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Clinical Characteristics of Coronavirus Disease 2019 in China



Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients.


We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death.


The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission.


During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)

In early December 2019, the first pneumonia cases of unknown origin were identified in Wuhan, the capital city of Hubei province.1 The pathogen has been identified as a novel enveloped RNA betacoronavirus2 that has currently been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has a phylogenetic similarity to SARS-CoV.3 Patients with the infection have been documented both in hospitals and in family settings.4-8

The World Health Organization (WHO) has recently declared coronavirus disease 2019 (Covid-19) a public health emergency of international concern.9 As of February 25, 2020, a total of 81,109 laboratory-confirmed cases had been documented globally.5,6,9-11 In recent studies, the severity of some cases of Covid-19 mimicked that of SARS-CoV.1,12,13 Given the rapid spread of Covid-19, we determined that an updated analysis of cases throughout mainland China might help identify the defining clinical characteristics and severity of the disease. Here, we describe the results of our analysis of the clinical characteristics of Covid-19 in a selected cohort of patients throughout China.



The study was supported by National Health Commission of China and designed by the investigators. The study was approved by the institutional review board of the National Health Commission. Written informed consent was waived in light of the urgent need to collect data. Data were analyzed and interpreted by the authors. All the authors reviewed the manuscript and vouch for the accuracy and completeness of the data and for the adherence of the study to the protocol, available with the full text of this article at


We obtained the medical records and compiled data for hospitalized patients and outpatients with laboratory-confirmed Covid-19, as reported to the National Health Commission between December 11, 2019, and January 29, 2020; the data cutoff for the study was January 31, 2020. Covid-19 was diagnosed on the basis of the WHO interim guidance.14 A confirmed case of Covid-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens.1 Only laboratory-confirmed cases were included in the analysis.

We obtained data regarding cases outside Hubei province from the National Health Commission. Because of the high workload of clinicians, three outside experts from Guangzhou performed raw data extraction at Wuhan Jinyintan Hospital, where many of the patients with Covid-19 in Wuhan were being treated.

We extracted the recent exposure history, clinical symptoms or signs, and laboratory findings on admission from electronic medical records. Radiologic assessments included chest radiography or computed tomography (CT), and all laboratory testing was performed according to the clinical care needs of the patient. We determined the presence of a radiologic abnormality on the basis of the documentation or description in medical charts; if imaging scans were available, they were reviewed by attending physicians in respiratory medicine who extracted the data. Major disagreement between two reviewers was resolved by consultation with a third reviewer. Laboratory assessments consisted of a complete blood count, blood chemical analysis, coagulation testing, assessment of liver and renal function, and measures of electrolytes, C-reactive protein, procalcitonin, lactate dehydrogenase, and creatine kinase. We defined the degree of severity of Covid-19 (severe vs. nonsevere) at the time of admission using the American Thoracic Society guidelines for community-acquired pneumonia.15

All medical records were copied and sent to the data-processing center in Guangzhou, under the coordination of the National Health Commission. A team of experienced respiratory clinicians reviewed and abstracted the data. Data were entered into a computerized database and cross-checked. If the core data were missing, requests for clarification were sent to the coordinators, who subsequently contacted the attending clinicians.


The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. These outcomes were used in a previous study to assess the severity of other serious infectious diseases, such as H7N9 infection.16 Secondary end points were the rate of death and the time from symptom onset until the composite end point and until each component of the composite end point.


The incubation period was defined as the interval between the potential earliest date of contact of the transmission source (wildlife or person with suspected or confirmed case) and the potential earliest date of symptom onset (i.e., cough, fever, fatigue, or myalgia). We excluded incubation periods of less than 1 day because some patients had continuous exposure to contamination sources; in these cases, the latest date of exposure was recorded. The summary statistics of incubation periods were calculated on the basis of 291 patients who had clear information regarding the specific date of exposure.

Fever was defined as an axillary temperature of 37.5°C or higher. Lymphocytopenia was defined as a lymphocyte count of less than 1500 cells per cubic millimeter. Thrombocytopenia was defined as a platelet count of less than 150,000 per cubic millimeter. Additional definitions — including exposure to wildlife, acute respiratory distress syndrome (ARDS), pneumonia, acute kidney failure, acute heart failure, and rhabdomyolysis — are provided in the Supplementary Appendix, available at


Laboratory confirmation of SARS-CoV-2 was performed at the Chinese Center for Disease Prevention and Control before January 23, 2020, and subsequently in certified tertiary care hospitals. RT-PCR assays were performed in accordance with the protocol established by the WHO.17 Details regarding laboratory confirmation processes are provided in the Supplementary Appendix.


Continuous variables were expressed as medians and interquartile ranges or simple ranges, as appropriate. Categorical variables were summarized as counts and percentages. No imputation was made for missing data. Because the cohort of patients in our study was not derived from random selection, all statistics are deemed to be descriptive only. We used ArcGIS, version 10.2.2, to plot the numbers of patients with reportedly confirmed cases on a map. All the analyses were performed with the use of R software, version 3.6.2 (R Foundation for Statistical Computing).



