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. 2020 Apr;8(Suppl 1):247–255. doi: 10.22038/abjs.2020.47754.2346

Prevalence of Comorbidities in COVID-19 Patients: A Systematic Review and Meta-Analysis

Ashkan Baradaran 1, Mohammad H Ebrahimzadeh 1, Aslan Baradaran 2, Amir R Kachooei 1
PMCID: PMC7296605  PMID: 32733980

Abstract

Background:

In this study, we aimed to assess the prevalence of comorbidities in the confirmed COVID-19 patients. This might help showing which comorbidity might pose the patients at risk of more severe symptoms.

Methods:

We searched all relevant databases on April 7th, 2020 using the keywords (“novel coronavirus” OR COVID-19 OR SARS-CoV-2 OR Coronavirus) AND (comorbidities OR clinical characteristics OR epidemiologic*). We reviewed 33 papers’ full text out of 1053 papers. There were 32 papers from China and 1 from Taiwan. There was no language or study level limit. Prevalence of comorbidities including hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, chronic kidney disease, malignancies, cerebrovascular diseases, chronic liver disease and smoking were extracted to measure the pooled estimates. We used OpenMeta and used random-effect model to do a single arm meta-analysis.

Results:

The mean age of the diagnosed patients was 51 years. The male to female ratio was 55 to 45. The most prevalent finding in the confirmed COVID-19 patients was hypertension, which was found in 1/5 of the patients (21%). Other most prevalent finding was diabetes mellitus (DM) in 11%, cerebrovascular disease in 2.4%, cardiovascular disease in 5.8%, chronic kidney disease in 3.6%, chronic liver disease in 2.9%, chronic pulmonary disease in 2.0%, malignancy in 2.7%, and smoking in 8.7% of the patients.

Conclusion:

COVID-19 infection seems to be affecting every race, sex, age, irrespective of health status. The risk of symptomatic and severe disease might be higher due to the higher age which is usually accompanied with comorbidities. However, comorbidities do not seem to be the prerequisite for symptomatic and severe COVID-19 infection, except hypertension.

Key Words: Comorbidities, Coronavirus, COVID-19, Systematic review

Introduction

We are still unaware of many facts about the new coronavirus that is called COVID-19. In the beginning of its spread in Wuhan, China, the transmission and infectivity rate was reported very low, leading everyone to believe that the mortality rate was even lower than a seasonal flu, and taking it even less seriously. The first reports were noting that it only affects older people with comorbidities and only they die because of inefficient immune system following multiple comorbidities. The threat was not considered seriously and later after it spread to other countries, doctors were seeing even younger patients that were dying despite having no past medical history, and the patient’s status was aggravating so fast in hours that no one could help save the lives. Further studies showed cytokine storm as a reason which is an exaggerated immune response to the infection regardless of having comorbidities. Thus, simply having chronic conditions does not determine the prognosis.

In this study, we aimed to assess the prevalence of comorbidities in the confirmed COVID-19 patients. This might help showing which comorbidity might pose the patients at risk of more severe symptoms.

Materials and Methods

Search strategy

In order to identify all relevant studies, databases including EMBASE, PubMed, and google scholar were searched and papers published in the past year were screened carefully until April 7th, 2020 using title and abstract. Records were imported to EndNote X9 citation manager and duplicates were excluded. The following search terms were employed to ensure inclusion of all relevant studies: (“novel coronavirus” OR COVID-19 OR SARS-CoV-2 OR Coronavirus) AND (comorbidities OR clinical characteristics OR epidemiologic*)

Inclusion and Exclusion Criteria

All articles with any design and study level (levels 1-4) that reported the prevalence of comorbidities among the confirmed COVID-19 patients were included. There was no language limit. We used the translated abstract of non-English articles. We excluded papers concerning children and papers without epidemiological information.

Data extraction and statistical analysis

All stages of the meta-analysis were done by the two authors (AK and AB) independently. Prevalence of comorbidities including hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, chronic kidney disease, malignancies, cerebrovascular diseases, chronic liver disease and smoking were extracted to measure the pooled estimates [Table 1]. The latest version of the statistical software OpenMeta [Analyst] (1) was used to do a single arm Meta-analysis. This analysis took study effects into account, and the results were calculated by a binary random-effect method (Dersimonian-Laird). A confidence interval of 95% was selected and the I2 statistic and Cochran’s Q test were measured to assess statistical heterogeneity. Forest plots were used to illustrate the prevalence with 95% confidence interval.

