prevalence and risks of severe events for cancer patients ...€¦ · 23/06/2020 · prevalence...

Prevalence and risks of severe events for cancer patients with COVID-19

infection: a systematic review and meta-analysis

Qiang Su a (MD), Jie-xuan Hua(MD), Hai-shan Lina(MD), Zheng Zhangb(MD),

Emily C. Zhuc(PhD), Chen-guang Zhangd*(PhD), Di-ya Wange*(PhD), Zu-hua

Gaof*(MD), and Bang-wei Caoa*(MD) a Department of Oncology, Beijing Friendship Hospital, Capital Medical University,

Beijing, 100050, China. b Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical

University, Beijing, 100050, China. c McCoy College of Business, Texas State University, 601 University Dr., San

Marcos, Texas 78666, USA. d Department of Biochemistry and Molecular Biology, School of Basic Medical

Sciences, Capital Medical University, Beijing, 100069, China. e Department of Radiation Oncology, University of California, San Francisco, CA,

USA. f Department of Pathology, Research Institute of McGill University Health Center,

Montreal, QC, Canada

* Correspondence authors

E-mail address:

[email protected] for Dr Chenguang Zhang;

[email protected] for Dr Di-ya Wang;

[email protected] for Dr Zu-hua Gao;

[email protected] for Dr Bang-wei Cao.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted June 24, 2020. ; https://doi.org/10.1101/2020.06.23.20136200doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

https://doi.org/10.1101/2020.06.23.20136200

http://creativecommons.org/licenses/by-nc-nd/4.0/

Summary

Background

The corona virus disease 2019 (COVID-19) pandemic poses a severe challenge

to public health, especially to those patients with underlying diseases. In this

meta-analysis, we studied the prevalence of cancer among patients with COVID-19

infection and their risks of severe events.

Methods

We searched the Pubmed, Embase and MedRxiv databases for studies between

December 2019 and May 3, 2020 using the following key words and terms:

sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, corona virus disease-2019,

clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology,

and epidemiological characteristics. We extracted data following PICO (patient,

intervention, comparison and outcome) chart. Statistical analyses were performed

with R Studio (version 3.5.1) on the group-level data. We assessed the studies’ risk of

bias in accordance to the adjusted Joanna Briggs Institute. We estimated the

prevalence or risks for severe events including admission into intensive care unit or

death using meta-analysis with random effects.

Findings

Out of the 2,551 studies identified, 32 studies comprising 21,248 participants

have confirmed COVID-19. The total prevalence of cancer in COVID-19 patients

was 3.97% (95% CI, 3.08% to 5.12%), higher than that of the total cancer rate

(0.29%) in China. Stratification analysis showed that the overall cancer prevalence of

COVID-19 patients in China was 2.59% (95% CI, 1.72% to 3.90%), and the

prevalence reached 3.79% in Wuhan (95% CI, 2.51% to 5.70%) and 2.31% (95% CI,

1.16% to 4.57%) in other areas outside Wuhan in China. The incidence of ICU

admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to

32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%), much higher

than the overall rates of COVID-19 patients in China. The fatality in COVID patients

with cancer was lower than those with cardiovascular disease (OR 0.49; 95% CI,

0.34 to 0.71; p=0.39), but comparable with other comorbidities such as diabetes (OR

1.32; 95% CI, 0.42 to 4.11; p=0.19), hypertension (OR 1.27; 95% CI, 0.35 to 4.62;

p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45).

Interpretation



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This comprehensive meta-analysis on the largest number of patients to date

provides solid evidence that COVID-19 infection significantly and negatively

affected the disease course and prognosis of cancer patients. Awareness of this could

help guide clinicians and health policy makers in combating cancer in the context of

COVID-19 pandemic.

Funding

Beijing Natural Science Foundation Program and Scientific Research Key

Program of Beijing Municipal Commission of Education (KZ202010025047).

Key words：COVID-19; Cancer; Prevalence; Fatality; Meta-analysis



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Introduction

The pandemic of severe acute respiratory syndrome corona virus 2

(SARS-CoV-2) has caused 4,794,932 confirmed cases and 316,420 deaths globally

by May 7, 2020, affecting almost all the countries in the world.1 WHO announced

COVID-19 as a public health emergency of international concern on January 30,

2020. Most COVID-19 cases experience mild to moderate respiratory symptoms or

signs such as fever and cough, and will recover without special treatment.

Unfortunately, the elder people, and those suffering underlying medical conditions

such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer are

more likely to develop into more serious conditions, including multiple organ

dysfunction syndrome or even death.2-4

Cancer is a major public health problem worldwide and is the second leading

cause of death in the United States. Recent research had shown that there were about

24.5 million cancer cases worldwide and 9.6 million cancer deaths in 2017. In 2020,

the number of new cancer cases and cancer related death is estimated to

reach1,806,590 and 606,520 in the US, respectively.5Cancer patients are more

susceptible to infections than those without cancer due to the systemic

immunosuppression caused by the malignancy itself or anticancer treatments, such as

chemo-radiation. 6-9 Therefore, it is conceivable that cancer patients are at higher risk

of COVID-19 infection. Moreover, unplanned interruptions of regular anti-tumor

therapies, due to the overloaded healthcare systems in the pandemic battle, expose

cancer patients’ adverse clinical risks of uncontrolled primary disease. Furthermore,

some tumor markers such as CEA, CA-125 can be elevated and positively correlated

with the pathological progression of COVID-19 infection, which may increase the

cancer detection rate. 10

The relationship between cancer and COVID-19 has drawn great attention from

the beginning of the pandemic. An early study by Liang et al reported a cancer

prevalence of 1.13% (95% CI, 0.61% to 1.65%) among 1,590 cases of COVID-19 in

China (only 18 patients with cancer), which was higher compared with the overall

cancer incidence of 0.29% in the Chinese population.11 More importantly, cancer

patients with COVID-19 had a higher risk of severe events (a composite end point

defined as the percentage of patients who were admitted to the intensive care unit



