Amazon Web Services · Web viewSupplementary materials. Table of Contents. Supplementary methods6. The Detailed Definitions of the Severity and Prognostic Endpoints of COVID-196

Supplementary materials

Table of Contents

Supplementary methods6

The Detailed Definitions of the Severity and Prognostic Endpoints of COVID-196

Table S1 Characteristics of the included studies.8

Table S2 Quantitative data synthesis for the associations of the epidemiological, comorbidity factors with prognosis of COVID-1916

Figure S1 Forest plot of association between sex and disease severity.20

Figure S2 Forest plot of association between smoking and disease severity.21

Figure S3 Forest plot of association between current smoker and disease severity.22

Figure S4 Forest plot of association between ex-smoker and disease severity.23

Figure S5 Forest plot of association between drinking and disease severity.24

Figure S6 Forest plot of association between local residents of Wuhan and disease severity.25

Figure S7 Forest plot of association between exposure history to Hubei province and disease severity.26

Figure S8 Forest plot of association between contact with confirmed or suspect cases and disease severity.27

Figure S9 Forest plot of association between family cluster and disease severity.28

Figure S10 Forest plot of association between Huanan seafood market exposure and disease severity.29

Figure S11 Forest plot of association between comorbidity and disease severity.30

Figure S12 Forest plot of association between hypertension and disease severity.31

Figure S13 Forest plot of association between diabetes and disease severity.32

Figure S14 Forest plot of association between malignancy and disease severity.33

Figure S15 Forest plot of association between cardiovascular disease and disease severity.34

Figure S16 Forest plot of association between coronary heart disease and disease severity.35

Figure S17 Forest plot of association between cerebrovascular disease and disease severity.36

Figure S18 Forest plot of association between cardiovascular/ cerebrovascular disease and disease severity.37

Figure S19 Forest plot of association between COPD and disease severity.38

Figure S20 Forest plot of association between respiratory system disease and disease severity.39

Figure S21 Forest plot of association between chronic kidney disease and disease severity.40

Figure S22 Forest plot of association between chronic liver disease and disease severity.41

Figure S23 Forest plot of association between hepatitis B infection and disease severity.42

Figure S24 Forest plot of association between lithiasis and disease severity.43

Figure S25 Forest plot of association between autoimmune disease and disease severity.44

Figure S26 Forest plot of association between abnormal lipid metabolism and disease severity.45

Figure S27 Forest plot of association between digestive disease and disease severity.46

Figure S28 Forest plot of association between thyroid disease and disease severity.47

Figure S29 Forest plot of association between tuberculosis and disease severity.48

Figure S30 Forest plot of association between nervous system disease and disease severity.49

Figure S31 Forest plot of association between endocrine system disease and disease severity.50

Figure S32 Forest plot of association between death and sex.51

Figure S33 Forest plot of association between death and smoking.52

Figure S34 Forest plot of association between death and current smoking.53

Figure S35 Forest plot of association between death and ex-smoking.54

Figure S36 Forest plot of association between death and contact with confirmed or suspect cases.55

Figure S37 Forest plot of association between death and Huanan seafood market exposure.56

Figure S38 Forest plot of association between death and comorbidities.57

Figure S39 Forest plot of association between death and hypertension.58

Figure S40 Forest plot of association between death and diabetes.59

Figure S41 Forest plot of association between death and malignancy.60

Figure S42 Forest plot of association between death and cardiovascular disease.61

Figure S43 Forest plot of association between death and coronary heart disease.62

Figure S44 Forest plot of association between death and cerebrovascular disease.63

Figure S45 Forest plot of association between death and COPD.64

Figure S46 Forest plot of association between death and respiratory system disease.65

Figure S47 Forest plot of association between death and chronic kidney disease.66

Figure S48 Forest plot of association between death and hepatitis B infection.67

Figure S49 Forest plot of association between death and autoimmune disease.68

Figure S50 Forest plot of association between admission to ICU and sex.69

Figure S51 Forest plot of association between admission to ICU and smoking.70

Figure S52 Forest plot of association between admission to ICU and drinking.71

Figure S53 Forest plot of association between admission to ICU and Huanan seafood market exposure.72

Figure S54 Forest plot of association between admission to ICU and comorbidities.73

Figure S55 Forest plot of association between admission to ICU and hypertension.74

Figure S56 Forest plot of association between admission to ICU and diabetes.75

Figure S57 Forest plot of association between admission to ICU and malignancy.76

Figure S58 Forest plot of association between admission to ICU and cardiovascular disease.77

Figure S59 Forest plot of association between admission to ICU and cerebrovascular disease.78

Figure S60 Forest plot of association between admission to ICU and COPD.79

Figure S61 Forest plot of association between admission to ICU and respiratory system disease.80

Figure S62 Forest plot of association between admission to ICU and chronic kidney disease.81

Figure S63 Forest plot of association between admission to ICU and chronic liver disease.82

Figure S64 Forest plot of association between composite endpoint and sex.83

Figure S65 Forest plot of association between composite endpoint and smoking.84

Figure S66 Forest plot of association between composite endpoint and current smoking.85

Figure S67 Forest plot of association between composite endpoint and ex-smoking.86

Figure S68 Forest plot of association between composite endpoint and contact with confirmed or suspect cases.87

Figure S69 Forest plot of association between composite endpoint and comorbidities.88

Figure S70 Forest plot of association between composite endpoint and hypertension.89

Figure S71 Forest plot of association between composite endpoint and diabetes.90

Figure S72 Forest plot of association between composite endpoint and malignancy.91

Figure S73 Forest plot of association between composite endpoint and cardiovascular disease.92

Figure S74 Forest plot of association between composite endpoint and coronary heart disease.93

Figure S75 Forest plot of association between composite endpoint and cerebrovascular disease.94

Figure S76 Forest plot of association between composite endpoint and COPD.95

Figure S77 Forest plot of association between composite endpoint and respiratory system disease.96

Figure S78 Forest plot of association between ARDS and sex.97

Figure S79 Forest plot of association between ARDS and hypertension.98

Figure S80 Forest plot of association between ARDS and diabetes.99

Figure S81 Forest plot of association between ARDS and cardiovascular disease.100

Figure S82 Forest plot of association between ARDS and cerebrovascular disease.101

Figure S83 Forest plot of association between ARDS and COPD.102

Figure S84 Forest plot of association between ARDS and respiratory system disease.103

Figure S85 Forest plot of association between invasive ventilation and sex.104

Figure S86 Forest plot of association between invasive ventilation and smoking.105

Figure S87 Forest plot of association between invasive ventilation and contact with confirmed or suspect cases.106

Figure S88 Forest plot of association between invasive ventilation and family cluster.107

Figure S89 Forest plot of association between invasive ventilation and comorbidities.108

Figure S90 Forest plot of association between invasive ventilation and hypertension.109

Figure S91 Forest plot of association between invasive ventilation and diabetes.110

Figure S92 Forest plot of association between invasive ventilation and malignancy.111

Figure S93 Forest plot of association between invasive ventilation and cardiovascular disease.112

Figure S94 Forest plot of association between invasive ventilation and cerebrovascular disease.113

Figure S95 Forest plot of association between invasive ventilation and COPD.114

Figure S96 Forest plot of association between invasive ventilation and respiratory system disease.115

Figure S97 Forest plot of association between cardiac abnormality and sex.116

Figure S98 Forest plot of association between cardiac abnormality and smoking.117

Figure S99 Forest plot of association between cardiac abnormality and exposure to Hubei Province.118

Figure S100 Forest plot of association between cardiac abnormality and contact with confirmed or suspect cases.119

Figure S101 Forest plot of association between cardiac abnormality and hypertension.120

Figure S102 Forest plot of association between cardiac abnormality and diabetes.121

Figure S103 Forest plot of association between cardiac abnormality and cardiovascular disease.122

Figure S104 Forest plot of association between cardiac abnormality and coronary heart disease.123

Figure S105 Forest plot of association between cardiac abnormality and COPD.124

Figure S106 Forest plot of association between cardiac abnormality and respiratory system disease.125

Figure S107 Forest plot of association between disease progression and sex.126

Figure S108 Forest plot of association between disease progression and smoking.127

Figure S109 Forest plot of association between disease progression and hypertension.128

Figure S110 Forest plot of association between disease progression and diabetes.129

Figure S111 Forest plot of association between disease progression and COPD.130

Figure S112 Forest plot of association between disease progression and respiratory system disease.131

Figure S113 Forest plot of association between age and severity.132

Figure S114 Forest plot of association between age and death.133

Figure S115 Forest plot of association between age and admission to ICU.134

Figure S116 Forest plot of association between age and composite endpoint.135

Figure S117 Forest plot of association between age and ARDS.136

Figure S118 Forest plot of association between age and invasive ventilation.137

Figure S119 Forest plot of association between age and cardiac abnormality.138

Figure S120 Forest plot of association between age and disease progression.139

Supplementary methods

The Detailed Definitions of the Severity and Prognostic Endpoints of COVID-19

1. The degree of severity of COVID-19 was determined using the American Thoracic Society guidelines for community-acquired pneumonia or the New Coronavirus Pneumonia Prevention and Control Guidelines of China [1, 2]. The latter grouped COVID-19 patients into four categories: (1) mild type: patients with mild clinical symptoms and no pulmonary changes on CT imaging; (2) common type: patients with symptoms of fever and signs of respiratory infection, and having pneumonia changes on CT imaging; (3) severe type: patients presenting with any one of the following conditions: a. respiratory distress, respiratory rate ≥ 30/min; b. oxygen saturation of finger ≤ 93% in resting condition; c. arterial partial pressure of oxygen (PaO2) /oxygen concentration (FiO2) ≤ 300 mmHg (1 mmHg = 0.133 kPa); (4) critical type: patients meeting any one of the following criteria: a. respiratory failure requiring mechanical ventilation; b. shock; c. concomitant failure of other organs and requirement for intensive care unit (ICU) monitoring and treatment. In our study, the severity of disease was classified into two categories, non-severe type and severe type. Non-severe type includes mild-type, common-type or both, and SpO2≥90%. Otherwise, severe-type, critical-type or both and SpO2<90% are defined as severe type.

2. The composite endpoint was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death [3].

3. Cardiac abnormality was defined by any one of following the course of disease: (1) complain of palpitation or chest distress; (2) TNT-HSST serum levels > 99th percentile upper reference limit (>28 pg/ml), the serum levels of troponin I (TNI) were above the 99th percentile of the upper reference limit (> 0.03 ug/L) using the Access AccuTnI+3 test, or increase in the levels of any of the other abovementioned cardiac markers; (3) NT-proBNP≥88.64 pg/mL is also characterized as a sign of Cardiac abnormality[4]; (4) new abnormalities on electrocardiography including sinus tachycardia [5-7].

