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. 2021 Mar 11;287:145–157. doi: 10.1016/j.jad.2021.03.016

The prevalence of psychiatric comorbidities during the SARS and COVID-19 epidemics: a systematic review and meta-analysis of observational studies

Yan-Jie Zhao a,b,c,#, Yu Jin d,#, Wen-Wang Rao a,b,c,#, Wen Li a,b,c,#, Na Zhao a,b,c,e,#, Teris Cheung f, Chee H Ng g, Yuan-Yuan Wang h, Qing-E Zhang i,⁎, Yu-Tao Xiang a,b,c,⁎
PMCID: PMC7948672  PMID: 33799032

Abstract

The coronavirus disease 2019 (COVID-19) and Severe Acute Respiratory Syndrome (SARS) are associated with various psychiatric comorbidities. This is a systematic review and meta-analysis comparing the prevalence of psychiatric comorbidities in all subpopulations during the SARS and COVID-19 epidemics. A systematic literature search was conducted in major international (PubMed, EMBASE, Web of Science, PsycINFO) and Chinese (China National Knowledge Internet [CNKI] and Wanfang) databases to identify studies reporting prevalence of psychiatric comorbidities in all subpopulations during the SARS and COVID-19 epidemics. Data analyses were conducted using the Comprehensive Meta-Analysis Version 2.0 (CMA V2.0). Eighty-two studies involving 96,100 participants were included. The overall prevalence of depressive symptoms (depression hereinafter), anxiety symptoms (anxiety hereinafter), stress, distress, insomnia symptoms, post-traumatic stress symptoms (PTSS) and poor mental health during the COVID-19 epidemic were 23.9% (95% CI: 18.4%-30.3%), 23.4% (95% CI: 19.9%-27.3%), 14.2% (95% CI: 8.4%-22.9%), 16.0% (95% CI: 8.4%-28.5%), 26.5% (95% CI: 19.1%-35.5%), 24.9% (95% CI: 11.0%-46.8%), and 19.9% (95% CI: 11.7%-31.9%), respectively. Prevalence of poor mental health was higher in general populations than in health professionals (29.0% vs. 11.6%; Q=10.99, p=0.001). The prevalence of depression, anxiety, PTSS and poor mental health were similar between SARS and COVID-19 epidemics (all p values>0.05). Psychiatric comorbidities were common in different subpopulations during both the SARS and COVID-19 epidemics. Considering the negative impact of psychiatric comorbidities on health and wellbeing, timely screening and appropriate interventions for psychiatric comorbidities should be conducted for subpopulations affected by such serious epidemics.

Keywords: Psychiatric comorbidities, COVID-19, SARS, depression, anxiety, stress

1. Introduction

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in Wuhan, Hubei province, China in December 2019 (World Health Organization, 2020, World Health Organization, 2020). Subsequently, the WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 (World Health Organization, 2020, World Health Organization, 2020). As of the end of February 2021, approximately 113 million cases had been confirmed and over 2.5 million deaths were reported worldwide (Johns Hopkins University, 2021). Severe acute respiratory syndrome (SARS) is an infectious disease caused by another coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV-1) (World Health Organization, 2004). SARS was first reported in southern China in November 2002, and later found in Hong Kong (World Health Organization, 2004) and many other Asian countries and territories. By 31 December 2003, a total of 8,096 SARS cases were confirmed worldwide (World Health Organization, 2003).

Clinical features of SARS and COVID-19 are similar in some aspects, but also different in others. For example, most patients with SARS suffered from a fever above 38.0°C, chills, headache, lethargy, and muscle pain. After 2 to 7 days, they may develop a dry, nonproductive cough with low blood oxygen levels. Most SARS patients developed shortness of breath and pneumonia subsequently, either primary viral pneumonia or secondary bacterial pneumonia (Centers for Diseases Control and Prevention, 2017). In contrast, COVID-19 patients usually experienced flu-like symptoms, including fever and/or dry cough. Severe cases may present difficult breathing, chest pain, sudden confusion, and bluish face or lips (Grant et al., 2020, Centers for Diseases Control and Prevention, 2020). Some COVID-19 patients eventually developed pneumonia, acute respiratory distress syndrome, sepsis, and kidney failure (World Health Organization, 2020). Further, SARS-CoV-1 and SARS-CoV-2 are different in both transmission characteristics and virulence. Compared to SARS-CoV-1, SARS-CoV-2 is more infectious with the reproduction number (R0) of around 3.3 (Liu et al., 2020, Xie et al., 2020), while the R0 of SARS-CoV-1 is around 2.7 (Riley et al., 2003, Lipsitch et al., 2003). The SARS-CoV-1 is more virulent than SARS-CoV-2. As of the end of 2003, SARS caused 774 deaths, resulting in a mortality rate of 9.2% (World Health Organization, 2003). In contrast, as of 18 October 2020, the mortality rate of COVID-19 was 2.8% (Johns Hopkins, 2020).

In any major catastrophes including bio-disasters, psychiatric comorbidities and related problems, such as depression, anxiety, sleep disturbances, fear, and stigmatization, are common and may act as barriers to accessing appropriate medical and mental health care. In order to prevent or minimise the negative outcomes caused by psychiatric comorbidities, understanding their patterns and associated factors is important. Previous studies on prevalence of psychiatric comorbidities found that confusion symptoms (27.9%), depression (32.6%), memory impairment (34.1%) insomnia (41.9%) and steroid-induced mania and psychosis (0.7%) were common in patients with SARS or Middle East respiratory syndrome (MERS) (Rogers et al., 2020). In addition, psychiatric comorbidities also persisted after the SARS epidemic, such as post-traumatic stress disorder (PTSD) (Hawryluck et al., 2004) and major depressive disorder (MDD) (Ma, 2009) in SARS survivors. Other subpopulations including family members and close contacts of SARS patients, health professionals, and the public also suffered from psychiatric problems during the epidemic (Cong et al., 2003), which could be associated with a range of negative consequences, such as decreased quality of life, increased treatment burden, and increased suicidality (Chinese Ministry of Health 2003). Similarly, psychiatric comorbidities, such as depression, anxiety, and sleep disturbance were common in COVID-19 patients (Deng et al., 2020), health professionals, and other subpopulations (Salazar de et al., 2020, Li et al., 2020).

