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Original Investigation | Oncology
Differences in Outcomes and Factors Associated With Mortality Among Patients
With SARS-CoV-2 Infection and Cancer Compared With Those Without Cancer
A Systematic Review and Meta-analysis
Emma Khoury, MRes; Sarah Nevitt, BSc, MSc, PhD; William Rohde Madsen, BSc; Lance Turtle, BSc, MBBS, PhD;
Gerry Davies, MSc, BMBS, PhD; Carlo Palmieri, BSc, MBBS, PhD
Abstract
IMPORTANCE SARS-CoV-2 infection has been associated with more severe disease and death in
patients with cancer. However, the implications of certain tumor types, treatments, and the age and
sex of patients with cancer for the outcomes of COVID-19 remain unclear.
OBJECTIVE To assess the differences in clinical outcomes between patients with cancer and SARS-
CoV-2 infection and patients without cancer but with SARS-CoV-2 infection, and to identify patients
with cancer at particularly high risk for a poor outcome.
DATA SOURCES PubMed, Web of Science, and Scopus databases were searched for articles
published in English until June 14, 2021. References in these articles were reviewed for
additional studies.
STUDY SELECTION All case-control or cohort studies were included that involved 10 or more
patients with malignant disease and SARS-CoV-2 infection with or without a control group (defined
as patients without cancer but with SARS-CoV-2 infection). Studies were excluded if they involved
fewer than 10 patients, were conference papers or abstracts, were preprint reports, had no full text,
or had data that could not be obtained from the corresponding author.
DATA EXTRACTION AND SYNTHESIS Two investigators independently performed data extraction
using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting
guideline. Meta-analysis was performed using a random-effects model.
MAIN OUTCOMES AND MEASURES The difference in mortality between patients with cancer and
SARS-CoV-2 infection and control patients as well as the difference in outcomes for various tumor
types and cancer treatments. Pooled case fatality rates, a random-effects model, and random-effects
meta-regressions were used.
RESULTS A total of 81 studies were included, involving 61 532 patients with cancer. Among 58 849
patients with available data, 30 557 male individuals (52%) were included and median age ranged
from 35 to 74 years. The relative risk (RR) of mortality from COVID-19 among patients with vs without
cancer when age and sex were matched was 1.69 (95% CI, 1.46-1.95; P< .001; I
2
= 51.0%). The RR of
mortality in patients with cancer vs control patients was associated with decreasing age (exp [b],
0.96; 95% CI, 0.92-0.99; P= .03). Compared with other cancers, lung cancer (RR, 1.68; 95% CI, 1.45-
1.94; P< .001; I
2
= 32.9%), and hematologic cancer (RR, 1.42; 95% CI, 1.31-1.54; P< .001; I
2
= 6.8%)
were associated with a higher risk of death. Although a higher point estimate was found for
genitourinary cancer (RR, 1.11; 95% CI, 1.00-1.24;P=.06;I2=21.5%),thefinding was not statistically
(continued)
Key Points
Question What are the clinical
outcomes for patients with both cancer
and SARS-CoV-2 infection?
Findings In this systematic review and
meta-analysis of 81 studies involving
61 532 patients with cancer, patients
who were younger, had lung cancer, or
had hematologic cancer were at an
increased risk of mortality from
COVID-19. Among anticancer
treatments, chemotherapy was
associated with the highest mortality
risk and endocrine therapy was
associated with the lowest risk.
Meaning Findings of this study suggest
that younger patients with cancer are a
high-risk population for poor outcomes
from COVID-19.
+Supplemental content
Author affiliations and article information are
listed at the end of this article.
Open Access. This is an open access article distributed under the terms of the CC-BY License.
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Abstract (continued)
significant. Breast cancer (RR, 0.51; 95% CI, 0.36-0.71; P< .001; I
2
= 86.2%) and gynecological
cancer (RR, 0.76; 95% CI, 0.62-0.93; P= .009; I
2
= 0%) were associated with a lower risk of death.
Chemotherapy was associated with the highest overall pooled case fatality rate of 30% (95% CI,
25%-36%; I
2
= 86.97%; range, 10%-100%), and endocrine therapy was associated with the lowest
at 11% (95% CI, 6%-16%; I
2
= 70.68%; range, 0%-27%).
CONCLUSIONS AND RELEVANCE Results of this study suggest that patients with cancer and SARS-
CoV-2 infection had a higher risk of death than patients without cancer. Younger age, lung cancer,
and hematologic cancer were also risk factors associated with poor outcomes from COVID-19.
JAMA Network Open. 2022;5(5):e2210880.doi:10.1001/jamanetworkopen.2022.10880
Introduction
Individuals with cancer are prone to respiratory viruses because of immunosuppression from either
the underlying disease or therapy. This susceptibility has been demonstrated with influenza, which is
associated with an increased mortality rate in patients with solid and hematologic cancer.
1,2
Furthermore, rhinovirus, if present before hematopoietic cell transplant, is associated with a
substantial increase in mortality.
3
With the emergence of the SARS-CoV-2 pandemic, there has been
an intense global effort to understand the impact of infection with SARS-CoV-2 and the outcomes
of COVID-19 for patients with cancer.
Understanding the possible risks, consequences, and complications of SARS-CoV-2 infection is
important for patients, family, and health care systems. For patients and their families, such
information enables informed decisions involving the risks of undergoing anticancer treatment
during the pandemic and the degree to which they should limit social and familial interactions. For
health care systems, these data are vital for informing decisions regarding treatment risk, protecting
patients with cancer, prioritizing a heterogeneous population by cancer type and treatment,
implementing preventive measures, and providing antiviral treatments. Such information is also vital
in planning the response to future pandemics.
The first large population-level data on outcomes of patients with COVID-19 were released by
the International Severe Acute Respiratory and Emerging Infections Consortium World Health
Organization Clinical Characterization Protocol UK.
4
The data revealed that 10% of the 20 133
patients who were hospitalized with COVID-19 had a history of malignant neoplasm.
4
A significant
increase in hospital mortality was reported in those patients (hazard ratio [HR], 1.13; 95% CI,
1.02-1.24; P= .02).
4
Meanwhile, population-level data from the Intensive Care National Audit and
Research Centre indicated that a lower proportion of patients with cancer were admitted to the
intensive care unit compared with patients with other viral pneumonias (non–COVID-19) (2.5% vs
5.8%).
5
If admitted to critical care, individuals with immunosuppressed systems had an increased
likelihood of death.
6
Numerous cancer-specific studies have been undertaken as part of the effort to understand the
consequences of COVID-19 in patients with cancer. These studies found that patients with cancer
and SARS-CoV-2 infection have a more severe disease course, with older patients with cancer
7-27
and
those with hematologic cancer reported to be at a particularly high risk.
7,8,11,23,28-33
However, many
of these studies reported disparate results, particularly regarding the association of outcomes with
cancer type and recent cancer treatment.
10,15,23,34,35
These studies varied in size and nature and were
limited by a lack of or small comparator groups of patients without cancer
12,14,16,18,19,21,23,26,29,36-45
as well as by selectivity in which tumor types or cancer therapies were included in their
analysis.
