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. 2020 May 5;8(2):e000406. doi: 10.1136/fmch-2020-000406

Clinicopathological characteristics of 8697 patients with COVID-19 in China: a meta-analysis

Jieyun Zhu 1, Zhimei Zhong 1, Pan Ji 1, Hongyuan Li 1, Bocheng Li 1, Jielong Pang 1, Jianfeng Zhang 1,✉, Chunling Zhao 1,✉
PMCID: PMC7229787  PMID: 32371463

Abstract

Objective

Our study aims to present a summary of the clinicopathological characteristics of patients affected by the coronavirus disease 2019 (COVID-19) that can be used as a reference for further research and clinical decisions.

Design

Studies were included in the meta-analysis if they had cohort, case–control or case series designs and provided sufficient details on clinical symptoms, laboratory outcomes and asymptomatic patients.

Setting

PubMed, Embase, Chinese Biomedical Literature Database, Wanfang, China Science and Technology Journal Database and China National Knowledge Infrastructure databases were electronically searched to identify related studies published between 1 January 2020 and 16 March 2020. Three reviewers independently examined the literature, extracted relevant data and assessed the risk of publication bias before including the studies in the meta-analysis.

Participants

The confirmed cases of COVID-19.

Results

A total of 55 unique retrospective studies involving 8697 patients with COVID-19 were identified. Meta-analysis showed that a higher proportion of infected patients were male (53.3%), and the two major symptoms observed were fever (78.4%) and cough (58.3%). Other common symptoms included fatigue (34%), myalgia (21.9%), expectoration (23.7%), anorexia (22.9%), chest tightness (22.9%) and dyspnoea (20.6%). Minor symptoms included nausea and vomiting (6.6%), diarrhoea (8.2%), headache (11.3%), pharyngalgia (11.6%), shivering (15.2%) and rhinorrhea (7.3%). About 5.4% of the patients were asymptomatic. Most patients showed normal leucocyte counts (64.7%) and elevated C reactive protein levels (65.9%). Lymphopaenia was observed in about 47.6% of the infected patients, along with abnormal levels of myocardial enzymes (49.4%) and liver function (26.4%). Other findings included leucopenia (23.5%), elevated D-dimer (20.4%), elevated erythrocyte sedimentation rate (20.4%), leucocytosis (9.9%), elevated procalcitonin (16.7%) and abnormal renal function (10.9%).

Conclusions

The most commonly experienced symptoms of patients with COVID-19 were fever and cough. Myalgia, anorexia, chest tightness and dyspnoea were found in some patients. A relatively small percentage of patients were asymptomatic and could act as carriers of the disease. Most patients showed normal leucocyte counts, elevated levels of C reactive protein and lymphopaenia, confirming the viral origin of the disease.

Keywords: communicable disease control

Introduction

In the spring of 2020, the coronavirus disease 2019 (COVID-19) pandemic has spread to more than 200 countries around the world.1 2 As of 27 March 2020, the total number of confirmed cases has exceeded 500 000.3 This pandemic has become a serious threat to global health and continues to challenge healthcare systems worldwide. It was determined to be caused by a novel coronavirus, the severe acute respiratory syndrome coronavirus 2.4 Therefore, it is critical to understand and identify the key clinical and laboratory characteristics of patients with COVID-19 in order to help in early detection and isolation of infected individuals, as well as minimise the spread of the disease.5

Although a number of studies have attempted to explore this subject, most of them were single-centre studies that were conducted in a specific hospital or region. Due to differences in study design and small samples, the clinical symptoms, laboratory findings and other key outcomes of these studies are complicated and unclear.6–8 For example, two recent systematic reviews9 10 of studies of patients with COVID-19 indicated a high incidence of fever (>88%) and cough (>68%), but only one10 reported symptoms of myalgia or fatigue (35.8%). Both reviews meta-analysed small samples pooled from 10 studies.

Therefore, the present meta-analysis was performed to provide the most extensive, up-to-date description so far of clinicopathological characteristics of patients with COVID-19 and to provide a reference for clinical decisions and future research.

Materials and methods

Search strategy and study eligibility

This meta-analysis was carried out based on the guidelines of the Preferred Reporting Items for Meta-Analyses of Observational Studies in Epidemiology Statement.11 We systematically examined the studies on clinicopathological characteristics of patients with COVID-19 indexed in the PubMed, Embase, Chinese Biomedical Literature, Wanfang, China Science and Technology Journal Database and China National Knowledge Infrastructure databases between 1 January 2020 and 16 March 2020. All references cited in these studies were also analysed manually to ensure that eligible papers were not overlooked. If multiple studies analysed the same patient population, we included only the one with more detailed information or the one published more recently. No language restrictions were incorporated during the literature search, and only literature published online was included. The following keywords were used, both separately and in combination, as part of the search strategy in each database: ‘Corona virus’, ‘Coronavirus’, ‘2019-nCoV’, ‘COVID-19’ or ‘SARS-CoV-2’ (box 1).

Box 1. Search strings used for the PubMed database.

# 1 Corona virus [Title/Abstract]

# 2 Coronavirus [Title/Abstract]

# 3 2019-nCoV [Title/Abstract]

# 4 COVID-19 [Title/Abstract]

# 5 SARS-CoV-2 [Title/Abstract]

# 6 # 1 OR # 2 OR # 3 OR # 4 OR # 5

Studies were included in the meta-analysis if they had cohort, case–control or case series designs and provided sufficient details on clinical symptoms, laboratory outcomes and asymptomatic patients. Only studies of more than 40 patients were included.