To date, very few studies have compared the psychiatric comorbidities of SARS and COVID-19 epidemics. Understanding their differences would be important to identify high-risk subpopulations, allocate health resources and provide appropriate treatments. A number of meta-analyses focused on psychiatric comorbidities of coronavirus diseases (Rogers et al., 2020, Kisely et al., 2020), but only one compared the epidemiological data of psychiatric comorbidities between multiple coronavirus diseases among health professionals (Salazar de et al., 2020). Several meta-analyses on prevalence of psychiatric comorbidities during the COVID-19 pandemic have been conducted, but most only focused on specific subpopulations, such as infected or suspected patients (Deng et al., 2020), health professionals (Pappa et al., 2020), or the public (Salari et al., 2020).

In order to better understand the psychiatric comorbidities of SARS and COVID-19, it is necessary to compare the prevalence of psychiatric comorbidities in all subpopulations during the SARS and COVID-19 epidemics. Therefore, we conducted this systematic review and meta-analysis of observational studies to compare the overall prevalence of psychiatric comorbidities (e.g., depressive symptoms [depression hereinafter], anxiety symptoms [anxiety hereinafter], stress, distress, insomnia symptoms [insomnia hereinafter], post-traumatic stress symptoms [PTSS], post-traumatic stress disorder [PTSD], and poor mental health) during the SARS and COVID-19 epidemics across all subpopulations studied. We also explored the moderating effects of sociodemographic characteristics (e.g., sex, education level and marital status) on the results. We hypothesized that the overall prevalence of psychiatric comorbidities during the COVID-19 epidemic would be similar to that during the SARS epidemic; 2) the overall prevalence of psychiatric comorbidities in healthcare professionals would be higher than that in the general population during the COVID-19 epidemic.

2. Methods

2.1. Literature search and selection

This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Moher et al., 2009), with the PROSPERO registration number of CRD42020211604. Literature search was systematically and independently conducted by three researchers (WWR, YJ, WL) in PubMed, EMBASE, Web of Science, PsycINFO, China National Knowledge Internet (CNKI) and WanFang databases from their inception to May 25, 2020, using the following search terms: “novel coronavir*”, “alphacoronavirus”, “betacoronavirus”, “COVID”, “COVID-19”, “severe acute respiratory syndrome” and “SARS”. For the psychiatric outcome category, the following search terms were used: “psychiatr*”, “mental”, “psycholog*”, “depress*”, “anxiety”, “posttraumatic stress disorder”, “PTSD”, “insomnia”, “sleep”, “epidemiology” and “prevalence”. The references of retrieved articles were also searched by hand for additional studies.

The same three researchers independently screened titles and abstracts, and then two of the researchers (YJZ and YJ) read the full texts of relevant articles for eligibility. Inclusion criteria were: 1) studies that examined psychiatric comorbidities during the SARS or COVID-19 epidemics in any subpopulations; 2) studies with available data on the prevalence of psychiatric comorbidities or relevant data that could generate the prevalence of psychiatric comorbidities during the SARS or COVID-19 epidemics in any subpopulations, as measured by standardized scales or diagnostic instruments; 3) case-control studies, cross-sectional or cohort studies. Case studies, reviews, systematic reviews, meta-analyses or commentaries were excluded. If more than one article were published using the same dataset, only the one with the most complete information or highest quality assessment score was included. Disagreement was resolved by consensus.

2.2. Data extraction

Relevant data were independently extracted by two researchers (YJZ and YJ) using a pre-designed data extraction sheet, including sex, education level, marital status, the first author, publication year, study design, study location, study period, study population, sample size, sampling method, prevalence of specific psychiatric co-morbidities. Disagreement was resolved by consensus, or a discussion with a senior researcher (YTX).

2.3. Quality assessment

The quality of included studies was evaluated using the Loney's 8-item scale (Loney et al., 1998) which has been widely used previously (Boyle, 1998, Yang et al., 2016). This scale assesses the quality of observational studies in eight domains: target population, probability sampling, response rate, non-responders, sample representative of the target population, standardized data collection method, validated criteria for outcomes, and confidence intervals (CI) of the prevalence of target outcomes. The total quality score ranges from 0 to 8, with ‘7-8’ as “high quality”, ‘4-6’ as “moderate quality” and ‘0-3’ as “low quality”. Two researchers (YJZ and YJ) independently evaluated the study quality, and disagreement was resolved by consensus or a discussion with the senior researcher (YTX).

2.4. Data analysis

Data analyses were performed using Comprehensive Meta-Analysis Version 2.0 (CMA V2.0, Biostat Inc., Englewood, New Jersey, USA). I2 test was used to evaluate heterogeneity between studies, with I2 > 50% indicating significant heterogeneity. The random-effects model was used in data syntheses due to different demographic characteristics between studies. In SARS related studies, December 31, 2003 was used as the cutoff date to classify acute SARS phase and SARS recovery phase. At least three articles were needed for data synthesis in each phrase. If the number of articles in either SARS phase was less than three, the relevant data in the two phrases were pooled.

Subgroup and meta-regression analyses were conducted to explore moderating effects of categorical (e.g. study population, sex, education level and marital status) and continuous variables (e.g., female percentage and quality assessment score) respectively, on the prevalence of psychiatric comorbidities in COVID-19 patients. Publication bias was examined by funnel plots, Egger's test and Duval and Tweedie trim-and-fill method. Two-tailed tests were conducted with the significance level of 0.05.

3. Results

3.1. Study characteristics

A total of 1,793 studies were identified in the literature search, and 82 met the eligibility criteria; of them, 74 studies with available data were included in the meta-analysis. Details of literature search, screening and selection are shown in Figure 1 . Study characteristics are presented in Table 1 . The included studies were conducted across 10 countries or areas including Asia, Europe, North America and South America.

Figure 1.

Figure 1

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Flow diagram

Table 1.

Characteristics of studies included in this systematic review and meta-analysis.

