. (2020a) Nurses China NR NR 773 73.18% Convenience HADS >10 20.70% 160 7
Xiong et al. (2020a) Nurses China NR 97.31 (217) 223 61.80% Convenience GAD-7 ≥10 12.11% 27 7
Zhu et al. (2020a) Frontline Nurses China NR NR 86 NR Convenience SAS ≥50 27.91% 24 5
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Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). NR = not reported; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale.

Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). NR = not reported; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale.

Table 3.

Characteristics of the studies included in the meta-analysis based on samples of medical doctors.

Author (Publication year) Population Country Mean age (SD) % Females (n) Sample size (n) Response rate (%) Sampling method Anxiety assessment Diagnostic Criteria Prevalence
 Quality assessment
% n
Almater et al. (2020) MD Saudi Arabia 32.9 (9.6) 43.9% (47) 107 30.60% Convenience sampling GAD-7 ≥10 21.50% 23 6
Civantos et al. (2020) MD USA NR 39.26% (137) 349 NR Convenience sampling GAD-7 ≥10 18.91% 66 7
Gupta et al. (2020b) MD India NR NR 749 79.45% Quota sampling HADS >7 35.25% 264 7
Huang et al. (2020a) MD China NR NR 70 93.50% Cluster Sampling SAS ≥50 14.29% 10 7
Keubo et al. (2020) MD Cameroon NR NR 74 NR Snowball sampling HADS >10 36.49% 27 5
Lai et al. (2020) MD China NR 54.77% (270) 493 68.69% Convenience sampling GAD-7 ≥10 11.56% 57 8
Liu et al. (2020a) MD China NR NR 858 NR Convenience sampling DASS-21 ≥10 11.31% 97 6
Ning et al. (2020) MD China NR 49.53% (157) 317 NR Snowball sampling SAS ≥50 12.62% 40 6
Que et al. (2020) MD China 33.69 (7.44) 63.49% (546) 860 NR Convenience sampling GAD-7 ≥10 11.98% 103 7
Şahin et al. (2020) MD Turkey NR NR 580 NR Convenience sampling GAD-7 ≥10 18.62% 108 6
Skoda et al. (2020) MD Germany NR 65.65% (323) 492 NR Convenience sampling GAD-7 ≥10 5.89% 29 7
Wang et al. (2020a) MD China NR NR 149 73.18% Convenience sampling HADS >10 20.13% 30 6
Zhu et al. (2020a) Frontline MD China NR 64.56% (51) 79 NR Convenience sampling SAS ≥50 11.39% 9 6
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Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). MD = Medical doctor; NR = not reported; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; SAS = Zung Self-Rating Scale.

Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). MD = Medical doctor; NR = not reported; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; SAS = Zung Self-Rating Scale.

Table 4.

Characteristics of the studies included in the meta-analysis based on samples of frontline healthcare workers.

Author (Publication year) Population Country Mean age (SD) % Females (n) Sample size (n) Response rate (%) Sampling method Anxiety assessment Diagnostic Criteria Prevalence
 Quality assessment
% n
Cai et al. (2020) Frontline HCW China 30.6 (8.8) 68.82% (819) 1173 NR Non-probabilistic sampling BAI ≥16 15.69% 184 7
Kannampallil et al. (2020) Frontline HCW USA NR 51.38% (112) 218 15.85% NR DASS-21 ≥8 21.56% 47 5
Lai et al. (2020) Frontline HCW China NR NR 522 68.69% Convenience sampling GAD-7 ≥10 16.09% 84 7
Li et al. (2020b) Frontline Nurses China NR 77.27% (136) 176 NR Convenience sampling HAMA ≥7 77.27% 136 6
Luceño-Moreno et al. (2020) Frontline HCW Spain 43.88 (10.82) 86.40% (1228) 1422 92.40% Non probabilistic sampling HADS ≥7 79.32% 1128 8
Sandesh et al. (2020) Frontline HCW Pakistan NR 42.86% (48) 112 NR Convenience sampling DASS-21 ≥10 85.71% 96 5
Stojanov et al. (2020) Frontline HCW Serbia 39.1 (7.3) 65.25% (77) 118 NR NR GAD-7 ≥10 31.36% 37 6
Tu et al. (2020) Frontline Nurses China 34.44 (5.85) 100% (100) 100 100% Cluster Sampling GAD-7 ≥10 7.00% 7 8
Wang et al. (2020a) Frontline HCW China NR NR 401 73.18% Convenience sampling HADS >10 24.69% 99 7
Wang et al. (2020b) Frontline HCW China NR 59.46% (393) 661 72.06% Convenience sampling HADS >7 28.74% 190 8
Wańkowicz et al. (2020) Frontline HCW Poland 40.47 (4.93) 56.31% (116) 206 NR NR GAD-7 >5 99.03% 204 6
Zhou et al. (2020) Frontline HCW China 35.77 (8.13) 81.19% (492) 606 NR NR GAD-7 >5 45.38% 275 7
Zhu et al. (2020a) Frontline HCW China 34.16 (8.06) 83.03% (137) 165 NR Convenience sampling SAS ≥50 20.00% 33 6
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Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). HCW = healthcare workers; NR = not reported; BAI = Beck Anxiety Inventory; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale.

