Prevalence of Clinical Manifestations and Comorbidities of Coronavirus (COVID-19) Infection: A Meta-Analysis

1. Introduction

The development of novel coronavirus, formally known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has introduced an exceptional test for the medicinal services network over the world. High infectivity, capacity to get transmitted in any event, during asymptomatic stage and generally low harmfulness have brought about fast transmission of this infection beyond geographic regions, leading a pandemic [1]. The first case of this disease, known as coronavirus disease 2019 (COVID-2019), happened on December 8, 2019 in Wuhan, the Hubei province of China [1]. From that point forward within a short span of just around 4 months, the infection has spread to 208 countries/ area/territories across the world, with 1210956 confirmed cases and 67594 deaths (World Health Organization statistics as on April 6, 2020) [2]. SARS-CoV-2 is a betacoronavirus that has a place with a generally notable viral family coronaviridae and the order nidovirales [3]. Modern, six coronavirus species have been distinguished to contaminate people and cause disease. Among them, 229E, OC43, NL63, and HKU1 diseases are often mild, generally caused normal cold side effects [4]. The opposite two species, severe acute respiratory syndrome coronavirus (SARS-CoV) which had an epidemic in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) which had an epidemic in 2012, might cause fatal illness [5]. SARS-CoV-2 is that the seventh member of the coronaviruses that infects humans [6]. Based on recent reports, the novel coronavirus infected patients predominantly can be identified through various symptoms such as fever, cough, dyspnea, myalgia, and fatigue [7-11]. Severity of COVID-19 infection was significant for several chronic conditions such as hypertension, cardiovascular disease and diabetes [12-15].

Reverse transcription-polymerase chain reaction (RT-PCR), chest computed tomography (CT) scanning and nucleic acid test can provide a more comprehensive overview of the disease, in order to help the clinical diagnosis and management [8, 9, 16].

A recent meta-analysis of symptoms in 46248 COVID-19 patients from 8 studies found that the highest prevalent of clinical symptom was fever, following cough, fatigue and dyspnea. On the other hand, the highest prevalent of co-morbidities were hypertension, diabetes, cardiovascular diseases and respiratory system disease [17]. This means that, complications could also be a risk factor for adverse outcomes. Estimating the prevalence of those chronic diseases is that the basis for preventing complications in patients with COVID-19 infections.

The case mortality rate is high for COVID-19 infection. According to World Health Organization statistics as on April 6, 2020, globally the death rate was 5.58%. The highest confirmed number of cases was reported in European region (655339) with the highest mortality rate 7.55%. Among this region mortality rate were for Italy (12.3%), Spain (9.5%), UK (10.3%), and France (11.58%). In Region of Americas the number of confirmed cases was 352592 with mortality rate 2.7%. In South-East Asia the number of confirmed cases was 8828 with death rate 3.89%. Among this region the highest mortality rate was observed in Bangladesh as 9.09% and for India was 2.68% [2]. In a recent meta-analysis it was found that 20.3% of total hospitalized patients required intensive care unit (ICU), 32.8% presented with acute respiratory distress syndrome (ARDS), 6.2% with shock and the case fatality rate (CFR) was higher than 13% among hospitalized patients [18].

This review is aimed toward providing a more convincing result, we’ll provide a meta-analysis aggregating all currently available data (indexed up to 2^nd April, 2020) from published studies of clinical symptoms and comorbidities predictive for severe illness with COVID-19.

2. Methods

2.1 Search Strategy

A systematic search was conducted on studies published from January 1, 2020 to April 2, 2020 through PubMed, Science Direct and Google Scholar using advanced search strategy with the following combined text heading as (“COVID-19” or “coronavirus” or “novel coronavirus”). Published peer reviewed articles were also included. Only English language articles and human based studies were included in the analysis. All the articles were screened by title and abstract to identify the suitable articles for this meta-analysis. Retrospective studies for coronavirus disease were only considered for analysis purpose. Cross-sectional study, case-control study and case reports were excluded for this meta-analysis. Also, editorials, and review articles were excluded. All the identified articles were searched by hand and not recognized by electronic inquiry. At last, duplicate articles were found out and removed. Titles and abstracts were searched independently by two reviewers. Controversial matters were solved by discussion.

