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Biomedicines 2022, 10, 2437. https://doi.org/10.3390/biomedicines10102437 www.mdpi.com/journal/biomedicines
Systematic Review
Association of Patients’ Epidemiological Characteristics and
Comorbidities with Severity and Related Mortality Risk of
SARS-CoV-2 Infection: Results of an Umbrella Systematic
Review and Meta-Analysis
Eduardo Reyna-Villasmil 1, Maria Giulia Caponcello 1, Natalia Maldonado 1, Paula Olivares 1, Natascia Caroccia 2,3,
Cecilia Bonazzetti 2,3, Beatrice Tazza 2, 3, Elena Carrara 4, Maddalena Giannella 2,3, Evelina Tacconelli 4,
Jesús Rodríguez-Baño 1,5,6,†, Zaira R. Palacios-Baena 1,6,*,† on behalf of the ORCHESTRA Study Group
1 Unit of Infectious Diseases and Clinical Microbiology, University Hospital Virgen Macarena, Institute of
Biomedicine of Seville (IBIS)/CSIC, 41008 Seville, Spain
2 Department of Medical and Surgical Sciences, University of Bologna, 40126 Bologna, Italy
3 Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
4 Infectious Diseases Division, Department of Diagnostics and Public Health, University of Verona,
37129 Verona, Italy
5 Department of Medicine, University of Seville, 41008 Seville, Spain
6 Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud
Carlos III, 28029 Madrid, Spain
* Correspondence: zaira.palacios.baena@hotmail.com
† These authors contributed equally to this work.
Abstract: The objective of this study was to assess the association between patients’ epidemiological
characteristics and comorbidities with SARS-CoV-2 infection severity and related mortality risk. An
umbrella systematic review, including a meta-analysis examining the association between patients’
underlying conditions and severity (defined as need for hospitalization) and mortality of COVID-
19, was performed. Studies were included if they reported pooled risk estimates of at least three
underlying determinants for hospitalization, critical disease (ICU admission, mechanical ventila-
tion), and hospital mortality in patients diagnosed with SARS-CoV-2 infection. Evidence was sum-
marized as pooled odds ratios (pOR) for disease outcomes with 95% confidence intervals (95% CI).
Sixteen systematic reviews investigating the possible associations of comorbidities with severity or
death from COVID-19 disease were included. Hospitalization was associated with age > 60 years
(pOR 3.50; 95% CI 2.97–4.36), smoking habit (pOR 3.50; 95% CI 2.97–4.36), and chronic pulmonary
disease (pOR 2.94; 95% CI 2.14–4.04). Chronic pulmonary disease (pOR 2.82; 95% CI 1.92–4.14), cer-
ebrovascular disease (pOR 2.74; 95% CI 1.59–4.74), and cardiovascular disease (pOR 2.44; 95% CI
1.97–3.01) were likely to be associated with increased risk of critical COVID-19. The highest risk of
mortality was associated with cardiovascular disease (pOR 3.59; 95% CI 2.83–4.56), cerebrovascular
disease (pOR 3.11; 95% CI 2.35–4.11), and chronic renal disease (pOR 3.02; 95% CI 2.61–3.49). In
conclusion, this umbrella systematic review provides a comprehensive summary of meta-analyses
examining the impact of patients’ characteristics on COVID-19 outcomes. Elderly patients and those
cardiovascular, cerebrovascular, and chronic renal disease should be prioritized for pre-exposure
and post-exposure prophylaxis and early treatment.
Keywords: COVID-19; SARS-CoV-2; meta-analysis; mortality; severe disease; predictors;
comorbidities
Citation: Reyna-Villasmil, E.;
Caponcello, M.G.; Maldonado, N.;
Olivares, P.; Caroccia, N.;
Bonazzetti, C.; Tazza, B.; Carrara, E.;
Giannella, M.; Tacconelli, E.; et al.
Association of Patients’
Epidemiological Characteristics and
Comorbidities with Severity and
Related Mortality Risk of
SARS-CoV-2 Infection: Results of an
Umbrella Systematic Review and
Meta-Analysis. Biomedicines 2022, 10,
2437. https://doi.org/10.3390/
biomedicines10102437
Academic Editors: Miguel A. Ortega,
Melchor Álvarez de Mon,
José-Antonio Girón-González,
Miguel A. Alvarez-Mon,
Jorge Monserrat, Natalio
García-Honduvilla and Luis G. Gui-
jarro
Received: 21 August 2022
Accepted: 19 September 2022
Published: 29 September 2022
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
tional affiliations.