9,11,18,23,25,29,32,40,43,46-59
The lack of a contemporaneous age- and sex-matched population
without cancer, variability in data collection and reporting, and variation in follow-up times also
limited the published cohort studies. In some studies that examined a cohort without cancer,
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historical patients or registry data were used.
36
Given all of these factors, confounding biases may be
present because of unmeasured confounders.
In the current systematic review and meta-analysis of the available published data, we aimed to
assess the differences in clinical outcomes between patients with cancer and SARS-CoV-2 infection
and patients without cancer but with SARS-CoV-2 infection, and to identify patients with cancer at
particularly high risk for a poor outcome. Such research and information, we believe, will further the
understanding of the implications of the SARS-CoV-2 pandemic and the possible novel risk factors
for poor outcome in this patient population that have not been identified in previous cohort studies.
Methods
Search Strategy and Literature Search
We conducted a systematic review and meta-analysis of the published literature and followed the
Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting
guideline.
60
Repeated searches of PubMed, Web of Science, and Scopus databases were performed
for articles that were published until June 14, 2021. References in these articles were also reviewed
to check for other relevant studies, with duplicate publications identified and removed. Search
strategies and results from the literature search are shown in eTables 1 to 3 in the Supplement.
Study Selection
Two of us (E.K. and C.P.) independently screened all titles and abstracts after the initial
de-duplication. The inclusion criteria were (1) any case-control or cohort study with or without a
control group (defined as patients without cancer but with SARS-CoV-2 infection) that (2) was
published in English as a full-text article and (3) involved patients with cancer and confirmed or
suspected SARS-CoV-2 infection or COVID-19 and (4) described 1 or more of their incidences,
presentations, management, or outcomes.
We excluded research with fewer than 10 patients, conference papers or abstracts, preprint
reports, articles with full text that could not be extracted, studies with data that could not be
obtained from the corresponding author, and animal studies. Among studies that reported
overlapping data sets, we selected those with the largest and most up-to-date cohorts. Discrepancies
were resolved by consensus.
Data Extraction and Quality Assessment
Two of us (E.K. and C.P.) independently extracted the following data: first author, study type, period
of data collection, country of data collection, number of male and female patients, median or mean
age, cancer treatment intervals before COVID-19 diagnosis or hospitalization, unadjusted and
adjusted odds ratios or HRs for severe disease and death for each cancer type and for each cancer
treatment type, and the number of patients with cancer and SARS-CoV-2 infection and their cancer
type. Study-level data on race and ethnicity are provided in eResults in the Supplement. Insufficient
data on race and ethnicity were available and thus were not incorporated into this meta-analysis.
The quality of the included studies was assessed using the Newcastle-Ottawa scale for case-
control and cohort studies (eFigure 1 in the Supplement).
61
Publication bias across studies was
assessed through visual inspection of a funnel plot for asymmetry (eFigure 2 in the Supplement).
Outcomes and Statistical Analysis
The main outcome of interest was the difference in mortality. We performed a meta-analysis to
compare mortality in patients with cancer and SARS-CoV-2 infection vs control patients and in
patients with cancer and a tumor type vs patients with cancer without that tumor type. Results of
this meta-analysis were presented as pooled risk ratios (RRs) with 95% CIs.
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We also performed meta-analyses to pool case fatality rates by tumor type and by type of
cancer treatment among patients with cancer. Results of these meta-analyses were presented as
pooled proportions and 95% CIs.
Clinical heterogeneity was assessed by examining study design, patient characteristics,
outcome definitions, and study quality in all included studies. Any important differences between
studies with regard to design, patient population, outcome definitions, and quality were described.
Between-study statistical heterogeneity was quantified according to random-effects heterogeneity
parameter tau, and I
2
statistics (defined as the percentage of the variability in effect estimates owing
to statistical heterogeneity rather than sampling error) were calculated for all meta-analyses.
Given that statistical heterogeneity was anticipated owing to the expected variability in study
design and participant characteristics, all meta-analyses were conducted using a random-effects
model, with restricted maximum likelihood to estimate between-studies heterogeneity. Statistical
heterogeneity was quantified using the I
2
statistic. To examine the implications of age and sex for
mortality among patients with cancer and control patients, random-effects meta-regressions were
conducted.
Meta-analyses and meta-regressions were performed with the admetan,
62
metaprop,
63
and
metareg
64
commands in Stata, version 14.1 (StataCorp LLC). Additional figures of study
characteristics were produced in R, version 4.0.4 (R Foundation for Statistical Computing) (eFigures
4 to 7, 10, 12, 14, and 16 in the Supplement). A ztest was used to compare 2 independent groups. A
2-sided P< .05 indicated statistical significance.
Results
The initial search retrieved 1150 articles for review (eFigure 3 in the Supplement). After the inclusion
of records that were identified through additional sources and the removal of duplicate articles, 1004
records were screened. We obtained 215 articles to assess for eligibility. Of these, 134 were excluded
(eFigure 3 in the Supplement). A total of 81 studies were included in this systematic review and meta-
analysis.
Global Distribution of Studies
The 81 studies involved 61 532 patients with cancer and SARS-CoV-2 infection (eFigure 4 and eTable 4
in the Supplement) and consisted of 61 retrospective
7,8,10-14,16-25,29-32,34,36-45,48,49,53-55,57-59,65-85
;
17 prospective
4,26-28,33,35,46,50-52,56,86-91
; and 3 retroprospective
9,15,47
(wherein data were collected
on both patients who were eligible before study commencement and those who entered after study
commencement) studies. The studies originated from 28 countries and 5 continents (eFigure 5,
eFigure 6, and eTable 5 in the Supplement). Eighty studies provided recruitment information by
country, and the following 5 countries had the highest numbers of recruited patients: US, UK, Italy,
France, and China.
Patient Characteristics and Cancer Types
Ten of 81 studies (12%) had cohorts that included both patients with cancer and SARS-CoV-2
infection and control patients
4,65,72,75,79,80,84,86,89,91
; however, most studies (n = 71 [88%]) reported
on cohorts of patients with cancer only. The number of patients with cancer in the 81 articles ranged
from 11 to 38 614. Where data were available, the cohorts comprised 52% male (n = 30 557 of
58 849) and 48% female (n = 28 269 of 58 849) patients, with a median age ranging from 35 to 74
years. The most frequently reported comorbid conditions were hypertension, diabetes,
cardiovascular disease, and pulmonary disease (eFigures 7 to 10 in the Supplement). Most patients
(34 117 [55%]) were hospitalized, and the rest of the patients were outpatients or unknown
(eFigure 11 and eTable 6 in the Supplement).
Most studies (55 of 81 [68%]) included patients with both solid and hematologic cancer
(n = 55 668),
4,7,8,10,12-16,19-22,24,26-28,30,31,33-39,41,42,44,45,65,66,68-76,78-91
and 18 studies (22%) focused
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on patients with hematologic cancer alone (n = 2526).
11,17,18,23,25,29,32,40,43,50,51,53,54,56,58,59,67,77
Of
these 18 studies, 4 reported exclusively on multiple myeloma
18,50,53,59
and 2 on chronic lymphocytic
leukemia.
17,67
Eight studies (10%) focused on patients with solid malignant neoplasms
(n = 3338),
9,46-49,52,55,57
of which 2 reported on thoracic cancers,
46,49
2 on gynecological
cancers,
48,57
and 2 on breast cancer.