Of the 7736 patients with Covid-19 who had been hospitalized at 552 sites as of January 29, 2020, we obtained data regarding clinical symptoms and outcomes for 1099 patients (14.2%). The largest number of patients (132) had been admitted to Wuhan Jinyintan Hospital. The hospitals that were included in this study accounted for 29.7% of the 1856 designated hospitals where patients with Covid-19 could be admitted in 30 provinces, autonomous regions, or municipalities across China (Figure 1).

Figure 1. Distribution of Patients with Covid-19 across Mainland China.

Shown are the official statistics of all documented, laboratory-confirmed cases of coronavirus disease 2019 (Covid-19) throughout China, according to the National Health Commission as of February 4, 2020. The numerator denotes the number of patients who were included in the study cohort and the denominator denotes the number of laboratory-confirmed cases for each province, autonomous region, or provincial municipality, as reported by the National Health Commission.

Table 1. Clinical Characteristics of the Study Patients, According to Disease Severity and the Presence or Absence of the Primary Composite End Point.

The demographic and clinical characteristics of the patients are shown in Table 1. A total of 3.5% were health care workers, and a history of contact with wildlife was documented in 1.9%; 483 patients (43.9%) were residents of Wuhan. Among the patients who lived outside Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city; 25.9% of nonresidents had neither visited the city nor had contact with Wuhan residents.

The median incubation period was 4 days (interquartile range, 2 to 7). The median age of the patients was 47 years (interquartile range, 35 to 58); 0.9% of the patients were younger than 15 years of age. A total of 41.9% were female. Fever was present in 43.8% of the patients on admission but developed in 88.7% during hospitalization. The second most common symptom was cough (67.8%); nausea or vomiting (5.0%) and diarrhea (3.8%) were uncommon. Among the overall population, 23.7% had at least one coexisting illness (e.g., hypertension and chronic obstructive pulmonary disease).

On admission, the degree of severity of Covid-19 was categorized as nonsevere in 926 patients and severe in 173 patients. Patients with severe disease were older than those with nonsevere disease by a median of 7 years. Moreover, the presence of any coexisting illness was more common among patients with severe disease than among those with nonsevere disease (38.7% vs. 21.0%). However, the exposure history between the two groups of disease severity was similar.


Table 2. Radiographic and Laboratory Findings.

Table 2 shows the radiologic and laboratory findings on admission. Of 975 CT scans that were performed at the time of admission, 86.2% revealed abnormal results. The most common patterns on chest CT were ground-glass opacity (56.4%) and bilateral patchy shadowing (51.8%). Representative radiologic findings in two patients with nonsevere Covid-19 and in another two patients with severe Covid-19 are provided in Figure S1 in the Supplementary Appendix. No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease.

On admission, lymphocytopenia was present in 83.2% of the patients, thrombocytopenia in 36.2%, and leukopenia in 33.7%. Most of the patients had elevated levels of C-reactive protein; less common were elevated levels of alanine aminotransferase, aspartate aminotransferase, creatine kinase, and d-dimer. Patients with severe disease had more prominent laboratory abnormalities (including lymphocytopenia and leukopenia) than those with nonsevere disease.


None of the 1099 patients were lost to follow-up during the study. A primary composite end-point event occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died (Table 3). Among the 173 patients with severe disease, a primary composite end-point event occurred in 43 patients (24.9%). Among all the patients, the cumulative risk of the composite end point was 3.6%; among those with severe disease, the cumulative risk was 20.6%.


A majority of the patients (58.0%) received intravenous antibiotic therapy, and 35.8% received oseltamivir therapy; oxygen therapy was administered in 41.3% and mechanical ventilation in 6.1%; higher percentages of patients with severe disease received these therapies (Table 3). Mechanical ventilation was initiated in more patients with severe disease than in those with nonsevere disease (noninvasive ventilation, 32.4% vs. 0%; invasive ventilation, 14.5% vs. 0%). Systemic glucocorticoids were given to 204 patients (18.6%), with a higher percentage among those with severe disease than nonsevere disease (44.5% vs. 13.7%). Of these 204 patients, 33 (16.2%) were admitted to the ICU, 17 (8.3%) underwent invasive ventilation, and 5 (2.5%) died. Extracorporeal membrane oxygenation was performed in 5 patients (0.5%) with severe disease.

Table 3. Complications, Treatments, and Clinical Outcomes.

The median duration of hospitalization was 12.0 days (mean, 12.8). During hospital admission, most of the patients received a diagnosis of pneumonia from a physician (91.1%), followed by ARDS (3.4%) and shock (1.1%). Patients with severe disease had a higher incidence of physician-diagnosed pneumonia than those with nonsevere disease (99.4% vs. 89.5%).