Table 1.

Data extracted using 33 papers

First Author Publication Date Country Number of patients Sex (M/F) Age (mean or median) Age (SD or range) HTN DM Malignancy Chronic Lung Disease Cardio vascular Disease Chronic Liver Disease Cerebro Vascular Disease Chronic Kidney Disease Smoking
Yingzhen Du, et al. 4/4/2020 China 85 62/23 66 14 32 19 6 2 10 5   3  
Lang Wang, et al. 4/3/2020 China 339 166/173 69 65-76 138 54 15 21 53 2 21 13  
ChaominWu, et al. 3/14/2020 China 201 128/73 51 43-60 32 22 1 5 8 7   2  
Xiao Tang, et al. 4/1/2020 China 148 105/43 62 47-69 70 35     31     9 43
Qingxian Cai, et al. 4/3/2020 China 298 149/149 47 33-61 38 19 4   11 8      
Wei Liu, et al. 3/3/2020 China 78 39/39 38 33-57 8 5 4 2         5
Xiao-Wei Xu, et al. 2/23/2020 China 62 35/27 41 32-52 5 1   1   7 1 1  
Tao Chen, et al. 3/29/2020 China 274 171/103 62 44-70 93 47 7 18 23 11 4 4 12
Jiangshan Lian, et al. 3/27/2020 China 652 349/303 41 11.38 73 33 3 0 5 25   5 46
Jianlei Cao, et al. 4/3/2020 China 102 53/49 54 37-67 28 11 4 10 5 2 6 4  
Zhongliang Wang, et al. 3/17/2020 China 69 32/37 42 35-62 9 7 4 4 8 1      
Hansheng Xie, et al. 4/3/2020 China 79 44/35 60 48-66 14 8     7        
Yan Deng, et al. 3/27/2020 China 109 death 73/36 69 62-74 40 17 6 22 13        
116 recovered 51/65 40 33-57 18 9 2 3 4        
Weina Guo, et al. 4/2/2020 China 174 76/98 59 49-67 43 37 17 14 32 8      
Wei-jie Guan, et al. 3/29/2020 China 1590 904/674 49 16 269 130 130 24 59 24 30 269  
Xi Jin, et al. 3/28/2020 China 74 GI symptoms 37/37 46 14 12 7 0 0 1 8   0  
577 no GI symptoms 294/283 45 14 88 41 6 1 4 17   6  
Wen-Hsin Hsih, et al. 3/30/2020 Taiwan 43 17/26 34 3-68 2 4 2 4 1 1      
Nanshan Chen, et al. 2/3/2020 China 99 67/32 56 13     1           3
Rui Wang, et al. 4/3/2020 China 5 3 to 2 58 47-67 4 1           5  
L. Zhang, et al. 4/1/2020 China 28 17/11 65 56-70   4   1   2      
Xiaoli Zhang, et al. 3/25/2020 China 72 normal imaging 33/39 35 14 4 4 0 0 0 2   0  
573 abnormal imaging 295/278 47 14 96 44 6 1 5 23   6  
Jin-Jin Zhang, et al. 2/23/2020 China 140 71/69 57 25-87 42 17   2 7   3 2 9
W. Guan, et al. 2/29/2020 China 1099 640/459 47 35-58 165 81 10 12 27   15 8 137
Chaolin Huang, et al. 1/28/2020 China 41 30/11 49 41-58 6 8 1 1 6 1     3
Yingxia Liu, et al. 2/13/2020 China 12 8 to 4 54 10-72 3 2 0 1 4 0   2  
DaweiWang, et al. 2/8/2020 China 138 75/63 56 42-68 43 14 10 4 20 4 7 4  
Jie Li, et al. 1/1/2020 China 17 9 to 8 45 13 1         1     3
Jian Wu, et al. 2/29/2020 China 80 39/41 46 15.42     1 0   1      
Kui Liu, et al. 2/12/2020 China 137 61/76 57 20-83 13 14 2 2 10        
Wenhua Liang, et al. 2/19/2020 China 1590           18           111
Yichun Cheng, et al. 4/6/2020 China 701 367/334 63 50-71 233 100 32 13       14  
Xiaobing Wang, et al. 4/7/2020 China 1012 524/488 50 39-58 46 27   20 15        
Ya-nan Han, et al. 4/7/2020 China 25 12 to 13 44 22-70 7 9              
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Results

Study characteristics

We included 33 studies for data extraction out of 1053 papers found on COVID-19 on April 7th, 2020 [Figure 1]. There were 32 papers from China and 1 paper from Taiwan (2). All 33 papers were in English language. In total, these studies included 9,249 patients comprising of 5,036 men and 4,191 women with confirmed COVID-19 since December 2019. (2-34)

Figure 1.