52,55
Tumor type was reported in 68 of 81 studies (84%) involving 43 676 patients (eTable 7 in the
Supplement).
7-40,42-59,66-70,73,74,76-78,81-83,85,87,90
The most frequent cancer types were hematologic
cancer, representing 9672 patients, followed by breast (8322 patients), genitourinary (7624
patients), skin and melanoma (6613 patients), gastrointestinal (4124 patients), and thoracic (2104
patients) cancers. For 1139 patients, the tumor type was documented as other or unknown (eFigures
10 and 12 in the Supplement).
Presenting Symptoms of Infection and Radiological Findings
Fifty-three of 81 studies (65%) reported presenting symptoms in patients with cancer and SARS-
CoV-2 infection, with fever and cough being the most commonly reported symptoms (eFigure 13 in
the Supplement).
9-11,13-15,17,19-21,23-25,27,28,31-35,37,41-43,45,46,48-50,52-58,66-70,74,76-78,81-85,87,88,90
In
addition, 27 studies (33%) reported on radiological findings in patients with cancer and SARS-CoV-2
infection (eTable 8 in the Supplement).
10,13,14,17,25,31-33,35,39,42,43,45,50,52,54-58,66,69,78,81,84,85,87
Both
symptoms and radiological findings are summarized in the eResults in the Supplement.
Mortality of Patients With Cancer vs Control Patients
Nineteen of 81 studies (24%) compared patients with cancer (n = 3926) and SARS-CoV-2 infection
with control patients (n = 38 847).
12,14,16,18,19,21,23,26,29,36-45
The details of these 19 studies, including
the matching of the 2 cohorts and the nature of the control patients, are described in the Table.No
obvious asymmetry across these studies was observed when assessing for publication bias (eFigure 2
in the Supplement).
We conducted a meta-analysis of these 19 studies. The pooled relative risk (RR) of mortality in
patients with cancer and SARS-CoV-2 infection compared with control patients was 2.12 (95% CI, 1.71-
2.62; P< .001; I
2
=84.4%)(Figure 1A). When pooling the results, the RR for mortality in 13 studies
that matched for age decreased to 1.69 (95% CI, 1.46-1.95; P< .001; I
2
= 51.0%) compared with 3.80
(95% CI, 2.53-5.71; P< .001; I
2
= 81.9%) from 6 studies without matching (Figure 1B). There was little
difference in the pooled RR of mortality between patients with any type of malignant neoplasm, solid
or hematologic (RR, 2.23; 95% CI, 1.68-2.95; I
2
= 88.7%), and hematologic cancer (RR, 1.81; 95% CI,
1.53-2.15; I
2
= 0.0%) alone vs control patients (eFigure 14 in the Supplement).
Fourteen studies provided data on the median or mean age of patients with cancer and SARS-
CoV-2 infection and control patients.
14,16,18,19,21,23,26,29,39-43,45
On univariate regression, when
assessing the association of age with mortality in patients with cancer vs control patients, the RR of
mortality statistically significantly decreased as age increased (exp [b], 0.96; 95% CI, 0.92-0.99;
P= .03), showing a greater difference in the RR of mortality between patients with cancer and
control patients of younger age (Figure 2A). Seventeen studies reported the sex (as proportion of
male patients) of both patients with cancer and control patients.
14,16,18,19,21,23,26,29,37-45
A small
increase in RR of mortality between patients with cancer and control patients was shown as the
proportion of male patients in the study increased, but this finding was not statistically significant
(exp [b], 1.19; 95% CI, 0.22-6.37; P= .83) (Figure 2B). When the combined impact of age and
proportion of male patients was explored by multivariable regression, age remained significant (exp
[b], 0.95; 95% CI, 0.91-0.99; P= .03), but male sex was not significant (exp [b], 3.5; 95% CI, 0.04-
300.71; P= .55) (eTable 9 in the Supplement).
Clinical Outcomes
Outcome data were available for 56 932 patients with cancer and SARS-CoV-2 infection from 68 of
the 81 studies (84%).
7-29,31-59,66-70,74,75,77,78,81-84,87,89,90
Where reported, 7% of patients (1170 of
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16 409) were admitted to the intensive care unit, and 5% of patients (2817 of 54 298) required
invasive mechanical ventilation (eTable 10 in the Supplement). At the time of reporting, 11% of
patients (473 of 4403) remained hospitalized, and 65% of patients (2841 of 4403) were discharged.
Median duration of hospital stay is shown in eTable 11 in the Supplement. Of the 56 932 patients with
cancer and SARS-CoV-2 infection, 12% died (6813). Median follow-up times varied, ranging from 5
to 69 days. Mortality was reported in all studies and ranged from 4% to 61% (eFigure 15 and eTable 12
in the Supplement). One study defined mortality as either transfer to hospice or death.
22
Fourteen different definitions of severe events were used across the studies (eFigure 16 in the
Supplement). In 12 studies, information on ethnicity and outcomes was reported
7,8,13,23,48,53,54,56,
59,70,84,89
(eResults in the Supplement). In unadjusted analyses, several factors were found to be
associated with severe events or death. These factors included increasing age (reported in 36
studies
9-11,13-21,24,25,27,30-33,37,40,42-44,46-49,52,54,58,68,69,77,87,88
) as well as increased levels of
Table. Outcome of PatientsWith or Without Hematologic Cancer in 19 Studie s
Source Comparison
a
No.
Features
Patients with
cancer and SARS-
CoV-2 infection
(n = 3926)
Deaths
(n = 774)
Control patients
(n = 38 847)
a
Deaths
(n = 2594)
Yigenoglu et al,
29
2021
b
Control patients matched by age,
sex, and comorbidities
740 102 740 50 Patient characteristics and
outcomes
Johannesen et al,
36
2021 Control patients 547 56 7841 158 Patient characteristics and
outcomes
Rüthrich et al,
37
2021 Control patients matched by age 435 97 2636 367 Patient characteristics and
outcomes
Montopoli et al,
38
2020 Control patients 430 75 4532 313 Patient characteristics and
outcomes
Miyashita et al,
12
2020 Control patients matched by age 334 37 5354 518 Patient outcomes
Lunski et al,
44
2021 Control patients (Ochsner Health
System)
157 56 1460 372 Patient characteristics,
laboratory markers, and
outcomes
Tian et al,
14
2020 Control patients matched 1:2 by
propensity score
232 46 519 56 Patient characteristics,
laboratory markers, and
outcomes
Mehta et al,
16
2020 Control patients matched 1:5 by
propensity score, age, and sex
218 61 1090 149 Patient characteristics and
outcomes
Martínez-López et al,
18
2020
b
Control patients matched by age
and sex
167 56 167 38 Patient characteristics,
laboratory markers, and
outcomes
Brar et al,
19
2020 Control patients matched 1:4 by
age, sex, and comorbidities
117 29 468 100 Patient characteristics and
outcomes
Meng et al,
39
2020 Control patients matched 1:3 by
propensity score
109 32 327 40 Patient characteristics,
laboratory markers, and
outcomes
Dai et al,
21
2020 Control patients matched by age 105 12 536 21 Patient characteristics and
outcomes
Cattaneo et al,
40
2020
b
Control patients matched by age,
sex, comorbidities, and
respiratory failure
102 40 102 24 Patient characteristics and
outcomes
Shah et al,
23
2020
b
Control patients matched by age
and sex
80 31 1115 223 Patient characteristics and
outcomes
Sun et al,
41
2021 Control patients 67 9 356 4 Patient characteristics and
outcomes
Joharatnam-Hogan et al,
45
2020
Control patients matched by age,
sex, and comorbidities
30 11 90 32 Patient characteristics,
laboratory markers, and
outcomes
Stroppa et al,
42
2020 Control patients matched by age,
sex, pneumonia, and antiviral
treatment
25 9 31 5 Patient characteristics and
outcomes
Liang et al,
26
2020 Control patients 18 7 1572 124 Patient characteristics and
outcomes
He et al,
43
2020
b
Health care workers without
cancer but with COVID-19
13 8 11 0 Patient characteristics,
laboratory markers, and
outcomes
a
Control patients were defined as patients without cancer but with SARS-CoV-2
infection.