During the initial phase of the Covid-19 outbreak, the diagnosis of the disease was complicated by the diversity in symptoms and imaging findings and in the severity of disease at the time of presentation. Fever was identified in 43.8% of the patients on presentation but developed in 88.7% after hospitalization. Severe illness occurred in 15.7% of the patients after admission to a hospital. No radiologic abnormalities were noted on initial presentation in 2.9% of the patients with severe disease and in 17.9% of those with nonsevere disease. Despite the number of deaths associated with Covid-19, SARS-CoV-2 appears to have a lower case fatality rate than either SARS-CoV or Middle East respiratory syndrome–related coronavirus (MERS-CoV). Compromised respiratory status on admission (the primary driver of disease severity) was associated with worse outcomes.

Approximately 2% of the patients had a history of direct contact with wildlife, whereas more than three quarters were either residents of Wuhan, had visited the city, or had contact with city residents. These findings echo the latest reports, including the outbreak of a family cluster,4 transmission from an asymptomatic patient,6 and the three-phase outbreak patterns.8 Our study cannot preclude the presence of patients who have been termed “super-spreaders.”

Conventional routes of transmission of SARS-CoV, MERS-CoV, and highly pathogenic influenza consist of respiratory droplets and direct contact,18-20 mechanisms that probably occur with SARS-CoV-2 as well. Because SARS-CoV-2 can be detected in the gastrointestinal tract, saliva, and urine, these routes of potential transmission need to be investigated21 (Tables S1 and S2).

The term Covid-19 has been applied to patients who have laboratory-confirmed symptomatic cases without apparent radiologic manifestations. A better understanding of the spectrum of the disease is needed, since in 8.9% of the patients, SARS-CoV-2 infection was detected before the development of viral pneumonia or viral pneumonia did not develop.

In concert with recent studies,1,8,12 we found that the clinical characteristics of Covid-19 mimic those of SARS-CoV. Fever and cough were the dominant symptoms and gastrointestinal symptoms were uncommon, which suggests a difference in viral tropism as compared with SARS-CoV, MERS-CoV, and seasonal influenza.22,23 The absence of fever in Covid-19 is more frequent than in SARS-CoV (1%) and MERS-CoV infection (2%),20 so afebrile patients may be missed if the surveillance case definition focuses on fever detection.14 Lymphocytopenia was common and, in some cases, severe, a finding that was consistent with the results of two recent reports.1,12 We found a lower case fatality rate (1.4%) than the rate that was recently reportedly,1,12 probably because of the difference in sample sizes and case inclusion criteria. Our findings were more similar to the national official statistics, which showed a rate of death of 3.2% among 51,857 cases of Covid-19 as of February 16, 2020.11,24 Since patients who were mildly ill and who did not seek medical attention were not included in our study, the case fatality rate in a real-world scenario might be even lower. Early isolation, early diagnosis, and early management might have collectively contributed to the reduction in mortality in Guangdong.

Despite the phylogenetic homogeneity between SARS-CoV-2 and SARS-CoV, there are some clinical characteristics that differentiate Covid-19 from SARS-CoV, MERS-CoV, and seasonal influenza infections. (For example, seasonal influenza has been more common in respiratory outpatient clinics and wards.) Some additional characteristics that are unique to Covid-19 are detailed in Table S3.

Our study has some notable limitations. First, some cases had incomplete documentation of the exposure history and laboratory testing, given the variation in the structure of electronic databases among different participating sites and the urgent timeline for data extraction. Some cases were diagnosed in outpatient settings where medical information was briefly documented and incomplete laboratory testing was performed, along with a shortage of infrastructure and training of medical staff in nonspecialty hospitals. Second, we could estimate the incubation period in only 291 of the study patients who had documented information. The uncertainty of the exact dates (recall bias) might have inevitably affected our assessment. Third, because many patients remained in the hospital and the outcomes were unknown at the time of data cutoff, we censored the data regarding their clinical outcomes as of the time of our analysis. Fourth, we no doubt missed patients who were asymptomatic or had mild cases and who were treated at home, so our study cohort may represent the more severe end of Covid-19. Fifth, many patients did not undergo sputum bacteriologic or fungal assessment on admission because, in some hospitals, medical resources were overwhelmed. Sixth, data generation was clinically driven and not systematic.

Covid-19 has spread rapidly since it was first identified in Wuhan and has been shown to have a wide spectrum of severity. Some patients with Covid-19 do not have fever or radiologic abnormalities on initial presentation, which has complicated the diagnosis.

Supported by the National Health Commission of China, the National Natural Science Foundation, and the Department of Science and Technology of Guangdong Province.

Disclosure forms provided by the authors are available with the full text of this article at

Drs. Guan, Ni, Yu Hu, W. Liang, Ou, He, L. Liu, Shan, Lei, Hui, Du, L. Li, Zeng, and Yuen contributed equally to this article.