Note. ⁎Quality score based on the Joanna Briggs Institute (JBI) standardized critical appraisal instrument for prevalence studies (Moola et al., 2017; see Table S2). HCW = healthcare workers; NR = not reported; BAI = Beck Anxiety Inventory; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale.

The sample size of the studies ranged from 46 to 8817 participants. Only 30 studies reported the mean age of participants, which ranged from 29 to 47 years. All but two studies included men and women, with women outnumbering men in most of the studies that included this data (58/65). Fifty studies included general samples of HCWs, with 13 of these also including data on specific subgroups: nurses and doctors (7 studies), frontline HCWs (4 studies), and nurses, doctors and workers assisting COVID-19 patients (Frontline HCW). Four studies included samples of pediatric HCWs, with data specifically on nurses and doctors in only one of these. Five studies only included frontline HCW, with one providing data on subsamples of frontline nurses and doctors. Three studies focused only on nurses, and another two on frontline nurses, while two studies only included doctors. The remaining 5 studies focused on professional groups considered to be HCW: dental workers, laboratory HCW, physiotherapists, and non-physician HCW (with a subpopulation of nurses). A total of 33 studies were conducted in China, 5 studies in Italy, 4 studies were from Turkey, 3 studies from each of India and USA, 2 studies from Ecuador, Peru, Saudi Arabia, Singapore, Spain, and a single study from each of Bolivia, Brazil, Cameroon, Croatia, Germany, Iran, Jordan, Kosovo, Libya, Nepal, Oman, Pakistan, Poland, Serbia, South Korea and Thailand. All studies were performed using online questionnaires, and of those that reported sampling methodology, all but four used non-random approaches. The response rate was reported by 38 studies and ranged from 18.5% to 100%. All studies measured anxiety by means of standardized scales, most commonly the Generalized Anxiety Disorder scale (GAD, 35 studies), the Zung Self-rating Anxiety Scale (SAS, 10 studies), the Hospital Anxiety and Depression Scale (HADS, 10 studies), and the Depression, Anxiety, and Stress Scale (DASS, 9 studies).

3.3. Quality assessment

The risk of bias scores ranged from 5 to 9, with a mean score of 7.01 (Table S2). The most common sources of bias were: (a) recruitment of inappropriate participants (67 studies), (b) response rate not reported or large number of non-responders (39 studies), and (c) sample size too small to ensure good precision of the final estimate (24 studies).

3.4. Meta-analysis of the prevalence of anxiety

The estimated overall prevalence of anxiety was 25% in overall samples of HCW (95% CI: 21%–29%) (Fig. 2 ), 27% in nurses (95% CI: 20%–34%) (Fig. 3 ), 17% in medical doctors (95% CI: 12%–22%) (Fig. 4 ), and 43% in Frontline HCW (95% CI: 25%–62%) (Fig. 5 ), with significant heterogeneity between studies (Q test: p < 0.001) in HCW overall, nurses, doctors and frontline HCW.