4. Disease progression included death, progression from non-severe to severe, or severe-type to critical-type [8].

References

1.Metlay JP, Waterer GW, Long AC, et al. Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med 2019; 200(7): e45-e67.

2.Commission HC. The New Coronavirus Pneumonia Prevention and Control Guidelines of China. Available at: http://www.nhc.gov.cn/.

3.Guan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020.

4.Gao L, Jiang D, Wen X, Cheng X, Sun M, al e. Prognostic value of NT-proBNP in patients with severe COVID-19. medRxiv 2020.

5.Liu R, Ming X, Xu O, Zhou J, Peng H, al e. Association of Cardiovascular Manifestations with In-hospital Outcomes in Patients with COVID-19: A Hospital Staff Data. medRxiv 2020.

6.Liu Y, Li J, Liu D, Song H, Chen C, al e. Clinical features and outcomes of 2019 novel coronavirus–infected patients with cardiac injury. medRxiv 2020.

7.Xu H, Hou K, Xu H, Li Z, Chen H, al e. Acute Myocardial Injury of Patients with Coronavirus Disease 2019. medRxiv 2020.

8.Hu L, Chen S, Fu Y, Gao Z, Long H, al e. Risk Factors Associated with Clinical Outcomes in 323 COVID-19 Patients in Wuhan, China. medRxiv 2020.

2

Table S1 Characteristics of the included studies.

Rank

First author

Country

Year

Date of recruitment

Reason of non-eligible of quantitative synthesis

Province/city

Hospital

PMID\DOI

Total number of cases

Endpoints

NOS quality score

1

Kaicai Liu

China

2020

2020.01.21-2020.02.03

Anhui

Six hospitals in Anhui province

32193037

73

severity

5

2

Jingyuan Liu

China

2020

2020.01.13-2020.01.31

Beijing

Beijing Ditan Hospital

10.1101/2020.02.10.20021584

61

severity

5

3

Sijia Tian

China

2020

2020.01.20-2020.02.10

Beijing

Beijing Emergency Medical Service

32112886

262

severity

5

4

Hui Hui

China

2020

2020.01.21-2020.02.03

duplicated patients

Beijing

Beijing Youan Hospital

10.1101/2020.02.24.20027052

41

severity

5

5

wen zhao

China

2020

2020.01.21-2020.02.08

Beijing

Beijing Youan Hospital

10.1101/2020.03.13.20035436.

77

severity, hospitalization duration >14 days

5

6

Xu Chen

China

2020

2020.01.23-2020.02.14

Changsha, Loudi

first Hospital of Changsha and Loudi Central Hospital

10.1101/2020.03.03.20030353

291

severity

5

7

Zhichao Feng

China

2020

2020.01.17-2020.02.01

Changsha

Third Xiangya Hospital,Changsha Public Health Treatment Center, and Second People’s Hospital of Hunan

10.1101/2020.02.19.20025296

141

progression

6

8

Huayuan Xu

China

2020

2020.01.02-2020.02.14

Chengdu

West China Second University Hospital

10.1101/2020.03.05.20031591.

53

cardiac abnormality

6

9

Lei Liu

China

2020

2020.01.20-2020.02.03

Chognqing

Chongqing Three Gorges Central Hospital

10.1101/2020.02.20.20025536

51

severity

6

10

Suxin Wan

China

2020

2020.01.26-2020.02.04

duplicated patients

Chognqing

Chongqing Three Gorges Central Hospital

10.1101/2020.02.10.20021832

123

severity

5

11

Di Qi

China

2020

2020.01.19-2020.02.16

Chognqing

Qianjiang central hospital of Chongqing, Chongqing three gorges central hospital and Chongqing public health medical center

10.1101/2020.03.01.20029397

267

severity

5

12

Kunhua Li

China

2020

2020.01-2020.02

Chognqing

the Second Affiliated Hospital of Chongqing Medical University

32164090

83

severity

5

13

Zhifeng Xu

China

2020

2020.01.20-2020.02.06

Foshan

the First people's hospital of Foshan

10.1101/2020.03.03.20030775

21

severity

5

14

Youbin Liu

China

2020

2020.01.10-2020.02.24

Guangzhou

Guangzhou Eighth People’s Hospital

10.1101/2020.03.11.20030957

291

cardiac abnormality

6

15

Yonghao Xu

China

2020

2020.01.14-2020.02.20

Guangzhou

the First Affiliated Hospital of Guangzhou Medical University, Dongguan People’s Hospital, etc

10.1101/2020.03.03.20030668.

45

Invasive ventilation

7

16

Shijiao Yan

China

2020

2020.01.22-2020.03.14

Hainan

the Second Affiliated Hospital of Hainan Medical University

10.1101/2020.03.19.20038539

168

severity

5

17

Xiaowei Fang

China

2020

2020.01.22-2020.02.18

Hefei

Anhui Provincial Hospital

79

severity

5

18

Rong Qu

China

2020

2020.01-2020.02

Huizhou

Huizhou municipal central hospital

32181903

30

severity

6

19

Tian Gu

China

2020

2019.12.18-2020.03.08

mainland China

10.1101/2020.03.23.20041848

321

death

6

20

Yishan Zheng

China

2020

Nanjing

the Second Hospital of Nanjing

10.1101/2020.02.19.20024885

88

severity

5

21

Hongzhou Lu

China

2020

beofe 2020.02.07

Shanghai

Shanghai CDC

10.1101/2020.02.19.20025031

265

severity

5

22

Min Cao

China

2020

2020.01.20-2020.02.15

Shanghai

Shanghai Public Health Clinical Centre

10.1101/2020.03.04.20030395.

198

ICU

6

23

Ying Wen

China

2020

2020.01.01-2020.02.28

Shenzhen

Shenzhen Center of Disease Control and Prevention

10.1101/2020.03.22.20035246

417

severity

5

24

Qingxian Cai

China

2020

2020.01.11-2020.02.06

Shenzhen

the Third People's Hospital of Shenzhen

10.1101/2020.02.17.20024018

298

severity

5

25

Sakiko Tabata

Japan

2020

2020.02.11-2020.02.25

Tokyo

Self-Defense Ofces Central Hospital

10.1101/2020.03.18.20038125

104

severity

5

26

Bo Zhou

China

2020

2020.02.05-2020.02.13

different grouping methods of disease severity

Wuhan

West District of Union Hospital of Tongji Medical College

32209382

34

severe vs very severe

5

27

Jiatao Lu

China

2020

2020.01.21-2020.02.05

Wuhan

Wuhan Hankou Hospital

10.1101/2020.02.20.20025510

577

severity

5

29

Min Liu

China

2020

2020.01.10-2020.01.31

Wuhan

Affiliated hospital of Jianghan University

32164090

30

severity

5

30

Wei liu

China

2020

2019.12.20-2020.01.15

Wuhan

three tertiary hospitals in Wuhan

32118640

78

Progression

6

31

Mingli Yuan

China

2020

2020.01.01-2020.01.25

Wuhan

Central Hospital of Wuhan

32191764

27

death

7

32

Yafei Wang

China

2020

2020.01.01-2020.02.10

Wuhan


10.1101/2020.03.02.20029306

110

severity

5

33

Ying Zhou

China

2020

2020.01.01-2020.02.28

Wuhan


10.1101/2020.03.24.20042119

377

severity

5

34

Yanli Liu

China

2020

2020.01.02-2020.02.01

Wuhan


10.1101/2020.02.17.20024166.

109

ARDS

7

35

Ru Liu

China

2020

2020.01.15-2020.01.24

Wuhan


10.1101/2020.02.29.20029348

41

cardiac abnormality

6

36

Chaolin Huang

China

2020

2019.12.16-2020.01.02

Wuhan

Jinyintan Hospital

31986264

41

ICU

6

37

Xiaobo Yang

China

2020

2019.12.24-2020.01.26

Wuhan

Jinyintan Hospital

32105632

52

death

7

38

Chaomin Wu

China

2020

2019.12.25-2020.01.26

Wuhan

Jinyintan Hospital

32167524

201

ARDS, death in ARDS

7

39

Fei Zhou

China

2020

2020.12.29-2020.01.31

Wuhan

Jinyintan Hospital and Wuhan Pulmonary Hospital

32171076

191

death

6

40

Jinjin Zhang

China

2020

2020.01.16-2020.02.03

Wuhan

No.7 hospital of Wuhan

32077115

140

severity

5

41

Qian Shi

China

2020

before 2020.02.15

unique endpoint

Wuhan

Renmin Hospital of Wuhan University

10.1101/2020.03.04.20031039

101

survival ≤3d

5

42

Luwen Wang

China

2020

2020.01.14-2020.02.13

Wuhan


10.1101/2020.02.19.20025288.

116

severity and ARDS

5

43

Yi Han

China

2020

2020.02.01-2020.02.18

Wuhan


10.1101/2020.03.24.20040162

47

severity

5

44

Ling Hu

China

2020

2020.01.08-2020.02.20

Wuhan

Tianyou Hospital

10.1101/2020.03.25.20037721

323

severity, unfavorable

7

28

Chen Chen

China

2020

2020.01-2020.02

different grouping methods of disease severity

Wuhan

Tongji Hospital

32141280

150

severity (critical vs non-critical)

5

45

Guang Chen

China

2020

2019.12.19-2020.01.27

Wuhan

Tongji hospital

10.1101/2020.02.16.20023903.

21

severity

5

46

Tao Chen

China

2020

2020.01.13-2020.02.12

Wuhan

Tongji Hospital

32217556

274

death

7

47

Zhihua Wang

China

2020

2020.02.23-2020.03.11

Wuhan

Tongji hospital

10.1101/2020.03.22.20041285

116

death

6

48

Chuan Qin

China

2020

2020.01.10-2020.02.12

Wuhan

Tongji Hospital

32161940

452

severity

5

49

Lin Fu

China

2020

2020.01.01-2020.01.30

Wuhan

Union Hospital of Huazhong University of Science and Technology

10.1101/2020.03.13.20035329.

200

death

6

50

Jing Liu

China

2020

2020.01.05-2020.01.24

Wuhan


10.1101/2020.02.16.20023671

40

severity

5

51

Ling Mao

China

2020

2020.01.16-2020.02.19

Wuhan


10.1101/2020.02.22.20026500.