This article was published on February 28, 2020, and last updated on March 6, 2020, at

We thank all the hospital staff members (see Supplementary Appendix for a full list of the staff) for their efforts in collecting the information that was used in this study; Zong-jiu Zhang, Ya-hui Jiao, Xin-qiang Gao, and Tao Wei (National Health Commission), Yu-fei Duan and Zhi-ling Zhao (Health Commission of Guangdong Province), and Yi-min Li, Nuo-fu Zhang, Qing-hui Huang, Wen-xi Huang, and Ming Li (Guangzhou Institute of Respiratory Health) for facilitating the collection of patients’ data; the statistical team members Zheng Chen, Dong Han, Li Li, Zhi-ying Zhan, Jin-jian Chen, Li-jun Xu, and Xiao-han Xu (State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, and Southern Medical University, respectively); Li-qiang Wang, Wei-peng Cai, Zi-sheng Chen (the Sixth Affiliated Hospital of Guangzhou Medical University) and Chang-xing Ou, Xiao-min Peng, Si-ni Cui, Yuan Wang, Mou Zeng, Xin Hao, Qi-hua He, Jing-pei Li, Xu-kai Li, Wei Wang, Li-min Ou, Ya-lei Zhang, Jing-wei Liu, Xin-guo Xiong, Wei-juna Shi, San-mei Yu, Run-dong Qin, Si-yang Yao, Bo-meng Zhang, Xiao-hong Xie, Zhan-hong Xie, Wan-di Wang, Xiao-xian Zhang, Hui-yin Xu, Zi-qing Zhou, Ying Jiang, Ni Liu, Jing-jing Yuan, Zheng Zhu, Jie-xia Zhang, Hong-hao Li, Wei-hua Huang, Lu-lin Wang, Jie-ying Li, Li-fen Gao, Cai-chen Li, Xue-wei Chen, Jia-bo Gao, Ming-shan Xue, Shou-xie Huang, Jia-man Tang, and Wei-li Gu (Guangzhou Institute of Respiratory Health) for their dedication to data entry and verification; Tencent (Internet-services company) for providing the number of hospitals certified to admit patients with Covid-19 throughout China; and all the patients who consented to donate their data for analysis and the medical staff members who are on the front line of caring for patients.

Author Affiliations

From the State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University (W.G., W.L., J.H., R.C., C.T., T.W., S.L., Jin-lin Wang, N.Z., J.H., W.L.), the Departments of Thoracic Oncology (W.L.), Thoracic Surgery and Oncology (J.H.), and Emergency Medicine (Z.L.), First Affiliated Hospital of Guangzhou Medical University, and Guangzhou Eighth People’s Hospital, Guangzhou Medical University (C.L.), and the State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University (C.O., P.C.), Guangzhou, Wuhan Jinyintan Hospital (Z.N., J.X.), Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (Yu Hu), the Central Hospital of Wuhan (Y.P.), Wuhan No. 1 Hospital, Wuhan Hospital of Traditional Chinese and Western Medicine (L.W.), Wuhan Pulmonary Hospital (P.P.), Tianyou Hospital Affiliated to Wuhan University of Science and Technology (Jian-ming Wang), and the People’s Hospital of Huangpi District (S.Z.), Wuhan, Shenzhen Third People’s Hospital and the Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases (L. Liu), and the Department of Clinical Microbiology and Infection Control, University of Hong Kong–Shenzhen Hospital (K.-Y.Y.), Shenzhen, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai (H.S.), the Department of Medicine and Therapeutics, Chinese University of Hong Kong, Shatin (D.S.C.H.), and the Department of Microbiology and the Carol Yu Center for Infection, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pok Fu Lam (K.-Y.Y.), Hong Kong, Medical ICU, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences (B.D.), and the Chinese Center for Disease Control and Prevention (G.Z.), Beijing, the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou (L. Li), Chengdu Public Health Clinical Medical Center, Chengdu (Y.L.), Huangshi Central Hospital of Edong Healthcare Group, Affiliated Hospital of Hubei Polytechnic University, Huangshi (Ya-hua Hu), the First Hospital of Changsha, Changsha (J. Liu), the Third People’s Hospital of Hainan Province, Sanya (Z.C.), Huanggang Central Hospital, Huanggang (G.L.), Wenling First People’s Hospital, Wenling (Z.Z.), the Third People’s Hospital of Yichang, Yichang (S.Q.), Affiliated Taihe Hospital of Hubei University of Medicine, Shiyan (J. Luo), and Xiantao First People’s Hospital, Xiantao (C.Y.) — all in China.

Address reprint requests to Dr. Zhong at the State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Rd., Guangzhou, Guangdong, China, or at .