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(ICU) and required invasive ventilation or who died) compared with those

COVID-19 patients without cancer (39% v 8%, HR: 5.34; 95%CI, 1.80 to 16.18; P =

0.0026). A pooled meta-analysis by Aakash Desai indicated that prevalence of cancer

in COVID-19 patients was 2.0% (95% CI, 2.0% to 3.0%; I2 = 83.2%), while the

prevalence was 3.0% (95% CI, 1.0% to 6.0%) in studies with a sample size smaller

than 100, and 2.0% (95% CI, 1.0% to 3.0%) in studies with a sample size not smaller

than 100.12 To guide clinical management and appropriate medical resources

allocation, it is necessary to validate the relationship of cancer and COVID-19 in a

much larger international scale. In the current study, we performed a meta-analysis of

21,248 participants in 32 clinical studies across six countries (China, Japan, Korea,

Italy, France, USA)11,13-43to exploit the prevalence of cancer in COVID-19 patients as

well as risks of severe events including ICU admission and death of these patients.

Methods

Search Methods and Study Selection

We searched Pubmed, Embase and MedRxiv databases for studies according to

the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel

coronavirus, Corona Virus Disease-2019, clinical, clinical characteristics, clinical

course, epidemiologic features, epidemiology, epidemiological characteristics. The

following data from all relevant studies between December 2019 and May, 2020

were included in our meta-analysis: (1) the study subjects were patients with

confirmed diagnosis of SARS-CoV-2 viral infection, based on the detection of the

viral nucleic acid by reverse transcription quantitative PCR (RT-qPCR) assay; (2) the

results included age, sex, epidemiology and cancer information; (3) study types

include cohort studies and clinical trials; (4) restriction of language to English only.

Animal studies, systematic reviews, case reports, and) small studies of fewer than 10

patients were excluded. This study was performed according to the Preferred

Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline.44

(eTable 1)

Data extraction

The two authors (Q.S and JX.H) independently extracted the data by checking

the titles, abstracts and full text. We then assessed the eligibility of the extracted

studies using the PICO (patient, intervention, comparison and outcome) chart.

Disagreements in the assessment were resolved via discussion with the third reviewer



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(CG.Z).45 Finally, they extracted the following information from all eligible studies:

the first author, study period, district, gender, age, diagnosis methods, the number of

total patients, the number of cancer patients, serious events, death toll, the number of

comorbidities, and death toll of comorbidities.

Data analysis

Statistical analyses were performed using R’s META package in R Studio

(version 3.5.1). We focused on the following parameters in COVID-19 patients: the

cancer prevalence, the ICU admission rate, and the cancer mortality. Different

subgroups of patient population with cancer were compared. The prevalence for

cancer in COVID-19 patients was expressed as percentage with 95% confidence

interval (95% Cis). Odds ratio (OR) was used to estimate the -risks of ICU admission

and death of cancer patients with COVID-19 compared with those suffering other

comorbidities including cardiovascular diseases, diabetes, hypertension or non-cancer

patients with COVID-19. Heterogeneity was evaluated using the Q test and Higgin’s

and Thompson’s I2 (I2) statistics. Defined as the percentage of variations in the

effect sizes that is not the cause of sampling errors, I2 statistics is robust and not

sensitive to the number of studies under analysis. We consider I2 value of over 50%

as moderate to high heterogeneity. P<0.05 was considered statistically significant.

We used random-effects model if the I2 value was more than 50%. We also

performed a multi-group subgroup analysis to explore the source of heterogeneity.

Specifically, subgroup design was based on the sample size (smaller or greater than

100), and the area of the patients (China/Outside China;

Japan/Korea/Italy/France/USA). The quality of the included studies was assessed by

the Joanna Briggs Institute (JBI),46and we slightly modified the table to make it adapt

with selected studies (eFigure 1). We also conducted sensitivity analysis by

sequentially removing individual studies one by one, and evaluating the stability of

the results. Begger test and Egger test were used to evaluate publication bias, caused

by possibly missing unpublished studies of low effect sizes. Finally, we developed a

funnel plot to describe the results of publication bias.

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis,

data interpretation, or writing of the report. The corresponding author had full access

to all the data in the study and had final responsibility for the decision to submit for



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publication.

Results

Eligible Studies and Characteristics

Using our searching principles, we retrieved 2,551 studies from three databases

between Dec 1st, 2019 and May 3rd, 2020, among which, 32 studies comprising

21,248 patients met our selection criteria (eTable 2). 22 of the 32 studies were

mutually exclusive cohorts of patients in China (Wuhan n=13; outside of Wuhan n=5;

unclear n=4), 2 studies based on cohorts of other Asian countries (Korean and Japan),

and 8 studies were based in Europe and USA (Italy n=1; France n=1; USA n=6). The

detailed research flow chart is shown in Figure 5, characteristics of the included

studies are shown in Table 1, and data on severe events in patients with COVID-19 is

shown in eTable3. Among the 21,248 patients in those 32 studies, valid data

including basic information, epidemiological characteristics, clinical characteristics,

prevalence and mortality of cancer or other complications were extracted from

COVID-19 patients. All patients presented exhibited mild to severe clinical

manifestations and were confirmed of COVID-19 infection RT-qPCR.