b
Study that examined patients with hematologic cancer.
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Figure 1. Forest Plot of RelativeRisk (RR) of Mor tality
Weight, %Study RR (95% CI)
Higher
mortality
for patients
Higher
mortality
for control
group
Brar et al,19 2020
Dai et al,21 2020
Cattaneo et al,40 2020
He et al,43 2020
Johannesen et al,36 2021
REML overall (I2
=
84.4%)
Unadjusted RR of mortality from presence of malignant disease
A
201010.5
RR (95% CI)
Joharatnam-Hogan et al,45 2020
Liang et al,26 2020
Lunski et al,44 2021
Martínez-López et al,18 2020
Mehta et al,16 2020
Meng et al,39 2020
Miyashita et al,12 2020
Montopoli et al,38 2020
Rüthrich et al,37 2021
Shah et al,23 2020
Stroppa et al,42 2020
Sun et al,41 2021
Tian et al,14 2020
Yigenoglu et al,29 2021
Weight, %Study RR (95% CI)
Higher
mortality
for patients
Higher
mortality
for control
group
Matched characteristics: yes
Brar et al,19 2020
Cattaneo et al,40 2020
Dai et al,21 2020
REML subtotal (I2
=
51.0%)
RR of mortality from presence of malignant disease, after matching for age and sex
B
201010.5
RR (95% CI)
Joharatnam-Hogan et al,45 2020
Martínez-López et al,18 2020
Mehta et al,16 2020
Meng et al,39 2020
Matched characteristics: no
He et al,43 2020
Johannesen et al,36 2021
Liang et al,26 2020
Lunski et al,44 2021
Montopoli et al,38 2020
Sun et al,41 2021
Miyashita et al,12 2020
Rüthrich et al,37 2021
Shah et al,23 2020
REML subtotal (I2
=
81.9%)
REML overall (I2
=
84.4%)
Stroppa et al,42 2020
Tian et al,14 2020
Yigenoglu et al,29 2021
1.60 (1.31-1.96)
5.08 (3.79-6.81)
1.84 (1.28-2.63)
2.45 (1.94-3.09)
2.05 (1.58-2.65)
8.60 (2.73-27.06)
1.94 (1.44-2.61)
2.23 (0.86-5.82)
4.93 (2.70-9.01)
1.47 (1.04-2.09)
14.57 (0.94-227.02)
1.67 (1.09-2.55)
2.04 (1.48-2.82)
1.14 (0.84-1.57)
2.40 (1.59-3.62)
2.92 (1.48-5.74)
1.03 (0.60-1.78)
1.16 (0.81-1.66)
2.53 (2.00-3.18)
6.74
6.31
5.96
6.60
6.47
2.35
6.28
2.95
4.57
6.00
0.56
5.58
6.16
6.20
5.66
4.17
4.89
5.95
6.60
2.12 (1.71-2.62) 100.00
2.12 (1.71-2.62)
3.80 (2.53-5.71)
14.57 (0.94-227.02)
1.94 (1.44-2.61)
1.84 (1.28-2.63)
4.93 (2.70-9.01)
1.14 (0.84-1.57)
8.60 (2.73-27.06)
2.23 (0.86-5.82)
2.45 (1.94-3.09)
2.04 (1.48-2.82)
5.08 (3.79-6.81)
1.67 (1.09-2.55)
1.69 (1.46-1.95)
2.05 (1.58-2.65)
2.40 (1.59-3.62)
1.60 (1.31-1.96)
1.03 (0.60-1.78)
1.47 (1.04-2.09)
1.16 (0.81-1.66)
2.92 (1.48-5.74)
2.53 (2.00-3.18)
100.00
26.99
0.56
6.28
5.96
4.57
6.20
2.35
2.95
6.60
6.16
6.31
5.58
73.01
6.47
5.66
6.74
4.89
6.00
5.95
4.17
6.60
Weights were calculated using random-effects
analysis. REML indicates restricted maximum
likelihood.
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proinflammatory markers (reported in 14 studies
14,16,23,35,43,53,58,66,69,74,76,77,85,87
) and infection-
related markers (reported in 14 studies
14,23,25,31 ,42,47,58,68,69,74,77,85,87,88
) (eFigure 17 in the
Supplement). Factors that were found in adjusted analysis to be associated with worsening severity
or mortality are shown in eFigure 17 in the Supplement; 22 studies found increasing age to be
associated with worsening severity or death.
7-27,47
Factors included in adjusted analysis are listed in
eFigure 18 in the Supplement.
Mortality and Case Fatality Rate by Cancer Type
Forest plots for pooled case fatality rate for each cancer type are shown in eFigure 19 in the
Supplement. The association of the stage of malignant neoplasm with clinical outcomes is
summarized in the eResults in the Supplement. There was a pooled case fatality rate for patients with
breast cancer and SARS-CoV-2 infection of 9% (95% CI, 7%-12%; I
2
= 89.8%; range, 0%-100%), with
an RR of mortality of 0.51 (95% CI, 0.36-0.71; P< .001; I
2
= 86.2%) compared with control patients
(eFigure 20 in the Supplement). The pooled case fatality rate for patients with gynecological cancers
and SARS-CoV-2 infection was 12% (95% CI, 8%-16%; I
2
= 38.47%; range, 0%-38%) and an
associated RR of 0.76 (95% CI, 0.62-0.93; P= .009; I
2
= 0%) compared with control patients
(eFigure 20 in the Supplement).
Gastrointestinal cancers in patients with SARS-CoV-2 infection were associated with a pooled
case fatality rate of 16% (95% CI, 12%-20%; I
2
= 78.66%; range, 0%-38%) and an RR of 1.13 (95% CI,
0.93-1.37; P= .21; I
2
= 54.8%) compared with control patients (eFigure 20 in the Supplement). Skin
cancer in patients with SARS-CoV-2 infection was associated with a pooled case fatality rate of 10%
(95% CI, 5%-15%; I
2
= 62.57%; range, 5%-50%) and an RR of mortality of 0.85 (95% CI, 0.60-1.20;
P= .35; I
2
= 51.4%) (eFigure 20 in the Supplement).