Study Language Disease Study design Survey period Country/territory Population Sampling method Sample size Female percentage (%) Age Response rate (%) Quality score Reference
Mean SD Min Max
Ahmed, M. Z. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China general population NR 1074 46.83 33.54 11.13 14 68 NR 4 (Ahmed et al., 2020)
Bo, H. X. et al. 2020 English COVID-19 cross-sectional 2020.3 Mainland China infected people NR 714 50.90 50.2 12.9 - - 97.80 6 (Bo et al., 2020)
Cai, W. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China health professionals NR 1521 75.54 - - 18 - NR 4 (Cai et al., 2020)
Cao, W et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China university students C 7143 69.65 - - - - 100.00 7 (Cao et al., 2020)
Chan, A. O. M. et al. 2004 English acute SARS cross-sectional 2 months after first case in Singapore Singapore health professionals NR 661 NR - - - - 67.00 4 (Chan and Huak, 2004)
Chang, J. et al. 2020 Chinese COVID-19 cross-sectional 2020.1-2020.2 Mainland China university students convenient 3881 63.05 20 - 18 - 91.38 5 (Chang et al., 2020)
Chen, C. S. et al. 2005 English acute SARS cross-sectional 2003.5 Taiwan health professionals NR 128 100.00 26.5 3.1 - - 69.57 4 (Chen et al., 2005)
Chen, Y. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China health professionals NR 105 90.5 32.6 6.5 - - 84.70 5 (Chen et al., 2020)
Cheng, S. K. et al. 2004 English acute SARS cross-sectional 2003.6 Hong Kong total sample NR 284 62.32 - - - - 60.17 5 (Cheng et al., 2004)
Chew, N. W. S. et al. 2020 English COVID-19 cross-sectional 2020.2-2020.4 Singapore, India health professionals NR 906 64.35 29 (median) - - - 90.60 5 (Chew et al., 2020)
Chong, M. Y. et al. 2004 English acute SARS cross-sectional 2003.5-2003.6 Taiwan health professionals NR 1257 81.07 31.8 6.4 21 59 50.28 5 (Chong et al., 2004)
Consolo, U. et al. 2020 English COVID-19 cross-sectional 2020.4 Italy health professionals C 356 39.61 - - - - 40.73 5 (Consolo et al., 2020)
Fang, Y. et al. 2004 Chinese acute SARS cross-sectional 2003.7-2003.10 Mainland China infected people NR 286 52.80 33.43 11.85 15 64 100.00 6 (Fang et al., 2004)
Gao, J. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China general population NR 4827 67.68 32.3 10.0 10 85 82.50 6 (Gao et al., 2020)
Hawryluck, L. et al. 2004 English acute SARS cross-sectional 2003.2-2003.6 Canada general population convenient 129 NR - - 18 66+ 0.86 4 (Hawryluck et al., 2004)
Hong, X. et al. 2009 English acute SARS cohort 2003.6-2007.9 Mainland China infected people NR 68 66.18 38.5 12.3 - - 97.14 6 (Hong et al., 2009)
Huang, J. Z. et al. 2020 Chinese COVID-19 cross-sectional 2020.2 Mainland China health professionals NR 230 81.30 32.6 6.2 22 59 93.50 5 (Huang et al., 2020)
Huang, Y. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China total sample NR 7236 54.62 35.3 5.6 - - 85.30 6 (Huang and Zhao, 2020)
Ko, C. H. et al. 2006 English SARS cross-sectional when the epidemic had just been controlled Taiwan general population R 1472 51.97 - - 15 51+ 94.85 6 (Ko et al., 2006)
Kwek, S. K. et al. 2006 English SARS cross-sectional 3 month post-discharge Singapore infected people NR 63 79.37 34.83 10.49 - - 43.45 5 (Kwek et al., 2006)
Lai, J. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China health professionals CMRS 1257 76.69 - - 18 40+ 68.69 5 (Lai et al., 2020)
Lam, M. H. B. et al.2009 English recovery SARS cross-sectional 2005.12-2007.7 Hong Kong infected people NR 181 68.51 43.3 13.7 - - 49.05 5 (Lam et al., 2009)
Lancee, W. J. et al. 2008 English recovery SARS cohort 2004.10-2005.9 Canada health professionals NR 139 87.05 45.0 9.6 - - 23.68 6 (Lancee et al., 2008)
Lau, J. T. F. et al. 2006 English acute SARS cross-sectional 2003.5-2003.6 Hong Kong general population R 818 50.24 - - 18 50+ 64.70 6 (Lau et al., 2006)
Lee, A. M. et al. 2007 English recovery SARS cohort 2004.4-2004.5 Hong Kong infected people NR 96 63.54 - - 18 61+ 80.00 5 (Lee et al., 2007)
Lei, L. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China general population convenient 1593 61.27 32.3 9.8 - - 80.17 5 (Lei et al., 2020)
Li, X. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China health professionals NR 948 76.79 - - 20 60+ NR 4 (Li et al., 2020)
Li, Y. et al. 2020 English COVID-19 prospective cohort 2020.2 Mainland China university students NR 1442 61.79 - - - - 71.20 4 (Li et al., 2020)
Liang, L. L. et al. 2020 English COVID-19 cross-sectional 2020.1 Mainland China general population convenient 584 61.82 - - 14 35 95.70 5 (Liang et al., 2020)
Liu, C. Y. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China health professionals NR 512 84.57 - - 18 60+ 85.33 5 (Liu et al., 2020)
Liu, N. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China general population NR 285 54.39 - - - - 95.00 5 (Liu et al., 2020)
Liu, X. et al. 2012 English recovery SARS cross-sectional 2006 Mainland China health professionals SR 549 76.50 - - - - 83.00 6 (Liu et al., 2012)
Liu, Z. R. et al. 2004 Chinese acute SARS cross-sectional 2003.5 Mainland China university students CS 6280 38.74 20.3 2.0 - - 92.35 6 (Liu et al., 2004)
Lü, S. H. et al. 2010 Chinese acute SARS retrospective 2003.3-2003.6 Mainland China general population MS 2379 45.61 39.12 13.67 18 69 93.96 6 (Lü et al., 2010)
Lu, W. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China health professionals NR 2299 77.64 - - - - 94.88 5 (Lu et al., 2020)
Lu, Y. C. et al. 2006 English acute SARS cross-sectional 2003.7-2004.3 Taiwan health professionals NR 127 58.27 - - - - 94.07 5 (Lu et al., 2006)
Lung, F. W. et al. 2009 English recovery SARS longitudinal 2004.7-2005.3 Taiwan health professionals NR 123 NR - - - - 96.85 5 (Lung et al., 2009)
Mak, I. W. C. et al. 2009 English recovery SARS cohort 2005.9-2006.3 Hong Kong infected people NR 90 62.22 41.1 12.1 - - 96.77 6 (Mak et al., 2009)
Maunder, R. G. et al. 2006 English recovery SARS cohort 2004.10-2005.9 Canada health professionals NR 769 86.87 - - - - 38.76 4 (Maunder et al., 2006)
Mazza, C. et al. 2020 English COVID-19 cross-sectional 2020.3 Italy general population NR 2766 71.66 32.94 13.2 18 90 98.36 5 (Mazza et al., 2020)
Mihashi, M. et al. 2009 English recovery SARS cross-sectional 2004.2-2004.3 Mainland China general population NR 187 36.90 26.3 8.0 - - 62.33 3 (Mihashi et al., 2009)
Ni, M. Y. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China total sample NR 1791 61.75 - - - - NR 5 (Ni et al., 2020)