2.2 Study Inclusion and Exclusion Criteria

We included articles that reported cases with demographic characteristics, clinical characteristics and comorbidities of SARS-CoV-2 infected patients confirmed by real time reverse transcriptase polymerase chain reaction (RT-PCR) or chest computed tomography (CT). Studies that included only pediatric patients were excluded due to heterogeneous results found among this group for coronavirus disease. Published articles and peer reviewed articles were included in the analysis. Unpublished articles were not included because of data uncertainty.

2.3 Data Extraction Process and Study Quality Assessment

The results of the initial search strategy were first screened by title and abstract. Full articles were inspected for inclusion and rejection criteria. Two reviewers independently extracted data from included articles for analysis. From each articles, various information following confirmation of SARS-CoV-2 infected patients, study design, time and place of data collections, year, country, total number of reported cases, gender, age, clinical characteristics (e. g., fever, cough, fatigue, etc.), comorbidities (e. g., hypertension, diabetes, cardiovascular disease, etc.) were extracted. PRISMA checklist was used for reporting the paper [19]. The quality assessment of the included studies were conducted by utilizing Newcastle-Ottawa technique in this meta-analysis [20].

2.4 Statistical Method

This meta-analysis was performed utilizing Stata, version 12. Pooled prevalence rate (PR) and 95% CI were estimated using random effects model [REM] [21] to summarize the weighted effect size for each study. REM were used due to between study heterogeneity [21]. In REM, effect size can vary among the study’s results [21, 22].  Heterogeneity of this meta-analysis was estimated by chi square test, tau square test ( ) and index. Chi-square test statistic (Q) was utilized to discover the nearness of heterogeneity among the investigations,  was used to discover between studies difference [21] and  was used to quantify the level of variety because of heterogeneity among the included study [23]. Consolidating just the published investigations may prompt a bias result in meta-analysis. Funnel plot was used to detect publication bias among the included studies [24].

3. Result

3.1 study selection and characteristics

Total 1903 articles were searched from PubMed, Science Direct and Google Scholar. Those articles were first screened by title and abstract and found 286 articles. Among total 286 articles, 146 were selected for full text assessment. Among those articles, 102 articles were excluded due to lack of information on clinical characteristics or comorbidities and duplication. Finally, 44 articles were included for quantitative analysis (Figure.1). Articles included in this study were published between 1 January, 2020 and 2 April, 2020. Most of the studies were from China and one from Hong Kong [25]. This meta-analysis was conducted for twenty one variables. Geological, demographical, clinical characteristics of the patients, study design, total number of patients (N) were described in Table 1. Quality assessment of the included variables were also appeared in Table 1.

Figure 1: PRISMA flowchart for search strategy and the process of selecting articles