214

severity

5

52

Zhongliang Wang

China

2021

2020.01.16-2020.01.29

Wuhan


32176772

69

Spo2<90%

5

53

Yudong Peng

China

2020

2020.01.20-2020.02.15

Wuhan


32120458

112

severity,death

6

54

Fan Zhang

China

2020

2019.12.25-2020.02.15

Wuhan

Wuhan No.1 Hospital

10.1101/2020.03.21.20040121

48

non-surviver

6

55

Jianmin Jin

China

2020

2020.01.29-2020.02.15

Wuhan

Wuhan Union Hospital by the medical team of Beijing Tongren Hospital

10.1101/2020.02.23.20026864

1056

death

6

56

Dawei Wang

China

2020

2020.01.01-2020.01.28

Wuhan

Zhongnan Hospital of Wuhan University

32031570

138

ICU

6

57

Pingzheng Mo

China

2020

2020.01.01-2020.02.05

unique endpoint

Wuhan


32173725

155

Refractory

6

58

Guqin Zhang

China

2020

2020.01.02-2020.02.10

Wuhan


10.1101/2020.03.02.20030452.

221

severity

5

59

Yao Na

China

2020

2020.01.21-2020.02.21

unique endpoint

Xi'an

Tangdu Hospital

32153170

40

liver injury

6

60

Weiliang Cao

China

2020

2020.01.01-2020.02.16

Xiangyang

the Xiangyang No.1 Hospital

10.1101/2020.02.23.20026963

128

severity

5

61

Jian Wu

China

2020

2020.01.20-2020.02.19

Yancheng

First People’s Hospital of Yancheng City, the Second People’s Hospital of Fuyang City, the Second People’s Hospital of Yancheng City, and the Fifth People’s Hospital of Wuxi

32220033

280

severity

5

62

Xiaowei Xu

China

2020

2020.01.10-2020.01.26

unique endpoint

Zhejiang

seven designated tertiary hospitals in Zhejiang province

32075786

62

Time since symptom onset ＞10 days

5

63

Bingwen Eugene FAN

Singapore

2020

2020.01.23-2020.02.28

Singapore

National Centre of Infectious Diseases of Singapore

32129508

67

ICU

6

64

Zhen Li

China

2020

2020.01.06-2020.02.21

Multiple cities

Wuhan Tongji hospital, Wuhan Pulmonary Hospital, Huangshi Central Hospital and Chongqing Southwest hospital

10.1101/2020.02.08.20021212.

193

severity

5

65

Yang Xu

China

2020

2020.02.07-2020.02.28

Multiple cities

Zhongnan Hospital of Wuhan University, Chinese PLA General Hospital, Peking Union Medical College Hospital, and affiliated hospitals of Shanghai University of Medicine & Health Sciences.

10.1101/2020.03.08.20031658

69

severity

5

66

Weijie guan

China

2020

2019.12.11-2020.01.31

Multiple cities

Wuhan Jinyintan hospital, Union Hospital Affiliated to Tongji Medical College of Huazhong University of science and technology, Wuhan Central Hospital, Wuhan first hospital, Chengdu Public Health Clinical Medical Center

32217650

1590

severity, composite endpoint, death, ICU, invasive ventilation

7

67

Weijie guan

China

2020

2019.12.11-2020.01.29

Multiple cities

32109013

1099

severity, composite endpoint

7

68

Lei Gao

China

2020

Wuhan

Hubei General Hospital

10.1101/2020.03.07.20031575

54

cardiac abnormality

7

69

Huoshenshan (unpublished)

China

2020

2020.02.03-2020.03.05

　

Wuhan

Huoshenshan Hospital

　

1780

severity, composite endpoint, death, ICU, Invasive ventilation, ARDS

7

Table S2 Quantitative data synthesis for the associations of the epidemiological, comorbidity factors with prognosis of COVID-19

Variables

No of studies

Total cases

P heterogeneity

I2 (%)

RR (95% CIs)

P value

P Egger

Death

Sex, male

10

4214

0.443

0.0

1.23 (1.14-1.33)

<0.001

0.276

Smoking

4

2445

0.246

27.7

1.15 (0.84-1.57)

0.395

0.061

Current smoking

2

2054

0.344

0.0

1.31 (0.64-2.67)

0.459

-

Ex-smoking

2

2054

0.318

0.0

0.87 (0.26-2.95)

0.826

-

Contact with confirmed or suspect cases

2

2054

<0.001

98.8

1.11 (0.07-16.85)

0.942

-

Huanan seafood market exposure

2

2054

0.918

0.0

5.84 (0.91-37.57)

0.063

-

Comorbidities

8

4499

<0.001

88.7

1.68 (1.32-2.13)

<0.001

0.248

Hypertension

11

4860

<0.001

84.4

1.74 (1.31-2.30)

<0.001

0.418

Diabetes

10

4748

0.001

67.1

1.75 (1.27-2.41)

0.001

0.057

Malignancy

6

3978

0.262

22.8

3.09 (1.59-6.00)

0.001

0.006

Cardiovascular disease

11

4860

<0.001

75.9

2.67 (1.60-4.43)

<0.001

0.654

Coronary heart disease

5

2452

<0.001

87.7

3.16 (1.45-6.91)

0.004

0.435

Cerebrovascular disease

6

3771

0.457

0.0

4.61 (2.51-8.47)

<0.001

0.766

COPD

4

3677

0.279

22.0

5.31 (2.63-10.71)

<0.001

0.107

Respiratory system disease

7

4472

0.185

31.8

3.22 (2.12-4.90)

<0.001

0.761

Chronic kidney disease

5

2219

0.477

0.0

7.10 (3.14-16.02)

<0.001

0.772

Hepatitis B infection

2

1864

0.973

0.0

1.18 (0.43-3.20)

0.752

-

Autoimmune disease

2

1864

0.576

0.0

2.04 (0.27-15.58)

0.491

-

Admission to ICU

Sex, male

5

2224

0.011

69.6

1.29 (1.13-1.47)

<0.001

0.651

Smoking

3

2019

0.742

0.0

0.85 (0.40-1.79)

0.669

0.437

Drinking

2

1978

0.638

0.0

0.51 (0.10-2.55)

0.411

-

Huanan seafood market exposure

3

1959

0.281

21.2

1.08 (0.44-2.69)

0.863

0.037

Comorbidities

5

3747

0.038

60.5

1.82 (1.45-2.29)

<0.001

0.646

Hypertension

5

3747

0.601

0.0

2.31 (1.97-2.70)

<0.001

0.312

Diabetes

5

3747

0.084

51.4

1.88 (1.10-3.23)

0.021

0.457

Malignancy

5

3747

0.427

0.0

2.52 (1.38-5.59)

0.003

0.158


5

3747

0.511

0.0

2.74 (1.92-3.92)

<0.001

0.692


3

3508

0.349

4.9

5.12 (2.86-9.17)

<0.001

0.273

COPD

4

3549

0.800

0.0

5.61 (2.68-11.76)

<0.001

0.740


4

3549

0.613

0.0

4.66 (2.59-8.40)

<0.001

0.637

Chronic kidney disease

2

1728

0.344

0.0

1.37 (0.36-5.15)

0.644

-

Chronic liver disease

3

377

0.906

0.0

0.50 (0.09-2.68)

0.416

0.816

Composite endpoint

Sex, male

2

2879

0.001

91.3

1.48 (0.95-2.29)

0.082

-

Smoking

2

2879

0.604

0.0%

2.67(1.91-3.73)

<0.001

-

Current smoking

2

2879

0.038

76.7

1.59 (0.64-3.98)

0.322

-

Ex-smoking

2

2879

0.035

77.4

2.34 (0.24-22.93)

0.466

-


2

2879

0.392

0.0

1.02 (0.84-1.24)

0.827

-

Comorbidities

2

3370

<0.001

95.3

1.96 (1.06-3.60)

0.031

-

Hypertension

2

3370

0.011

84.5

2.20 (1.44-3.36)

<0.001

-

Diabetes

2

3370

0.002

89.2

2.20 (0.86-5.66)

0.101

-

Malignancy

2

3370

0.072

69.1

3.76 (1.00-14.16)

0.051

-


2

3370

0.927

0.0

3.09 (2.09-4.57)

<0.001

-


2

3370

0.473

0.0

3.36 (2.15-5.25)

<0.001

-


2

3370

0.225

32.0

4.10 (2.34-7.18)

<0.001

-

COPD

2

3370

0.185

43.0

8.52 (4.36-16.65)

<0.001

-


2

3370

0.185

43.0

8.52 (4.36-16.65)

<0.001

-

ARDS

Sex, male

3

2090

0.464

0.0

1.15 (1.01-1.30)

0.033

0.353

Hypertension

3

2090

0.377

0.0

1.90 (1.57-2.30)

<0.001

0.520

Diabetes

3

2090

0.068

62.9

3.07 (1.28-7.36)

0.012

0.066


3

2090

0.244

29.2

2.26 (1.43-3.58)

<0.001

0.422


2

1889

0.152

51.2

3.15 (1.23-8.04)

0.016

-

COPD

2

1889

0.140

54.1

2.59 (0.94-7.17)

0.066

-


2

1889

0.303

5.6

2.44 (1.20-4.97)

0.014

-

Invasive ventilation

Sex, male

2

1825

0.403

0.0

1.35 (1.11-1.64)

0.002

-

Smoking

2

1825

0.657

0.0

0.94 (0.41-2.15)

0.885

-


2

1825

0.020

81.5

1.44 (0.77-2.71)

0.253

-

Family cluster

2

1825

0.646

0.0

1.58 (1.13-2.14)

0.006

-

Comorbidities

3

3415

0.005

81.2

1.83 (1.19-2.79)

0.006

0.569

Hypertension

3

3415

0.131

50.9

2.35 (1.92-2.89)

<0.001

0.366

Diabetes

3

3415

0.131

50.8

1.85 (1.24-2.76)

0.003

0.021

Malignancy

3

3415

0.397

0.0

1.79 (0.66-4.88)

0.252

0.110


3

3415

0.844

0.0

2.90 (1.63-5.15)

<0.001

0.618


2

3370

0.602

0.0

3.98 (1.77-8.93)

0.001

-

COPD

2

3370

0.383

0.0

6.53 (2.70-15.84)

<0.001

-


3

3415

0.260

25.7

4.34 (2.04-9.26)

<0.001

0.567

Cardiac abnormality

Sex, male

4

439

0.211

33.6

1.33 (1.02-1.72)

0.036

0.624

Smoking

2

94

0.448

0.0

1.12 (0.33-3.73)

0.860

-

Exposure to Hubei Province

2

344

0.464

0.0

1.18 (0.76-1.83)

0.473

-


2

94

0.408

0.0

0.94 (0.65-1.36)

0.735

-

Hypertension

4

439

0.947

0.0

2.97 (1.65-5.34)

<0.001

0.610

Diabetes

4

439

0.695

0.0

1.85 (0.90-3.81)

0.094

0.247


4

439

0.915

0.0

4.90 (1.82-13.21)

0.002

0.177


3

386

0.819

0.0

5.37 (1.74-16.54)

0.003

0.408

COPD

3

148

0.881

0.0

2.30 (0.48-11.02)

0.296

0.480


3

148

0.881

0.0

2.30 (0.48-11.02)

0.296

0.480

Disease progression

Sex, male

2

219

0.853

0.0

1.38 (0.93-2.05)

0.106

-

Smoking

2

219

0.068

70.0

2.70 (0.14-51.96)

0.511

-

Hypertension

2

219

0.547

0.0

2.90 (1.45-5.81)

0.003

-

Diabetes

2

219

0.746

0.0

3.30 (1.08-10.07)

0.036

-

COPD

2

219

0.848

0.0

7.48 (1.60-35.05)

0.011

-


2

219

0.848

0.0

7.48 (1.60-35.05)

0.011

-

Figure S1 Forest plot of association between sex and disease severity.