Data extraction and quality assessment

The literature selected was independently assessed by three reviewers based on the eligibility criteria, and relevant data were extracted. Disagreements were resolved by consensus. The titles and abstracts were first screened to identify the eligible articles, followed by a full-text review to obtain detailed information. When required, the authors were contacted directly to obtain further information and clarifications regarding their study. The following data were extracted from each included study: surname of first author; date of publication; study design; number, age and sex of patients; clinical and laboratory outcomes; and data relevant for assessing publication bias. The quality of observational case series was independently evaluated by the three reviewers based on the British National Institute for Clinical Excellence12 guidelines. This evaluation was conducted based on a set of eight criteria, and studies with a score greater than 4 were considered to be of high quality (total score=8).

Statistical analyses

All statistical analyses were performed using STATA V.12. Original incidence rates r were transformed by the double arcsine method to ensure a normal distribution, and the resulting transformed rate tr was used in single-arm meta-analysis. The heterogeneity between studies was analysed using a χ2 test (p<0.10) and quantified using the I2 statistic. When no statistical heterogeneity was observed, a fixed-effects model was used. Otherwise, potential sources of clinical heterogeneity were identified using subgroup and sensitivity analyses; these sources were eliminated, and the meta-analysis was repeated using a random-effects model. Pooled incidence rates R were back-calculated from transformed rates tr using the R=[sin (tr / 2)].2 A two-tailed p<0.05 was considered statistically significant. Publication bias was evaluated using a funnel plot along with Egger’s regression test and Begg’s test.

Results

Literature screening and assessment

A total of 5576 records were identified from the various databases examined. After a detailed assessment based on the inclusion criteria, 55 unique studies6–8 13–64 involving 8697 patients with COVID-19 were included in the meta-analysis (figure 1).

Figure 1.

Figure 1

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Flow chart depicting literature screening process.

Characteristics of included studies

All studies included in the meta-analysis were conducted in China and published between 6 February 2020 and 16 March 2020. These retrospective studies examined Chinese patients distributed across 31 provinces. A large proportion of these studies (n=40) were based on data collected from a single centre, with no clear explanation regarding eligibility criteria. Follow-up data were reported for most patients. All studies received quality scores of 5–8, indicating high quality (table 1).

Table 1.