Table 1: Characteristics table for the included studies

3.2 Clinical manifestations and Comorbidities

Summary of the meta-analysis for clinical manifestations and comorbidities of COVID-19 infected patients were shown in Table 2. Following the clinical symptoms, the prevalence rate (PR) were found for fever 84.49 % [95 % CI (81.87- 87.11), z = 63.13, p < 0.001] (Figure. 2), cough 56.39% [95% CI (51.06 - 61.73), z = 20.72, p < 0.001] (Figure. S1) and fatigue 33.65% [95% CI (26.77 – 40.54), z = 9.58, p < 0.001] (Figure. S2), which were the most frequent clinical symptoms for COVID-19 infected patient. PR for clinical manifestations were also found 8.51% [95% CI (6.63 – 10.39), z = 8.87, p < 0.001] for diarrhea (Figure. S3), 16.26% [95% CI (12.93 – 19.59), z = 9.57, p < 0.001] for myalgia (Figure. S4), 9.04% [95% CI (6.22 – 11.86), z = 6.29, p < 0.001] for headache (Figure. S5), 14.05% [95% CI (9.54 – 18.56), z = 6.10, p < 0.001] for chest pain (Figure. S6), 4.35% [95% CI (3.14 – 5.57), z = 7.00, p < 0.001] for vomiting (Figure. S7), 22.34% [95% CI (17.18 – 27.50), z = 8.49, p < 0.001] for dyspnea (Figure. S8), 11.00% [95% CI (7.24 – 14.76), z = 5.73, p < 0.001] for sore throat (Figure. S9), 20.72% [95% CI (11.18 – 30.27), z = 4.25, p < 0.001] for sputum (Figure. S10), 15.20 % [95% CI (8.49 – 21.91), z = 4.44, p < 0.001] for poor appetite (Figure. S11), 9.45 % [95% CI (5.09 – 13.81), z = 4.25, p < 0.001] for chills (Figure. S12).

Several comorbidities were also presented among SARS-CoV-2 infected patients, among them hypertension [PR: 20.00%, 95% CI (16.53 – 23.46), z = 11.33, p < 0.001] (Figure. S13), cardiovascular disease [PR: 11.19%, 95% CI (6.76 – 15.62), z = 4.95, p < 0.001] (Figure. S14) and diabetes [PR: 9.78%, 95% CI (8.02 – 11.55), z = 10.88, p < 0.001] (Figure. S15)  were significantly high. The PR were also found for chronic kidney disease [PR: 1.24%, 95% CI (0.87 – 1.61), z = 6.60, p < 0.001] (Figure. S16), chronic liver disease [PR: 2.65%, 95% CI (1.85 – 3.46), z = 6.48, p < 0.001] (Figure. S17), chronic pulmonary disease [PR: 1.97%, 95% CI (1.06 – 2.87), z = 4.26, p < 0.001] (Figure. S18), malignancies [PR: 2.28%, 95% CI (1.49 – 3.08), z = 5.63, p < 0.001] (Figure. S19)  and cerebrovascular disease [PR: 2.63%, 95% CI (1.06 – 4.19), z = 3.29, p < 0.001] (Figure. S20).

Table 2: Summary table for meta-analysis results

Figure.2: Forest plot for the prevalence of fever among SARS-CoV-2 infected patients

Figure.3: Funnel plot for detecting publication bias in the data for variable fever

Significant heterogeneity were found among the results for most of the clinical characteristics and some comorbidities since Q statistics were high with p < 0.001. Funnel plot was used to find the publication bias among the included studies. Publication bias was found for variable fever (Figure. 3). Publication bias were also found for all of the other variables except chills using funnel plot (Figure. S21 –Figure. S40).

4. Discussion

Novel Corona virus has become an epidemic in Western Pacific Region (especially China), European Region (Italy, Spain, The United Kingdom etc.), Eastern Mediterranean Region (especially Iran), Regions of the Americas (United States of America). Nowadays South-East Asian Region and African region have faced an outbreak of this virus. The extensive distribution of this virus has led to a serious concern, globally [2].

In this meta-analysis, we tried to summarize clinical and comorbidity data on COVID-19 confirmed cases that were published up to 2 April, 2020. Clinical data were collected from total of 7673 infected patients.  Heterogeneity was found highly since PR for all studies were not same for included studies. Forest plot was drawn for all of the variables and robustness of the result were notable. For this reason random effects model (REM) of meta-analysis was conducted. The assumption of this model is that the effect sizes are not identical in every study and follows a distribution [21]. Pooled estimate and 95% CIs refer to the center of the distribution of pooled prevalence in meta-analysis. In REM, 95% CI measures the uncertainty of pooled estimate [21]. In this meta-analysis 95% CI was calculated along with prevalence rate (PR). In forest plot, pooled PR estimates were presented with a diamond and individual studies weight were presented with a square.