Result of meta-analyses

Prevalence of cancer in COVID-19 patients

We first analyzed the cancer prevalence rates in COVID-19 patients. As shown

in Figure 1, the total prevalence of cancer in COVID-19 patients was 3.97% (95% CI,

3.08% to 5.12%), which is higher than the total cancer rate (0.29%) in China.

When analyzing in subgroups of different sample size, the prevalence showed

some variation. The prevalence of cancer was 4.16% (95% CI, 2.80% to 6.14%) in

the subgroup whose sample size was ≤100, and 3.99% (95% CI, 2.96% to 5.37%) in

the subgroup whose sample size was >100 (Figure 2). This observation is consistent

and comparable with the analysis by Aakash Desai.12

We then analyzed the cancer prevalence based on patients’ geographic location.

The overall cancer prevalence of COVID-19 patients was 2.59% (95% CI, 1.72% to

3.90%) in China, 3.85% (95% CI, 1.45% to 9.80%) in Japan, and 3.57% (95% CI,

0.50% to 21.42%) in Korea (Figure 1). The prevalence was higher in Italy and USA,

which reached 11.35% (95% CI, 10.19% to 12.61%) and 6.09% (95% CI, 5.13% to

7.21%), respectively (Figure 1). Moreover, the cancer prevalence of COVID-19

patients in Wuhan was 3.79% (95% CI, 2.51% to 5.70%), higher than the average



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level of China (Figure 3).

Our analyses clearly showed that the cancer prevalence of COVID-19 patients

was higher than that of the general population. In USA and European countries, the

prevalence was higher than in China and other Asian countries. In China, the

prevalence in Wuhan was higher than in other areas of China (Figure1 and 3).

Risk of severe events of cancer in COVID-19 patients

In China, the incidence of ICU admission in COVID19 patients with cancer was

38.77% (95% CI, 26.04% to 53.24%) (Figure 4). In Wuhan, the incidence of ICU

admission and death in COVID19 patients with cancer were 42.80 (95% CI, 23.38 %

to 64.72%) and 59.61% (95% CI, 28.86% to 84.30%) respectively. The incidence of

ICU admission in COVID19 patients with cancer were 23.71% (95% CI, 18.41% to

29.99%) outside China, and the fatality reached 17.45% (95% CI, 11.92% to 24.83%)

in Europe. The COVID19 patients in Wuhan apparently had a worse outcome than

the rest of the China. Moreover, COVID-19 patients with cancer had a significantly

higher ICU admission rate than those without cancer in China (OR 2.77; 95% CI,

1.28 to 6.00; p=0.46), and had overall higher fatality irrelative with areas (OR 4.87;

95% CI, 1.52 to 15.59; p=0.74/OR 3.97; 95% CI, 1.10 to 14.32; p=0.88) (eFigure 4).

This may imply the existence of complex interrelationship between COVID-19 and

cancer, where cancer was able to exaggerate COVID-19 infection and vice versa.

We further compared the rate of ICU admission rate and death of cancer with

other underlying diseases in COVID-19 patients. As shown in eFigure 2, there was

no significantly difference in the ICU admission rate of COVID 19 patients with

cancer from COVID 19 patients with other comorbidities, including cardiovascular

diseases (OR 0.89; 95% CI, 0.52 to 1.53; p=0.91), diabetes (OR 0.81; 95% CI, 0.48

to 1.37; p=0.85), hypertension (OR 0.88; 95% CI, 0.53 to 1.44; p=0.64), and

respiratory disease (OR 0.65;95% CI, 0.37 to 1.15; p=0.45). Similarly, mortality rate

of COVID 19 patients with cancer is close to COVID 19 patients with diabetes (OR

1.32; 95% CI, 0.42 to 4.11; p=0.39), hypertension (OR 1.27; 95% CI, 0.35 to 4.62;

p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45).

Nevertheless, it is worth noting that COVID19 patients with cancer showed higher

mortality rate than cardiovascular diseases (OR 0.49; 95% CI, 0.34 to 0.71; p=0.39)

(eFigure 3).

Quality assessment and publication bias



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The quality of the included literature was satisfactory. Insufficient number of

patients and incomplete coverage of the population were the main cause of low

quality literature (eFigure 1), which we excluded. We performed sensitivity analysis

of the included studies to assess their stability. After each study was excluded in turn,

we found no significant difference in the results of the analysis (eTable 4,5). The

funnel plot and Egger test results showed the risk of publication bias is low (p-value

= 0.35) (eFigure 5).

The heterogeneity among our study subgroups was high. In the subgroup

mortality analysis, the heterogeneity parameter I2 were >50% in all subgroups except

group IA (sample size ≤100). So, the random-effects model was deployed.

Discussion

A comprehensive analysis of cancer prevalence and severe events among

patients with COVID-19 can serve as an important reference for oncology clinicians

and healthcare policy makers. A few existing studies focused mainly on the

prevalence analysis of cancer patients with COVID-19, lacking a comparison of the

prevalence or risk for severe events among different areas. 11,12We performed a

systematic review on the prevalence of cancer and risks of severe events among

patients with COVID-19 using a collection of sparse binomial data from published

studies. To our knowledge, this is the largest and most comprehensive meta-analysis

of the prevalence and fatality analysis of cancer among patients with COVID-19 to

date.

In this study, we found that the total prevalence of cancer in COVID-19 patients

was 3.97% (95% CI, 3.08% to 5.12%), which was significantly higher than that of

the total cancer rate (0.29%) in China. 47In other words, approximately 3 in 100

patients with COVID-19 had suffered from superimposed malignancy. The higher

prevalence of cancer in COVID19 patients could be due to multiple factors. Firstly,

caner is more common among senior patients, who are also susceptible to COVID19.