The pooled case fatality rate for patients with lung cancer and SARS-CoV-2 infection was 30%
(95% CI, 24%-37%; I
2
= 83.71%; range, 0%-60%). The RR of mortality in those with lung cancer
compared with other cancer types was significantly higher at 1.68 (95% CI, 1.45-1.94; P< .001;
I
2
= 32.9%) (eFigure 20 in the Supplement). The pooled case fatality rate for patients with
genitourinary cancers and SARS-CoV-2 infection was 22% (95% CI, 16%-27%; I
2
= 92.61%; range,
8%-50%), with an RR of mortality of 1.11 (95% CI, 1.00-1.24; P= .06; I
2
= 21.5%) compared with
control patients (eFigure 20 in the Supplement). The pooled case fatality rate for patients with
hematologic cancer and SARS-CoV-2 infection was 32% (95% CI, 28%-37%; I
2
= 93.10%; range,
11%-100%). The overall RR of mortality in patients with hematologic cancer and SARS-CoV-2
infection compared with those with solid malignant neoplasms was 1.42 (95% CI, 1.31-1.54; P< .001;
I
2
= 6.8%) (eFigure 20 in the Supplement).
Figure 2. Meta-regression Bubble Plot of Association of Mortality With Age and Sex of Patients vs Control Group
3
2
1
0
log Risk ratio
Age, y
Risk ratio of mortality by mean or median age
A
35 40 45 50 55 60 65 70 75
3
2
1
0
log Risk ratio
Male individuals, %
Risk ratio of mortality by male sex
B
40 50 60 70 80 90 100
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Cancer Treatment and Course of COVID-19
Data on timing of cancer treatment and outcome were available in 64 of 81 studies
(79%).
7-11,13-22,24-28,30-40,42-56,58,59,66-70,73,74,76-78,81-83,85,87,88
These studies comprised 54 335
patients, of whom 7567 (14%) had undergone or received some form of treatment for their
malignant disease. Where data were available, the most common treatment modality was
chemotherapy (3792 patients), followed by targeted therapy (1700 patients) and immunotherapy
(817 patients). A total of 3896 patients had not received any form of treatment. The pooled case
fatality rates for various cancer treatments is shown in eTable 13 in the Supplement.
Surgery within 3 months of a COVID-19 diagnosis in patients with cancer was associated with a
pooled case fatality rate of 19% (95% CI, 9%-29%; I
2
= 58.49%; range, 0%-40%) (Figure 3A). In
general, these studies examined all patients, aside from 1 study that was focused on patients who
underwent surgery for gynecological malignant neoplasms.
48
Patients with cancer who had received
chemotherapy and had a COVID-19 diagnosis had an overall pooled case fatality rate of 30% (95%
CI, 25%-36%; I
2
= 86.97%; range, 10%-100%) (Figure 3B), whereas patients who had endocrine
therapy had a pooled case fatality rate of 11% (95% CI, 6%-16%; I
2
= 70.68%; range, 0%-27%)
(Figure 3C). None of the 9 studies specified which cancer type was treated with endocrine
therapy.
7,9,27,30,38,44,47,69,85
Immunotherapy was associated with a pooled case fatality rate of 19% (95% CI, 13%-25%;
I
2
= 36.98%; range, 0%-50%) (Figure 4A). One of the immunotherapy studies specified the tumor
type involved (lung cancer
49
), but the remaining immunotherapy studies did not specify the tumor
type.
7,9,16,21,27,30,34,42,44,45,66,69,85
Data on mortality after radiotherapy were provided in 9
studies,
9,16,21,27,30,34,44,66,85
and radiotherapy was associated with a pooled case fatality rate of 23%
(95% CI, 12%-33%; I
2
= 74.84%; range, 0%-50%) (Figure 4B). The tumor type treated
(gynecological cancer) was specified in only 1 study.
48
Targeted therapy was associated with an
overall risk of mortality of 18% (95% CI, 12%-23%; I
2
= 59.72%; range, 0%-57%) (Figure 4C). The
nature of the targeted therapy was not specified in most cases, and the specific cancer type was
stated in only 4 of 19 studies (21%), of which 2 involved patients with hematologic cancer,
25,58
1
involved patients with lung cancer,
49
and 1 involved patients with gynecological cancer and SARS-
CoV-2 infection.
48
Discussion
Population data at the start of the SARS-CoV-2 pandemic rapidly identified patients with cancer as a
group with poor outcomes.
4
Subsequent reports on SARS-CoV-2 infection in patients with cancer
have ranged from small series
26,43,57,58,81,82,90
to larger registry-based collaborative
studies
4,7,37,46,47,92,93
and have varied in geographical location and tumor type focus (eTable 4 in the
Supplement). In this systematic review and meta-analysis, we analyzed the important global effort
by the cancer research community by reviewing all of the available and published data as of June 14,
2021. Our objective was to assess the clinical outcomes among patients with cancer and SARS-
CoV-2 infection vs the outcomes among control patients; we also aimed to identify patients with
cancer who are at high risk for a poor outcome.
This systematic review found a number of potential limitations with the available data and
literature, including (1) lack of contemporaneous populations without cancer for comparative
analysis
12,19,23,36,38,40
; (2) heterogeneity of definitions between studies, such as severity of COVID-19
as demonstrated by the 14 definitions of severity used across studies (eFigure 16 in the Supplement);
(3) predominantly retrospective nature of the studies (61 of 81) (eTable 4 in the Supplement); (4)
variable follow-up times; (5) heterogeneity or poor description of the control cohorts, such as
inclusion of patients who were not hospitalized
12,21,29,36-38,44
or health care workers with
COVID-19
43
; and (6) lack of detail on the systemic cancer treatment used.
4,11,12,22,29,33,37,39,41,42,50,52,
55-57,65,70-72,75,76,79-81,84,86-91
In studies with a control cohort, the data were generally historical or
based on registry data
36,37
or were not contemporaneous with the cancer cohort.