In this study co-morbidity symptoms were considered because the co-morbidity symptoms such as hypertension, cardiovascular disease, diabetes, chronic liver disease, cerebrovascular disease, malignancies, chronic pulmonary disease, and chronic kidney disease may be linked to the pathogenesis of COVID 19. As mentioned earlier in the result section, fever prevalent (84.49%) was higher compared to the other clinical symptoms. Other clinical symptoms prevalence were cough (56.39%), fatigue (33.65%), dyspnea (22.34%), sputum (20.72%), myalgia (16.26%), poor appetite (15.20%), chest pain (14.05%), sore throat (11.0%), chills (9.45%), headache (9.04%), diarrhea (8.51%), and vomiting (4.35%). According to the findings of the current study, hypertension prevalent (20.0%) was higher among the co-morbidity symptoms. Followed by the other co-morbidity symptoms were cardiovascular disease (11.19%), diabetes (9.78%), chronic liver disease (2.65%), cerebrovascular disease (2.63%), malignancies (2.28%), chronic pulmonary disease (1.97%), and chronic kidney disease (1.24%).

From many individual studies of China it was found that clinical features fever and cough were the most predominant among COVID-19 infected patients [7-11]. Fatigue, myalgia, dyspnea were also found among COVID-19 infected patients [47, 50, 54, 55]. Those symptoms occurred more frequently among older patients [10, 13, 42]. The rate of infections among males was significantly high compared to women for many studies [11, 28, 37]. Patients presented with different comorbidities such as hypertension, diabetes and cardiovascular can play a vital role for prognosis of this disease [54-56, 59]. Cancer patients have a high risk for this disease [54, 55, 59]. From a single center cluster study of China, it was found that 26% of patients required admission to the intensive care unit (ICU) [55]. From another study it was found that the rate of admission to ICU was 32% among total hospitalized patients [60]. From e systematic review it was notified that the rate of admission to ICU was higher among the patients presented with different comorbidities and older patients [61]. From two studies in China, the death rate was found as 4.3% and 15% respectively [55, 60]. The case fatality rate is increasing day by day and spreads all over the world. Until 6^th April, total of 1210956 confirmed cases were reported globally with death rate of 5.58% [2]. Most confirmed cases were found in the European region with death rate 7.55% [2].

The ongoing outbreak of corona virus has gotten perhaps the greatest danger to the worldwide economy and monetary markets. Coronavirus impact on the worldwide economy have shaken markets around the world, plunging stock costs and security yields [62].

Although SARS-CoV-2 is an emerging virus, no specific treatment is currently available and pathophysiology of this condition is still unknown but we can control this epidemic followed by some rules and regulations such as: washing hands frequently and avoiding touching the eyes, nose, and mouth with contaminated hands, avoiding close contact, especially with those who have a fever, coughing or sneezing, avoiding contact with live animals and consuming raw animal products [63, 64]. Since SARS-CoV-2 is highly contagious, medical staff have a high risk of infection. Adult infected patients with COVID-19 in the perioperative period have a high risk of death. Therefore, there are some responsibilities for health policymakers in this critical situation in order to implement comprehensive protective measures such as quarantine, avoid traveling, and avoid gathering in public places essential to control this death trap.

4.1 Limitations

Limitations of this meta-analysis should be addressed. Firstly, all the studies included here from China, so the generalizability of findings to other countries and populations is not clear. The Chinese may differ to other populations in terms of their culture, prevalence of different clinical and comorbidities symptom, as well as their access to high quality medical services. Secondly, only English language articles were included in the analysis. Thirdly, the high heterogeneity could be found in the sample size. Fourthly, biasness and high heterogeneity among the results of the included studies were found for several variables. Fifthly, an evidence of publication bias was found for most of the variable but any remedial measure was not taken to solve it.