Secondly, due to primary disease and the anticancer treatment, cancer patients are

immune compromised, hence they are more vulnerable to viral infection. Thirdly,

COVID19 infection has been shown to elevate the serum level of some cancer

biomarkers such as CEA and CA19-9 etc., which may increase the cancer detection

rate in this population.10

The cancer prevalence in COVID19 patients in the city of Wuhan was



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significantly higher than the rest of China. Meanwhile, it was significantly higher in

European and American countries than in Asian countries. The geographical

differences cannot be explained by factors like race or climate. It seems to be

associated with overall incidence of COVID19 infection in those affected areas.

Therefore, effective control of COVID19 infection, especially non-pharmaceutical

interventions, could potentially reduce the cancer prevalence.48

In addition to the high prevalence, we found that nearly a third of cancer patients

with COVID-19 experienced aggravated conditions and were admitted to ICU, which

was apparently higher than total COVID-19 patients. Moreover, fatality in cancer

patients with COVID-19 was about 24.32% (95% CI, 13.95% to 38.91%), also

significantly higher than the rate of total patients with COVID-19 (about 3-7%).49

Factors that affected cancer prevalence mentioned above might also explain the

worse prognosis of cancer patients with COVID19. Firstly, individuals with cancer

are generally older,50 which has been shown to express higher levels of

angiotensin-converting enzyme II (ACE2), a receptor for SARS-CoV-2 entry into the

cell.51 Secondly, cancer patients often suffer from immunosuppression, like

lymphopenia, which often results in the impairment of the required immune response

and may lead to viral propagation, tissue destruction, and progression to severe stages

of the disease in ACE2-rich tissues .52 Meanwhile, the overload in the hospitals may

prevent timely and sufficient medical care for COVID19 patients with cancer, also

leading to high fatality. Severe events are not only a major public health issue, but

also a serious economic concern. Although health care personnel and resources were

heavily allocated to ICU and treating severe cases, the outcomes were far from

satisfactory. Thus, prevention of CoVID19 infection using strict quarantine and

social distance measures is essential to protect cancer patients from worsening their

health conditions.

The outcome of cancer patients with COVID19 was also compared with other

morbidities, especially chronic diseases, which were thought as risk factors for severe

event in patients with COVID-19. A meta-analysis by Yang et al showed that

COVID-19 patients with hypertension, cardiovascular diseases and respiratory

disease have higher risk for developing severe complications.53 Du et al found that

61.9% patients with hypertension and 57.1% patients with cardiovascular or

cerebrovascular diseases developed severe events.54 Kang et al found that



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superimposed heart disease is the highest cause of death among all complications for

COVID-19 patients. 55From our analyses, the ICU admission rate for cancer

patients with COVID19 was comparable with diabetes, hypertension, cardiovascular

diseases and respiratory diseases. Meanwhile, the fatality of cancer patients with

COVID19 was also similar with other chronic diseases above expect cardiovascular

diseases. COVID19 patients with cardiovascular diseases showed higher mortality

rate than cancer. Thus, cancer should become one of the notable superimposed

comorbidities together with diabetes, cardiovascular and chronic respiratory diseases,

contributing to COVID-19 infection and severe events.

Our findings are of values to both cancer patients and oncology clinicians in

COVID-19 outbreak countries and territories. Future studies should rely on more

detailed analyses. For example, the prevalence and risks of severe events can vary

with different cancer types. A recent cohort study by Dai et al. indicated that

COVID-19 patients in Wuhan with hematologic cancer had the highest frequency of

severe events, which was followed by lung cancer, and patients with metastatic

cancer hade much higher incidence of severe events than those with non-metastatic

cancers. Moreover, cancer patients who had received surgery tended to have higher

risks of severe events than other treatments except immunotherapy. 56 We anticipate

further investigations on these issues in broader geographic and clinical settings.

Limitations

Our meta-analysis studied the cancer prevalence and risks of severe events in

COVID-19 patients by accommodating fully reported and censored data from diverse

literature. But this meta-analysis has limitations. Small-study effects were observed

when studies with smaller sample sizes had different incidences and wider CIs for

fatality in patients with COVID-19 and cancer. Although the forest plots showed no

asymmetry favoring cancer fatality studies, this meta-analysis is subject to

publication bias given that all of our analyses were based on publications. In addition,

this study is subject to any biases or errors of the original investigators, and the

results are generalizable only to patient groups with COVID-19. Meanwhile, it

currently cannot summarize the prevalence or risk of severe events of different types

of cancer in COVID-19 patients due to the lack of enough data.

Conclusions:



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In summary, using the largest dataset to date, our meta-analysis has clearly

demonstrated that (1) there is higher prevalence of COVID19 infection in cancer

patients, (2) when COVID19 infection occur in a patient with cancer, the patient is

more likely to develop severe clinical events. Patients with chronic health conditions

require more stringent preventive measures and more delicate therapeutic strategies

during the COVID19 pandemic.

Author contributions

QS, JXH and EC. Z had access to all the data included in the study and are

responsible for the completeness of the data and the accuracy of our analysis. JXH,

HSL and ZZ helped to design the study. QS, DYW, and BWC contributed to the

statistical analysis and the revision of this manuscript. ZHG and CGZ approved the

final manuscript.

Declaration of interests

All other authors declare no competing interests.

Acknowledgments

This work was supported by Beijing Natural Science Foundation Program and

Scientific Research Key Program of Beijing Municipal Commission of Education

(KZ202010025047).

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SARS-CoV-2 patients in the Province of Reggio Emilia, Italy. https://wwwmedrxivorg/ 2020.

37. Basse C, Diakite S, Servois V, Frelaut M, Noret A. Characteristics and outcome of SARS-CoV-2

infection in cancer patients. https://wwwmedrxivorg/ 2020.