Characteristics of included studies

Study Publication date Sample size (n) Study design Study population Age*
(years）		 Follow-up Outcomes reported Quality score
Zhaoet al 6 March 3 101 Retrospective. Patients with COVID-19 in Radiology Quality Control Center, Hunan. 21–50 NA ① 7
Xiong et al 7 March 3 42 Retrospective. Patients with COVID-19 in Tongji Hospital, Huazhong University of Science and Technology. 26–75 11 January–15 February ①② 7
Zhou et al 8 March 11 191 Retrospective, multicentre. Patients with COVID-19 in Wuhan Jinyintan Hospital and Wuhan Pulmonary Hospital. 56.0 December 2019–31 January 2020 ①② 7
Li et al 13 February23 54 Retrospective, single centre. Patients with COVID-19 in Wuhan Fourth Hospital. 51.5 January–February ①② 7
Xiao et al 14 February 27 143 Retrospective, single centre. Patients with COVID-19 in Chongqing Three Gorges Central Hospital. 45.1±1.0 23 January–8 February ①② 6
Sun et al 15 February 24 88 Retrospective, single centre. Patients with COVID-19 in Tianjin Haihe Hospital. 48.5±15.7 21 January–8 February 8 ① 7
Xu et al 16 February 25 45 Retrospective, single centre. Patients with COVID-19 in Hubei Provincial Hospital of Integrated Chinese and Western Medicine. 54.58±17 22 January–5 February ① 7
Lu et al 17 February 10 50 Retrospective, single centre. NA. 50.4±16.8 NA ①② 6
Wang et al 18 February 25 52 Retrospective, single centre. Patients with COVID-19 in The First Affiliated Hospital of Zhejiang University. 44±14 9 January–3 February ① 6
Liao et al 19 February 26 42 Retrospective, single-centre cohort. Patients with COVID-19 in Zhongnan Hospital of Wuhan University. 51.6 16 January–18 February ② 6
Yu et al 20 February 26 40 Retrospective, single centre. Patients with COVID-19 in Wenzhou Sixth People′s Hospital. 45.9 17 January–28 January ① 6
Liu et al 21 February 18 41 Retrospective, single centre. Patients with COVID-19 in Xiaochang First People's Hospital. 48.45 NA ② 6
Cheng and Li22 February 19 54 Retrospective, single centre. Patients with COVID-19 in The Affiliated Puren Hospital of Wuhan University of Science and Technology. 60.1±17 1 January–31 January ①② 7
Yang et al 23  March 3 57 Retrospective, single centre. Patients with COVID-19 in Nanjing Public Health Medical Centre. 37 NA ①② 7
Xiang et al 24  March 2 49 Retrospective, single centre. Patients with COVID-19 in The First Affiliated of Nanjing University. 42.9 21 January–27 January ① 6
Ma et al 25 March 10 75 Retrospective, multicentre. Patients with COVID-19 from four hospitals in Fuyang City. 43.9±15.1 20 January–18 February ② 7
Xue et al 26  March 10 66 Retrospective, single centre. Patients with COVID-19 in Shanghai Public Health Clinical Center. 46.0±15.6 NA ① 6
Gong et al 27  March 9 225 Retrospective, single centre Patients with COVID-19 in Chongqing Three Gorges Central Hospital. 0–82 20 January–16 February ① 7
Ran et al 28  March 6 209 Retrospective, multicentre. Patients with COVID-19 from four hospitals in Fuyang City. 46.5±15.7 25 January–10 February ① 7
Yuan et al 29 March 6 223 Retrospective, single centre. Patients with COVID-19 in Chongqing Public Medical Center. 46.5±16.1 24 January–23 February ①② 6
Shi et al 30 March 5 67 Retrospective, single centre. Patients with COVID-19 in Shanghai Public Health Clinical Center. 36±53.7 January–February ① 7
Xiong et al 31 March 3 89 Retrospective, single centre. Patients with COVID-19 in Renmin Hospital of Wuhan University. 53±16.9 17 January–20 February ① 6
Chen et al 32 March 13 139 Retrospective, single centre. Patients with COVID-19 in Chongqing Three Gorges Central Hospital. 15–79 January–February ① 6
Fang et al 33 Machr 12 308 Retrospective, single centre. Patients with COVID-19 in Hubei Huangshi Chinese Medicine Hospital. 30–86 25 January–20 February ① 7
Zhou et al 34 March 13 537 Retrospective, multicentre. All cases of COVID-19 in Shandong Province. 26–86 December 2019–15 February 2020 ①② 7
Li et al 35 March 12 524 Retrospective, multicentre. COVID-19 patients from hospitals in Henan Province. 45 2 January–20 February ① 8
Song et al 36 March 12 60 Retrospective, multicentre. Patients with COVID-19 in Gansu Provincial Designated Hospital. 39.5±17.7 21 January–22 February ① 7
Cheng et al 37 Mar 12 463 Retrospective, single centre. Patients with COVID-19 in Wuhan Jinyintan Hospital 15–90 December 2019–6 February 2020 ①② 6
Chen et al 38 March 10 76 Retrospective, single centre. Patients with COVID-19 in Puren Hospital of Wuhan University of Science and Technology. 59.5 January–February ①② 6
Cheng et al 39 March 2 1079 Retrospective, multicentre. All cases of COVID-19 in Henan Province. 46 December 2019–29 February 2020 ① 7
Han et al 40 March 16 150 Retrospective. Patients with COVID-19 from two hospitals in Wuhan. 53±14 12 January–16 February ①② 6
Xu et al 41 March 16 62 Retrospective, single centre. Critically ill patients with COVID-19 in Zhongnan Hospital of Wuhan University. 62.9 8 January–14 February ① 6
 Dong et al 42 March 13 135 Retrospective, multicentre. All reported confirmed cases of COVID-19 in Tianjin. 48.6±16.8 December 2019–24 February 2020 ① 7
Sun et al 43 March 15 391 Retrospective. COVID-19 cases reported in Zhejiang province. NA NA ① 7
Li et al 44 February 29 83 Retrospective. Patients with COVID-19 in The Second Affiliated Hospital of Chongqing Medical University 45.5±12.3 January–February ①② 7
Wu et al 45 February 21 80 Retrospective, single centre. Patients with COVID-19 from three tertiary hospitals in Jiangsu. 46.1 22 January–14 February ①② 7
Xu et al 46 February 28 90 Retrospective, single centre. Patients with COVID-19 in Guangzhou Eighth People’s Hospital. 50 2 3January–4 February ①② 6
Xu et al 47 February 25 50 Retrospective, single centre. Patients with COVID-19 in The Fifth Medical Centre of Chinese PLA General Hospital. NA 1 January–2 February ①② 6
Yang et al 48 February 26 149 Retrospective, multicentre. Patients with COVID-19 from three tertiary hospitals in Wenzhou. 45.1±13.4 17 January–10 February ①② 7
Xu et al 49 February 19 62 Retrospective, multicentre. Patients with COVID-19 from seven hospitals in Zhejiang Province. 41 10 January–26 January ①② 6
Zhang et al 50 February 23 140 Retrospective, single centre. Patients with COVID-19 in No.7 Hospital in Wuhan. 57.0 16 January–3 February ①② 6
Wang et al 51 February 8 138 Retrospective, single-centre case series. Patients with COVID-19 in Zhongnan Hospital of Wuhan University. 56 (42–68） 1 January–28 January ①② 6
Liu et al 52 February 18 137 Retrospective, multicentre. Patients with COVID-19 from nine tertiary hospitals in Hubei Province. 55±16 30 December 2019–24 January 2020 ①② 6
Huang et al 53 February 15 41 Retrospective, single centre. Patients with COVID-19 in Hubei Province. 49 (41–58) December 2019–2 January 2020 ①② 6
Chen et al 54 February 15 99 Retrospective, single centre. Patients with COVID-19 in Wuhan Jinyintan Hospital. 55.5±13.1 1 January–20 January ①② 6
Guan et al 55 February 6 1099 Retrospective, multicentre. Patients with COVID-19 from 552 hospitals in 31 provinces. 47.0 NA ①② 8
Bernhem et al 56 February 20 121 Retrospective case series. Patients with COVID-19 from four hospitals in four Chinese provinces. 45.3 18 January–2 February ① 8
Wu et al 57 February 21 80 Retrospective, multicentre. Patients with COVID-19 from three hospitals in Chongqing. 44±11 January–February ①② 7
Shi et al 58 February 21 81 Retrospective, multicentre cohort. Patients with COVID-19 in Wuhan Jinyintan Hospital and Union Hospital of Tongji Medical College. 49.5–11.0 18 January–2 February ①② 7
Yang et al 59 February 24 52 Retrospective, single centre. Critically ill patients with COVID-19 in Wuhan Jinyintan Hospital. 59.7–13.3 2 December 2019–23 January 2020 ① 6
Zhou et al 60 March 5 62 Retrospective. Patients with COVID-19 in Huazhong University of Science and Technology. 52.8–12.2 16 January–30 January ①② 6
Wang et al 61 February 28 50 Retrospective, multicentre. Patients with COVID-19 from four hospitals in Jilin Province. 44.5±16.1 28 January–21 February ① 8
Fang et al 62 February 25 79 Retrospective, single centre. Patients with COVID-19 in Anhui Provincial Hospital. 45.1±16.6 22 January–18 February ①② 5
Yu et al 63 February 17 40 Retrospective, single centre. Patients with COVID-19 in the Chinese People's Liberation Army General Hospital. 39.9±18.2 21 January–February ①② 6
Zhang et al 64 February 19 42 Retrospective, single centre. Patients with COVID-19 in Nanjing Hospital, affiliated to Nanjing University of Traditional Chinese Medicine. 43±16.8 19 January–February ①② 5
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① Clinical symptoms; ② laboratory findings.