Figure 2.

Figure 2

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Forest plot for the prevalence of anxiety among healthcare workers

Figure 3.

Figure 3

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Forest plot for the prevalence of anxiety among nurses.

Figure 4.

Figure 4

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Forest plot for the prevalence of anxiety among medical doctors.

Figure 5.

Figure 5

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Forest plot for the prevalence of anxiety among frontline healthcare workers.

3.5. Meta-regression and subgroup analysis

Potential sources of heterogeneity were investigated across the studies. Our subgroup analysis showed that the prevalence of anxiety was lower for studies using the DASS-21, a convenience sampling method, with high methodological quality, or studies conducted in China (Table 5 ).

Table 5.

Overall prevalence rates of anxiety according to study characteristics.

Healthcare Workers (HCW)
 Nurses
 Medical Doctors
 Frontline HCW
No. of studies Prevalence (%) (95% CI) p⁎ No. of studies Prevalence (%) (95% CI) p⁎ No. of studies Prevalence (%) (95% CI) p⁎ No. of studies Prevalence (%) (95% CI) p⁎
Anxiety assessment 0.179 0.919 0.502 0.711
GAD-7 29 23 (18–28) 8 18 (11–26) 7 16 (11–22) 5 41 (11–76)
HADS 8 40 (30–51) 4 40 (23-59) 2 33 (30–36) 3 44 (10–82)
DASS-21 8 18 (12–26) 1 13 (11–15) 1 11 (9–14) 2 43 (38–49)
SAS 9 17 (13–22) 3 24 (19–29) 3 13 (10–16) 1 20 (14–27)
STAI 2 71 (67–75) - - - - - -
Other (BAI/HAMA/GADS/STAI) 3 23 (21–25) 1 77 (70–83) - - - 2 22 (20–25)
 
Country 0.005 0.190 0.077 0.051
China 26 19 (14–24) 10 22 (14–31) 7 12 (11–14) 8 28 (17–40)
Other 33 30 (25–36) 7 35 (21–55) 6 21 (12–33) 5 68 (37–92)
 
Sampling method 0.857 0.640 0.070 0.747
Convenience 36 24 (19–29) 11 25 (17–34) 9 14 (11–17) 6 42 (23–62)
Other 14 26 (19–33) 6 30 (19–43) 4 24 (11–39) 3 31 (0–83)
 
Quality rating 0.632 0.168 0.721 0.164
Medium (<7) 13 27 (16–38) 11 32 (21–44) 7 18 (13–23) 5 67 (30–95)
High (≥ 7) 46 25 (21–29) 6 19 (11–28) 6 15 (8–25) 7 30 (11–53)
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Note: 95%CI = 95% Confidence interval; HCW = healthcare workers; NR = not reported; BAI = Beck Anxiety Inventory; DASS-21 = Depression, Anxiety and Stress scales; GAD-7 = General Anxiety Disorder-7; HADS = Hospital Anxiety and Depression Scale; HAMA = Hamilton Anxiety Rating Scale; SAS = Zung Self-Rating Scale; STAI = State-trait Anxiety Scale.

⁎

P value obtained from univariate meta-regression.

3.6. Sensitive analysis

Excluding each study one-by-one from the analysis did not substantially change the pooled prevalence of anxiety. This indicated that no single study had a disproportional impact on the overall prevalence (data not shown).

3.7. Risk of publication bias

Visual inspection of the funnel plot (Fig. 6 ) suggested a small publication bias for the estimation of the pooled prevalence in HCW, nurses and medical doctors, confirmed by significant Egger’s test results (p < 0.05). In contrast, no publication bias was identified in the estimation of the pooled prevalence in frontline HCW (Egger’s test: p = 0.804)

Figure 6.

Figure 6

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Funnel plot for the prevalence of anxiety.