38. McMichael TM, Currie DW, Clark S, et al. Epidemiology of Covid-19 in a long-term care facility

in King County, Washington. New England Journal of Medicine 2020.

39. Petrilli CM, Jones SA, Yang J, et al. Factors associated with hospitalization and critical illness

among 4,103 patients with COVID-19 disease in New York City. https://wwwmedrxivorg/ 2020.

40. Cummings MJ, Baldwin MR, Abrams D, et al. Epidemiology, clinical course, and outcomes of

critically ill adults with COVID-19 in New York City: a prospective cohort study.


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Patients in New York City. https://wwwmedrxivorg/ 2020.

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Patients with COVID-19 in New York: retrospective case series. https://wwwmedrxivorg/ 2020.

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outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020.

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clinical questions. AMIA annual symposium proceedings; 2006: American Medical Informatics

Association; 2006. p. 359.

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Liu Za Zhi 2019; 41(1): 19-28.

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containing the COVID-19 outbreak in China. https://wwwmedrxivorg/ 2020.

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countries and 6 states of the United States. https://wwwmedrxivorg/ 2020.

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causes-prevention/risk/age.

51. Hoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 Cell Entry Depends on ACE2

and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell 2020; 181(2): 271-80.e8.

52. Zheng M, Gao Y, Wang G, et al. Functional exhaustion of antiviral lymphocytes in COVID-19

patients. Cellular molecular immunology 2020; 17(5): 533-5.

53. Yang J, Zheng Y, Gou X, et al. Prevalence of comorbidities in the novel Wuhan coronavirus

(COVID-19) infection: a systematic review and meta-analysis. International Journal of Infectious

Diseases 2020.

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pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J 2020; 55(5).

55. Kang Y, Chen T, Mui D, et al. Cardiovascular manifestations and treatment considerations in

covid-19. Heart 2020.

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Multicenter Study during the COVID-19 Outbreak. Cancer discovery 2020; 10(6): 783-91.



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Table 1. List of the 32 Studies Included in This Meta-Analysis

Study Year District Diagnosis methods

Total patients

Cancer patients

Cancer- related Death

Death toll

Huang C13 12-6,19 1-2,20 Wuhan, CHN RT-PCR and

NGS 41 1

NA 6

Chen N14 1-1,20 1-20,20

Wuhan, CHN RT-PCR 99 1

NA 11

Wang D15 1-1,20 1-28,20

Wuhan, CHN

bron-

choalveolar

lavage fluid

138 10

NA 6

Huang Y16 12-21,2019

1-28,2020

Wuhan, CHN unclear 34 3

NA 0

Zhang J17 1-16,2020

1-3,2020


NA NA

Yang X18 12，2019

1-26,2020


NA 32

Shi H19 12-20,2019

1-23,2020

Wuhan, CHN RT-PCR and

NGS 81 4

NA 3

. C

C-B

Y-N

C-N

D 4.0 International license

It is made available under a

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he copyright holder for this preprint this version posted June 24, 2020.

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Zhou F20 12-29,2019

1-31,2020

Wuhan, CHN unclear 191 2

NA 54

Chen L21 12,2019 2-3,2020

Wuhan, CHN RT-PCR 29 2 1 2

Wu C22 12-30,2019

1-15,2020


NA 44

Liu W23 12-30,2019

1-15,2020


NA 2

Zhang L24 1-13,2020

2-26,2020


Chen T25 1-13,2020

2-12,2020


Liu K26 12-30,2019

1-24,2020 NA, CHN RT-PCR 137 2 NA 16

Wang J27 12-17,2019

3-18,2020 NA, CHN RT-PCR 283 283 50 50

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Zhu W28 1-24,2020

2-20,2020

outside of

WH,CHN RT-PCR 32 2 NA NA

Yang W29 1-17,2020

2-10,2020

outside of

WH,CHN unclear 149 2

NA 0

Wu J30 1-22,2020

2-14,2020

outside of

WH,CHN RT-PCR 80 1

NA 0

Xu X31 1-23,2020

2-4,2020

outside of

WH,CHN RT-PCR 90 2

NA NA

Leung KS-S32 1-26,2020

2-28,2020

outside of

WH,CHN RT-PCR 50 1

NA NA

Guan W33 12,2019 1

29,2020 NA, CHN RT-PCR 1099 10

NA 15

Liang W11 12,2019 1

31,2020 NA, CHN unclear 1590 18

NA NA

Kim ES34 12,2019 2,2020 Korea unclear 28 1

NA 0

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Tabata S35 2-11,2020

2-25,2020 Japan RT-PCR 104 4

NA 0

Rossi PG36 2-27,2020

4-2,2020 Italy PCR 2653 301 44 217

Basse C37 3,2020 5,2020 France RT-PCR 141 141 30 30

McMichael TM38 2-28,2020

3-18,2020

King County,

Washington, US RT-PCR 167 15

NA 34

Petrilli CM39 3-1,2020 4-2,2020 New York, US RT-PCR 4103 185

NA 176

Cummings MJ40 3-2,2020 4-1,2020 New York, US RT-PCR 257 18

NA 86

Paranjpe I41 2-27,2020

4-2,2020 New York, US RT-PCR 2199 151

NA 310

Argenziano MG42 3-1,2020

4-15,2020 New York, US RT-PCR 1000 66

NA 172

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Richardson S43 3-1,2020 4-4,2020 New York, US RT-PCR 5700 320

NA 553

Note：RT-PCR and NGS：Real-time RT-PCR and next-generation sequencing; NA: Not available; NA, CHN: The patients were from China, but

was not sure whether from Wuhan or outside of Wuhan

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Figure legends Figure 1. Prevalence of cancer in COVID-19 patients in China and outside China.