Nickell, L. A. et al. 2004 English acute SARS cross-sectional 2003.4 Canada health professionals NR 510 80.59 - - - - 11.91 4 (Nickell et al., 2004)
Ozamiz-Etxebarria, N. et al. 2020 English COVID-19 cross-sectional 2020.3 Spain general population NR 976 81.15 - - 18 78 40.67 4 (Ozamiz-Etxebarria et al., 2020)
Peng, E. Y. C. et al. 2010 English acute SARS cross-sectional 2003.11 Taiwan general population SR 1278 49.69 41.6 16.6 18 89 68.31 5 (Peng et al., 2010)
Reynolds, D. L. et al. 2008 English acute SARS cross-sectional 2003.3-2003.6 Canada total sample NR 1057 61.12 - - - - 55.28 6 (Reynolds et al., 2008)
Shacham, M. et al. 2020 English COVID-19 cross-sectional 2020.3-2020.4 Israel health professionals NR 338 58.58 46.39 11.18 24 74 NR 4 (Shacham et al., 2020)
Sim, K. et al. 2004 English acute SARS cross-sectional 2003.7 Singapore health professionals NR 277 85.20 38.0 12.7 - - 92.03 5 (Sim et al., 2004)
Sim, K. et al. 2010 English acute SARS cross-sectional 2003.7 Singapore general population consecutive 415 40.72 36.6 13.9 - - 78.01 4 (Sim et al., 2010)
Su, T. P. et al. 2007 English acute SARS prospective 2003.4-2003.6 Taiwan health professionals NR 102 100.00 25.4 3.7 - - 95.33 5 (Su et al., 2007)
Tan, W. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China general population NR 673 25.56 30.8 7.4 - - 50.87 4 (Tan et al., 2020)
Tang, W. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China university students convenient 2485 61.37 19.81 1.55 16 27 68.84 4 (Tang et al., 2020)
Tham, K. Y. et al. 2004 English acute SARS cross-sectional 2003.11 Singapore health professionals NR 96 68.75 - - - - 77.42 4 (Tham et al., 2004)
Tian, B. C. et al. 2007 Chinese recovery SARS cross-sectional 2006.3-2006.4 Mainland China general population convenient 2424 45.46 39.12 13.67 - - 101.00 5 (Tian et al., 2007)
Tian, F. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China general population convenient 1060 48.21 35.01 12.8 13 76 93.64 5 (Tian et al., 2020)
Wang, C. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China general population convenient 1210 67.27 - - 12 59 92.79 5 (Wang et al., 2020)
Wang, S. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China health professionals NR 123 90.24 33.75 8.41 20 50+ 50.00 4 (Wang et al., 2020)
Wu, K. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China health professionals NR 60 26.67 33.5 12.4 25 59 NR 4 (Wu and Wei, 2020)
Yin, Q. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China health professionals convenient 371 61.46 35.30 9.48 20 40+ 98.41 5 (Yin et al., 2020)
Zhang, C. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China health professionals convenient 1563 82.73 - - 18 60+ 80.32 6 (Zhang et al., 2020)
Zhang, K. R. et al. 2005 Chinese acute SARS cross-sectional 2003.9-2003.10 Mainland China total sample NR 296 67.57 34 12 8 81 NR 4 (Zhang et al., 2005)
Zhang, W. R. et al. 2020 English COVID-19 cross-sectional 2020.2-2020.3 Mainland China health professionals NR 2182 64.21 - - 16 60+ NR 4 (Zhang et al., 2020)
Zhang, Y. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China general population convenient 263 59.70 37.7 14.0 18 50+ 65.75 5 (Zhang and Ma, 2020)
Zhu, J. et al. 2020 English COVID-19 cross-sectional 2020.2 Mainland China health professionals NR 165 83.03 34.16 8.06 - - 100.00 6 (Zhu et al., 2020)
Zhu, S. et al. 2020 English COVID-19 cross-sectional 2020.2-2020.3 Mainland China total sample NR 2279 59.72 - - - - NR 4 (Zhu et al., 2020)
Shi, T. Y. et al. 2005 Chinese acute SARS cross-sectional 2003.12-2004.1 Mainland China total sample C 162 79.63 - - - - 93.1 6 (Shi et al., 2005)
Zhang, X. J. et al. 2003 Chinese acute SARS cross-sectional 2003.4-2003.5 Mainland China general population C 1031 35.89 33.17 - 16 86 91.73 6 (Zhang et al., 2003)
He, L. P. et al. 2004 Chinese acute SARS cross-sectional 2003.5 Mainland China general population CR 1016 NR 27.30 9.62 - - 94.69 6 (He et al., 2004)
Zhao, Q. et al. 2020 Chinese COVID-19 cross-sectional 2020.2 Mainland China infected people NR 106 56.60 35.90 11.92 21 65 100.00 6 (Zhao et al., 2020)
Gao, H. S. et al. 2006 Chinese acute SARS longitudinal 2003.9-2004.6 Mainland China infected people NR 67 68.66 25.32 8.54 15 67 88.16 5 (Gao et al., 2006)
Gao, H. S. et al. 2006 Chinese SARS longitudinal 2003.6-2004.6 Mainland China infected people NR 67 68.66 - - - - NR 4 (Gao et al., 2006)
Wei, L. P. et al. 2005 Chinese SARS longitudinal within 2 weeks and after 3 months of post-charge Mainland China infected people NR 22 86.36 - - - - NR 4 (Wei et al., 2005)
Cheng, S. K. et al. 2004 English acute SARS cross-sectional 2003.5-2003.7 Hong Kong infected people NR 180 66.67 36.9 11.1 18 70 42.35 5 (Cheng et al., 2004)
Wu, K. K. et al. 2005 English SARS longitudinal at 1 month and 3 months after discharge from hospital Hong Kong infected people NR 131 56.49 41.82 14.01 18 84 27.52 4 (Wu et al., 2005)
Lee, D. T. S. et al. 2006 English acute SARS case-control 2003.4-2003.6 Hong Kong pregnant women consecutive 235 100.00 29.6 5.4 - - 57.6 4 (Lee et al., 2006)
Wu, Y. et al. 2020 English COVID-19 cross-sectional 2020.1-2020.2 Mainland China pregnant women NR 1285 100.00 - - 27 32 NR 4 (Wu et al., 2020)
Xie, X. et al. 2020 English COVID-19 cross-sectional 2020.2-2020.3 Mainland China children NR 1784 43.27 - - - - 76.57 4 (Xie et al., 2020)
Zhou, S. J. et al. 2020 English COVID-19 cross-sectional 2020.3 Mainland China adolescents NR 8079 53.55 16 - 12 18 99.25 5 (Zhou et al., 2020)
Yuan, R. et al. 2020 English COVID-19 cross-sectional 2020/NR Mainland China the parents of children hospitalized or not hospitalized NR 100 57.00 - - - - NR 4 (Yuan et al., 2020)
Nguyen, H. C. et al. 2020 English COVID-19 cross-sectional 2020.2-2020.3 Vietnam outpatients NR 3947 55.66 44.4 17.0 18 60+ 97.96 6 (Nguyen et al., 2020)
Han, Z. H. et al. 2020 Chinese COVID-19 longitudinal 2020.1-2020.3 Mainland China suspected infected people NR 72 41.67 - - 11 73 100 5 (Han et al., 2020)
Wan, I. Y. P. et al. 2004 English acute SARS cross-sectional 2003.4 Hong Kong patients on a waiting list for thoracic surgery NR 57 31.58 59.77 14.5 17 83 31.67 4 (Wan et al., 2004)
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Abbreviations: COVID-19: Coronavirus disease 2019; SARS: Severe acute respiratory syndrome; M: multistage; SD: standard deviation; S: stratified; C: cluster; R: random; NR: not reported.