*Reported variously as range or mean±SD or median, and IQR values.

NA, not reported.

Meta-analysis results

Gender distribution

Relevant data regarding the clinicopathological characteristics of 8697 patients with COVID-19 was collected.6–8 13–64 Significant heterogeneity was observed across all included studies (I2=93.7%), therefore, a random-effects model was used in the meta-analysis. We found that 53.3% (95%CI 50.3 to 56.4) of the patients were male.

Clinical symptoms

Two major symptoms, including fever (78.4%) and cough (58.3%), were highly prevalent among patients. Fatigue (34%), myalgia (21.9%), expectoration (23.7%), anorexia (22.9%), chest tightness (22.9%) and dyspnoea (20.6%) also occurred frequently. Less frequent symptoms were nausea and vomiting (6.6%), diarrhoea (8.2%), headache (11.3%), pharyngalgia (11.6%), shivering (15.2%) and rhinorrhea (7.3%). Only 5.4% of patients with COVID-19 were found to be asymptomatic (table 2).

Table 2.

Clinical symptoms observed in patients with COVID-19

Symptom No. of studies No. of patients Heterogeneity Model Meta-analysis
P value I2 (%) R (95% CI) P value
Fever 51 8473 <0.001 95.9 Random 0.784 (0.736 to 0.828) <0.001
Cough 52 8539 <0.001 97.2 Random 0.583 (0.515 to 0.649) <0.001
Fatigue 45 7848 <0.001 96.9 Random 0.340 (0.277 to 0.405) <0.001
Myalgia 37 5625 <0.001 93.0 Random 0.219 (0.177 to 0.264) <0.001
Headache 34 6414 <0.001 88.5 Random 0.113 (0.089 to 0.140) <0.001
Diarrhoea 43 7904 <0.001 87.5 Random 0.082 (0.064 to 0.102) <0.001
Expectoration 33 6408 <0.001 95.8 Random 0.237 (0.185 to 0.294) <0.001
Dyspnoea 25 3670 <0.001 87.5 Random 0.206 (0.133 to 0.290) <0.001
Chest tightness 30 5773 <0.001 97.2 Random 0.229 (0.163 to 0.304) <0.001
Nausea and vomiting 24 4941 <0.001 82.2 Random 0.066 (0.048 to 0.086) <0.001
Pharyngalgia 31 5947 <0.001 88.7 Random 0.116 (0.090 to 0.145) <0.001
Rhinorrhoea 13 3111 <0.001 91.2 Random 0.073 (0.042 to 0.113) <0.001
Anorexia 19 3274 <0.001 96.9 Random 0.229 (0.143 to 0.326) <0.001
Shivering 16 4394 <0.001 96.8 Random 0.152 (0.090 to 0.228) <0.001
Asymptomatic 10 878 0.002 66.3 Random 0.054 (0.031 to 0.084) <0.001
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Pathological characteristics

A large proportion of patients had normal leucocyte counts (64.7%) and high levels of C reactive protein (65.9%) (figures 2 and 3). Lymphopaenia was observed in many patients (47.6%), along with elevated levels of myocardial enzymes (49.4%) and abnormal liver function (26.4%). Also observed were leucopenia (23.5%), leucocytosis (9.9%), abnormal renal function (10.9%), elevated levels of D-dimer (20.4%), elevated erythrocyte sedimentation rate (20.4%) and elevated procalcitonin (16.7%) (table 3).

Figure 2.

Figure 2

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Transformed incidence rate of normal leucocyte count in patients with COVID-19.

Figure 3.

Figure 3

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Transformed incidence rate of high C reactive protein levels in patients with COVID-19.

Table 3.

Pathological characteristics of patients with COVID-19

Characteristic No. of studies No. of patients Heterogeneity Model Meta-analysis
P value I2 (%) R (95% CI） P value
Leucocytosis 21 3936 <0.001 90.6 Random 0.099 (0.069 to 0.134) <0.001
Normal leucocyte count 23 3267 <0.001 89.5 Random 0.647 (0.591 to 0.700) <0.001
Leucopenia 27 4233 <0.001 89.6 Random 0.235 (0.194 to 0.279) <0.001
Lymphopaenia 32 4660 <0.001 94.4 Random 0.476 (0.413 to 0.540) <0.001
High C reactive protein 23 2912 <0.001 93.2 Random 0.659 (0.586 to 0.728) <0.001
High procalcitonin 13 2190 <0.001 96.6 Random 0.167 (0.083 to 0.274) <0.001
High D-dimer 9 2354 <0.001 90.4 Random 0.204 (0.147 to 0.267) <0.001
High erythrocyte sedimentation rate 7 455 <0.001 90.4 Random 0.204 (0.147 to 0.267) <0.001
Abnormal liver function 11 2524 <0.001 90.1 Random 0.264 (0.204 to 0.329) <0.001
Abnormal renal function 8 2183 <0.001 96.1 Random 0.109 (0.045 to 0.196) <0.001
High myocardial enzymes 11 2541 <0.001 96.1 Random 0.494 (0.264 to 0.725) <0.001
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Subgroup analysis

Patients were stratified into two groups based on the date of initial diagnosis: group 1 included all patients and group 2 included those diagnosed between December 2019 and 31 January 2020 (table 4). We found that all patients diagnosed before 31 January had higher incidence rates of fever and cough. No significant difference was observed in the heterogeneity between the subgroups and the overall heterogeneity, indicating that the date of initial diagnosis was not the main source of heterogeneity.