43
Only 3 of the 19
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Figure 3. Forest Plot of OverallCase Fatality Rate for Surgery, Chemotherapy, and Endocrine Therapy
Weight, %Study ES (95% CI)
Passamonti et al,11 2020
Sanchez-Pina et al,25 2020
Lunski et al,44 2021
Song et al,85 2021
Özdemir et al,47 2020
Overall (I2
=
86.97%; P
<.001)
Case fatality rate in chemotherapy
B
Yang et al,66 2020
Mehta et al,16 2020
Grivas et al,7 2021
Dai et al,21 2020
Lattenist et al,58 2020
Luo et al,49 2020
Rogado et al,24 2020
Lièvre et al,9 2020
Stroppa et al,42 2020
Mehta et al,34 2021
Erdal et al,83 2021
Ferrari et al,27 2021
de Joode et al,30 2020
Joharatnam-Hogan et al,45 2020
Albiges et al,69 2020
Fox et al,54 2020
Cattaneo et al,40 2020
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
Weight, %Study ES (95% CI)
Montopoli et al,38 2020
Lièvre et al,9 2020
Song et al,85 2021
Albiges et al,69 2020
Grivas et al,7 2021
Overall (I2
=
70.68%; P
<.001)
Case fatality rate in endocrine therapy
C
Özdemir et al,47 2020
de Joode et al,30 2020
Ferrari et al,27 2021
Lunski et al,44 2021
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
5
4
56
42
8
31
25
0
0
12
17
2
4
6
0.19 (0.09-0.29)
0.00 (0.00-0.43)
0.00 (0.00-0.49)
0.21 (0.13-0.34)
0.40 (0.27-0.56)
0.25 (0.07-0.59)
0.13 (0.05-0.29)
0.24 (0.11-0.43)
100
11.48
9.30
20.27
16.69
7.77
19.33
15.16
1.6
2.0
4.3
12.0
7.6
16.7
10.1
69
19
112
31
42
19
205
26
27
32
17
14
385
60
4
80
802
25
8
3
59
153
13
7
18
15
8
6
20
9
10
8
2
4
126
31
4
17
144
12
3
2
20
49
0.30 (0.25-0.36)
0.19 (0.11-0.30)
0.37 (0.19-0.59)
0.16 (0.10-0.24)
0.48 (0.32-0.65)
0.19 (0.10-0.33)
0.32 (0.15-0.54)
0.10 (0.06-0.15)
0.35 (0.19-0.54)
0.37 (0.22-0.56)
0.25 (0.13-0.42)
0.12 (0.03-0.34)
0.29 (0.12-0.55)
0.33 (0.28-0.38)
0.52 (0.39-0.64)
1.00 (0.51-1.00)
0.21 (0.14-0.31)
0.18 (0.15-0.21)
0.48 (0.30-0.67)
0.38 (0.14-0.69)
0.67 (0.21-0.94)
0.34 (0.23-0.47)
0.32 (0.25-0.40)
100
5.74
3.44
6.16
4.13
5.24
3.56
6.52
4.01
4.02
4.62
4.56
3.15
6.45
5.08
2.79
5.79
6.65
3.78
2.04
0.99
5.20
6.07
37.1
3.8
67.1
15.1
15.6
28.9
13.5
25.5
38.0
12.9
16.2
46.7
30.0
100
10.3
40.4
16.1
96.4
32.0
23.1
3.6
43.4
31
169
48
483
57
4
4
51
16
2
6
13
47
10
0
0
7
3
0.11 (0.060.16)
0.06 (0.020.21)
0.04 (0.020.08)
0.27 (0.170.41)
0.10 (0.070.13)
0.18 (0.100.29)
0.00 (0.000.49)
0.00 (0.000.49)
0.14 (0.070.26)
0.19 (0.070.43)
100.00
13.18
21.69
8.86
21.86
11.64
2.86
2.86
12.16
4.88
15.6
11.1
13.7
9.7
4.4
1.6
0.9
16.3
9.6
0 0.75 1.000.50
ES (95% CI)
0.25
0 0.75 1.000.50
ES (95% CI)
0.25
0 0.75 1.000.50
ES (95% CI)
0.25
Weight, %Study ES (95% CI)
Lunski et al,44 2021
Lièvre et al,9 2020
Yang et al,66 2020
Dai et al,21 2020
Song et al,85 2021
Case fatality rate in surgery
A
de Joode et al,30 2020
Mehta et al,34 2021
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
Overall (I2
=
58.49%; P
=.02)
ES indicates effect size.
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Figure 4. Forest Plot of OverallCase Fatality Rate for Immunonotherapy, Radiotherapy, and Targeted Therapy
Weight, %Study ES (95% CI)
Song et al,85 2021
Mehta et al,16 2020
Lunski et al,44 2021
Yang et al,66 2020
Lièvre et al,9 2020
Overall (I2
=
36.98%; P
=.08)
Case fatality rate in immunotherapy
A
Grivas et al,7 2021
Dai et al,21 2020
Mehta et al,34 2021
Ferrari et al,27 2021
Albiges et al,69 2020
Stroppa et al,42 2020
de Joode et al,30 2020
Joharatnam-Hogan et al,45 2020
Luo et al,49 2020
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
0 0.75 1.000.50
ES (95% CI)
0.25
Weight, %Study ES (95% CI)
Lunski et al,44 2021
Ferrari et al,27 2021
Song et al,85 2021
Yang et al,66 2020
Lièvre et al,9 2020
Overall (I2
=
74.84%; P
<.001)
Case fatality rate in radiotherapy
B
Mehta et al,34 2021
Dai et al,21 2020
Mehta et al,16 2020
de Joode et al,30 2020
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
0 0.75 1.000.50
ES (95% CI)
0.25
Weight, %Study ES (95% CI)
Lunski et al,44 2021
Özdemir et al,47 2020
Mehta et al,34 2021
Dai et al,21 2020
Song et al,85 2021
Overall (I2
=
59.72%; P
<.001)
Case fatality rate in targeted therapy
C
Yang et al,66 2020
Lattenist et al,58 2020
Lièvre et al,9 2020
Luo et al,49 2020
Ferrari et al,27 2021
Sanchez-Pina et al,25 2020
Grivas et al,7 2021
de Joode et al,30 2020
Albiges et al,69 2020
Joharatnam-Hogan et al,45 2020
No. of
deaths
No. of
participants
Proportion
of patients
treated, %
0 0.75 1.000.50
ES (95% CI)
0.25
57
7
4
16
6
4
12
11
5
4
62
26
248
3
16
1
2
3
2
0
3
1
1
0
22
5
39
0
0.19 (0.13-0.25)
0.28 (0.18-0.41)
0.14 (0.03-0.51)
0.50 (0.15-0.85)
0.19 (0.07-0.43)
0.33 (0.10-0.70)
0.00 (0.00-0.49)
0.25 (0.09-0.53)
0.09 (0.02-0.38)
0.20 (0.04-0.62)
0.00 (0.00-0.49)
0.35 (0.25-0.48)
0.19 (0.09-0.38)
0.16 (0.12-0.21)
0.00 (0.00-0.56)
100
12.89
4.45
1.43
7.11
2.32
4.35
4.88
8.38
2.65
4.35
12.64
9.69
21.80
3.05
16.2
23.3
2.0
9.6
5.7
1.3
6.1
5.9
1.8
16.0
4.8
25.5
5.0
1.2
2
9
95
13
49
8
67
10
21
0
3
30
1
11
2
24
5
1
0.23 (0.12-0.33)
0.00 (0.00-0.66)
0.33 (0.12-0.65)
0.32 (0.23-0.41)
0.08 (0.01-0.33)
0.22 (0.13-0.36)
0.25 (0.07-0.59)
0.36 (0.25-0.48)
0.50 (0.24-0.76)
0.05 (0.01-0.23)
100.00
4.63
7.07
15.62
13.33
14.62
7.29
14.71
7.02
15.72
0.6%
4.4%
7.4%
12.4%
17.4%
4.0%
19.1%
4.0%
11.3%
30
1
114
56
55
5
22
7
12
10
12
4
693
6
13
2
0
28
17
5
2
2
4
2
1
6
0
104
2
4
0.18 (0.12-0.23)
0.07 (0.02-0.21)
0.00 (0.00-0.79)
0.25 (0.18-0.33)
0.30 (0.20-0.43)
0.09 (0.04-0.20)
0.40 (0.12-0.77)
0.09 (0.03-0.28)
0.57 (0.25-0.84)
0.17 (0.05-0.45)
0.10 (0.02-0.40)
0.50 (0.25-0.75)
0.00 (0.00-0.49)
0.15 (0.13-0.18)
0.33 (0.10-0.70)
0.31 (0.13-0.58)
100.00
11.80
0.81
12.63
9.43
12.88
1.52
9.45
2.01
4.91
5.83
3.13
3.52
16.36
1.91
3.79
18.0
7.7
8.8
16.0
3.6
12.8
11.1
23.3
4.8
9.8
5.9
3.8
14.0
3.2
2.9
ES indicates effect size.