A list of investigators in the China Medical Treatment Expert Group for Covid-19 study is provided in the Supplementary Appendix, available at

Smoking Upregulates Angiotensin-Converting Enzyme-2 Receptor: A Potential Adhesion Site for Novel Coronavirus SARS-CoV-2 (Covid-19)

Samuel James Brake (1), Kathryn Barnsley (2), Wenying Lu (1) , Kielan Darcy McAlinden (1), Mathew Suji Eapen (1) and Sukhwinder Singh Sohal (1),*

(1) Respiratory Translational Research Group, Department of Laboratory Medicine, School of Health Sciences, University of Tasmania, Launceston, Tasmania 7248, Australia; (S.J.B.); (W.L.); (K.D.M.); (M.S.E.)
(2) School of Medicine, University of Tasmania, Hobart, Tasmania 7001, Australia;
* Correspondence:; Tel.: +61-424-753-373

J. Clin. Med. 20209(3), 841;

Received: 17 March 2020; Accepted: 18 March 2020; Published: 20 March 2020

(This article belongs to the Section Pulmonology)


The epicenter of the original outbreak in China has high male smoking rates of around 50%, and early reported death rates have an emphasis on older males, therefore the likelihood of smokers being overrepresented in fatalities is high. In Iran, China, Italy, and South Korea, female smoking rates are much lower than males. Fewer females have contracted the virus. If this analysis is correct, then Indonesia would be expected to begin experiencing high rates of Covid-19 because its male smoking rate is over 60% (Tobacco Atlas). Smokers are vulnerable to respiratory viruses. Smoking can upregulate angiotensin-converting enzyme-2 (ACE2) receptor, the known receptor for both the severe acute respiratory syndrome (SARS)-coronavirus (SARS-CoV) and the human respiratory coronavirus NL638. This could also be true for new electronic smoking devices such as electronic cigarettes and “heat-not-burn” IQOS devices. ACE2 could be a novel adhesion molecule for SARS-CoV-2 causing Covid-19 and a potential therapeutic target for the prevention of fatal microbial infections, and therefore it should be fast tracked and prioritized for research and investigation. Data on smoking status should be collected on all identified cases of Covid-19.
Little attention has been given to the role of smoking in either the transmission of the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, actual virus) or mortality rate of Covid-19 (name of the disease caused). Smokers contract more respiratory ailments, including colds (commonly rhinoviruses, but also coronaviruses) than non-smokers. Smokers also show double the influenza rate and increased rates of bacterial pneumonia and tuberculosis [1,2,3,4,5]. The damage caused to the lungs by smoking makes patients more susceptible to pulmonary infections, both bacterial and viral [6]. Smokers are 34% more likely than non-smokers to contract the flu [6]. Han and colleagues conclude that literature evidence showed that smoking was consistently associated with a higher risk of hospital admissions after influenza infection [7]. Smoking is the primary etiological factor behind chronic obstructive pulmonary disease (COPD) in the developed world, but environmental pollution and degrading air quality are also responsible in developing countries. It is now the fourth leading cause of death in the world [8]. Vaccination against influenza is strongly recommended for patients with COPD, as the frequency and progression of exacerbations are strongly linked to respiratory viruses in 30% of cases [1]. Rubin et al. found that COPD patients who were prone to viral infections had higher exacerbation rates, more inflammation, and loss of lung function compared to those with existing exacerbating disease conditions [9]. Symptomatology and mortality in influenza-infected smokers were also enhanced [9]. According to the WHO, comorbidities are associated with a high percentage of Covid-19 related deaths [10,11]. In conjunction with the complications arising from comorbidities in patients who smoke [12], we put forth the question of whether smoking, smoking-induced health conditions, and comorbidities, in combination, is culminating in a high risk demographic for both contraction of the virus and the severe presentation of Covid-19.
China has a high male smoking rate at around 50% in rural areas and is estimated to be about 44.8% overall [13]. Most of the deaths identified from the epicenter of the Covid-19 outbreak were in men from older age groups and those with underlying conditions such as chronic respiratory disease, cancer, hypertension, diabetes, or cardiovascular disease. The initial age distribution of Covid-19 cases was skewed towards older age groups with a median age of 45 years (IQR 33–56) for patients who were alive or who had an unknown outcome at the time of reporting. The median age of patients who had died at the time of reporting was 70 years (IQR 65–81) as reported by Sun and colleagues [14]. This data was also supported by an early epidemiological study of 99 Covid-19 cases from Wuhan, China [14].
Fatality rates are given as the percentage of the defined group with confirmed Covid-19 that died, and therefore will not add up to 100%. The Table 1 was adapted from Coronavirus Disease (Covid-19) Research and Statistics [15].
Table 1. Risk factor-based fatality rates of Covid-19 from early data in China.
Age group Fatality rates
0–9 years 0%
10–19 years 0.2%
20–29 years 0.2%
30–39 years 0.2%
40 – 49 years 0.4%
50–59 years 1.3%
60–69 years 3.6%
70–79 years 8%
80 years and above 14.8%
Underlying health conditions
Cardiovascular disease 10.5%
Diabetes 7.3%
Chronic respiratory disease 6.3%
Hypertension 6%
Cancer 5.6%
No underlying health conditions 0.9%