Figure S2 Forest plot of association between smoking and disease severity.

Figure S3 Forest plot of association between current smoker and disease severity.

Figure S4 Forest plot of association between ex-smoker and disease severity.

Figure S5 Forest plot of association between drinking and disease severity.

Figure S6 Forest plot of association between local residents of Wuhan and disease severity.

Figure S7 Forest plot of association between exposure history to Hubei province and disease severity.

Figure S8 Forest plot of association between contact with confirmed or suspect cases and disease severity.

Figure S9 Forest plot of association between family cluster and disease severity.

Figure S10 Forest plot of association between Huanan seafood market exposure and disease severity.

Figure S11 Forest plot of association between comorbidity and disease severity.

Figure S12 Forest plot of association between hypertension and disease severity.

Figure S13 Forest plot of association between diabetes and disease severity.

Figure S14 Forest plot of association between malignancy and disease severity.

Figure S15 Forest plot of association between cardiovascular disease and disease severity.

Figure S16 Forest plot of association between coronary heart disease and disease severity.

Figure S17 Forest plot of association between cerebrovascular disease and disease severity.

Figure S18 Forest plot of association between cardiovascular/ cerebrovascular disease and disease severity.

Figure S19 Forest plot of association between COPD and disease severity.

Figure S20 Forest plot of association between respiratory system disease and disease severity.

Figure S21 Forest plot of association between chronic kidney disease and disease severity.

Figure S22 Forest plot of association between chronic liver disease and disease severity.

Figure S23 Forest plot of association between hepatitis B infection and disease severity.

Figure S24 Forest plot of association between lithiasis and disease severity.

Figure S25 Forest plot of association between autoimmune disease and disease severity.

Figure S26 Forest plot of association between abnormal lipid metabolism and disease severity.

Figure S27 Forest plot of association between digestive disease and disease severity.

Figure S28 Forest plot of association between thyroid disease and disease severity.

Figure S29 Forest plot of association between tuberculosis and disease severity.

Figure S30 Forest plot of association between nervous system disease and disease severity.

Figure S31 Forest plot of association between endocrine system disease and disease severity.

Figure S32 Forest plot of association between death and sex.

Figure S33 Forest plot of association between death and smoking.

Figure S34 Forest plot of association between death and current smoking.

Figure S35 Forest plot of association between death and ex-smoking.

Figure S36 Forest plot of association between death and contact with confirmed or suspect cases.

Figure S37 Forest plot of association between death and Huanan seafood market exposure.

Figure S38 Forest plot of association between death and comorbidities.

Figure S39 Forest plot of association between death and hypertension.

Figure S40 Forest plot of association between death and diabetes.

Figure S41 Forest plot of association between death and malignancy.

Figure S42 Forest plot of association between death and cardiovascular disease.

Figure S43 Forest plot of association between death and coronary heart disease.

Figure S44 Forest plot of association between death and cerebrovascular disease.

Figure S45 Forest plot of association between death and COPD.

Figure S46 Forest plot of association between death and respiratory system disease.

Figure S47 Forest plot of association between death and chronic kidney disease.

Figure S48 Forest plot of association between death and hepatitis B infection.

Figure S49 Forest plot of association between death and autoimmune disease.

Figure S50 Forest plot of association between admission to ICU and sex.

Figure S51 Forest plot of association between admission to ICU and smoking.

Figure S52 Forest plot of association between admission to ICU and drinking.

Figure S53 Forest plot of association between admission to ICU and Huanan seafood market exposure.

Figure S54 Forest plot of association between admission to ICU and comorbidities.

Figure S55 Forest plot of association between admission to ICU and hypertension.

Figure S56 Forest plot of association between admission to ICU and diabetes.

Figure S57 Forest plot of association between admission to ICU and malignancy.

Figure S58 Forest plot of association between admission to ICU and cardiovascular disease.

Figure S59 Forest plot of association between admission to ICU and cerebrovascular disease.

Figure S60 Forest plot of association between admission to ICU and COPD.

Figure S61 Forest plot of association between admission to ICU and respiratory system disease.

Figure S62 Forest plot of association between admission to ICU and chronic kidney disease.

Figure S63 Forest plot of association between admission to ICU and chronic liver disease.

Figure S64 Forest plot of association between composite endpoint and sex.

Figure S65 Forest plot of association between composite endpoint and smoking.

Figure S66 Forest plot of association between composite endpoint and current smoking.

Figure S67 Forest plot of association between composite endpoint and ex-smoking.

Figure S68 Forest plot of association between composite endpoint and contact with confirmed or suspect cases.

Figure S69 Forest plot of association between composite endpoint and comorbidities.

Figure S70 Forest plot of association between composite endpoint and hypertension.

Figure S71 Forest plot of association between composite endpoint and diabetes.

Figure S72 Forest plot of association between composite endpoint and malignancy.

Figure S73 Forest plot of association between composite endpoint and cardiovascular disease.

Figure S74 Forest plot of association between composite endpoint and coronary heart disease.

Figure S75 Forest plot of association between composite endpoint and cerebrovascular disease.

Figure S76 Forest plot of association between composite endpoint and COPD.

Figure S77 Forest plot of association between composite endpoint and respiratory system disease.

Figure S78 Forest plot of association between ARDS and sex.

Figure S79 Forest plot of association between ARDS and hypertension.

Figure S80 Forest plot of association between ARDS and diabetes.

Figure S81 Forest plot of association between ARDS and cardiovascular disease.

Figure S82 Forest plot of association between ARDS and cerebrovascular disease.

Figure S83 Forest plot of association between ARDS and COPD.

Figure S84 Forest plot of association between ARDS and respiratory system disease.

Figure S85 Forest plot of association between invasive ventilation and sex.

Figure S86 Forest plot of association between invasive ventilation and smoking.

Figure S87 Forest plot of association between invasive ventilation and contact with confirmed or suspect cases.

Figure S88 Forest plot of association between invasive ventilation and family cluster.

Figure S89 Forest plot of association between invasive ventilation and comorbidities.

Figure S90 Forest plot of association between invasive ventilation and hypertension.

Figure S91 Forest plot of association between invasive ventilation and diabetes.

Figure S92 Forest plot of association between invasive ventilation and malignancy.

Figure S93 Forest plot of association between invasive ventilation and cardiovascular disease.

Figure S94 Forest plot of association between invasive ventilation and cerebrovascular disease.

Figure S95 Forest plot of association between invasive ventilation and COPD.

Figure S96 Forest plot of association between invasive ventilation and respiratory system disease.

Figure S97 Forest plot of association between cardiac abnormality and sex.

Figure S98 Forest plot of association between cardiac abnormality and smoking.

Figure S99 Forest plot of association between cardiac abnormality and exposure to Hubei Province.

Figure S100 Forest plot of association between cardiac abnormality and contact with confirmed or suspect cases.

Figure S101 Forest plot of association between cardiac abnormality and hypertension.

Figure S102 Forest plot of association between cardiac abnormality and diabetes.

Figure S103 Forest plot of association between cardiac abnormality and cardiovascular disease.

Figure S104 Forest plot of association between cardiac abnormality and coronary heart disease.

Figure S105 Forest plot of association between cardiac abnormality and COPD.

Figure S106 Forest plot of association between cardiac abnormality and respiratory system disease.

Figure S107 Forest plot of association between disease progression and sex.

Figure S108 Forest plot of association between disease progression and smoking.

Figure S109 Forest plot of association between disease progression and hypertension.

Figure S110 Forest plot of association between disease progression and diabetes.

Figure S111 Forest plot of association between disease progression and COPD.

Figure S112 Forest plot of association between disease progression and respiratory system disease.

Figure S113 Forest plot of association between age and severity.

Figure S114 Forest plot of association between age and death.

Figure S115 Forest plot of association between age and admission to ICU.

Figure S116 Forest plot of association between age and composite endpoint.

Figure S117 Forest plot of association between age and ARDS.

Figure S118 Forest plot of association between age and invasive ventilation.

Figure S119 Forest plot of association between age and cardiac abnormality.

Figure S120 Forest plot of association between age and disease progression.