Table 4.

Analysis of clinical symptoms observed in patients with COVID-19, stratified by date of initial diagnosis*

Clinical symptom No. of studies No. of patients Heterogeneity Model Meta-analysis
P value I2 (%) R (95% CI) P value
Fever
 Group 1 51 8473 <0.001 95.9 Random 0.784 (0.736 to 0.828) <0.001
 Group 2 14 2162 <0.001 97.9 Random 0.813 (0.667 to 0.924) <0.001
Fatigue
 Group 1 45 7848 <0.001 96.9 Random 0.340 (0.277 to 0.405) <0.001
 Group 2 11 1971 <0.001 93.9 Random 0.366 (0.268 to 0.470) <0.001
Cough <0.001
 Group 1 52 8539 <0.001 97.2 Random 0.583 (0.515 to 0.649) <0.001
 Group 2 14 2162 <0.001 86.6 Random 0.640 (0.574 to 0.703) <0.001
Myalgia <0.001
 Group 1 37 5625 <0.001 93.0 Random 0.219 (0.177 to 0.264) <0.001
 Group 2 10 1938 <0.001 91.7 Random 0.271 (0.193 to 0.358) <0.001
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*Group 1: all patients; group 2: diagnosed before 31 January 2020.

Sensitivity analysis

A sensitivity analysis was carried out by excluding one study at a time and reanalysing the entire dataset. We found that the pooled incidence rates did not change substantially, indicating the reliability and stability of our meta-analysis (eg, figure 4).

Figure 4.

Figure 4

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Sensitivity analysis of the incidence rate of expectoration in patients with COVID-19.

Evaluation of publication bias

The p values derived using the Egger’s and the Begg’s test for all the clinicopathological characteristics showed no obvious publication bias (table 5). A funnel plot based on the incidence rate of fever showed p values of 0.091 in Egger’s test and 0.703 in Begg’s test (figure 5). These results confirm that there is no publication bias.

Table 5.

Evaluation of publication bias using the Egger’s and the Begg’s test

Characteristic P (Egger’s) P (Begg’s) Characteristic P (Egger’s) P (Begg’s)
Fever 0.091 0.703 Shivering 0.642 0.137
Cough 0.259 0.776 Asymptomatic 0.840 0.589
Fatigue 0.094 0.018 Leucocytosis 0.087 0.238
Myalgia <0.001 <0.001 Normal leucocyte count 0.760 0.195
Headache 0.034 0.015 Leucopenia 0.790 0.587
Diarrhoea 0.001 0.004 Lymphopenia 0.062 0.910
Expectoration 0.208 0.018 High C reactive protein 0.001 0.138
Dyspnoea 0.386 0.088 High procalcitonin 0.022 0.222
Chest tightness 0.234 0.164 High D-dimer 0.363 0.466
Nausea and vomiting 0.102 0.092 High erythrocyte sedimentation rate 0.028 0.048
Pharyngalgia 0.089 0.086 Abnormal liver function 0.050 0.640
Rhinorrhea 0.748 0.059 Abnormal renal function 0.015 0.686
Anorexia 0.002 0.006 High myocardial enzymes 0.791 0.350
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Figure 5.

Figure 5

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Evaluation of publication bias using a funnel plot based on the incidence rate of fever.

Discussion

In this meta-analysis, we examined 55 independent studies6–8 13–64 reporting clinicopathological data on 8697 patients with COVID-19 distributed across 31 provinces in China. The studies included in this analysis comprise the latest research available on COVID-19 through 16 March 2020. Our results indicate that there is a slightly higher proportion of male patients (53.3%) and that the main symptoms of this disease are fever (78.4%), cough (58.3%) and fatigue (34%). Compared with previous results,9 10 our findings reveal lower incidence rates of the two major symptoms of this disease, which we found to depend to some extent on whether diagnosis was made before or after 31 January 2020, reflecting with the progress of the epidemic, the number of atypical manifestations has growed gradually. For example, some patients developed gastrointestinal symptoms, such as diarrhoea, nausea and vomiting. These results highlight the importance of also taking into account non-respiratory symptoms of the disease.

Most patients with COVID-19 showed normal leucocyte counts and lymphopqenia. Few patients had leucocytosis and elevated procalcitonin levels, confirming that this disease is transmitted by a virus. Therefore, it is essential for clinicians to use such pathological findings to rule out the presence of bacterial infections. In this study, 49.4% of the patients presented with myocardial enzyme spectrum abnormalities, which manifested as an increase in lactate dehydrogenase levels. Studies have shown that elevated levels of lactate dehydrogenase can be a risk factor for rapid progression from mild to critical COVID-19.65 Therefore, monitoring the function of important organs during treatment is critical, and treatment should be adjusted as needed to preserve and maintain organ function.

Infected people who are asymptomatic can act as a source of infection,66 especially since the estimated median incubation period is 5–6 days (range 0–14 days). An analysis by the Chinese Center for Disease Control and Prevention conducted through 17 February 2020 suggested that the proportion of asymptomatic patients was only around 1%,67 but our results suggest that the proportion is closer to 5%. This increase may reflect the growing experience of hospitals with this novel disease and increasing screening of suspected COVID-19 cases for viral infection, allowing the correct diagnosis of greater proportions of patients showing no or less typical manifestations. Therefore, to control the spread of this disease, general practitioners should carefully monitor individuals with histories of contact in areas where outbreaks have occurred or who had contact with suspected or confirmed cases of COVID-19 within 14 days before onset of symptoms.68 Epidemiological history of patients should be investigated in detail, and asymptomatic infected people in the community should be identified as quickly as possible to control spread of the disease.