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studies that compared patients with cancer and SARS-CoV-2 infection with control patients used
propensity score matching.
14,16,39
Therefore, within the current literature, biases from unmeasured
confounders may be present.
Analyzing 19 studies that involved 3926 patients with cancer and SARS-CoV-2 infection and
38 847 control patients, we found that malignant disease was associated with an increased risk of
severe COVID-19 or death compared with the risk in control patients (RR, 2.12; 95% CI, 1.76-2.62;
P< .001; I
2
= 84.4%). However, when patients were matched for age and sex, the risk decreased to
1.69 (95% CI, 1.46-1.95; P< .001; I
2
= 51.0%). This finding demonstrates a potential overestimation of
the true risk to patients with cancer in studies that did not adjust for age and sex. Furthermore, it
highlights the importance of a comparator control cohort in understanding the true implications of
SARS-CoV-2 infection within a population with cancer. No significant sex-based differences in
outcome were seen when patients with cancer and control patients were compared, in contrast to a
number of studies that have reported male sex as a risk factor.
7,10,18,22,23,28,31,66
However, the
proportion of male patients included in each study varied, which may introduce uncertainty to the
interpretation of the results of the meta-regression.
In the regression analysis, we found that younger age in patients with cancer and SARS-CoV-2
infection was associated with a worse clinical outcome than in age-matched control cohorts. To date,
all of the cohort studies, which by their nature lacked an age-matched control group, have
consistently reported increasing age as a risk factor for poor clinical outcome.
7-27
Although it is true
that older patients have worse absolute outcomes than younger patients, the RR data we found were
highest for younger patients. This observation has been reported within the International Severe
Acute Respiratory and Emerging Infections Consortium World Health Organization Clinical
Characterization Protocol UK.
92,93
A recent analysis of more than 20 000 patients with cancer vs
155 000 patients without cancer found that patients younger than 50 years, particularly those
receiving active cancer treatment, were 5 times more likely to die than patients without cancer of a
similar age (HR, 5.22; 95% CI, 4.19-6.52; P< .001).
93
Compared with patients without cancer, the RR
of death (the cancer attributable risk) decreased with age.
93
The reasons for this finding were likely
associated with the type of cancer, the intensity of treatments, or behavioral factors such as
increased social mixing vs that of an older population.
We found that patients with lung cancer, followed by those with hematologic cancer, were at
greatest risk of mortality from COVID-19, compared with patients with other cancers. Hematologic
cancers have been consistently reported as a risk factor for poor clinical outcomes.
7,8,11,23,28,31,32
The
increased susceptibility to poor outcomes among patients with hematologic cancer is consistent
with the more profound immune suppression that affects this patient group, whereas the increase in
mortality among patients with lung cancer is likely associated with age, reduced lung reserve,
comorbidities, and cancer treatment. The reason for the lower mortality from COVID-19 that we
found in patients with breast and gynecological cancers is not clear. It could be associated with the
protective feature of the female sex, although we found no sex-based difference in outcomes within
the meta-analysis we conducted. An alternative explanation could be low circulating estradiol levels
often seen in patients with breast and gynecological cancers. Use of androgen deprivation therapy in
prostate cancer has been associated with protection from SARS-CoV-2 infection.
38
There is a need
to understand if a similar outcome is seen in female patients with lower estradiol levels.
In the pooled case fatality analysis, we found that endocrine therapy had the lowest pooled case
fatality rate at 11% and chemotherapy had the highest at 30%. The higher mortality seen with
chemotherapy compared with other treatments was likely associated with the immunosuppression
after chemotherapy. Cohort studies have reported disparate results regarding the risk of
chemotherapy.
8,9,13-16,20,24-28,30,31,34-36,40,46,48,58,66,68,74,85
Given the lack of patient-level data, we
were unable to define the risk of mortality for patients who were receiving anticancer therapy and
contracted COVID-19. Similarly, we could not compare the implications of cancer treatment for the
risk of mortality between these patients and control patients. A comparison of risk by different
treatment modalities and by individual drugs also was not possible given that it was not clear if
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patients had received more than 1 treatment modality and the individual drugs were not named.
More granular data, as well as use of a population without cancer controlled by age and sex, are
needed to ascertain the risk of different anticancer therapies in the context of COVID-19.
Ongoing studies such as the International Severe Acute Respiratory and Emerging Infections
Consortium World Health Organization Clinical Characterization Protocol UK
93
will enable a more
comprehensive comparison of patients with cancer vs control patients, with adjustments for age,
sex, and other comorbidities, and the identification of the true risk of different tumor types and
treatments while controlling for patient-level factors. In addition, studies are needed that assess the
outcome of mitigation and treatment measures over the course of the SARS-CoV-2 pandemic
between patients with cancer and control patients given that many of the treatment studies did not
recruit patients with cancer. eTable 14 in the Supplement lists completed and current studies relevant
to this topic.
The global effort to understand the implications of SARS-CoV-2 infection for patients with
cancer has resulted in a rich data resource that should be used for an individual patient-level meta-
analysis. Such data will maximize learning and knowledge and may be used to prepare the cancer
research community for subsequent pandemics, which will inevitably occur.
Limitations
This study has several limitations. First, we assessed outcomes in 7 tumor types as outcome data
because these were most frequently reported in the studies we analyzed. Second, it was not possible
to compare patients with solid cancers with a control cohort because we found no suitable studies
that identified these cancer types. Third, we were unable to explore the potential outcomes of
different SARS-CoV-2 variants because this information was not available. Furthermore,
interpretation of the funnel plots may be difficult because of the heterogeneous evidence base and
the presence of observational studies. Fourth, the data included in this meta-analysis were from the
prevaccination and antiviral medication phases of the SARS-CoV-2 pandemic; therefore, vaccinations
and active treatments may have affected our observations.
Conclusions
This large, comprehensive systematic review and meta-analysis found a higher risk of death from
COVID-19 in patients with cancer than in patients without cancer, although the risk was lower than
that reported in individual cohort studies. Younger patients were at a particularly increased risk for
poor clinical outcomes compared with age-matched control patients. Patients with lung cancer had
the highest risk of mortality, followed by those with hematologic cancer. Given these data, younger
patients may be considered in certain settings to be a high-risk population for poor outcomes from
COVID-19.
ARTICLE INFORMATION
Accepted for Publication: March 21, 2022.
Published: May 9, 2022. doi:10.1001/jamanetworkopen.2022.10880
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Khoury E
et al. JAMA Network Open.