The term “coronaviruses” arose from their crown-like appearance when imaged, the Latin for crown being corona. The distinguishing crown-like feature of coronaviruses is attributed to the presence of large type 1 transmembrane spike (S) glycoproteins. This heavily glycosylated cell surface protein contains two distinct functional domains (S1 and S2) which are thought to mediate host cell entry by the virus. The S1 domain contains the angiotensin-converting enzyme-2 (ACE2) receptor-binding domain and is responsible for first stage host cell entry [16]. The S2 domain facilitates fusion between cell and virus membrane, required for cellular infiltration [17]. S proteins are enzymatically modified, exposing the fusion site for cellular adhesion. This is achieved through cleavage by cellular proteases, mediated by protein convertase called “furin” [17,18]. Furin is expressed significantly in the lungs, and respiratory viruses also utilize this system to convert their surface proteins [17]. Although the S protein cleavage site is less observed in coronavirus with similar genomic sequence [17], it is essential to note that more pathogenic influenza viruses share similar cleavage sites [19].
The ACE2 receptor provides a human cell-binding site for the S protein for the SARS-coronavirus (SARS-CoV) [20,21,22] (a virus that was first identified in 2003 in a southern province of China [23,24,25]), the coronavirus NL63 [20,26], and now SARS-CoV-2 [27]. Recent studies have found that the modified S protein of SARS-CoV-2 has a significantly higher affinity for ACE2 and is 10- to 20-fold more likely to bind to ACE2 in human cells than the S protein of the previous SARS-CoV [28,29]. This increase in affinity may enable easier person-to-person spread of the virus and thus contribute to a higher estimated R0 for SARS-CoV-2 than the previous SARS virus. The ACE2 protein is expressed on the surface of lung type-2 pneumocytes [30]. It could thus act as a novel adhesion molecule for Covid-19 and be a potential therapeutic target for the prevention of fatal microbial infections in the community.
An early suggestion is that ACE2 is upregulated on the airway epithelium of smokers. Guoshuai Cai recently reported higher ACE2 gene expression in smoker samples compared to never-smokers. Zhao et al. observed that ACE2 is expressed explicitly in type-2 pneumocytes, in which genes regulating viral reproduction and transmission are highly expressed [31]. This indicates that smokers may be more susceptible to infection by SARS-CoV-2, and possibly Covid-19. We recently identified enhanced ACE2 expression in resected lung tissue from patients with COPD and healthy lung function smokers, albeit comparably less in the latter, while entirely absent in heathy non-smoking individuals (Figure 1). ACE2 expression was quite evident in the type-2 pneumocytes, alveolar macrophages, and the apical end of the small airway epithelium. COPD patients showed significantly higher levels of ACE2, suggesting that COPD further exaggerates ACE2 and potential SARS-CoV-2 adhesion site. ACE2 expression could also be true for patients with another chronic lung disease such as idiopathic pulmonary fibrosis [32]. The attachment of the virus to cell surface ACE2 protects them from immune surveillance mechanisms, leaving them tagged to the host for relatively longer periods, thus making them an efficient carrier and vulnerable host for future infections and spread. The eventual engulfment of ACE2 further provides the virus access to the host cells system, thus providing a flourishing environment, not just to sustain and proliferate but also to mutate and modify host evasion mechanisms. Previous observations using in vivo knockout mice models suggest that SARS-CoV-2 adhesion on ACE2 could also downmodulate the expression of ACE2 itself. This, in turn, increases the production and activation of other related ACE enzymes. This differential modulation and the drastic reduction in ACE2 results in severe acute respiratory failure [33,34].
Jcm 09 00841 g001 550
Figure 1. Surgically resected lung tissue stained for the angiotensin-converting enzyme-2 (ACE2) receptor. Current smoker with chronic obstructive pulmonary disease (COPD-CS), (A) showing positive staining in the small airway epithelium but also apical including cilia (B) red arrows indicating positive staining in type-2 pneumocytes and black arrows showing alveolar macrophages positive for the ACE2 receptor. Normal lung function smoker (NLFS), (C) and (D) showing similar pattern for COPD-CS although a little less staining is observed. Normal controls (NC), (E) and (F) no staining observed in any of the areas. This is the first immunohistochemical human lung evidence for ACE2 receptor expression in smokers and patients with COPD.
Wang et al. also noted an ACE2 connection to smoking and Covid-19 [35]. The increases seen in smokers further raises the question of whether this is also true for people engaged in waterpipe smoking [36] and those switching over to the more recent alternatives such as electronic cigarettes and “heat-not-burn” IQOS devices. It is essential to recognize that these devices are not “safer”, they are still a tobacco product that produces vapor or smoke and similarly could cause infectious lung damage as we see with traditional cigarettes [37,38,39].
Further research on these products and their influence on the virulence of coronaviruses is urgently needed. Following the outbreak in New York City, Mayor Bill de Blasio announced that “If you are a smoker or a vaper that does make you more vulnerable,” urging that now is the perfect time to quit [40]. Smokers, as a vulnerable group, must be supported to quit and should be advised to avoid areas where they may be liable to be exposed to Covid-19, especially smokers with pre-existing respiratory health concerns. Smokers should be prioritized for vaccination when a vaccine is developed, particularly if it is found they are a key transmission source.
Research on smoking and potential exacerbations of Covid-19 transmission and mortality should include waterpipes, electronic smoking devices, and “heat-not-burn” devices, such as IQOS devices. Further compounding this link between smoking and Covid-19 vulnerability are the comorbidities that have been identified as a significant increased risk factor for severe and fatal Covid-19. The link between smoking and comorbidities, such as diabetes and cardiovascular disease, have long been established [12]. As a research community, we must ask the questions:
(1) Are COPD and other smoking-related illnesses associated with fatal Covid-19 cases?
(2) Are smokers more likely to contract and transmit SARS-CoV-2 than non-smokers?
(3) Are demographics with high smoking rates more vulnerable to Covid-19 outbreaks?
WHO and all countries should ensure that the smoking status of patients identified with Covid-19, including deaths, is recorded and incorporated in data sets, so the smoker’s relationship to Covid-19 can be determined.
Status data collection could be simple in four categories,
1. active smoker,
2. passive smoker (those living in households with smokers or working in smoky environments),
3. former smoker (12 months or longer abstinence),
4. non-smoker.
Governments should act to reduce smoking rates in all countries in accordance with the WHO Framework Convention on Tobacco Control (FCTC), and initiate a stimulus package for health, as they have done for business, at the time of this outbreak/pandemic including all communicable pulmonary diseases and Covid-19, as it is possible that smoking exacerbates contraction, transmission, and mortality. It appears that smoking has the potential to upregulate the ACE2 receptor, making smokers and COPD patients more vulnerable to Covid-19. The new electronic smoking devices also do not seem to be safer options. ACE2 thus could be a potential therapeutic target for SARS-CoV-2 and should be prioritized for further research.