Copyright: © 2022 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).
Biomedicines 2022, 10, 2437 2 of 15
1. Introduction
By the end of April 2022, the COVID-19 pandemic, caused by the new coronavirus
SARS-CoV-2, had caused more than 500 million cases and more than 6 million deaths
worldwide [1]. SARS-CoV-2 frequently causes asymptomatic or mild infection; however,
some patients progress to a severe disease, which is associated with high mortality [2].
Identifying the patients’ conditions associated with the development of severe forms of
COVID-19 and mortality is helpful because it allows identifying the patients who would
benefit most from specific preventive interventions, including enhanced transmission-
protective measures, being prioritized in vaccination campaigns, and, more recently, re-
ceiving antivirals or monoclonal antibodies, which may avoid the progression from mild
to severe disease.
Several patient conditions, including age, gender, and several chronic underlying
comorbidities, have been associated with worse outcomes [2]. However, the generaliza-
bility of the estimates for the relative impact of each of these conditions in the different
studies performed may be hampered because the different studies may be affected by se-
lection and information biases and lack of statistical power. As a result, the estimations of
the risk associated with underlying conditions of the patients for the development of se-
vere COVID-19 or mortality are frequently heterogeneous, if not contradictory.
The aim of this study was to conduct an umbrella systematic review and meta-anal-
ysis in order to assess the association between patients’ epidemiological characteristics
and comorbidities with severity and related mortality risk of SARS-CoV-2 infection.
2. Methods
2.1. Design, Data Sources, and Search Strategy
An umbrella literature review was conducted according to the Joanna Briggs Institute
recommendations [3] and reported according to Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) [4]. The study protocol was registered in PROS-
PERO (CRD42021267368). Patients were not involved in the design, conduct, interpreta-
tion, and writing up of the results of this study.
For this umbrella review, the PICO question was defined as follows. The patients
were outpatients and inpatients diagnosed with SARS-CoV-2 infection; the exposures
were the epidemiological characteristics and chronic underlying conditions of the pa-
tients; the comparator was the absence of exposure to these characteristics and conditions;
and the outcomes considered were hospital admission, severe/critical COVID-19, and in-
hospital mortality. Published systematic reviews and meta-analyses on the association of
patients’ epidemiological characteristics and comorbidities with hospitalization due to
COVID-19, development of severe or critical COVID-19 defined as mechanical ventilation
and need of intensive care unit (ICU) admission, and death were considered only in ad-
mitted patients with a first episode of infection.
The literature search was conducted in PubMed, MEDLINE, EMBASE, Web of Sci-
ence, Scopus, Cochrane Library databases, and the JBI database of Systematic Reviews
and Implementation Reports, with no language restrictions. The initial search was con-
ducted on 1 August 2021 and updated on 30 September 2021. The full search strategy used
is shown in Table S1, Supplementary Materials.
Biomedicines 2022, 10, 2437 3 of 15
2.2. Inclusion and Exclusion Criteria
Articles were eligible if they were published between December 2019 and August
2021 and if they included a meta-analysis of the association of patients’ epidemiological
characteristics and comorbidities with the severity or mortality from COVID-19. Studies
had to meet the following criteria: (a) they were conducted on patients diagnosed with
SARS-CoV-2 infection by PCR or antigen test in nasopharyngeal or respiratory tract sam-
ples; (b) they included the evaluation of at least three epidemiological characteristics and
comorbidities in order to be able to assess the confounding effect of one condition on oth-
ers; (c) they provided quantitative data of patients with and without the conditions and
their outcomes; (d) they provided a pooled estimation of the association of the conditions
and the outcomes. Studies in which severity or mortality was not the primary outcome,
narrative reviews, meta-analyses including fewer than 5 studies, and preprints were ex-
cluded. Systematic reviews reporting outcomes in vaccinated patients or in pregnant
women and children (aged less than 18 years) were also excluded, as these groups may
have specific outcome determinants.