Figure 1

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PRISMA flow diagram. (From Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group [2009]. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 6[7]:e1000097. https://doi.org/10.1371/journal.pmed.1000097.)

Test of heterogeneity

We performed statistical testing for heterogeneity to determine if the prevalence of comorbidities was the same in all studies. Cochran Q result rejected the null hypothesis that there is no heterogeneity between studies if P value was <0.05. Moreover, I2 revealed that how much percent of the variation across the studies was because of heterogeneity rather than sampling error and chance. Following rule of thumb, we considered I2 greater than 40% as substantial heterogeneity. Considering the presence of heterogeneity in all fields, we used random-effects model to conduct the meta-analysis.

Age distribution

Based on the random effect model after inclusion of 33 studies, the mean age of the confirmed COVID-19 patients was 51 years (95% CI, 49-54 years old). Cochran Q statistics showed 95% heterogeneity among studies which was high and significant (Q=655, P<0.001, I2=95) [Figure 2].

Figure 2.

Figure 2

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Forest plot of the age distribution using random effect model

Sex distribution

Based on the random effect model after inclusion of 32 studies, 55% (95% CI, 53%-57%) of the patients were men while 45% (95% CI, 43%-47%) were women. Cochran Q statistics showed 74% heterogeneity among studies which was high and significant (Q=120, P<0.001, I2=74) [Figures 3; 4].

Figure 3.

Figure 3

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Forest plot of the Sex (male) ratio using random effect model

Figure 4.

Figure 4.

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Forest plot of the Sex (Female) ratio using random effect model

Prevalence of cerebrovascular disease

Based on the random effect model after inclusion of 8 studies, the prevalence of cerebrovascular disease among the confirmed COVID-19 patients was 2.4% (95% CI, 1.5%-3.4%). Cochran Q statistics showed 64% heterogeneity among studies which was high and significant (Q=19, P=0.007, I2=64) [Figure 5].

Figure 5.

Figure 5

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Forest plot of the cerebrovascular disease prevalence among COVID-19 patients using random effect model

Prevalence of cardiovascular disease

Based on the random effect model after inclusion of 23 studies, the prevalence of cardiovascular disease among the confirmed COVID-19 patients was 5.8% (95% CI, 4.5%-7.1%). Cochran Q statistics showed 91% heterogeneity among studies which was high and significant (Q=250, P<0.001, I2=91) [Figure 6].

Figure 6.

Figure 6

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Forest plot of the cardiovascular disease prevalence among COVID-19 patients using random effect model

Prevalence of chronic kidney disease

Based on the random effect model after inclusion of 17 studies, the prevalence of chronic kidney disease among the confirmed COVID-19 patients was 3.6% (95% CI, 2.0%-5.1%). Cochran Q statistics showed 96% heterogeneity among studies which was high and significant (Q=368, P<0.001, I2=96) [Figure 7].

Figure 7.

Figure 7

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Forest plot of the chronic kidney disease prevalence among COVID-19 patients using random effect model

Prevalence of diabetes mellitus

Based on the random effect model after inclusion of 29 studies, the prevalence of diabetes mellitus among the confirmed COVID-19 patients was 11% (95% CI, 8.9%-12.7%). Cochran Q statistics showed 89% heterogeneity among studies which was high and significant (Q=252, P<0.001, I2=89) [Figure 8].

Figure 8.

Figure 8

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Forest plot of the diabetes melittus prevalence among COVID-19 patients using random effect model

Prevalence of hypertension

Based on the random effect model after inclusion of 29 studies, the prevalence of hypertension among the confirmed COVID-19 patients was 21% (95% CI, 17%-24.6%). Cochran Q statistics showed 96% heterogeneity among studies which was high and significant (Q=687, P<0.001, I2=96) [Figure 9].