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Figure 2. Prevalence of cancer in COVID-19 patients in subgroups based on sample size over or not over 100.



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Figure 3. Prevalence of cancer in COVID-19 patients based on areas.



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Figure 4. The incidence of ICU admission and fatality of cancer patients with COVID-19 in different areas.

A:ICU admission incidence of cancer patients with COVID-19 in Wuhan, B:ICU

admission incidence of cancer patients with COVID-19 outside Wuhan, C:ICU

admission incidence of cancer patients with COVID-19 outside China, A+B:ICU

admission incidence of cancer patients with COVID-19 in China, A+B+C:ICU

admission incidence of cancer patients with COVID-19 in all areas(all available

statistics), D:Fatality of cancer patients with COVID-19 in Wuhan; E:Fatality of

cancer patients with COVID-19 in Europe; D+E: Fatality of cancer patients with

COVID-19 in all areas(all available statistics)



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Figure 5. Flowchart depicting the studies selection process.



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eTable 1: Checklist of items included when reporting a systematic review and meta- analysis Section/topic Item No Checklist item Reported on page No Title Title 1 Identify the report as a systematic review, meta-analysis, or both 1 Abstract

Structured summary 2 Provide a structured summary including, as applicable, background, objectives, data

sources, study eligibility criteria, participants, interventions, study appraisal and synthesis

methods, results, limitations, conclusions and implications of key findings, systematic

review registration number

1-2

Introduction Rationale 3 Describe the rationale for the review in the context of what is already known 2-4

Objectives 4 Provide an explicit statement of questions being addressed with reference to participants,

interventions, comparisons, outcomes, and study design (PICOS) 3-4

Methods

Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (such as web address),

and, if available, provide registration information including registration number 4

Eligibility criteria 6 Specify study characteristics (such as PICOS, length of follow-up) and report

characteristics (such as years considered, language, publication status) used as criteria for

eligibility, giving rationale

4

Information sources 7 Describe all information sources (such as databases with dates of coverage, contact with

study authors to identify additional studies) in the search and date last searched 4

Search 8 Present full electronic search strategy for at least one database, including any limits used,

such that it could be repeated 4

Study selection 9 State the process for selecting studies (that is, screening, eligibility, included in systematic

review, and, if applicable, included in the meta-analysis) 4

Data collection process 10 Describe method of data extraction from reports (such as piloted forms, independently, in

duplicate) and any processes for obtaining and confirming data from investigators 4

Data items 11 List and define all variables for which data were sought (such as PICOS, funding sources)

and any assumptions and simplifications made 4

Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies

(including specification of whether this was done at the study or outcome level), and how

this information is to be used in any data synthesis

5

Summary measures 13 State the principal summary measures (such as risk ratio, difference in means). 4-5

Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including

measures of consistency (such as I2 statistic) for each meta-analysis 4-5

Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (such as

publication bias, selective reporting within studies) 5

Additional analyses 16 Describe methods of additional analyses (such as sensitivity or subgroup analyses,

meta-regression), if done, indicating which were pre-specified 5

Results

Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with

reasons for exclusions at each stage, ideally with a flow diagram 5

Study characteristics 18 For each study, present characteristics for which data were extracted (such as study size,

PICOS, follow-up period) and provide the citations 5

Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome-level assessment

(see item 12). 7

Results of individual studies 20 For all outcomes considered (benefits or harms), present for each study (a) simple

summary data for each intervention group and (b) effect estimates and confidence intervals,

ideally with a forest plot

5-7

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of

consistency 5

Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see item 15) 7

Additional analysis 23 Give results of additional analyses, if done (such as sensitivity or subgroup analyses,

meta-regression) (see item 16) 6-7

Discussion

Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome;

consider their relevance to key groups (such as health care providers, users, and policy

makers)

7-10

Limitations 25 Discuss limitations at study and outcome level (such as risk of bias), and at review level

(such as incomplete retrieval of identified research, reporting bias) 10

Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and

implications for future research 10

Funding

Funding 27 Describe sources of funding for the systematic review and other support (such as supply of

data) and role of funders for the systematic review 11



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eTable 2. Search performed in Pubmed and Embase

Recent queries in EMBASE No. Query Results #3 #1 AND #2 1061

#2 'clinical characteristics':ab,ti OR clinical: ab,ti OR 'clinical course': ab,ti OR 'epidemiologic features': ab,ti OR epidemiology: ab,ti OR 'epidemiological characteristics': ab,ti

4990590

#1 'sars cov 2':ti,ab OR 'covid 19':ab,ti OR '2019 ncov':ab,ti OR '2019 novel coronavirus': ab,ti OR 'corona virus disease 2019':ab,ti

5612

Recent queries in medrxiv

#1

covid-19;sars-cov-2;2019 novel coronavirus;2019-ncov;Corona Virus Disease-2019;clinical;clinical characteristics; clinical course; epidemiologic features;epidemiology;epidemiological characteristics

173

Recent queries in pubmed

Search Query Items found

#3 #1 AND #2 1317

#2 Search (((((sars-cov-2) OR covid-19) OR 2019 novel coronavirus) OR 2019-ncov) OR Corona Virus Disease-2019) Filters: Humans

1677

#1 Search ((((((clinical) OR clinical characteristics) OR clinical course) OR epidemiologic features) OR epidemiology) OR epidemiological characteristics) Filters: Humans

257113



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eTable 3. List of studies that reported at least one comorbidity-related severe events