3.2. Prevalence of psychiatric comorbidities during the COVID-19 epidemic

Of the 36 studies on COVID-19, 21 studies reported prevalence of depression during the COVID-19 epidemic and the pooled prevalence of depression was 23.9% (95% CI: 18.4% - 30.3%; I2=99.43%, p<0.001; Supplementary Figure 1). Twenty-four studies reported prevalence of anxiety during the COVID-19 epidemic and the pooled prevalence of anxiety was 23.4% (95% CI: 19.9% - 27.3%; I2=98.78%, p<0.001; Supplementary Figure 2). Five studies reported the prevalence of stress during the COVID-19 epidemic and the pooled prevalence was 14.2% (95% CI: 8.4% - 22.9%; I2=98.65%, p<0.001; Supplementary Figure 3). Three studies reported prevalence of distress the COVID-19 epidemic and the pooled prevalence of distress was 16.0% (95% CI: 8.4% - 28.5%; I2=97.77%, p<0.001; Supplementary Figure 4). Eight studies reported the prevalence of insomnia during the COVID-19 epidemic and the pooled prevalence of insomnia was 26.5% (95% CI: 19.1% - 35.5%; I2=98.79%, p<0.001; Supplementary Figure 5). Thirteen studies reported prevalence of PTSS during the COVID-19 epidemic and the pooled prevalence of PTSS was 24.9% (95% CI: 11.0% - 46.8%; I2=99.68%, p<0.001; Supplementary Figure 6). Five studies reported the prevalence of poor mental health during the COVID-19 epidemic and the pooled prevalence of poor mental health was 19.9% (95% CI: 11.7% - 31.9%; I2=98.92%, p<0.001; Supplementary Figure 7). Details of pooled prevalence of psychiatric comorbidities are presented in Table 2 .

Table 2.

Prevalence of psychiatric comorbidities during the COVID-19 epidemic in all subpopulations

Psychiatric outcomes Number of studies Events Sample size Prevalence (%) 95% CI (%) I2 (%) p Publication bias (Egger's test)
Depression 21 10025 39542 23.9 18.4 - 30.3 99.43 < 0.001 t = 1.28, p = 0.22
Anxiety 24 11690 45253 23.4 19.9 - 27.3 98.78 < 0.001 t = 1.28, p = 0.21
Stress 5 1440 6531 14.2 8.4 - 22.9 98.65 < 0.001 t = 3.37, p = 0.04
Distress 3 555 2840 16.0 8.4 - 28.5 97.77 < 0.001 t = 1.40, p = 0.39
Insomnia 8 3481 14042 26.5 19.1 - 35.5 98.79 < 0.001 t = 0.61, p = 0.57
PTSS 13 4268 11983 24.9 11.0 - 46.8 99.68 < 0.001 t = 2.26, p = 0.04
Poor mental health 5 1216 6406 19.9 11.7 - 31.9 98.92 < 0.001 t = 0.14, p = 0.90
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Notes: I2 statistic was used to assess the heterogeneity of the studies.

The minimum number of studies required to synthesize data is 3.

3.3. Comparisons of prevalence of psychiatric comorbidities between COVID-19 and SARS epidemics

Of the 38 studies on SARS, 6 studies reported prevalence of depression during the acute SARS phase, while 3 studies reported that during the SARS recovery phase, with the pooled prevalence of 27.5% (95% CI: 17.3% - 40.6%; I2=94.95%, p<0.001) and 26.0% (95% CI: 15.6% - 40.0%; I2=87.59%, p<0.001), respectively. No significant difference in prevalence of depression between SARS and COVID-19 epidemics was found (Q=0.34, p=0.85). Nine studies reported prevalence of anxiety during the SARS epidemic and the pooled prevalence of anxiety was 17.7% (95% CI: 8.2% - 34.1%; I2=97.37%, p<0.001), with no significant difference compared to that during the COVID-19 epidemic (Q=0.59, p=0.44). Fifteen studies reported the prevalence of PTSS during the SARS epidemic and the pooled prevalence of PTSS was 16.8% (95% CI: 12.9% - 21.5%; I2=93.94%, p<0.001), with no significant difference compared to that during the COVID-19 epidemic (Q=0.89, p=0.35).