Corresponding Author: Carlo Palmieri, BSc, MBBS, PhD, Department of Molecular and Clinical Cancer Medicine,
Institute of Translational Medicine, University of Liverpool, Sherrington Building, Ashton Street, Liverpool,
L69 3GE, United Kingdom (c.palmieri@liverpool.ac.uk).
Author Affiliations: Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Institute of
Translational Medicine, Liverpool, United Kingdom(Khour y, Palmieri); University of Liverpool, School of Medicine,
Liverpool, United Kingdom (Khoury); Department of Health Data Science, Institute of Population Health,
University of Liverpool, United Kingdom (Nevitt); Department of Political Science and School of Public Policy,
JAMA Network Open | Oncology Differences in Outcomes and Factors Associated With Mortality Among Patients With or Without Cancer and SARS-CoV-2
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Downloaded From: https://jamanetwork.com/ on 05/12/2022
University College London, London, United Kingdom (Madsen); Department of Political Science, University of
Copenhagen, Copenhagen, Denmark (Madsen); Tropical and Infectious Disease Unit, Liverpool University
Hospitals National Health Service (NHS) Foundation Trust,Member of Liverpool Health Partners, Liverpool, United
Kingdom (Turtle); Department of Clinical Infection Microbiology and Immunology, Department of Clinical
Infection, University of Liverpool, Liverpool, United Kingdom (Davies); University of Liverpool Instituteof Infec tion
and Global Health, Veterinary and Ecological Sciences, Liverpool, United Kingdom (Davies); The Clatterbridge
Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom (Palmieri).
Author Contributions: Ms Khoury had full access to all the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data analysis.
Concept and design: Khoury, Turtle, Palmieri.
Acquisition, analysis, or interpretation of data: Khoury, Nevitt, Rohde Madsen, Davies, Palmieri.
Drafting of the manuscript: Khoury, Rohde Madsen, Turtle, Palmieri.
Critical revision of the manuscript for important intellectual content: Khoury, Nevitt, Turtle, Davies, Palmieri.
Statistical analysis: Nevitt, Rohde Madsen, Davies.
Administrative, technical, or material support: Khoury.
Supervision: Nevitt, Turtle, Palmieri.
Conflict of Interest Disclosures: Dr Turtle reported receiving personal fees paid to the University of Liverpool
from Eisai Ltd. Dr Palmieri reported receiving grants from Pfizer and Daiichi Sankyo as well as personal fees from
Pfizer, Roche, Daiichi Sankyo, Novartis, Exact Sciences, Gilead, SeaGen, and Eli Lilly outside the submitted work.
No other disclosures were reported.
Funding/Support: Dr Palmieri and Dr Turtle were supported by grant MR/V028979/1 from UK Research
Innovation-Department for Health and Social Care COVID-19 RapidResponse Rolling Call. Dr Palmieri was
supported by grant C18616/A25153from the Liverpool Experimental Cancer Medicine Centre, by Cancer Research
UK, and by the Clatterbridge Cancer Charity and North West Cancer. Ms Khoury was supported by award CR1054
from North West Cancer Research Fund to support a Master of Research. Dr Turtle was supported by contract
75F40120C00085 from the US Food and Drug Administration Medical Countermeasures Initiative, by fellowship
205228/Z/16/Z from the Wellcome Trust, and by grant NIHR200907 from the National Institute for Health
Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections at the University of
Liverpool in partnership with Public Health England, in collaboration with Liverpool School of Tropical Medicine
and the University of Oxford.
Role of the Funder/Sponsor:The funders had no role in the design and conduc t of the study; collection,
management, analysis, and interpretation of the data; preparation, review, or approvalof the manuscript; and
decision to submit the manuscript for publication.
Disclaimer: The views expressed herein are those of the authors and do not reflect the official policy or position
of the National Health Service, the NIHR, the Department of Health, or Public Health England.
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SUPPLEMENT.
eTable 1. Search Strategy Used for the SystematicReview in PubMed
eTable 2. Search Strategy Used for the Systematic Review in Web of Science
eTable 3. Search Strategy Used for the Systematic Review in Scopus
eFigure 1. Newcastle-Ottawa Scale for Quality Assessment of the Included Studies
eFigure 2. Funnel Plot of the Main Analysis of Mortality in Cancer Patients With COVID-19 to Control Patients
eFigure 3. PRISMA Chart for Study Selection
eFigure 4. Number of Cancer Patients Across 81 Studies, a Total of 61,532 Patients
eTable 4. Overview of Studies Included in Review
eFigure 5. Distribution of Cancer Patients Across 80 Studies
eFigure 6. Countries Included Across All Studies
eTable 5. Distribution of Cancer Patientsby County
eFigure 7. Co-morbidities Experienced by Cancer Patients, Representing 21,697 Patients Across 56 Studies
eFigure 8. Percentage of Cancer Patients Without Co-morbidities, or With One or More Co-morbidities, Where
Reported
eFigure 9. Summary of Reported Co-morbidities
eFigure 10. Chest Radiograph Chest X Ray Imaging on Admission With RadiologicalChanges Documented, Where
Reported
eFigure 11. Setting of Care Across 80 Studies, Representing 22,918Cancer Patients
eTable 6. Inpatient vs Outpatient Care Across 73 Studies as Wellas Possible or Probable Nosocomial Infection
eTable 7. Tumour Prevalence Across Studies Included
eFigure 12. Tumour Type Breakdown Across 68 Studies, Where Reported
eResults. Presenting Symptoms of SARS-CoV-2 Infection, Radiological Findings, Ethnicity and Stage of Malignancy
and Outcome
eReferences
eFigure 13. Presenting Symptoms, Includes 9,196 Patients
eTable 8. Chest Radiograph±Che stX Ray Imaging on Admission With Radiological Changes Documented, Where
Reported *Nodules/Interstitial Thickening/Erratic Paving
eFigure 14. Forest Plot of Relative Risk of Mortality in Subgroup Analysis
eTable 9.Meta-Regression Results on the Impact of (A) Age, (B) Male Gender, and (C) Both Age and Male Gender
eTable 10.Patient Outcomes in 68 Studies
eTable 11. Median Duration of Hospital Stay (Days)
eFigure 15. Mortality Reported Across 81 Studies
eTable 12. Mortality of Cancer Patients Reported Across Studies
eFigure 16. Definition of Severe Event
eFigure 17. Significant Variables in Unadjusted (A) and Adjusted (B) Analyses Across Studies
eFigure 18. Variables Included in Adjusted Analysis Across Studies
eFigure 19. Forest Plot of Overall Pooled Case Fatality in Subgroup Analysis
eTable 13. Pooled Case Fatality Rates for Various Cancer Treatments
eTable 14. Table of Completed and Ongoing Cancer Observational Studies Related to the SARS-CoV-2/COVID-19
Pandemic in Patients With Malignant Disease
eFigure 20. Forest Plot of Relative Risk of Mortality in Different Cancer Types in the Subgroup Analysis
JAMA Network Open | Oncology Differences in Outcomes and Factors Associated With Mortality Among Patients With or Without Cancer and SARS-CoV-2
JAMA Network Open. 2022;5(5):e2210880.doi:10.1001/jamanetworkopen.2022.10880 (Reprinted) May 9, 2022 19/19
Downloaded From: https://jamanetwork.com/ on 05/12/2022