2.3. Article Selection and Data Extraction
All the identified references were managed with a reference management program,
and duplicates were removed. The titles and abstracts were screened, and the full texts of
the selected articles were then reviewed for eligibility and data extraction by two investi-
gators (ZRP-B and ER-V). A third coauthor (JR-B) resolved any disagreement that could
not be resolved by consensus. The data extracted included: author, year of publication,
number of participants, number and type of studies included, quality assessment instru-
ment used, method of analysis, patients’ epidemiological characteristics and comorbidi-
ties and outcomes assessed, heterogeneity, and the estimated associations between all con-
ditions and the outcomes. The AMSTAR 2 tool [5] was used to assess methodological
quality and assign an overall rating for the reviews included Table S2.
The patients’ epidemiological characteristics and comorbidities were grouped into
categories. The definitions used in the included systematic review were reviewed for ho-
mogeneity. The outcomes considered were hospitalization due to COVID-19, develop-
ment of severe/critical disease (i.e., need for ICU admission, high-flow oxygen or mechan-
ical ventilation), and mortality. Data of the association of the conditions and outcomes
were collected as rate ratios (RR), odds ratios (OR), and hazard ratios (HR), with 95% con-
fidence intervals (CI).
2.4. Data Analysis
The characteristics and results of the included studies were synthesized and pre-
sented in tables and forest diagrams. Pooled OR (pOR) with 95% confidence intervals (CI)
were calculated for conditions investigated in at least 3 meta-analyses using the DerSi-
monian and Laird random-effects method, which accounts for inter- and intra-study var-
iance. For dichotomous variables, a summary of estimations was produced by using a
logarithmic scale to maintain symmetry in the analysis. An estimate of publication bias
was calculated with Egger’s regression test. The I2 statistic was used as an estimate of true
variance in the summary estimate and was used as an estimate of the proportion of vari-
ance reflecting the true differences in effect size. The degree of overlap of primary studies
included in the different meta-analyses was investigated by a citation matrix including
the systematic reviews in columns and the primary studies included in rows; the degree
of overlap was quantified using the corrected covered area (CCA). The overlap was cate-
gorized as very high (>15%), high (11–15%), moderate (6–10%), or light (0–5%) [6]. CCA is
a validated method for quantifying the degree of overlap between two or more reviews.
Biomedicines 2022, 10, 2437 4 of 15
3. Results
The initial search identified 411 potentially eligible studies. After discarding dupli-
cates, 225 were screened, and finally, 16 systematic reviews and meta-analyses were in-
cluded [7–22] (Figure 1). These systematic reviews included 568 primary studies, with a
range of 7 and 77 per meta-analysis.
Figure 1. Flow chart of included articles according to PRISMA.
Biomedicines 2022, 10, 2437 5 of 15
The characteristics of the selected studies are shown in Table 1. Overall, the risk esti-
mates for the association of 12 underlying patient conditions with some of the outcomes
in patients diagnosed with COVID-19 were available, including: age, sex, smoking status,
obesity, hypertension, diabetes mellitus (DM), cardiac disease (CD, including arrhythmia
or chronic heart failure), chronic pulmonary diseases (CPD), cancer (hematological cancer,
solid cancer, any malignant tumor), cerebrovascular diseases (CVD, including stroke and
transient ischemic attack), chronic kidney disease (CKD), and chronic liver disease (CLD).
Seven systematic reviews provided information on the risk of hospitalization for 11 char-
acteristics and comorbidities (all except CLD) [7–11,16,22]. Nine reported the risk of se-
vere/critical illness from 10 determinants (all except age and hypertension) [10–18], and
six reported risk estimates for the mortality ratio of 11 factors (all except age) [14,17–21].
Overall, four considered adjusted estimations for the individual conditions [11,14,18,19].
Table 1. Summary of features of the systematic reviews and meta-analyses investigating the out-
come impact of patients’ characteristics and underlying conditions included in this study.
Author
Last Date
of Search
Names of Databases
Searched
Number of
Selected Ar-
ticles
Number of
Selected Pa-
tients
Determinant Factors
Outcomes
Related to
COVID-19
Instrument
of Quality
Appraisal
Amstar-2
Score
Matsu-
shita et
al. [19].
3 April,
2020
PubMed and Embase
25
76,638
Age, Male sex, Hyper-
tension, DM, CD
Death
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Dorjee
et al.