Nine studies reported prevalence of poor mental health in acute SARS phase while 3 studies reported that in SARS recovery phase, with the pooled prevalence of 26.6% (95% CI: 11.7% - 49.8%; I2=99.61%, p<0.001) and 32.8% (95% CI: 12.4% - 62.8%; I2=96.44%, p<0.001), respectively. The pooled prevalence of poor mental health in SARS was similar with that during the COVID-19 epidemic (Q=1.06, p=0.59). Three studies reported prevalence of PTSD in acute SARS phase while 3 studies reported that in SARS recovery phase, with the pooled prevalence of 29.4% (95% CI: 9.3% - 63.0%; I2=96.62%, p<0.001) and 15.3% (95% CI: 6.7% - 31.3%; I2=89.83%, p<0.001), respectively. No study on prevalence of PTSD during the COVID-19 epidemic was published by the date of literature search; therefore, comparison between SARS and COVID-19 could not be made. Detailed comparisons of psychiatric comorbidities between COVID-19 and SARS epidemics are shown in Table 3 .

Table 3.

Comparison of prevalence of psychiatric comorbidities during the COVID-19 and SARS epidemics

Psychiatric outcomes Condition Number of studies Events Sample size Prevalence (%) 95% CI (%) I2 (%) p (within subgroup) Q (p across subgroups)
Depression COVID-19 21 10025 39542 23.9 18.4 - 30.3 99.43 < 0.001 Q = 0.34, p = 0.85
Acute SARS 6 348 1780 27.5 17.3 - 40.6 94.95 < 0.001
SARS Recovery 3 175 712 26.0 15.6 - 40.0 87.59 < 0.001
Anxiety COVID-19 24 11690 45253 23.4 19.9 - 27.3 98.78 < 0.001 Q = 0.59, p = 0.44
SARS 9 275 2892 17.7 8.2 - 34.1 97.37 < 0.001
PTSS COVID-19 13 4268 11983 24.9 11.0 - 46.8 99.68 < 0.001 Q = 0.89, p = 0.35
SARS 15 938 5653 16.8 12.9 - 21.5 93.94 < 0.001
Poor mental health COVID-19 5 1216 6406 19.9 11.7 - 31.9 98.92 < 0.001 Q = 1.06, p = 0.59
Acute SARS 9 2034 9907 26.6 11.7 - 49.8 99.61 < 0.001
SARS Recovery 3 129 406 32.8 12.4 - 62.8 96.44 < 0.001
PTSD Acute SARS 3 89 421 29.4 9.3 - 63.0 96.62 < 0.001 Q = 0.95, p = 0.33
SARS Recovery 3 71 410 15.3 6.7 - 31.3 89.83 < 0.001
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Note: Acute SARS refers to study period before January 1, 2004; Recovery SARS refers to study period after January 1, 2004.

Studies involving anxiety during SARS were not divided into “acute SARS/recovery SARS” because only 2 studies were conducted during recovery phase of SARS and they did not reach the minimum number of studies to synthesize data. Studies involving stress, distress, insomnia were not compared between COVID-19 and SARS due to the similar reason.

3.4. Subgroup analyses in prevalence of psychiatric comorbidities during the COVID-19 epidemic

The pooled prevalence of poor mental health in the general population and health professionals during the COVID-19 epidemic was 29.0% (95% CI: 18.1% - 43.1%) and 11.6% (95% CI: 9.2% - 14.6%), respectively. Subgroup analyses revealed that compared with health professionals, general populations were more likely to have poorer general mental health (Q=10.99, p=0.001). No significant difference was found between health professionals (28.0%, 95% CI: 9.5% - 59.0%) and general populations (19.2%, 95% CI: 4.6% - 54.2%) in prevalence of PTSS (Q=0.21, p=0.63). The prevalence estimates of depression and anxiety during the COVID-19 were similar between the general population and health professionals (Q=0.01, p=0.91 for depression; Q=0.23, p=0.64 for anxiety). Details of the comparisons are presented in Table 4 . No significant differences were found in prevalence of depression, anxiety, insomnia and PTSS during the COVID-19 epidemic between different sex, between different education levels and between different marital status (all p values > 0.05; Table 5 ).

Table 4.

Prevalence of psychiatric comorbidities during the COVID-19 epidemic in all subpopulations

Psychiatric outcomes Population Number of studies Events Sample size Prevalence (%) 95% CI (%) I2 (%) p (within subgroup) Q (p across subgroups)
Depression General population 10 6016 20644 23.2 16.6 - 31.4 99.38 < 0.001 Q = 0.01, p = 0.91
Health professionals 11 2809 11922 23.9 15.0 - 35.9 99.32 < 0.001
Anxiety General population 10 5118 20599 21.2 16.6 - 26.7 98.74 < 0.001 Q = 0.23, p = 0.64
Health professionals 14 3584 13020 23.2 17.1 - 30.8 98.77 < 0.001
PTSS General population 5 1164 3015 19.2 4.6 - 54.2 99.57 < 0.001 Q = 0.21, p = 0.63
Health professionals 5 2190 4327 28.0 9.5 - 59.0 99.59 < 0.001
Poor mental health General population 3 742 2575 29.0 18.1 - 43.1 97.93 < 0.001 Q = 10.99, p = 0.001
Health professionals 3 402 3327 11.6 9.2 - 14.6 83.06 < 0.001
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Note: Only the first visit of longitudinal studies was included in order to avoid data duplication.

Studies involving stress, distress, insomnia were not compared between different populations because their numbers of studies in at least one population did not reach the minimum number of studies to synthesize data.

Table 5.

Prevalence of psychiatric comorbidities during the COVID-19 epidemic by sex, education level and marital status.