Author Contributions

All authors contributed towards the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.


Clifford Craig Foundation Launceston General Hospital, Rebecca L. Cooper Medical Research Foundation, Cancer Council Tasmania.

Conflicts of Interest

The authors declare no conflict of interest.


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E-cigarette users are exposed to potentially harmful levels of metal linked to DNA damage

Zinc excess in the body correlates with oxidative stress


February 20, 2020

Researchers at the University of California, Riverside, have completed a cross-sectional human study that compares biomarkers and metal concentrations in the urine of e-cigarette users, nonsmokers, and cigarette smokers.

They found that the biomarkers, which reflect exposure, effect, and potential harm, are both elevated in e-cigarette users compared to the other groups and linked to metal exposure and oxidative DNA damage.

“Our study found e-cigarette users are exposed to increased concentrations of potentially harmful levels of metals — especially zinc — that are correlated to elevated oxidative DNA damage,” said Prue Talbot, a professor of cell biology, who led the research team.

Zinc, a dietary nutrient, plays key roles in growth, immune function, and wound healing. Too little of this essential trace element can cause death; too much of it can cause disease. Its deficiency, as well as its excess, cause cellular oxidative stress, which, if unchecked, can lead to diseases such as atherosclerosis, coronary heart disease, pulmonary fibrosis, acute lymphoblastic leukemia, and lung cancer.

Electronic cigarettes consist of a battery, atomizing unit, and refill fluid. Metals in e-cigarette aerosols come mainly from the metal components in the atomizer— nichrome wire, tin solder joints, brass clamps, insulating sheaths, and wicks — as well as the e-fluids that the atomizers heat.

The study, which appears in BMJ Open Respiratory Research, marks the first time researchers have examined and quantified urinary biomarkers of effect and potential harm in relation to metals in e-cigarette users.

A biomarker is a quantifiable characteristic of a biological process. Biomarkers allow researchers and physicians to measure a biological or chemical substance that is indicative of a person’s physiological state. Previous e-cigarette studies with humans have examined biomarkers of exposure — for example, nicotine or nicotine metabolites — but none have studied biomarkers of potential harm or shown how this harm correlates with metal exposure.

The biomarkers studied by the UC Riverside researchers were 8-hydroxydeoxyguanosine (8-OHdG), a biomarker of oxidative DNA damage; 8-isoprostane, an indicator of the oxidative degradation of lipids; and metallothionein, a metal response protein. All three biomarkers were significantly elevated in e-cigarette users compared to the concentrations in cigarette smokers.

“Our findings reaffirm that e-cigarette use is not harm free,” said Shane Sakamaki-Ching, a graduate student in the Cell, Molecular and Developmental Biology Graduate Program and the research paper’s first author. “Indeed, prolonged use may lead to disease progression.”

The researchers advise physicians to exercise caution when recommending e-cigarettes to their patients. Electronic cigarette aerosols contain potentially harmful chemicals, cytotoxic flavor chemicals, metals, ultrafine particles, and reaction products. E-cigarette use has been linked to adverse health effects such as respiratory diseases, increased risk for cardiovascular disease, and impaired wound healing following surgery.

“Pregnant women, especially, should not be encouraged to use e-cigarettes,” Talbot said. “Excess of zinc in their bodies can lead to nausea and diarrhea. Given the recent deaths and pulmonary illnesses related to e-cigarette usage, everyone should be made aware of the potential health risks linked to e-cigarette usage.”