NOTE: Weights are from random effects analysis

Overall (I-squared = 27.2%, p = 0.078)

Yishan Zheng

Study

Weijie Guan

Guqin Zhang

ID

Sakiko Tabata

Kaicai Liu

Yudong Peng

Weiliang Cao


Jian Wu

Luwen Wang

Ling Hu

Guang Chen

Yi Han

Di Qi

Chuan Qin

Jingyuan Liu

Qingxian Cai

Jing Liu

Zhen Li

Jinjin Zhang

Ying Zhou

Zhongliang Wang

Xu Yang

Ling Mao

Chuan Qin

Xu Chen

Jiatao Lu

Zhifeng Xu

Ying Wen

Shijiao Yan

wen zhao

Sijia Tian

Xiaowei Fang

1.20 (1.13, 1.27)

1.19 (0.67, 2.12)

0.99 (0.86, 1.14)

1.45 (1.11, 1.88)

RR (95% CI)

1.27 (0.82, 1.96)

0.66 (0.39, 1.11)

1.23 (0.76, 1.99)

1.27 (0.83, 1.95)

1.08 (0.98, 1.19)

1.01 (0.80, 1.28)

1.00 (0.74, 1.37)

1.07 (0.86, 1.32)

1.30 (0.83, 2.03)

1.81 (1.02, 3.20)

1.54 (1.26, 1.87)

1.12 (0.93, 1.36)

1.23 (0.74, 2.04)

1.23 (0.95, 1.60)

1.82 (0.84, 3.92)

1.37 (1.04, 1.81)

1.23 (0.89, 1.70)

1.48 (1.19, 1.84)

1.10 (0.60, 2.00)

1.04 (0.64, 1.68)

1.47 (1.06, 2.02)

1.12 (0.93, 1.36)

1.10 (0.83, 1.47)

1.10 (0.87, 1.38)

1.33 (0.55, 3.22)

1.40 (1.07, 1.83)

1.28 (0.92, 1.79)

1.36 (0.82, 2.26)

1.21 (0.90, 1.62)

1.53 (1.07, 2.18)

100.00

0.96

%

7.60

3.61

Weight

1.61

1.17

1.33

1.66

9.73

4.20

2.80

4.80

1.53

0.98

5.30

5.52

1.23

3.63

0.56

3.31

2.64

4.74

0.90

1.35

2.68

5.52

3.19

4.41

0.43

3.50

2.51

1.22

3.12

2.27

1.20 (1.13, 1.27)

1.19 (0.67, 2.12)

0.99 (0.86, 1.14)

1.45 (1.11, 1.88)

RR (95% CI)

1.27 (0.82, 1.96)

0.66 (0.39, 1.11)

1.23 (0.76, 1.99)

1.27 (0.83, 1.95)

1.08 (0.98, 1.19)

1.01 (0.80, 1.28)

1.00 (0.74, 1.37)

1.07 (0.86, 1.32)

1.30 (0.83, 2.03)

1.81 (1.02, 3.20)

1.54 (1.26, 1.87)

1.12 (0.93, 1.36)

1.23 (0.74, 2.04)

1.23 (0.95, 1.60)

1.82 (0.84, 3.92)

1.37 (1.04, 1.81)

1.23 (0.89, 1.70)

1.48 (1.19, 1.84)

1.10 (0.60, 2.00)

1.04 (0.64, 1.68)

1.47 (1.06, 2.02)

1.12 (0.93, 1.36)

1.10 (0.83, 1.47)

1.10 (0.87, 1.38)

1.33 (0.55, 3.22)

1.40 (1.07, 1.83)

1.28 (0.92, 1.79)

1.36 (0.82, 2.26)

1.21 (0.90, 1.62)

1.53 (1.07, 2.18)

100.00

0.96

%

7.60

3.61

Weight

1.61

1.17

1.33

1.66

9.73

4.20

2.80

4.80

1.53

0.98

5.30

5.52

1.23

3.63

0.56

3.31

2.64

4.74

0.90

1.35

2.68

5.52

3.19

4.41

0.43

3.50

2.51

1.22

3.12

2.27

1.25513.92



ID


Weijie Guan-2

Yonghao Xu

Study

1.79 (0.97, 3.29)

RR (95% CI)

1.46 (0.66, 3.21)

2.85 (1.64, 4.93)

1.07 (0.43, 2.68)

100.00

Weight

31.15

42.61

26.24

%

1.79 (0.97, 3.29)

RR (95% CI)

1.46 (0.66, 3.21)

2.85 (1.64, 4.93)

1.07 (0.43, 2.68)

100.00

Weight

31.15

42.61

26.24

%

1.20314.93




Weijie Guan-2

Study

ID

Yonghao Xu

2.16 (0.77, 6.09)

1.81 (0.26, 12.67)

3.83 (0.90, 16.19)

RR (95% CI)

0.63 (0.06, 6.41)

100.00

28.48

51.66

%

Weight

19.85

2.16 (0.77, 6.09)

1.81 (0.26, 12.67)

3.83 (0.90, 16.19)

RR (95% CI)

0.63 (0.06, 6.41)

100.00

28.48

51.66

%

Weight

19.85

1.061116.4



ID


Study

Weijie Guan-2

Yonghao Xu

2.95 (1.68, 5.17)

RR (95% CI)

2.50 (1.01, 6.23)

3.49 (1.57, 7.73)

2.50 (0.51, 12.29)

100.00

Weight

37.88

%

49.71

12.42

2.95 (1.68, 5.17)

RR (95% CI)

2.50 (1.01, 6.23)

3.49 (1.57, 7.73)

2.50 (0.51, 12.29)

100.00

Weight

37.88

%

49.71

12.42

1.0814112.3



ID

Weijie Guan-2

Study


4.03 (1.79, 9.09)

RR (95% CI)

4.74 (1.72, 13.07)

3.02 (0.78, 11.74)

100.00

Weight

64.12

%

35.88

4.03 (1.79, 9.09)

RR (95% CI)

4.74 (1.72, 13.07)

3.02 (0.78, 11.74)

100.00

Weight

64.12

%

35.88

1.0765113.1



Weijie Guan-2

ID


Study

7.10 (2.91, 17.37)

8.11 (3.15, 20.83)

RR (95% CI)

2.25 (0.14, 36.56)

100.00

89.72

Weight

10.28

%

7.10 (2.91, 17.37)

8.11 (3.15, 20.83)

RR (95% CI)

2.25 (0.14, 36.56)

100.00

89.72

Weight

10.28

%

1.0274136.6



Study


ID

Yonghao Xu

Weijie Guan-2

4.71 (1.90, 11.69)

2.16 (0.55, 8.51)

RR (95% CI)

3.75 (0.42, 33.36)

8.11 (3.15, 20.83)

100.00

%

32.28

Weight

15.15

52.57

4.71 (1.90, 11.69)

2.16 (0.55, 8.51)

RR (95% CI)

3.75 (0.42, 33.36)

8.11 (3.15, 20.83)

100.00

%

32.28

Weight

15.15

52.57

1.03133.4



ID

Youbin Liu

Ru Liu

Lei Gao

Huayuan Xu

Study

1.35 (0.98, 1.87)

RR (95% CI)

1.66 (1.19, 2.31)

1.30 (0.60, 2.82)

1.60 (0.83, 3.09)

0.88 (0.53, 1.47)

100.00

Weight

41.44

14.05

18.24

26.28

%

1.35 (0.98, 1.87)

RR (95% CI)

1.66 (1.19, 2.31)

1.30 (0.60, 2.82)

1.60 (0.83, 3.09)

0.88 (0.53, 1.47)

100.00

Weight

41.44

14.05

18.24

26.28

%

1.32413.09



Huayuan Xu

ID

Ru Liu

Study

1.07 (0.31, 3.68)

0.77 (0.17, 3.45)

RR (95% CI)

2.13 (0.24, 18.73)

100.00

67.64

Weight

32.36

%

1.07 (0.31, 3.68)

0.77 (0.17, 3.45)

RR (95% CI)

2.13 (0.24, 18.73)

100.00

67.64

Weight

32.36

%

1.0534118.7



Study

ID

Huayuan Xu

Youbin Liu

1.13 (0.73, 1.75)

RR (95% CI)

1.53 (0.60, 3.93)

1.04 (0.64, 1.70)

100.00

%

Weight

21.14

78.86

1.13 (0.73, 1.75)

RR (95% CI)

1.53 (0.60, 3.93)

1.04 (0.64, 1.70)

100.00

%

Weight

21.14

78.86

1.25413.93



Ru Liu

ID

Study

Huayuan Xu

0.99 (0.71, 1.40)

1.06 (0.72, 1.56)

RR (95% CI)

0.77 (0.36, 1.62)

100.00

79.11

Weight

%

20.89

0.99 (0.71, 1.40)

1.06 (0.72, 1.56)

RR (95% CI)

0.77 (0.36, 1.62)

100.00

79.11

Weight

%

20.89

1.36312.75




Study

Yi Han

Chuan Qin

Di Qi

ID

Yafei Wang

Ling Hu

Weijie Guan

Jingyuan Liu

Chuan Qin

Xu Yang

Jiatao Lu

1.56 (0.95, 2.56)

1.04 (0.79, 1.39)

1.28 (0.32, 5.10)

0.44 (0.10, 1.92)

6.12 (3.89, 9.61)

RR (95% CI)

1.00 (0.50, 2.03)

1.90 (0.99, 3.64)

1.68 (1.21, 2.33)

2.59 (0.40, 16.93)

0.44 (0.10, 1.92)

2.64 (0.47, 14.75)

2.13 (0.36, 12.48)

100.00

14.21

%

6.94

6.42

13.28

Weight

11.47

11.90

14.00

4.79

6.42

5.37

5.19

1.56 (0.95, 2.56)

1.04 (0.79, 1.39)

1.28 (0.32, 5.10)

0.44 (0.10, 1.92)

6.12 (3.89, 9.61)

RR (95% CI)

1.00 (0.50, 2.03)

1.90 (0.99, 3.64)

1.68 (1.21, 2.33)

2.59 (0.40, 16.93)

0.44 (0.10, 1.92)

2.64 (0.47, 14.75)

2.13 (0.36, 12.48)

100.00

14.21

%

6.94

6.42

13.28

Weight

11.47

11.90

14.00

4.79

6.42

5.37

5.19

1.0591116.9



Lei Gao

Study

Ru Liu

ID

Huayuan Xu

Youbin Liu

2.84 (1.71, 4.74)

4.00 (0.97, 16.55)

3.60 (0.18, 70.54)

RR (95% CI)

2.30 (0.51, 10.36)

2.74 (1.50, 5.00)

100.00

12.97

%

2.95

Weight

11.54

72.54

2.84 (1.71, 4.74)

4.00 (0.97, 16.55)

3.60 (0.18, 70.54)

RR (95% CI)

2.30 (0.51, 10.36)

2.74 (1.50, 5.00)

100.00

12.97

%

2.95

Weight

11.54

72.54

1.0142170.5



Huayuan Xu

Youbin Liu

ID

Study

Lei Gao

Ru Liu

2.00 (0.98, 4.07)

2.30 (0.51, 10.36)

2.91 (0.97, 8.74)

RR (95% CI)

1.33 (0.35, 5.03)

0.71 (0.05, 10.55)

100.00

22.39

41.84

Weight

%

28.82

6.95

2.00 (0.98, 4.07)

2.30 (0.51, 10.36)

2.91 (0.97, 8.74)

RR (95% CI)

1.33 (0.35, 5.03)

0.71 (0.05, 10.55)

100.00

22.39

41.84

Weight

%

28.82

6.95

1.0475121



Youbin Liu

Ru Liu

Study

Lei Gao

Huayuan Xu

ID

5.23 (2.16, 12.67)

6.13 (1.85, 20.34)

2.16 (0.09, 50.04)

6.40 (0.86, 47.69)

3.83 (0.48, 30.60)

RR (95% CI)