4. Discussion

4.1. Summary of main findings

The COVID-19 pandemic is having an unprecedented impact on HCW, with anxiety being one of most commonly reported mental condition. The present study provides an up-to-date meta-analysis of studies reporting the prevalence of anxiety in HCW during the COVID-19 pandemic. Our meta-analysis is based on 71 studies, and to the best of our knowledge, we are the first to report overall prevalence rates of anxiety in different occupational categories of HCW (i.e., nurses, medical doctors and frontline HCW). Our findings show that HCW, nurses and doctors report high anxiety levels (25%, 27% and 17%, respectively), and up to 43% of the frontline HCW display high levels of anxiety symptoms. The prevalence found in HCW is similar to the one reported by Pappa et al. (23.2%) (Pappa et al., 2020) but lower than the one reported by Luo et al. (33%) (Luo et al., 2020). These discrepancies might be explained in light of the different number of studies included or the origin of the samples (i.e., China).

Some previous systematic reviews and meta-analyses have been conducted to report the prevalence of anxiety in the general population. A recent meta-analysis conducted from December 2019 to August 2020 and based on 43 population-based studies reported an overall prevalence of anxiety of 25% (Santabárbara et al., 2021). Similarly, a recent systematic review (Xiong et al., 2020a) found that the prevalence of anxiety symptoms ranged from 6.3% to 50.9% in the general population during the COVID-19 outbreak (based on 11 population-based studies). The present study reports similar prevalence rates of anxiety in HCW to that reported in the general population, although the proportion of anxiety in frontline HCW appears to be higher (43%).

The finding of frontline HCW as the workers with the highest levels of anxiety is consistent with previous literature (Alshekaili et al., 2020; Buselli et al., 2020; Cabarkapa et al., 2020). Frontline HCW are directly responsible for caring for patients with COVID-19, and are exposed to several risk factors for anxiety such as burnout, lack of treatment guidelines, and feeling inadequately supported (Lai et al., 2020; Rajkumar, 2020). The exposure to critical medical situation and death and trauma make frontline HCW especially vulnerable to post-traumatic stress disorder (PTSD) (Carmassi et al., 2020). Despite the fact that the present meta-analysis is based on studies that evaluated anxiety symptoms with standardized questionnaire, they correspond to overall anxiety, but do not specifically assess the presence of PSTD. However, symptoms of PSTD might be also indirectly captured by these instruments.

The relatively high anxiety levels observed in nurses (27%) is also consistent with some previous reports showing that nurses are particularly affected by severe emotional distress (Dennison Himmelfarb and Baptiste, 2020). Nurses have direct contact with patients and therefore have more direct emotional contact. Recent systematic reviews have shown that being a nurse in the COVID-19 pandemic is a risk factor for anxiety (Cabarkapa et al., 2020; Sanghera et al., 2020). Factors promoting anxiety include fear of being infected or infecting others (Mo et al., 2020), lack of personal protective equipment, lack of access to COVID-19 testing, and lack of accurate information about the disease (Shanafelt et al., 2020).

4.2. Strengths and limitations

Some strengths of our meta-analysis are the inclusion of a large body of literature and the use of a rigorous approach to identify publication bias (i.e., Egger’s test). These results show that there is a small bias in the estimation of the pooled prevalence of anxiety for HCW, nurses and medical doctors but null for frontline HCW.

However, some limitations should be considered when interpreting our results. First, the majority of the studies included were based on cross-sectional data and non-probabilistic samples. The epidemiological status of COVID-19 is constantly changing worldwide, and thus longitudinal studies are necessary to determine whether the elevated levels anxiety are sustained, reduced or increased over time (Pierce et al., 2020). Second, the studies used a variety of self-report anxiety scales, and the use of some tests was associated with significantly higher prevalence of anxiety than others. Ideally, studies should use the same measure of anxiety, and if possible, include a diagnosis based on clinical interviews. However, this is not always possible, and the use of brief, self-reported standardized questionnaires appears as a common practice in epidemiological studies. Finally, while we were able to include studies from many countries, the majority of studies were focused on Asian countries, particularly on Chinese samples. Europe and America are both highly affected by the COVID-19 pandemic, thus there is still need for epidemiological studies in these regions using randomized sampling design, when possible.