A recent study suggests that, considering different scenarios, highly effective contact tracing and case isolation are sufficient to control a new outbreak of COVID-19 within 3 months.69 Therefore, isolation, quarantine, social distancing, and community containment measures should be rapidly implemented in high-risk countries or regions.70 In China, community engagement has been the first line of defence in the battle against the COVID-19 pandemic. General practitioners act as both gatekeepers and health promoters by educating the public and guiding the community in the fight against this disease.71 Monitoring people at designated checkpoints, intercepting transmission routes in a timely manner and preventing local outbreaks are critical to prevent repeat epidemics.72

Although this study rigorously analysed clinical and laboratory data collected from a large sample of patients with COVID-19, we were unable to eliminate the significant heterogeneity observed between studies. For example, the course and the severity of the disease varied across studies. Given that most of the studies included in our meta-analysis were single-centre, retrospective studies, it was difficult for us to control for the effects of several confounding factors, including bias in patient admission and selection, as well as differences in disease severity and course. Further research is required to verify and extend our results for China. Continued surveillance across multiple countries, along with transparent and accurate reporting of patient characteristics and testing policies, will help us gain a better understanding of this global pandemic.73

Conclusion

In summary, Current evidence showed that the most commonly experienced symptoms of patients with COVID-19 were fever and cough. Myalgia, anorexia, chest tightness and dyspnoea were found in some patients. A relatively small percentage of patients were asymptomatic and could act as carriers of the disease. Most patients showed normal leucocyte counts, elevated levels of C reactive protein and lymphopenia, confirming the viral origin of the disease. Due to limited quality and quantity of the included studies, more high-quality prospective studies are required to verify above conclusions.

Footnotes

Contributors: PJ, JP and BL collected and analysed the data. HL helped collect the data. JZ acquired the funding. JZ designed the study, and wrote the first draft of the manuscript. CZ and JZ designed and supervised the study, and finalised the manuscript, which all authors read and approved.

Funding: This study was supported by grants from the National Natural Science Foundation of China (81960343); the Emergency Science and Technology Brainstorm Project for the Prevention and Control of COVID-19, which is part of the Guangxi Key Research and Development Plan (2020AB39028).

Competing interests: None declared.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement: Data are available on reasonable request.