100.00

54.50

7.93

%

19.42

18.15

Weight

5.23 (2.16, 12.67)

6.13 (1.85, 20.34)

2.16 (0.09, 50.04)

6.40 (0.86, 47.69)

3.83 (0.48, 30.60)

RR (95% CI)

100.00

54.50

7.93

%

19.42

18.15

Weight

1.02150



Youbin Liu

Ru Liu

Study

Lei Gao

ID

5.60 (2.11, 14.89)

6.13 (1.85, 20.34)

2.16 (0.09, 50.04)

6.40 (0.86, 47.69)

RR (95% CI)

100.00

66.58

9.69

%

23.73

Weight

5.60 (2.11, 14.89)

6.13 (1.85, 20.34)

2.16 (0.09, 50.04)

6.40 (0.86, 47.69)

RR (95% CI)

100.00

66.58

9.69

%

23.73

Weight

1.02150



Study

ID

Huayuan Xu

Ru Liu

Lei Gao

2.20 (0.45, 10.78)

RR (95% CI)

1.53 (0.15, 15.89)

2.16 (0.09, 50.04)

4.03 (0.20, 80.21)

100.00

%

Weight

46.19

25.57

28.24

2.20 (0.45, 10.78)

RR (95% CI)

1.53 (0.15, 15.89)

2.16 (0.09, 50.04)

4.03 (0.20, 80.21)

100.00

%

Weight

46.19

25.57

28.24

1.0125180.2



Ru Liu

Study

ID

Huayuan Xu

Lei Gao

2.20 (0.45, 10.78)

2.16 (0.09, 50.04)

RR (95% CI)

1.53 (0.15, 15.89)

4.03 (0.20, 80.21)

100.00

25.57

%

Weight

46.19

28.24

2.20 (0.45, 10.78)

2.16 (0.09, 50.04)

RR (95% CI)

1.53 (0.15, 15.89)

4.03 (0.20, 80.21)

100.00

25.57

%

Weight

46.19

28.24

1.0125180.2



Zhichao Feng

ID

Wei liu

Study

1.37 (0.93, 2.03)

1.43 (0.79, 2.60)

RR (95% CI)

1.33 (0.80, 2.22)

100.00

42.43

Weight

57.57

%

1.37 (0.93, 2.03)

1.43 (0.79, 2.60)

RR (95% CI)

1.33 (0.80, 2.22)

100.00

42.43

Weight

57.57

%

1.38412.6



ID

Study

Zhichao Feng

Wei liu

2.70 (0.14, 51.96)

RR (95% CI)

0.53 (0.03, 8.83)

9.14 (1.72, 48.62)

100.00

Weight

%

42.82

57.18

2.70 (0.14, 51.96)

RR (95% CI)

0.53 (0.03, 8.83)

9.14 (1.72, 48.62)

100.00

Weight

%

42.82

57.18

1.0192152



ID

Study

Zhichao Feng

Wei liu

3.01 (1.51, 5.99)

RR (95% CI)

3.36 (1.54, 7.34)

2.03 (0.47, 8.81)

100.00

Weight

%

77.94

22.06

3.01 (1.51, 5.99)

RR (95% CI)

3.36 (1.54, 7.34)

2.03 (0.47, 8.81)

100.00

Weight

%

77.94

22.06

1.11318.81



ID

Wei liu

Zhichao Feng

Study

3.31 (1.08, 10.14)

RR (95% CI)

4.06 (0.76, 21.61)

2.80 (0.62, 12.65)

100.00

Weight

44.87

55.13

%

3.31 (1.08, 10.14)

RR (95% CI)

4.06 (0.76, 21.61)

2.80 (0.62, 12.65)

100.00

Weight

44.87

55.13

%

1.0463121.6



Weijie Guan

Study


ID

1.21 (0.84, 1.76)

1.48 (1.02, 2.16)

1.02 (0.74, 1.40)

RR (95% CI)

100.00

46.56

%

53.44

Weight

1.21 (0.84, 1.76)

1.48 (1.02, 2.16)

1.02 (0.74, 1.40)

RR (95% CI)

100.00

46.56

%

53.44

Weight

1.46312.16



Wei liu

ID

Zhichao Feng

Study

7.56 (1.61, 35.45)

6.09 (0.41, 90.40)

RR (95% CI)

8.40 (1.27, 55.35)

100.00

32.82

Weight

67.18

%

7.56 (1.61, 35.45)

6.09 (0.41, 90.40)

RR (95% CI)

8.40 (1.27, 55.35)

100.00

32.82

Weight

67.18

%

1.0111190.4



Study

Shijiao Yan

Chuan Qin

Kaicai Liu

Ling Mao

Guqin Zhang

Xiaowei Xu

wei-jie guan

Jinjin Zhang

Jiatao Lu

Yi Han

Rong Qu

Yishan Zheng

Zhen Li

Luwen Wang

Jingyuan Liu

Di Qi

Sakiko Tabata

Guang Chen

Xu Chen

ID

Ying Zhou

Zhifeng Xu

Jian Wu


wen zhao

Jing Liu

Zhongliang Wang

Qingxian Cai

Xu Yang

Sijia Tian

Xiaowei Fang

Yudong Peng

Chuan Qin

0.73 (0.53, 0.94)

0.66 (0.29, 1.04)

0.57 (0.37, 0.76)

1.67 (1.11, 2.23)

0.63 (0.35, 0.91)

0.55 (0.24, 0.86)

0.45 (-0.05, 0.96)

0.41 (0.24, 0.57)

0.88 (0.52, 1.23)

0.25 (0.02, 0.47)

0.01 (-0.56, 0.58)

0.73 (-0.47, 1.94)

-2.06 (-2.84, -1.27)

0.72 (0.41, 1.03)

0.73 (0.35, 1.10)

0.30 (-0.26, 0.86)

1.70 (1.36, 2.04)

0.30 (-0.14, 0.73)

1.07 (0.15, 1.99)

-0.99 (-1.30, -0.67)

SMD (95% CI)

1.11 (0.88, 1.34)

1.33 (0.33, 2.33)

1.66 (1.37, 1.95)

0.52 (0.42, 0.62)

1.45 (0.89, 2.01)

1.42 (0.68, 2.15)

2.47 (1.75, 3.19)

1.42 (1.11, 1.72)

1.06 (0.54, 1.58)

1.42 (1.08, 1.76)

1.14 (0.63, 1.65)

-0.50 (-1.04, 0.03)

0.57 (0.37, 0.76)

100.00

%

3.34

3.64

2.94

3.52

3.46

3.06

3.67

3.39

3.60

2.91

1.64

2.42

3.47

3.34

2.94

3.41

3.22

2.14

3.45

Weight

3.59

1.99

3.50

3.72

2.94

2.54

2.57

3.47

3.03

3.41

3.05

3.00

3.64

0.73 (0.53, 0.94)

0.66 (0.29, 1.04)

0.57 (0.37, 0.76)

1.67 (1.11, 2.23)

0.63 (0.35, 0.91)

0.55 (0.24, 0.86)

0.45 (-0.05, 0.96)

0.41 (0.24, 0.57)

0.88 (0.52, 1.23)

0.25 (0.02, 0.47)

0.01 (-0.56, 0.58)

0.73 (-0.47, 1.94)

-2.06 (-2.84, -1.27)

0.72 (0.41, 1.03)

0.73 (0.35, 1.10)

0.30 (-0.26, 0.86)

1.70 (1.36, 2.04)

0.30 (-0.14, 0.73)

1.07 (0.15, 1.99)

-0.99 (-1.30, -0.67)

SMD (95% CI)

1.11 (0.88, 1.34)

1.33 (0.33, 2.33)

1.66 (1.37, 1.95)

0.52 (0.42, 0.62)

1.45 (0.89, 2.01)

1.42 (0.68, 2.15)

2.47 (1.75, 3.19)

1.42 (1.11, 1.72)

1.06 (0.54, 1.58)

1.42 (1.08, 1.76)

1.14 (0.63, 1.65)

-0.50 (-1.04, 0.03)

0.57 (0.37, 0.76)

100.00

%

3.34

3.64

2.94

3.52

3.46

3.06

3.67

3.39

3.60

2.91

1.64

2.42

3.47

3.34

2.94

3.41

3.22

2.14

3.45

Weight

3.59

1.99

3.50

3.72

2.94

2.54

2.57

3.47

3.03

3.41

3.05

3.00

3.64

0-3.1903.19



Fan Zhang


Tian Gu

Mingli Yuan

Zhihua Wang

Jianmin Jin

Tao Chen

ID

Chaomin Wu

Fei Zhou

Study

1.06 (0.85, 1.26)

1.03 (0.41, 1.66)

1.06 (0.73, 1.38)

0.83 (0.60, 1.06)

0.84 (0.03, 1.66)

0.62 (0.07, 1.17)

1.42 (1.09, 1.76)

1.01 (0.75, 1.26)

SMD (95% CI)

0.87 (0.58, 1.16)

1.77 (1.32, 2.22)

100.00

6.89

13.06

15.57

4.78

8.13

12.80

14.88

Weight

13.86

10.02

%

1.06 (0.85, 1.26)

1.03 (0.41, 1.66)

1.06 (0.73, 1.38)

0.83 (0.60, 1.06)

0.84 (0.03, 1.66)

0.62 (0.07, 1.17)

1.42 (1.09, 1.76)

1.01 (0.75, 1.26)

SMD (95% CI)

0.87 (0.58, 1.16)

1.77 (1.32, 2.22)

100.00

6.89

13.06

15.57

4.78

8.13

12.80

14.88

Weight

13.86

10.02

%

0-2.2202.22



Chaolin Huang

Dawei Wang

ID

Study

Min Cao

Bingwen Eugene FAN

0.77 (0.59, 0.95)

0.80 (0.55, 1.05)

0.00 (-0.66, 0.66)

0.84 (0.45, 1.24)

SMD (95% CI)

0.97 (0.48, 1.45)

0.86 (0.14, 1.58)

100.00

51.39

7.48

20.95

Weight

%

13.90

6.28

0.77 (0.59, 0.95)

0.80 (0.55, 1.05)

0.00 (-0.66, 0.66)

0.84 (0.45, 1.24)

SMD (95% CI)

0.97 (0.48, 1.45)

0.86 (0.14, 1.58)

100.00

51.39

7.48

20.95

Weight

%

13.90

6.28

0-1.5801.58



Study

ID

Weijie guan


0.88 (0.56, 1.21)

SMD (95% CI)

1.05 (0.80, 1.30)

0.72 (0.49, 0.95)

100.00

%

Weight

48.71

51.29

0.88 (0.56, 1.21)