5. Conclusion

Like other respiratory diseases, COVID-19 disease also presented symptoms as fever, cough, fatigue, and dyspnea. The case fatality rate was high for COVID-19 infection among patients with a chronic conditions. A large number of COVID-19 infected patients needed intensive care unit, which is limited in the developing countries. COVID-19 outbreak is really a threat for most of the developing countries due to the living style of their population and also for limited resources.

Funding source

This research has no fund.

Ethical approval

Ethical approval is not required.

Conflict of Interest

Authors have no conflict of interest.

Authors Contributions

FMN and MMI formulated the research questions, designed the study, developed the search strategy, searched and collected the articles, conducted the quality assessment, analyzed the data and drafted the manuscript. Finally, both authors have read and approved the final version of the manuscript.

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Supplementary Information:

Forest plot:

S1 Figure: Forest Plot for the prevalence of variable cough.

S2 Figure: Forest Plot for the prevalence of variable fatigue.

S3 Figure: Forest Plot for the prevalence of variable diarrhea.

S4 Figure: Forest Plot for the prevalence of variable myalgia.

S5 Figure: Forest Plot for the prevalence of variable headache.

S6 Figure: Forest Plot for the prevalence of variable chest pain.

S7 Figure: Forest Plot for the prevalence of variable vomiting.

S8 Figure: Forest Plot for the prevalence of variable dyspnea.

S9 Figure: Forest Plot for the prevalence of variable sore throat.

S10 Figure: Forest Plot for the prevalence of variable sputum production.

S11 Figure: Forest Plot for the prevalence of variable poor appetite.

S12 Figure: Forest Plot for the prevalence of variable chills.

S13 Figure: Forest Plot for the prevalence of variable hypertension.

S14 Figure: Forest Plot for the prevalence of variable cardiovascular disease.

S15 Figure: Forest Plot for the prevalence of variable diabetes.

S16 Figure: Forest Plot for the prevalence of variable chronic kidney disease.

S17 Figure: Forest Plot for the prevalence of variable chronic liver disease.

S18 Figure: Forest Plot for the prevalence of variable chronic pulmonary disease.

S19 Figure: Forest Plot for the prevalence of variable malignancies.

S20 Figure: Forest Plot for the prevalence of variable cerebrovascular disease.

Funnel Plot:

S21 Figure: Funnel Plot for detecting publication bias of variable cough.

S22 Figure: Funnel Plot for detecting publication bias of variable fatigue.

S23 Figure: Funnel Plot for detecting publication bias of variable diarrhea.

S24 Figure: Funnel Plot for detecting publication bias of variable myalgia.

S25 Figure: Funnel Plot for detecting publication bias of variable headache.

S26 Figure: Funnel Plot for detecting publication bias of variable chest pain.

S27 Figure: Funnel Plot for detecting publication bias of variable vomiting.

S28 Figure: Funnel Plot for detecting publication bias of variable dyspnea.

S29 Figure: Funnel Plot for detecting publication bias of variable sore throat.

S30 Figure: Funnel Plot for detecting publication bias of variable sputum production.

S31 Figure: Funnel Plot for detecting publication bias of variable poor appetite.

S32 Figure: Funnel Plot for detecting publication bias of variable chills.

S33 Figure: Funnel Plot for detecting publication bias of variable hypertension.

S34 Figure: Funnel Plot for detecting publication bias of variable cardiovascular disease.

S35 Figure: Funnel Plot for detecting publication bias of variable diabetes.

S36 Figure: Funnel Plot for detecting publication bias of variable chronic kidney disease.

S37 Figure: Funnel Plot for detecting publication bias of variable chronic liver disease.

S38 Figure: Funnel Plot for detecting publication bias of variable chronic pulmonary disease.

S39 Figure: Funnel Plot for detecting publication bias of variable malignancies.

S40 Figure: Funnel Plot for detecting publication bias of variable cerebrovascular disease.