References

  • 1. WHO Who Director-General's opening remarks at the media briefing on COVID-19 -11. Available: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-11-march-2020 [Accessed 29 Mar 2020].
  • 2. WHO Coronavirus disease(COVID-19) outbreak situation. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 [Accessed 29 Mar 2020].
  • 3. WHO Coronavirus disease 2019 (COVID-19)Situation Report-67). Available: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200327-sitrep-67-covid-19.pdf?sfvrsn=b65f68eb_4 [Accessed 29 Mar 2020].
  • 4. WHO Naming the coronavirus disease (COVID-19) and the virus that causes it. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it [Accessed 29 Mar 2020].
  • 5. Zhang MQ, Wang XH, Chen YL, et al. [Clinical features of 2019 novel coronavirus pneumonia in the early stage from a fever clinic in Beijing]. Zhonghua Jie He He Hu Xi Za Zhi 2020;43:E013. 10.3760/cma.j.issn.1001-0939.2020.0013 [DOI] [PubMed] [Google Scholar]
  • 6. Zhao W, Zhong Z, Xie X, et al. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. AJR Am J Roentgenol 2020:1–6. 10.2214/AJR.20.22976 [DOI] [PubMed] [Google Scholar]
  • 7. Xiong Y, Sun D, Liu Y, et al. Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes. Invest Radiol 2020. 10.1097/RLI.0000000000000674. [Epub ahead of print: 03 Mar 2020]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395:1054–62. 10.1016/S0140-6736(20)30566-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Sun P, Qie S, Liu Z, et al. Clinical characteristics of 50466 hospitalized patients with2019-nCoV infection. J Med Virol 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. LQ L, Huang T, Wang YQ, et al. COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis. J Med Virol 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Stroup DF, Berlin JA, Morton SC, et al. Meta-Analysis of observational studies in epidemiology: a proposal for reporting. meta-analysis of observational studies in epidemiology (moose) group. JAMA 2000;283:2008–12. 10.1001/jama.283.15.2008 [DOI] [PubMed] [Google Scholar]
  • 12. NICE Appendix 4: quality of case series. Available: https://www.nice.org.uk/guidance/cg3/documents/appendix-4-quality-of-case-series-form2 [Accessed 29 Mar 2020].
  • 13. Li YY, Wang WN, Lei Y, et al. [Comparison of the clinical characteristics between RNA positive and negative patients clinically diagnosed with 2019 novel coronavirus pneumonia]. Zhonghua Jie He He Hu Xi Za Zhi 2020;43:E023. 10.3760/cma.j.cn112147-20200214-00095 [DOI] [PubMed] [Google Scholar]
  • 14. Xiao KF, Shui LL, Pang XH, et al. The clinical features of the 143 patients withCOVID-19 in north-east of Chongqing. Journal of Third Military Medical University 2020. [Google Scholar]
  • 15. Sun HY, YF B, Zhu ZG, et al. A preliminary study on TCM syndrome characteristics of 88 patients with COVID-19 in Tianjin. J Tradit Chin Med 2020. [Google Scholar]
  • 16. Xu B, Fan CY, Zou YL, et al. Analysis of traditional Chinese medicine syndromes of 46 cases of COVID-19. Chinese journal of experimental traditional medical formulae 2020. [Google Scholar]
  • 17. YF L, Yang ZG, Wang M. Analysis on Chinese medical clinical characteristics of 50 patients with 2019-nCoV-infected pneumonia. Academic Journal of Shanghai University of traditional Chinese medicine, 2020. [Google Scholar]
  • 18. Wang J, Liu JP, Wang YY, et al. [Dynamic Changes of Chest CT Imaging in Patients With Corona Virus disease-19 (COVID-19)]. Zhejiang Da Xue Xue Bao Yi Xue Ban 2020;49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Liao XN, Zhou J, Cao J, et al. Chest CT features comparison between COVID⁃19 and bacterial pneumonia. Medical Journal of Wuhan university 2020;26. [Google Scholar]
  • 20. XT Y, Ye H, Yang SF, et al. Chest CT features of COVID-19. The Journal of Practical Medicine 2020. [Google Scholar]
  • 21. Liu FM, Ding HL, Gong XM, et al. Chest CT performance and clinical characteristics of coronavirus disease 2019 (COVID-19). Radiologic Practice 2020. [Google Scholar]
  • 22. Cheng DZ, Li Y. Clinical effectiveness and case analysis in 54 NCp patients treated with lanhuaqingwen granules. World Chinese Medicine 2020. [Google Scholar]
  • 23. Yang K, Ren MH, Xiao YL, et al. Epidemiological and clinical characteristics of 57 casesof new coronavirus pneumonia in non-epidemic areas. J Third Mil Med Univ 2020. [Google Scholar]
  • 24. Xiang TX, Liu JM, Xu F, et al. Analysis of clinical characteristics of 49 patientswith novel coronavirus pneumonia in Jiangxi Province. Chinese J Respir Crit Care Med 2020. [Google Scholar]
  • 25. PQ M, Yuan YS, Zhang L, et al. Manifestations of the initial chest CT and its association with laboratory tests in 75 COVID-19 patients. Int J Med Radiol 2020. [Google Scholar]
  • 26. Xue HH, Zhang HY, Pang ZH, et al. Analysis on TCM clinical characteristics of66 COVID-19 cases in the recover period. Shan J Tradit Chin Med 2020. [Google Scholar]
  • 27. Gong X, Mou FZ, Wei DR, et al. The clinical characteristics and medication analysis of corona virus disease 2019. World Chinese Medicine 2020. [Google Scholar]
  • 28. Ran J, YP L, QT L, et al. Study of TCM syndrome in 209 novel coronavirus pneumonia cases of Chongqing in 2020. Journal of Emergency in Traditional Chinese Medicine 2020. [Google Scholar]
  • 29. Yuan J, Sun YL, Zuo YJ, et al. Clinical characteristics of 223 novel coronavirus pneumonia cases in Chongqing. Journal of Southwest University(Natural Science Edition) 2020. [Google Scholar]
  • 30. Shi J, Yang ZG, Chen Y, et al. Clinical observation on 49 cases of non-critical coronavirus disease 2019 in Shanghai treated by integrated traditional Chinese and Western medicine. Shanghai Journal of Traditional Chinese Medicine 2020. [Google Scholar]
  • 31. Xiong J, Jiang WL, Zhou Q, et al. Clinical characteristics, treatment, and prognosis in89 cases of COVID‐2019. Medical Journal of Wuhan University(Health Sciences) 2020. [Google Scholar]
  • 32. Chen X, Tong J, Xiang JH, et al. Retrospective study on the epidemiological characteristics of 139 patients with novel coronavirus pneumonia on the effects of severity. Chongqing Medicine 2020. [Google Scholar]
  • 33. Fang L, Zhu QG, Cheng W, et al. Retrospective analysis on 308 cases of COVID-19 and clinical application program of Kang Yi Qiang Shen Gong exercise prescription. Shanghai Journal of Traditional Chinese Medicine 2020. [Google Scholar]
  • 34. Zhou SY, Wang CT, Zhang W, et al. Clinical characteristics and treatment effect of 537 cases of novel coronavirus pneumonia in Shandong Province. Journal of Shandong University(Health Sciences) 2020. [Google Scholar]
  • 35. JS L, SY L, Xie Y, et al. Clinical characteristics and TCM syndrome distribution in 524 cases of novel coronavirus pneumonia in Henan. Journal of Traditional Chinese Medicine 2020. [Google Scholar]
  • 36. Song ZY, Yong WX, Li J, et al. Analysis on TCM syndrome rules of 60 Casesof novel coronavirus pneumonia in Gansu area. Chinese Journal of Information on TCM 2020. [Google Scholar]
  • 37. Cheng KB, Wei M, Shen H, et al. Clinical characteristics of 463 patients with common and severe type coronavirus disease 2019. Shanghai Medical Journal 2020. [Google Scholar]