SMD (95% CI)

1.05 (0.80, 1.30)

0.72 (0.49, 0.95)

100.00

%

Weight

48.71

51.29

0-1.301.3


Yanli Liu

ID


Chaomin Wu

Study

0.83 (0.67, 0.99)

0.80 (0.41, 1.19)

SMD (95% CI)

0.81 (0.59, 1.03)

0.87 (0.58, 1.16)

100.00

16.52

Weight

54.10

29.37

%

0.83 (0.67, 0.99)

0.80 (0.41, 1.19)

SMD (95% CI)

0.81 (0.59, 1.03)

0.87 (0.58, 1.16)

100.00

16.52

Weight

54.10

29.37

%

0-1.1901.19



ID

Yonghao Xu

Study

0.84 (0.54, 1.14)

0.90 (0.55, 1.24)

SMD (95% CI)

0.66 (0.05, 1.26)

100.00

75.34

Weight

24.66

%

0.84 (0.54, 1.14)

0.90 (0.55, 1.24)

SMD (95% CI)

0.66 (0.05, 1.26)

100.00

75.34

Weight

24.66

%

0-1.2601.26



Youbin Liu

Huayuan Xu

ID

Study

Lei Gao

Ru Liu

0.92 (0.44, 1.41)

0.88 (0.36, 1.41)

1.46 (0.85, 2.08)

SMD (95% CI)

1.11 (0.54, 1.69)

0.23 (-0.40, 0.85)

100.00

26.90

24.11

Weight

%

25.20

23.80

0.92 (0.44, 1.41)

0.88 (0.36, 1.41)

1.46 (0.85, 2.08)

SMD (95% CI)

1.11 (0.54, 1.69)

0.23 (-0.40, 0.85)

100.00

26.90

24.11

Weight

%

25.20

23.80

0-2.0802.08



ID

Zhichao Feng

Wei liu

Study

2.37 (0.00, 4.74)

SMD (95% CI)

1.19 (0.63, 1.74)

3.61 (2.75, 4.46)

100.00

Weight

50.95

49.05

%

2.37 (0.00, 4.74)

SMD (95% CI)

1.19 (0.63, 1.74)

3.61 (2.75, 4.46)

100.00

Weight

50.95

49.05

%

0-4.7404.74




Study

Weijie Guan

ID

2.17 (0.61, 7.70)

1.17 (0.59, 2.31)

4.22 (1.81, 9.84)

RR (95% CI)

100.00

51.97

%

48.03

Weight

2.17 (0.61, 7.70)

1.17 (0.59, 2.31)

4.22 (1.81, 9.84)

RR (95% CI)

100.00

51.97

%

48.03

Weight

1.10219.84



Jingyuan Liu

Study

ID

Yafei Wang


Ling Hu

0.83 (0.48, 1.44)

2.22 (0.87, 5.66)

RR (95% CI)

0.40 (0.15, 1.09)

0.67 (0.42, 1.07)

0.88 (0.47, 1.62)

100.00

19.44

%

Weight

17.91

34.01

28.63

0.83 (0.48, 1.44)

2.22 (0.87, 5.66)

RR (95% CI)

0.40 (0.15, 1.09)

0.67 (0.42, 1.07)

0.88 (0.47, 1.62)

100.00

19.44

%

Weight

17.91

34.01

28.63

1.14616.84



Ying Zhou

Weijie Guan

ID

Study

Zhen Li

Sijia Tian

0.66 (0.32, 1.36)

(Excluded)

1.11 (0.94, 1.32)

RR (95% CI)

0.46 (0.32, 0.66)

0.49 (0.21, 1.16)

100.00

0.00

38.64

Weight

%

36.07

25.29

0.66 (0.32, 1.36)

(Excluded)

1.11 (0.94, 1.32)

RR (95% CI)

0.46 (0.32, 0.66)

0.49 (0.21, 1.16)

100.00

0.00

38.64

Weight

%

36.07

25.29

1.20614.85



Kaicai Liu

Zhen Li

Study

ID

Ying Wen

Weijie Guan

Xu Chen

Shijiao Yan

wen zhao

Jian Wu

Di Qi

Sijia Tian

1.21 (0.88, 1.65)

1.30 (1.02, 1.66)

3.28 (0.81, 13.31)

RR (95% CI)

1.09 (0.82, 1.47)

2.48 (2.16, 2.85)

1.19 (0.86, 1.65)

0.99 (0.77, 1.26)

0.65 (0.41, 1.03)

1.20 (0.98, 1.46)

1.59 (0.86, 2.95)

0.66 (0.40, 1.07)

100.00

11.48

3.54

%

Weight

11.16

12.07

10.89

11.50

9.74

11.78

8.32

9.52

1.21 (0.88, 1.65)

1.30 (1.02, 1.66)

3.28 (0.81, 13.31)

RR (95% CI)

1.09 (0.82, 1.47)

2.48 (2.16, 2.85)

1.19 (0.86, 1.65)

0.99 (0.77, 1.26)

0.65 (0.41, 1.03)

1.20 (0.98, 1.46)

1.59 (0.86, 2.95)

0.66 (0.40, 1.07)

100.00

11.48

3.54

%

Weight

11.16

12.07

10.89

11.50

9.74

11.78

8.32

9.52

1.0751113.3



Ying Zhou

ID

Xiaowei Fang

Shijiao Yan

Study

wen zhao

Kaicai Liu

Weijie Guan


Jingyuan Liu

Sijia Tian

Di Qi

Xu Chen

Jian Wu

Zhen Li

0.98 (0.86, 1.13)

(Excluded)

RR (95% CI)

0.97 (0.76, 1.23)

0.79 (0.42, 1.46)

2.49 (1.04, 6.00)

0.29 (0.07, 1.18)

1.03 (0.90, 1.18)

1.06 (0.95, 1.19)

0.80 (0.30, 2.10)

1.19 (0.89, 1.58)

1.00 (0.74, 1.34)

0.75 (0.48, 1.17)

0.16 (0.02, 1.18)

0.38 (0.15, 0.94)

100.00

0.00

Weight

14.49

4.07

%

2.19

0.91

21.08

22.42

1.82

12.02

11.75

6.76

0.45

2.05

0.98 (0.86, 1.13)

(Excluded)

RR (95% CI)

0.97 (0.76, 1.23)

0.79 (0.42, 1.46)

2.49 (1.04, 6.00)

0.29 (0.07, 1.18)

1.03 (0.90, 1.18)

1.06 (0.95, 1.19)

0.80 (0.30, 2.10)

1.19 (0.89, 1.58)

1.00 (0.74, 1.34)

0.75 (0.48, 1.17)

0.16 (0.02, 1.18)

0.38 (0.15, 0.94)

100.00

0.00

Weight

14.49

4.07

%

2.19

0.91

21.08

22.42

1.82

12.02

11.75

6.76

0.45

2.05

1.0212147.1



Xu Chen

wen zhao

Study

Sijia Tian

Shijiao Yan


ID

0.95 (0.86, 1.04)

0.79 (0.51, 1.21)

1.07 (0.60, 1.89)

1.03 (0.76, 1.40)

0.89 (0.65, 1.23)

0.95 (0.85, 1.06)

RR (95% CI)

100.00

4.59

2.61

%

9.10

8.30

75.40

Weight

0.95 (0.86, 1.04)

0.79 (0.51, 1.21)

1.07 (0.60, 1.89)

1.03 (0.76, 1.40)

0.89 (0.65, 1.23)

0.95 (0.85, 1.06)

RR (95% CI)

100.00

4.59

2.61

%

9.10

8.30

75.40

Weight

1.51111.96



Study

Jian Wu

ID

Jing Liu


Guang Chen

Guqin Zhang

1.79 (0.38, 8.35)

30.86 (4.10, 232.07)

RR (95% CI)

4.15 (0.41, 41.74)

0.45 (0.23, 0.92)

0.30 (0.04, 2.46)

1.81 (0.45, 7.33)

100.00

%

18.30

Weight

16.69

25.28

17.86

21.87

1.79 (0.38, 8.35)

30.86 (4.10, 232.07)

RR (95% CI)

4.15 (0.41, 41.74)

0.45 (0.23, 0.92)

0.30 (0.04, 2.46)

1.81 (0.45, 7.33)

100.00

%

18.30

Weight

16.69

25.28

17.86

21.87

1.004311232



Jing Liu

Guqin Zhang

Jinjin Zhang

Study

Weijie Guan-2

Zhen Li

Ling Mao

Sakiko Tabata

Xu Chen

wen zhao

ID


Di Qi

Chuan Qin

Xiaowei Fang

Qingxian Ca

Guang Chen

Chuan Qin

1.72 (1.44, 2.06)

2.08 (0.92, 4.68)

3.18 (2.30, 4.39)

1.48 (1.16, 1.88)

2.51 (2.14, 2.95)

1.37 (1.06, 1.75)

1.47 (1.05, 2.05)

1.44 (0.99, 2.09)

2.18 (1.59, 3.00)

2.04 (1.08, 3.83)

RR (95% CI)

1.39 (1.25, 1.55)

2.50 (1.44, 4.37)

1.54 (1.21, 1.97)

0.42 (0.23, 0.74)

2.48 (1.84, 3.34)

2.27 (0.56, 9.20)

1.54 (1.21, 1.97)

100.00

3.18

6.92

7.67

%

8.29

7.59

6.82

6.44

6.98

4.26

Weight

8.59

4.82

7.64

4.63

7.15

1.40

7.64

1.72 (1.44, 2.06)

2.08 (0.92, 4.68)

3.18 (2.30, 4.39)

1.48 (1.16, 1.88)

2.51 (2.14, 2.95)

1.37 (1.06, 1.75)

1.47 (1.05, 2.05)

1.44 (0.99, 2.09)

2.18 (1.59, 3.00)

2.04 (1.08, 3.83)

RR (95% CI)

1.39 (1.25, 1.55)

2.50 (1.44, 4.37)

1.54 (1.21, 1.97)

0.42 (0.23, 0.74)

2.48 (1.84, 3.34)

2.27 (0.56, 9.20)

1.54 (1.21, 1.97)

100.00

3.18

6.92

7.67

%

8.29

7.59

6.82

6.44

6.98

4.26

Weight

8.59

4.82

7.64

4.63

7.15

1.40

7.64

1.10919.2



Xu Chen

Jiatao Lu

Di Qi

Yudong Peng

Weijie Guan-2


Guqin Zhang

Ling Hu

Guang Chen

ID

Jing Liu

Jingyuan Liu

Ying Zhou

Luwen Wang

wen zhao

Chuan Qin

Ling Mao

Zhongliang Wang

Jinjin Zhang

Chuan Qin

Shijiao Yan

Yihan

Xiaowei Fang

Hongzhou Lu

Study

2.09 (1.73, 2.52)