5. Conclusions

This meta-analysis confirms the huge mental toll of the COVID-19 pandemic in HCW, especially in frontline HCW and nurses. Therefore, there is an urgent need to prevent and treat common mental health problems in this population. Several strategies have been recommended to support the mental health and well-being of HCW. These include accurate work-related information, regular breaks, adequate rest and sleep, a healthy diet, physical activity, peer support, family support, avoidance of unnecessary coping strategies, limiting the use of social media and professional counselling or psychological services (Pollock et al., 2020). In addition, psychological support based on coping strategies should be provided to control anxiety. Spiritual care programs and other approaches developed for end-of-life and palliative care could also be helpful (Chirico et al., 2020).

Since the beginning of the COVID-19 pandemic, psychological support for health workers has been implemented in different countries. In China, different groups of mental health providers used social networking platforms and telephone aids to implement mental health support by offering guidance and supervision to solve psychological problems (Chen et al., 2020a; Cheng et al., 2020a). In Iran, consistent measures were taken to prevent, screen and treat mental health disorders among staff serving patients with COVID-19 (Zandifar et al., 2020). In the UK, in the first three weeks of the outbreak, a digital learning package was developed and evaluated. This e-learning package included evidence-based guidance, support and signage related to psychological well-being for all health care workers in the UK (Blake et al., 2020). In Spain, the Ministry of Health and the General Council of Psychologists activated a telephone support line for professionals with direct intervention in the management of the pandemic, such as health frontline care workers (Spanish Government, 2020). Finally, it is worth to mention the use of interventions based on cognitive behavior therapy to mitigate maladaptive coping strategies and change cognitive bias (Ho et al., 2020). These interventions can be delivered through e-health platforms (such as the Internet) and have been widely proved to be cost-effective for treating anxiety disorders (Zhang and Ho, 2017).

These are only examples of the psychological support being given in different countries. We advocate that the psychological support provided should be adapted to specific groups of workers and that further research should be carried out into which measures are most effective in minimizing the psychological impact of the COVID-19 pandemic in HCW.

Ethical statement for solid state ionics

Hereby, I Beatriz Olaya consciously assure that for the manuscript “Prevalence of anxiety in health care professionals during the COVID-19 pandemic: A rapid systematic review with meta-analysis” the following is fulfilled:

  • 1)

    This material is the authors' own original work, which has not been previously published elsewhere.

  • 2)

    The paper is not currently being considered for publication elsewhere.

  • 3)

    The paper reflects the authors' own research and analysis in a truthful and complete manner.

  • 4)

    The paper properly credits the meaningful contributions of co-authors and co-researchers.

  • 5)

    The results are appropriately placed in the context of prior and existing research.

  • 6)

    All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper reference.

  • 7)

    All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.

The violation of the Ethical Statement rules may result in severe consequences.

To verify originality, your article may be checked by the originality detection software iThenticate. See also http://www.elsevier.com/editors/plagdetect.

I agree with the above statements and declare that this submission follows the policies of Solid State Ionics as outlined in the Guide for Authors and in the Ethical Statement.

Funding

This work has been supported by grants from the Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spanish Ministry of Economy and Competitiveness, Madrid, Spain [grants 94/1562, 97/1321E, 98/0103, 01/0255, 03/0815, 06/0617, G03/128 and 19/01874]. BO’s work is supported by the PERIS program 2016-2020 “Ajuts per a la Incorporació de Científics i Tecnòlegs” [grant number SLT006/17/00066], with the support of the Health Department from the Generalitat de Catalunya, and by the Miguel Servet programme (reference CP20/00040), funded by Instituto de Salud Carlos III and co-funded by European Union (ERDF/ESF, "Investing in your future"). Sponsors had no involvement in the study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the article for publication.

Declaration of Competing Interest

None.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pnpbp.2021.110244.

Appendix A. Supplementary data

Table S1. PRISMA checklist.

Table S2. Quality assessment with the JBI appraisal checklist for prevalence studies.

mmc1.docx (41.4KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. PRISMA checklist.

Table S2. Quality assessment with the JBI appraisal checklist for prevalence studies.

mmc1.docx (41.4KB, docx)

Articles from Progress in Neuro-Psychopharmacology & Biological Psychiatry are provided here courtesy of Elsevier

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