Figure 9.

Figure 9

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Forest plot of the hypertension prevalence among COVID-19 patients using random effect model

Prevalence of liver disease

Based on the random effect model after inclusion of 20 studies, the prevalence of liver disease among the confirmed COVID-19 patients was 2.9% (95% CI, 2.0%-3.7%). Cochran Q statistics showed 62% heterogeneity among studies which was high and significant (Q=50, P<0.001, I2=62) [Figure 10].

Figure 10.

Figure 10

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Forest plot of the chronic liver disease prevalence among COVID-19 patients using random effect model

Prevalence of pulmonary disease

Based on the random effect model after inclusion of 25 studies, the prevalence of chronic pulmonary disease among the confirmed COVID-19 patients was 2.0% (95% CI, 1.4%-2.5%). Cochran Q statistics showed 85% heterogeneity among studies which was high and significant (Q=162, P<0.001, I2=85) [Figure 11].

Figure 11.

Figure 11

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Forest plot of the chronic pulmonary disease prevalence among COVID-19 patients using random effect model

Prevalence of malignancy

Based on the random effect model after inclusion of 24 studies, the prevalence of malignancy among the confirmed COVID-19 patients was 2.7% (95% CI, 1.9%-3.5%). Cochran Q statistics showed 87% heterogeneity among studies which was high and significant (Q=181, P<0.001, I2=87) [Figure 12].

Figure 12.

Figure 12

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Forest plot of the malignancy prevalence among COVID-19 patients using random effect model

Prevalence of smoking

Based on the random effect model after inclusion of 10 studies, the prevalence of smoking among the confirmed COVID-19 patients was 8.6% (95% CI, 5.8%-11.4%). Cochran Q statistics showed 88% heterogeneity among studies which was high and significant (Q=74, P<0.001, I2=88) [Figure 13].

Figure 13.

Figure 13

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Forest plot of the smoking prevalence among COVID-19 patients using random effect model

Discussion

In this study, we aimed to assess the prevalence of accompanying comorbidities in patients tested positive for COVID-19. It seems that the most prevalent comorbidity was hypertension while other comorbidities are counting for less than 10% among the patients.

One main limitation of our study was that most of the studies were from the Chinese population which differences in race and ethnicity may play a role particularly when we see differences in mortality rates around the world. We assume that the prevalence and rates might differ in future papers that consider other populations. Another limitation of our study is that we are not sure if the prevalence reporting were complete because most of these papers emerged at the time that papers were published too fast with no peer review. We are only trusting the current literature so far from China and we have to be vigilant about the accuracy of data. Thus, we did not perform quality assessment because we were to include all published papers.

The mean age of the diagnosed patients was 51 years. It is intuitive that younger age groups are not showing too severe symptoms to make them refer to the hospitals and thus they remain undiagnosed. The male to female ratio was 55/45 showing slightly higher infection rate or probably slightly more severe presentation to refer the patients to the hospitals. This might be due to the fact that men are spending more time out of house at work, which causes men contracting the disease first and slightly more severe.

The most prevalent finding in the confirmed COVID-19 patients was hypertension, which was found in 1/5 of the patients and is consistent with the tendency of the virus binding with ACE2 receptor. Other most prevalent finding was diabetes mellitus (DM) in almost 10% of the patients. Other comorbidities were less than 10%. It seems that these rates are also found with other common conditions and is not supporting the idea that more comorbidities increase the risk of symptomatic COVID-19 unless hypertension is present.

Other systematic reviews that included limited studies in the beginning of this outbreak reported the same finding about accompanying hypertension as the most prevalent risk to symptomatic COVID-19 infection. Although other reports have noted accompanying of different comorbidities such chronic liver, kidney, heart and even lung diseases, we presume that the comorbidities only cause the patients refer more to the hospital, but the comorbidities do not seem to be the cause of disease severity or even mortality. There is limited data about smoking as an influencing factor on the severity of the disease. Thus so far, we cannot make any conclusion, but obviously smoking recession improves airway clearance.

COVID-19 infection seems to be affecting every race, sex, age, irrespective of health status. The risk of symptomatic and severe disease might be higher due to the higher age which is usually accompanied with comorbidities. However, comorbidities do not seem to be the prerequisite for symptomatic and severe COVID-19 infection, except hypertension.

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