Death

Study Basse C37 Chen T25 Rossi PG36 Wang J27 Zhang L24 Chen L21

Total patients 141 274 2653 283 28 29

Death toll 30 113 217 50 8 2

Single Comorbidities

Cancer NA 7 301 283 28 2

Cancer-related death

NA 5 44 50 8 1

Hypertension NA 93 430 NA NA 8

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Hypertension-related death

NA 54 87 NA NA 0

Diabetes NA 47 284 NA NA 5

Diabetes-related death

NA 24 51 NA NA 0

Cardiac disease NA 23 490 NA NA NA

Cardiac disease-related death NA 16 130 NA NA NA

Respiratory disease NA 18 128 NA NA NA

Respiratory disease -related death

NA 11 24 NA NA NA

ICU Admission

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Chen L21 Liang W11 Wang D15 Guan W33 Huang C13 Zhang J17 Argenziano MG42 Basse C37

Total patients 29 1590 138 1099 41 140 1000 141

Patients in ICU 5 NA 36 67 13 58 231 11

Single Comorbidities

Cancer 2 18 10 10 1 6 66 NA

Cancer patients in ICU

1 7 4 3 0 3 14 NA

Hypertension 8 NA 43 165 6 42 598 NA

Hypertension patients in ICU NA NA 21 24 2 22 157 NA

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Diabetes 5 NA 14 81 8 17 372 NA

Diabetes patients in ICU

NA NA 8 18 1 8 100 NA

Cardiac disease NA NA 20 27 6 12 233 NA

Cardiac disease patients in ICU

NA NA 9 6 3 8 53 NA

Respiratory disease NA NA 4 12 1 4 438 NA

Respiratory disease patients in ICU

NA NA 3 7 1 4 106 NA

Note: Single Comorbidities:COVID-19 patients with a single comorbidity; NA: Not available; Cancer-related

death: Deaths of COVID-19 patients with cancer; Patients in ICU: Covid-19 patients admitted to the ICU in the studies; Cancer patients

in ICU:Covid-19 patients with cancer admitted to the ICU . C

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eTable4. Results of sensitivity analysis of the included studies Proportion p-value 95%CI I2

Omitting Chen L 0.0392 [0.0303; 0.0507] 90.3% Omitting Liang W 0.0437 [0.0342; 0.0555] 87.8% Omitting Wang D 0.0385 [0.0296; 0.0500]] 90.3% Omitting Guan W 0.0433 [0.0338; 0.0554] 88.7% Omitting Liu K 0.0407 [0.0315; 0.0525] 90.2% Omitting Yang X 0.0398 [0.0307; 0.0514] 90.3% Omitting Zhou F 0.0411 [0.0318; 0.0529] 90.1% Omitting Huang C 0.0400 [0.0309; 0.0516] 90.3% Omitting Chen N 0.0405 [0.0314; 0.0523] 90.2% Omitting Zhu W 0.0393 [0.0303; 0.0508] 90.3% Omitting Shi H 0.0394 [0.0304; 0.0510] 90.3% Omitting Wu C 0.0410 [0.0317; 0.0528] 90.1% Omitting Yang W 0.0408 [0.0315; 0.0526] 90.2% Omitting Zhang J 0.0396 [0.0305; 0.0513] 90.3% Omitting Wu J 0.0404 [0.0312; 0.0521] 90.2% Omitting Huang Y 0.0388 [0.0299; 0.0502] 90.3% Omitting Xu X 0.0403 [0.0311; 0.0520] 90.3% Omitting Chen T 0.0406 [0.0313; 0.0525] 90.1% Omitting Liu W 0.0394 [0.0303; 0.0510] 90.3% Omitting Kim ES 0.0398 [0.0307; 0.0514] 90.3% Omitting McMichael TM 0.0380 [0.0292; 0.0493] 90.3% Omitting Tabata S 0.0398 [0.0306; 0.0515] 90.3% Omitting Leung KS-S 0.0401 [0.0310; 0.0518] 90.3% Omitting Petrilli CM 0.0389 [0.0296; 0.0512] 89.3% Omitting Rossi PG 0.0385 [0.0307; 0.0481] 80.3% Omitting Cummings MJ 0.0384 [0.0295; 0.0500] 90.3% Omitting Paranjpe I 0.0377 [0.0284; 0.0498] 90.3% Omitting Argenziano MG 0.0382 [0.0291; 0.0500] 90.3% Omitting Richardson S 0.0377 [0.0280; 0.0505] 90.1% Pooled estimate 0.0397 [0.0308; 0.0512] 90.0% eTable5. Results of sensitivity analysis of the included studies(subgroup: prognosis of cancer)

Proportion p-value 95%CI I2 Omitting Chen T 0.1616 [0.1141; 0.2240] 69.50% Omitting Rossi PG 0.2060 [0.1142; 0.3429] 80.60% Omitting Argenziano MG 0.1818 [0.1102; 0.2849] 81.20% Omitting Wang J 0.1981 [0.1067; 0.3382] 81.80%



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Omitting Zhang L 0.1704 [0.1081; 0.2581] 79.40%



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eFigure 1. Quality assessment for studies

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eFigure 2. The incidence of ICU admission of Cancer patients VS Other Comorbidities with COVID-19

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eFigure 3. Fatality of Cancer patients VS Other Comorbidities with COVID-19

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eFigure 4. The incidence of ICU admission and fatality of cancer patients with COVID-19 VS Non-cancer patients

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eFigure 5. Results of publication bias

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Dear Editor,

We have enclosed a manuscript titled “Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis” by Su et al. In this study, a meta-analysis was carried out to evaluate the prevalence of cancer patients in COVID19 infection and their risks of severe events. We searched related studies from diverse literatures, extracted data following PICO chart, and carried out statistical analyses using R Studio (version 3.5.1) on the group-level data. We estimated the overall prevalence and risks for severe events including admission into intensive care unit or death in cancer patients with COVID19 worldwide and also made preliminary comparisons among different countries and territories. Meanwhile, comparisons between cancer and other comorbidities including diabetes, hypertension, cardiovascular and respiratory diseases were also made concerning the prevalence, risks for ICU admission and death during COVID19 infection.