4.58 (2.64, 7.93)

1.34 (0.92, 1.96)

4.03 (2.02, 8.03)

0.73 (0.50, 1.08)

2.56 (2.06, 3.18)

1.66 (1.44, 1.90)

2.80 (1.81, 4.34)

1.49 (1.07, 2.07)

3.64 (0.48, 27.33)

RR (95% CI)

10.38 (1.35, 80.07)

2.59 (0.97, 6.92)

2.47 (1.90, 3.21)

0.90 (0.56, 1.45)

2.85 (1.23, 6.58)

2.03 (1.42, 2.91)

2.41 (1.47, 3.97)

4.91 (1.51, 15.93)

1.56 (0.94, 2.57)

2.03 (1.42, 2.91)

3.10 (1.52, 6.33)

1.20 (0.58, 2.49)

5.04 (1.97, 12.93)

2.63 (1.54, 4.49)

100.00

4.50

5.65

3.68

5.58

6.69

7.06

5.24

5.98

0.77

Weight

0.75

2.43

6.43

4.98

2.98

5.79

4.83

1.88

4.80

5.79

3.56

3.46

2.57

4.60

%

2.09 (1.73, 2.52)

4.58 (2.64, 7.93)

1.34 (0.92, 1.96)

4.03 (2.02, 8.03)

0.73 (0.50, 1.08)

2.56 (2.06, 3.18)

1.66 (1.44, 1.90)

2.80 (1.81, 4.34)

1.49 (1.07, 2.07)

3.64 (0.48, 27.33)

RR (95% CI)

10.38 (1.35, 80.07)

2.59 (0.97, 6.92)

2.47 (1.90, 3.21)

0.90 (0.56, 1.45)

2.85 (1.23, 6.58)

2.03 (1.42, 2.91)

2.41 (1.47, 3.97)

4.91 (1.51, 15.93)

1.56 (0.94, 2.57)

2.03 (1.42, 2.91)

3.10 (1.52, 6.33)

1.20 (0.58, 2.49)

5.04 (1.97, 12.93)

2.63 (1.54, 4.49)

100.00

4.50

5.65

3.68

5.58

6.69

7.06

5.24

5.98

0.77

Weight

0.75

2.43

6.43

4.98

2.98

5.79

4.83

1.88

4.80

5.79

3.56

3.46

2.57

4.60

%

1.0125180.1



Weijie Guan-2


Guqin Zhang

Yudong Peng

Guang Chen

Xiaowei Fang

Ling Hu

Jiatao Lu

Hongzhou Lu

Di Qi

Luwen Wang

Jinjin Zhang

Ying Zhou

Jingyuan Liu

Xu Chen

Ling Mao

Jing Liu

Zhongliang Wang

ID

wen zhao

Yihan

Chuan Qin

Chuan Qin

Shijiao Yan

Study

1.94 (1.60, 2.36)

2.78 (1.99, 3.89)

1.57 (1.25, 1.98)

1.41 (0.61, 3.27)

1.26 (0.49, 3.23)

1.82 (0.19, 17.12)

2.29 (0.62, 8.41)

2.07 (1.15, 3.72)

0.98 (0.49, 1.97)

4.42 (1.91, 10.24)

3.95 (1.98, 7.88)

1.29 (0.55, 3.04)

1.26 (0.52, 3.06)

2.22 (1.54, 3.21)

3.88 (0.71, 21.24)

2.25 (0.97, 5.23)

1.43 (0.74, 2.78)

4.15 (0.87, 19.83)

23.57 (3.08, 180.21)

RR (95% CI)

1.42 (0.28, 7.19)

1.44 (0.36, 5.80)

1.40 (0.88, 2.21)

1.40 (0.88, 2.21)

5.13 (1.73, 15.22)

100.00

9.53

11.19

3.82

3.26

0.72

1.93

6.05

4.94

3.84

4.99

3.74

3.53

9.01

1.20

3.82

5.26

1.40

0.86

Weight

1.31

1.72

7.65

7.65

2.60

%

1.94 (1.60, 2.36)

2.78 (1.99, 3.89)

1.57 (1.25, 1.98)

1.41 (0.61, 3.27)

1.26 (0.49, 3.23)

1.82 (0.19, 17.12)

2.29 (0.62, 8.41)

2.07 (1.15, 3.72)

0.98 (0.49, 1.97)

4.42 (1.91, 10.24)

3.95 (1.98, 7.88)

1.29 (0.55, 3.04)

1.26 (0.52, 3.06)

2.22 (1.54, 3.21)

3.88 (0.71, 21.24)

2.25 (0.97, 5.23)

1.43 (0.74, 2.78)

4.15 (0.87, 19.83)

23.57 (3.08, 180.21)

RR (95% CI)

1.42 (0.28, 7.19)

1.44 (0.36, 5.80)

1.40 (0.88, 2.21)

1.40 (0.88, 2.21)

5.13 (1.73, 15.22)

100.00

9.53

11.19

3.82

3.26

0.72

1.93

6.05

4.94

3.84

4.99

3.74

3.53

9.01

1.20

3.82

5.26

1.40

0.86

Weight

1.31

1.72

7.65

7.65

2.60

%

1.005551180



Jing Liu

Hongzhou Lu

Ling Hu

Xiaowei Fang

Luwen Wang

Weijie Guan-2

Jian Wu

wen zhao

Chuan Qin


Ling Mao

Zhongliang Wang

Shijiao Yan

ID

Chuan Qin

Study

1.61 (0.98, 2.64)

0.40 (0.02, 7.78)

2.21 (0.27, 18.08)

9.66 (0.54, 173.35)

0.78 (0.03, 18.42)

11.39 (1.52, 85.36)

5.22 (2.09, 13.02)

1.58 (0.27, 9.30)

0.95 (0.10, 8.62)

1.45 (0.46, 4.55)

0.76 (0.38, 1.54)

0.89 (0.30, 2.64)

1.31 (0.15, 11.65)

0.72 (0.04, 14.65)

RR (95% CI)

1.45 (0.46, 4.55)

100.00

2.53

4.62

2.66

2.25

4.96

14.15

6.09

4.27

11.13

17.57

11.84

4.33

2.46

Weight

11.13

%

1.61 (0.98, 2.64)

0.40 (0.02, 7.78)

2.21 (0.27, 18.08)

9.66 (0.54, 173.35)

0.78 (0.03, 18.42)

11.39 (1.52, 85.36)

5.22 (2.09, 13.02)

1.58 (0.27, 9.30)

0.95 (0.10, 8.62)

1.45 (0.46, 4.55)

0.76 (0.38, 1.54)

0.89 (0.30, 2.64)

1.31 (0.15, 11.65)

0.72 (0.04, 14.65)

RR (95% CI)

1.45 (0.46, 4.55)

100.00

2.53

4.62

2.66

2.25

4.96

14.15

6.09

4.27

11.13

17.57

11.84

4.33

2.46

Weight

11.13

%

1.005771173



Guqin Zhang

Xu Chen

Kunhua Li

Zhongliang Wang

Hongzhou Lu

Chuan Qin

Shijiao Yan

Ying Zhou

Jinjin Zhang

wen zhao

Jingyuan Liu

Weijie Guan-2

ID


Yihan

Jiatao Lu

Chuan Qin

Xiaowei Fang

Yudong Peng

Study

2.74 (2.03, 3.70)

4.36 (1.97, 9.64)

3.44 (1.14, 10.41)

6.81 (0.29, 161.62)

6.55 (1.77, 24.16)

4.42 (1.51, 12.94)

4.64 (1.42, 15.19)

3.67 (1.26, 10.69)

2.89 (1.30, 6.40)

1.89 (0.44, 8.11)

5.70 (1.57, 20.68)

7.50 (0.32, 175.65)

2.70 (1.60, 4.55)

RR (95% CI)

1.51 (1.06, 2.15)

3.83 (0.46, 31.79)

1.74 (0.88, 3.44)

4.64 (1.42, 15.19)

4.58 (0.44, 48.16)

1.15 (0.76, 1.76)

100.00

7.49

5.01

0.85

3.96

5.22

4.56

5.25

7.47

3.34

4.03

0.86

10.64

Weight

12.88

1.79

8.66

4.56

1.48

11.97

%

2.74 (2.03, 3.70)

4.36 (1.97, 9.64)

3.44 (1.14, 10.41)

6.81 (0.29, 161.62)

6.55 (1.77, 24.16)

4.42 (1.51, 12.94)

4.64 (1.42, 15.19)

3.67 (1.26, 10.69)

2.89 (1.30, 6.40)

1.89 (0.44, 8.11)

5.70 (1.57, 20.68)

7.50 (0.32, 175.65)

2.70 (1.60, 4.55)

RR (95% CI)

1.51 (1.06, 2.15)

3.83 (0.46, 31.79)

1.74 (0.88, 3.44)

4.64 (1.42, 15.19)

4.58 (0.44, 48.16)

1.15 (0.76, 1.76)

100.00

7.49

5.01

0.85

3.96

5.22

4.56

5.25

7.47

3.34

4.03

0.86

10.64

Weight

12.88

1.79

8.66

4.56

1.48

11.97

%

1.005691176



Yudong Peng

Hongzhou Lu

Yihan

Xiaowei Fang


wei-jie guan

ID

Jinjin Zhang

Ying Zhou

Study

2.03 (1.39, 2.97)

1.15 (0.76, 1.76)

4.42 (1.51, 12.94)

3.83 (0.46, 31.79)

4.58 (0.44, 48.16)

1.51 (1.06, 2.15)

3.15 (1.47, 6.76)

RR (95% CI)

1.89 (0.44, 8.11)

2.89 (1.30, 6.40)

100.00

24.48

9.22

2.96

2.43

26.89

14.50

Weight

5.69

13.83

%

2.03 (1.39, 2.97)

1.15 (0.76, 1.76)

4.42 (1.51, 12.94)

3.83 (0.46, 31.79)

4.58 (0.44, 48.16)

1.51 (1.06, 2.15)

3.15 (1.47, 6.76)

RR (95% CI)

1.89 (0.44, 8.11)

2.89 (1.30, 6.40)

100.00

24.48

9.22

2.96

2.43

26.89

14.50

Weight

5.69

13.83

%

1.0208148.2



Xi

Documents

Amazon Web Services · Web viewSupplementary materials. Table of Contents. Supplementary methods6. The Detailed Definitions of the Severity and Prognostic Endpoints of COVID-196