Psychiatric outcomes Categories Number of studies Events Sample size Prevalence (%) 95% CI (%) I2 (%) p (within subgroup) Q (p across subgroups)
Depression Male 5 1770 5892 32.4 20.1 - 47.6 99.00 < 0.001 Q = 0.02, p = 0.90
Female 5 3234 9478 33.7 20.1 - 50.7 99.53 < 0.001
Anxiety Male 8 2748 9663 25.7 21.0 - 31.1 96.25 < 0.001 Q = 0.64, p = 0.42
Female 8 4928 17907 28.7 23.8 - 34.1 98.07 < 0.001
Insomnia Male 5 848 4089 25.2 19.7 - 31.6 87.08 < 0.001 Q = 1.07, p = 0.30
Female 5 1818 7048 31.7 21.6 - 43.9 98.72 < 0.001
Senior high school or below 3 62 147 43.3 28.5 - 59.5 52.96 0.12 Q = 1.15, p = 0.28
University or above 3 860 2486 34.6 31.4 - 38.1 56.12 0.10
Married 3 606 1775 34.6 31.0 - 38.3 47.55 0.15 Q = 0.17, p = 0.68
Unmarried 3 316 859 35.8 31.140.9 43.56 0.17
PTSS Male 4 235 993 19.1 4.2 - 56.3 98.65 < 0.001 Q = 0.08, p = 0.78
Female 4 907 2199 25.4 5.1 - 68.3 99.56 < 0.001
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Note: Only studies reported all categories of sex and education level were included.

The minimum number of studies required to synthesize data is 3.

3.5. Meta-regression analyses

Meta-regression analyses revealed that the prevalence estimates of depression (r=2.31), stress (r=4.54) and insomnia (r=3.97) were positively and significantly associated with proportion of female participants. Studies with higher quality scores reported higher prevalence of depression (r=0.64), anxiety (r=0.40) and PTSS (r=2.08). Details of meta-regression analyses are shown in Supplementary Table 2.

3.6. Prevalence of psychiatric comorbidities in special subpopulations

A case-control study in Hong Kong reported that the prevalence of depression in pregnant women during the SARS epidemic was 12.3% (Lee et al., 2006), while another cross-sectional study in mainland China reported that the prevalence of depression in pregnant women during the COVID-19 epidemic was 29.6% (Wu et al., 2020). Two cross-sectional studies conducted in mainland China reported that the prevalence of depression in children and adolescents during the COVID-19 epidemic ranged from 22.6% to 43.7%, and the prevalence of anxiety in children and adolescents during the COVID-19 epidemic ranged from 18.9% to 37.4% (Xie et al., 2020, Zhou et al., 2020). A cross-sectional study conducted in mainland China reported that during the COVID-19 epidemic, parents of children hospitalized for any reason had significantly more severe depression and anxiety than parents of non-hospitalized children (48.0% vs. 8.0% in depression; 42.0% vs. 8.0% in anxiety) (Yuan et al., 2020).

A longitudinal study in mainland China reported that inpatients with COVID-19 or suspected COVID-19 had high levels of anxiety (86.1% before psychological intervention vs. 58.3% after psychological intervention; p<0.05) (Han et al., 2020), while a cross-sectional study in Vietnam reported that outpatients with suspected COVID-19 symptoms had significantly higher prevalence of depression than those without (64.3% vs. 35.7%; p<0.001) (Nguyen et al., 2020). A cross-sectional study in Hong Kong reported that during the SARS epidemic mental health problems were common in patients on a waiting list for thoracic surgeries, of whom 26.3% had depression, and 42.1% had anxiety (Wan et al., 2004).

3.7. Quality assessment and publication bias

Of the 82 included studies, the mean quality assessment score was 4.9, ranging from 3 to 7. Eighty studies are rated as “moderate quality”, while one study was rated as “low quality” and one study was rated as “high quality” (Supplementary Table 1). Egger's test found marginal publication bias in studies on PTSS during the COVID-19 epidemic (t=2.26, p=0.04; shown in Table 2). Funnel plots are shown in Supplementary Figures 8-15. A sensitivity analysis using the trim-and-fill method was performed with one imputed study, producing an approximately symmetrical funnel plot (Supplementary Figure 14). Using the trim-and-fill method, the adjusted pooled prevalence of PTSS was 53.1% (95% CI: 30.2% - 74.7%).

4. Discussion

To the best of our knowledge, this was the first systematic review that compared the prevalence of psychiatric comorbidities between the SARS and COVID-19 epidemics in all sub-populations. We found that psychiatric comorbidities were common in different subpopulations in both epidemics, and the prevalence estimates of psychiatric comorbidities were similar between both epidemics.

The overall prevalence of depression in all subpopulations studied during the COVID-19 epidemic was 23.9% (95% CI: 18.4%-30.3%) in this systematic review, which is similar to the findings of an earlier meta-analysis (18.9%; 95% CI: 13.0% - 26.6%) of depression during the COVID-19 epidemic (Li et al., 2020). We found the overall prevalence of anxiety in all subpopulations studied during the COVID-19 epidemic was 23.4% (95% CI: 19.9% - 27.3%), which is significantly lower than the corresponding figure in an earlier meta-analysis (44.5%; 95% CI: 29.8% - 60.1%) (Li et al., 2020). The reasons might be that the previous meta-analysis included studies published on or before 6 March 2020 (early stage of the COVID-19 epidemic), and conducted specifically on frontline health professionals, confirmed cases and quarantined populations. Another meta-analysis on COVID-19 patients also found higher prevalence of depression (45%; 95% CI 37% - 54%) and anxiety (47%; 95% CI 37% - 57%) (Deng et al., 2020), probably due to uncertainty about the novel virus, lack of specific treatments and fear of transmission to vulnerable populations (Xiang et al., 2020). The pooled prevalence of insomnia in this systematic review was 26.5% (95% CI: 19.1% - 35.5%), which is comparable with the findings of two earlier meta-analyses (49.8%, 95% CI: 18.6% - 81.1% (Li et al., 2020); and 34%, 95% CI: 19% - 50% (Deng et al., 2020)). The overall prevalence of stress and PTSS in this systematic review was 14.2% (95% CI: 8.4% - 22.9%) and 24.9% (95% CI: 11.0% - 46.8%), respectively, both of which are comparable with the corresponding figure in the previous meta-analysis (21.6%; 95% CI: 3.4%-68.1%) conducted in early stage of the COVID-19 epidemic (Li et al., 2020).