The major findings from this study can be described as, 1) The total prevalence of cancer in COVID-19 patients was 3.97% (95% CI, 3.08% to 5.12%) worldwide, while in China was 2.59% (95% CI, 1.72% to 3.90%), 3.79% in Wuhan (95% CI, 2.51% to 5.70%). 2) The prevalence of cancer in COVID19 was higher in USA and European countries than in Asian countries.

3) The incidence of ICU admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to 32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%).

4) The fatality in COVID patients with cancer was lower than those with cardiovascular disease, but comparable with other comorbidities such as diabetes, hypertension, and respiratory diseases.

The listed authors have participated and have made significant contribution to the study, and will take the responsibility for the appropriateness regarding the experimental design, acquired methodology, the collection, analysis and summary of the data. As the corresponding author, I have reviewed the final version of the manuscript and approve it for submission.

We also certify that this manuscript has not been published in whole or as part for publication elsewhere. I have read and abided by the statement of ethical standards for manuscripts submitted to your journal. The attached are the required submission items. We believe that the results from this manuscript fit the field readers’ interests and warrant for a publication in “medRxiv” as a full length article. Please consider our manuscript seriously and feedback us with valuable scientific critiques. Thank you very much.

Sincerely yours,

Chen-guang Zhang, Ph.D.

School of Basic Medical Sciences

Capital Medical University, Beijing, China.

E-mail: [email protected]

Di-ya Wang, Ph.D.

Department of Radiation Oncology, University of California, San Francisco, CA, United States.

E-mail: [email protected].

Zu-hua Gao, MD.

Department of Pathology, Research Institute of McGill University Health Center, Montreal, QC,



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Canada.


Bang-wei Cao, MD.

Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing,

100050, China.




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Research in context

Evidence before this study

We searched Pubmed, Embase and MedRxiv databases for cohort

studies and clinical trials of evaluating prevalence of cancer patients with

COVID-19 as well as risks of severe clinical events including ICU

admission and death in those patients between Dec 1st, 2019 and May 3rd,

2020. Meanwhile, preliminary comparisons concerning cancer prevalence

and risks for severe events among different countries were made.

Furthermore, risks for ICU admission and death were also made between

cancer patients with patients suffering other comorbidities including

diabetes, hypertension, cardiovascular and respiratory diseases.

Conclusions of previous similar meta-analyses were limited due to small

sample sizes, which also did not make comparisons among different

countries or territories. Meanwhile, the relative risks of severe events for

cancer over other underlying diseases for COVID19 patients were largely

unknown. Our search did not retrieve any systematic review of

information on prevalence and risks of severe events of cancer patients in

COVID19 infection.

Added value of this study

We did a systematic review of 32 studies comprising 21,248 cases

with confirmed COVID-19. To our knowledge, this is the largest and

most comprehensive meta-analysis to date on the prevalence of cancer



https://doi.org/10.1101/2020.06.23.20136200


patients with COVID19 infection and their risks of ICU admission or

death. According to our analyses, the total prevalence of cancer in

COVID-19 patients was 3.97% worldwide. In China, the overall cancer

prevalence of COVID-19 patients was 2.59%, and in Wuhan, the city

where COVID-19 was first reported, the prevalence reached 3.79% while

2.31%in other areas outside Wuhan in China. The prevalence was higher

in Italy and USA, reaching 11.35% and 6.09%, respectively. The

incidence of ICU admission in cancer patients with COVID-19 was

26.80% worldwide, and the mortality was 24.32% (95% CI, 13.95% to

38.91%) according to the data available from Wuhan and European

countries. The fatality in COVID patients with cancer was lower than

those with cardiovascular disease (OR 0.49; 95% CI, 0.34 to 0.71;

p=0.39), but comparable with other comorbidities such as diabetes,

hypertension, and respiratory diseases. The ICU admission rate was close

with each other in cancer patients and patients with other comorbidities

mentioned above in COVID19 infection.

Implications of all the available evidence

Our findings based on larger population provide some clarification in

multiple aspects and have implications for cancer patients, oncology

clinicians as well as policy makers all around the world, especially in

COVID19 outbreak countries. The prevalence of cancer in COVID19 is

higher than cancer rates in total population. Special attention should paid



https://doi.org/10.1101/2020.06.23.20136200


on cancer patients during the pandemic, and more strict quarantine

measures, longer social distances are applicable to cancer patients, and

more appropriate personal protective equipment (eg, face masks) should

be provided for them, especially in circumstances of high risks. Besides

high prevalence, high rates of ICU admission and death were also

observed in cancer patients with COVID19, possibly resulting from

infection itself or a delay of timely medication for cancer. Thus, the

balance between protection from COVID19 infection and cancer

progression due to inadequate or delayed medication should be well

handled for oncologists and health policy makers. The ICU admission

rate of cancer patients is similar to common chronic diseases like diabetes,

hypertension, cardiovascular and respiratory diseases. However, the

mortality of cardiovascular disease patients with COVID19 is higher than

patients with cancer, diabetes, hypertension and respiratory disease. Thus,

the policies of medical resource allocation were supposed to include

cancer as one priority, at least equal to other common chronic diseases.



https://doi.org/10.1101/2020.06.23.20136200


prevalence and risks of severe events for cancer patients ...€¦ · 23/06/2020 · prevalence...

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