The study involved 53 participants from the Buffalo, New York, area. Talbot and Sakamaki-Ching were joined in the study by Monique Williams, My Hua, Jun Li, Steve M. Bates, Andrew N. Robinson, and Timothy W. Lyons of UCR; and Maciej L. Goniewicz of the Roswell Park Comprehensive Cancer Center, Buffalo, New York.

The study was supported by grants from the National Institutes of Health.


Disparities by income, age persist in tobacco use among Ohio adults


The Ohio Health Issues Poll is conducted every year to learn more about the health opinions, behaviors and status of Ohio adults. In 2019, OHIP asked Ohio adults several questions about tobacco use and their opinion on tobacco policies.


Ohio adults with lower incomes more likely to be current smokers

More than 2 in 10 Ohio adults (24%) reported being current smokers. This has remained relatively stable since OHIP began asking about smoking status in 2006. However, Ohio adults have consistently been more likely to smoke than adults across the nation. In 2018, the most recent year for which national data are available, 14% of adults nationwide reported being current smokers.1

Responses varied by household income. Ohio adults whose household income was 200% of the Federal Poverty Guidelines2 or less (42%) were nearly three times more likely to report being current smokers than those with household incomes greater than 200% FPG (15%). Since 2006, the percentage of current smokers has declined among Ohio adults with higher income but not among those living in or just above poverty.

Younger adults more likely to have tried e-cigarettes

Electronic cigarettes or e-cigarettes are also known as vapes, vape pens or e-hookahs and many are known by their brand names.3 The majority of
e-cigarettes contain nicotine and are not safe for children, young adults, pregnant women or anyone who is not a current smoker.4

Ohio adults ages 18 to 45 (44%) are about twice as likely as older adults (17%) to have ever tried an e-cigarette. This trend has continued since OHIP began asking about e-cigarette use in 2015. (See graph.)

About 1 in 10 Ohio adults (11%) reported using e-cigarettes some days, every day or rarely. The Behavioral Risk Factor Surveillance System asks a similar question and found that 5% of adults in the nation used e-cigarettes some days or every day in 2017, the most recent year for which data are available.5 While these questions are similar, it is important to recognize that OHIP allowed the response “rarely” in addition to “every day” and “some days.” Therefore it is not possible to make a direct comparison between the state and national percentages.

Ohio adults’ opinions about tobacco policies

OHIP asked Ohio adults whether they favored or opposed other policies that affect tobacco use:

  • An excise tax on the sale of e-cigarettes: 67% favor, 28% oppose. Support has increased since 2018 when 56% favored a tax.
  • A law that raises the age of sale from 18 to 21 on all tobacco products including e-cigarettes: 53% favor, 43% oppose. (See What’s Happening Now below.)
  • A $1 per pack increase in the cost of cigarettes: 43% favor, 54% oppose.

Why we ask these questions

Tobacco use is the leading cause of preventable death in the United States. It is responsible for about 20% of all deaths annually.6 Although cigarette use has declined in recent decades, the percentage of adults in Ohio who are current cigarette smokers continues to be higher than the nation. In addition, some people have not experienced this decline in smoking. These include adults living in or just above poverty. These smokers experience more tobacco-related health issues and often lack access to health care that could help treat these issues.7

Assessing public opinion about policies that reduce access to tobacco products is key to understanding the tobacco landscape. E-cigarettes are a newer product that have garnered much media attention in recent months. OHIP aims to understand who is using the products while monitoring the policy landscape across the state.

What’s Happening Now

In October 2019, Ohio’s Tobacco 21 law went into effect, making it illegal to give, sell or distribute all tobacco products – including e-cigarettes – to anyone under the age of 21. As of January 2020, Ohio is one of 19 states with such a law, along with more than 500 cities and municipalities.8 However, Ohio’s law does not include enforcement strategies. Many local jurisdictions, including Cincinnati, are implementing effective enforcement strategies such as compliance checks with retailers and tobacco retailer licensing programs. For more information about the Tobacco 21 legislation visit

1. Centers for Disease Control and Prevention. (2019). BRFSS Prevalence & Trends Data – Current Smoker Status. Retrieved from
2. In 2018, 200% of the Federal Poverty Guidelines for a family of four was $50,200.
3. Centers for Disease Control and Prevention. (2020). About Electronic Cigarettes (E-Cigarettes). Retrieved from
4. Centers for Disease Control and Prevention. (n.d.). Electronic Cigarettes: What’s the Bottom Line? Retrieved from
5.  Centers for Disease Control and Prevention. (2019). BRFSS Prevalence & Trends Data – E-Cigarette Use. Retrieved from
6. Centers for Disease Control and Prevention. (2019). Smoking and Tobacco Use. Retrieved from
7. Campaign for Tobacco-Free Kids. (2015). Tobacco and Socioeconomic Status. Retrieved from
8. Tobacco 21. (2019). State by State. Retrieved from