  • 38. Chen T, Jiang ZY, Xu W, et al. Clinical features and CT imaging analysis of 76patiens with corona virus disease 2019. Journal of Jinan University 2020. [Google Scholar]
  • 39. Cheng JL, Huang C, Zhang GJ, et al. [Epidemiological characteristics of novel coronavirus pneumonia in Henan]. Zhonghua Jie He He Hu Xi Za Zhi 2020;43:E027. 10.3760/cma.j.cn112147-20200222-00148 [DOI] [PubMed] [Google Scholar]
  • 40. Han J, Dong XF, Hu F, et al. Clinical characteristics of 120 patients infected with SARS-CoV-2. Guangdong Medical Journal 2020. [Google Scholar]
  • 41. Xu S, HT H, YG H, et al. Clinical features of 62 cases of coronavirus disease 2019complicated with acute renal injury. Medical Journal of Wuhan University 2020:1–5. [Google Scholar]
  • 42. Dong XC, Li JM, Bai JY, et al. [Epidemiological characteristics of confirmed COVID-19 cases in Tianjin]. Zhonghua Liu Xing Bing Xue Za Zhi 2020;41:638–42. 10.3760/cma.j.cn112338-20200221-00146 [DOI] [PubMed] [Google Scholar]
  • 43. Sun WW, Ling F, Pan JR, et al. [Epidemiological characteristics of 2019 novel coronavirus family clustering in Zhejiang Province]. Zhonghua Yu Fang Yi Xue Za Zhi 2020;54:E027. 10.3760/cma.j.cn112150-20200227-00199 [DOI] [PubMed] [Google Scholar]
  • 44. Li K, Wu J, Wu F, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol 2020;2020. 10.1097/RLI.0000000000000672. [Epub ahead of print: 29 Feb 2020]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Wu J, Liu J, Zhao X, et al. Clinical characteristics of imported cases of COVID-19 in Jiangsu Province: a multicenter descriptive study. Clin Infect Dis 2020. 10.1093/cid/ciaa199. [Epub ahead of print: 29 Feb 2020]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Xu X, Yu C, Qu J, et al. Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2. Eur J Nucl Med Mol Imaging 2020;47:1275–80. 10.1007/s00259-020-04735-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Xu Y-H, Dong J-H, An W-M, et al. Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2. J Infect 2020;80:394–400. 10.1016/j.jinf.2020.02.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Yang W, Cao Q, Qin L, et al. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China. J Infect 2020;80:388–93. 10.1016/j.jinf.2020.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Xu X-W, Wu X-X, Jiang X-G, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ 2020;368:m606. 10.1136/bmj.m606 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Zhang J-J, Dong X, Cao Y-Y, et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 2020. 10.1111/all.14238. [Epub ahead of print: 19 Feb 2020]. [DOI] [PubMed] [Google Scholar]
  • 51. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061. 10.1001/jama.2020.1585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Kui L, Fang YY, Deng Y, et al. Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chin Med J 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506. 10.1016/S0140-6736(20)30183-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13. 10.1016/S0140-6736(20)30211-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Guan WJ, ZY N, Hu Y, et al. Clinical characteristics of 2019 novel coronavirus infection in China. N Engl J Med 2020. [Google Scholar]
  • 56. Bernheim A, Mei X, Huang M, et al. Chest CT findings in coronavirus Disease-19 (COVID-19): relationship to duration of infection. Radiology 2020:200463. 10.1148/radiol.2020200463 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Wu J, Wu X, Zeng W. Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features, 2020. Available: https://www-ncbi-nlm-nih-gov.library.westernwyoming.edu/pubmed/?term=32091414 10.1097/RLI.0000000000000670 [DOI] [PMC free article] [PubMed]
  • 58. Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis 2020;20:425–34. 10.1016/S1473-3099(20)30086-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59. Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020. 10.1016/S2213-2600(20)30079-5. [Epub ahead of print: 24 Feb 2020]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60. Zhou S, Wang Y, Zhu T, et al. Ct features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China. AJR Am J Roentgenol 2020:1–8. 10.2214/AJR.20.22975 [DOI] [PubMed] [Google Scholar]
  • 61. Wang T, Shi L, Chen YY, et al. Clinical efficacy analysis of 50 cases of corona virus disease 2019 in traditional Chinese medicine. Jilin Journal of Chinese Medicine 2020. [Google Scholar]
  • 62. Fang XW, Mei Q, Yang TJ, et al. Clinical characteristics and treatment analysis of 79 cases of COVID-19. Chinese Pharmacological Bulletin 2020. [Google Scholar]
  • 63. SM Y, Cui YF, Wang ZX, et al. Analysis of the relationship between clinical features and tongue manifestations of 40 cases with novel coronavirus pneumonia. Beijing Journal Of Traditional Chinese Medicine 2020. [Google Scholar]
  • 64. Zhang X, Li L, Dai GC, et al. A preliminary study on the clinical characteristicsandchinese medical syndrome of 42 cases of COVID-19 in Nanjing. Journal of Nanjing University of Traditional Chinese Medicine 2020. [Google Scholar]
  • 65. Phan LT, Nguyen TV, Luong QC, et al. Importation and human-to-human transmission of a novel coronavirus in Vietnam. N Engl J Med 2020;382:872–4. 10.1056/NEJMc2001272 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66. National Health Commission Office The Guideline for the diagnosis and treatmentof novel coronavirus pneumonia (trial version sixth). Available: http://www.nhc.gov.cn/xcs/zhengcwj/202002/8334a8326dd94d329df351d7da8aefc2/files/b218cfeb1bc54639af227f922bf6b817.pdf [Accessed 29 Mar 2020].
  • 67. Strategy and Policy Working Group for NCIP Epidemic Response [Urgent research agenda for the novel coronavirus epidemic: transmission and non-pharmaceutical mitigation strategies]. Zhonghua Liu Xing Bing Xue Za Zhi 2020;41:1–6. 10.3760/cma.j.issn.0254-6450.2020.02.001 [DOI] [PubMed] [Google Scholar]
  • 68. Razai MS, Doerholt K, Ladhani S, et al. Coronavirus disease 2019 (covid-19): a guide for UK GPs. BMJ 2020;368:m800. 10.1136/bmj.m800 [DOI] [PubMed] [Google Scholar]
  • 69. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health 2020;8:e488–96. 10.1016/S2214-109X(20)30074-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Hopman J, Allegranzi B, Mehtar S. Managing COVID-19 in low- and middle-income countries. JAMA 2020. 10.1001/jama.2020.4169. [Epub ahead of print: 16 Mar 2020]. [DOI] [PubMed] [Google Scholar]
  • 71. Li DKT, Zhu S. Contributions and challenges of general practitioners in China fighting against the novel coronavirus crisis. Fam Med Community Health 2020;8:e000361. 10.1136/fmch-2020-000361 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72. ZB H, Song C. Discovery and management of novel coronavirus pneumonia infectedwith latent infection. Chinese Journal of Preventive Medicine 2020;54. [Google Scholar]
  • 73. Onder G, Rezza G, Brusaferro S. Case-Fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA 2020. 10.1001/jama.2020.4683. [Epub ahead of print: 23 Mar 2020]. [DOI] [PubMed] [Google Scholar]

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