We found that the prevalence of depression and anxiety in all subpopulations studied between the SARS and COVID-19 epidemics were similar (Q=0.34, p=0.85 for depression; Q=0.59, p=0.44 for anxiety), which is also consistent with the findings in health professionals (Q=1.153, p=0.283 for depression; Q=0.557, p=0.456 for anxiety) (Salazar de et al., 2020). We found that the prevalence of PTSS in all subpopulations studied between the SARS and COVID-19 epidemics were similar (Q=0.89, p=0.35). However, in an earlier meta-analysis the prevalence of PTSD features in health professionals during the SARS, MERS and COVID-19 epidemics were different (16.7% in SARS, 40.7% in MERS, and 7.7% in COVID-19 epidemics; Q=22.74, p<0.001) (Salazar de et al., 2020). This may be because only one COVID-19 study with very low prevalence of PTSD features was included (Salazar de et al., 2020).

Subgroup analyses revealed that compared with health professionals, the general population was more likely to have poor general mental health status during the COVID-19 epidemic. This could be due to several reasons. Widespread misinformation on social mass media may have resulted in panic, fear and other mental health problems at the early phase of COVID-19 epidemic (Apuke and Omar, 2020, Pennycook et al., 2020, Brennen et al., 2020). Compared to health professionals, the general population may have less relevant medical knowledge to appraise the appropriate level of risks (O'Connor and Murphy, 2020), and may be more likely to suffer from mental health problems. In addition, substantial mental health services and psychological assistances were specifically developed for health professionals during the COVID-19 epidemic, which reduced the risk of adverse mental health effects (Liu et al., 2020, Li et al., 2020).

The prevalence of depression and anxiety between the general population and health professionals during the COVID-19 epidemic are comparable, consistent with previous findings (Li et al., 2020) in which the prevalence of depression was 12.6% (95% CI: 7.2%-21.2%) in the general population and 13.4% (95% CI: 5.3% - 29.7%) in health professionals during the COVID-19 epidemic, while the corresponding figures of anxiety was 40.6% (95% CI: 15.1% - 72.4%) and 41.1% (95% CI: 28.4% - 55.1%), respectively (Li et al., 2020). In contrast to the previous study, no significant difference in the prevalence of PTSS between the general population and health professionals was found in this meta-analysis. In the previous study, the prevalence of stress-related symptoms in health professionals (73.4%, 95% CI: 71.1% - 75.5%) was higher than in the general population (2.3%, 95% CI: 0.6% - 8.7%) (Li et al., 2020). However, the previous study only had one survey each on stress-related symptoms in the general population and in health professionals respectively (Li et al., 2020), which could lead to unreliable results.

Subgroup analyses revealed that no gender difference was found in the prevalence of depression, anxiety, insomnia and PTSS in all subpopulations studied during the COVID-19 epidemic in this meta-analysis, which is consistent with earlier meta-analyses conducted in COVID-19 patients (Deng et al., 2020) and health professionals (Pappa et al., 2020). However, meta-regression analysis found that female gender was positively associated with higher risk of depression, stress and insomnia. An earlier meta-analysis found that female health professionals were more likely to suffer from distress in coronavirus disease epidemics (Salazar de et al., 2020). This may be attributed to hormonal and cultural differences in females, for instance, socially sanctioned expression of emotions is encouraged in females more than males (Burt and Stein, 2002, Albert, 2015, Zhang and Wing, 2006, Barsky et al., 2001, Jayaratne et al., 1983). Marital status and education level did not moderate the prevalence of insomnia in this meta-analysis. As no other meta-analysis examined this potential association, direct comparisons could not be made. We also found that higher quality studies were associated with higher prevalence of depression, anxiety and PTSS. Due to random sampling, large sample size, strict study design and better trained interviewers that were adopted in high quality studies, mental health problems were more likely to be identified compared to lower quality studies (Rao et al., 2020, Xu et al., 2018, Wang et al., 2018).

The strengths of this systematic review included first, psychiatric comorbidities of all subpopulations studied during the COVID-19 and SARS epidemics were included, while previous meta-analyses focused either on COVID-19 or SARS alone (Deng et al., 2020, Li et al., 2020, Salari et al., 2020), and only on certain subpopulations (Rogers et al., 2020, Deng et al., 2020, Salazar de et al., 2020, Kisely et al., 2020). Second, the number of included studies and the total sample size were large, which enabled us to perform sophisticated analyses, such as subgroup and meta-regression analyses and test publication bias. However, several methodological limitations should be noted when interpreting the results. First, only studies published in English and Chinese languages were included. Second, even after subgroup analyses were performed, significant between-study heterogeneity was found. Such heterogeneity is unavoidable in the meta-analyses of epidemiological studies (Rotenstein et al., 2016, Wang et al., 2017). Third, some factors related to psychiatric comorbidities, such as pre-existing psychiatric disorders, social support, and severity and treatments of SARS and COVID-19, were not examined due to insufficient data.

In conclusion, psychiatric comorbidities were common in different subpopulations during both the SARS and COVID-19 epidemics. Although clinical features of both diseases are different, their prevalence of psychiatric comorbidities were almost similar. Considering the negative impact of psychiatric comorbidities on health and wellbeing during serious epidemics, timely screening and appropriate interventions for psychiatric comorbidities should be conducted for vulnerable subpopulations. Further public mental health education and psychological assistance hotlines should also be provided for the affected populations.

Contributors

Study design: Qing-E Zhang, Yu-Tao Xiang.

Data collection, analysis and interpretation: Yan-Jie Zhao, Yu Jin, Wen-Wang Rao, Wen Li, Na Zhao, Yuan-Yuan Wang.

Drafting of the manuscript: Yan-Jie Zhao, Yu Jin, Teris Cheung, Yu-Tao Xiang.

Critical revision of the manuscript: Chee H. Ng.

Approval of the final version for publication: all co-authors.

Declaration of Competing Interest

There is no conflict of interest related to the topic of this manuscript.

Acknowledgments

Acknowledgments

None.

Role of funding

The study was supported by the National Science and Technology Major Project for investigational new drug (2018ZX09201-014), the Beijing Municipal Science & Technology Commission (No. Z181100001518005), and the University of Macau (MYRG2019-00066-FHS).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2021.03.016.

Appendix. Supplementary materials

mmc1.docx (136.3KB, docx)

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