[11]
31
August,
2020
Medline, Embase, Web
of Science, and the
WHOOVID-19 data-
base
77
38,906
Age, Male sex, Smoking,
CKD, Hypertension,
CLD, DM, CPD, CD
Death, sever-
ity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Khan et
al. [21]
1 May,
2020
Medline, Web of Sci-
ence, Scopus, and CI-
NAHL
41
27,670
Malignancies, CKD, Hy-
pertension, CLD, DM,
CPD, CD, CVD
Death
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Zhou et
al. [12].
25 April,
2020
PubMed, Embase, and
Cochrane Library
34
16,110
Obesity, Malignancies,
CKD, Hypertension,
CLD, DM, CPD, CD,
CVD
Sever-
ity/Death
Not reported
High
Del Sole
et al. [7]
28 May,
2020
PubMed, ISI Web of
Science, SCOPUS, and
Cochrane databases
12
2794
Male sex, Smoking, Hy-
pertension, DM, CPD,
CD, CVD
Severity
Not reported
Moderate
Yang et
al. [13]
25
February,
2020
PubMed, EMBASE,
and Web of Science
7
1576
Hypertension, CPD, CD
Severity
Not reported
High
Ssen-
tongo et
al. [4]
7 July,
2020
MEDLINE, SCOPUS,
OVID, and Cochrane
Library databases and
medrxiv.org
25
484
Malignancies, CKD, Hy-
pertension, DM, CD
Mortality
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Li J et al.
[16]
28
February,
2021
PubMed, Embase, Web
of Science, and
Cochrane Library for
epidemiological stud-
ies
41
21,060
Male sex, Smoking, Obe-
sity, malignancies, CKD,
Hypertension, CLD,
DM, CPD, CD, CVD
Severity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Booth et
al. [22]
9 July,
2020
PubMed and SCOPUS
66
1,786,001
Age, Male sex, Obesity,
Malignancies
Severity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
Moderate
Biomedicines 2022, 10, 2437 6 of 15
Cheng
et al. [8]
1 April,
2020
PubMed, Embase,
China National
Knowledge Infrastruc-
ture (CNKI), and Wan-
fang Database
22
3286
Malignancies, Hyperten-
sion, DM, CPD, CD,
CVD
Severity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Honar-
doost et
al. [9]
February
2021
Electronic literature
28
6270
Hypertension, DM,
CPD, CD, CVD
Severity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
Low
Yin et al.
[10]
18
January,
2021
PubMed, Web of Sci-
ence, and CNKI
41
12,526
Malignancies, CKD, Hy-
pertension, CLD, DM,
CPD, CD, CVD
Severity
Not reported
High
Sahu et
al. [15]
24 May,
2020
PubMed, Embase, and
Web of Science
22
4380
Obesity, Malignancies,
CKD, Hypertension,
DM, CPD, CD
Severity
Not reported
High
Li X et
al. [20]
14 April,
2020
PubMed, Embase, and
Cochrane Library
12
2445
Malignancies, Hyperten-
sion, DM, CPD, CD,
CVD
Severity
Newcastle
Ottawa Qual-
ity Assess-
ment Scale
High
Giri et
al. [17]
20
Novem-
ber, 2020
PubMed, Scopus, Em-
base, and Web of Sci-
ence
41
16,495
Malignancies, Hyperten-
sion, DM, CD, CVD
Severity
Methodologi-
cal Index for
Non-Ran-
domized
Studies
High
Fernán-
dez et
al. [18]
28 May,
2020
MEDLINE, bioRXiv,
and MedRXiv,
74
44,672
CKD, Hypertension,
CD, CVD
Severity (One
parameter for
mortality)
ROBINS-I
tools
High
DM: diabetes mellitus; CD: cardiac disease (including arrhythmia or chronic heart failure); CKD:
chronic kidney disease; CLD: chronic liver disease; CPD: chronic pulmonary diseases; CVD: cere-
brovascular diseases (including stroke and transient ischemic attack).
Thirteen of the sixteen systematic reviews and meta-analyses were rated as high
quality [8,10–15,19–22], two were considered of moderate quality [7,16], and one was con-
sidered of low quality [9], as it did not meet two of the seven critical domains. When over-
laps of the primary studies were analyzed (Figure 2), 266 primary studies appeared in at
least two reviews. The degree of overlap ranged from 0% to 16% (Figure 2). One study
[13] showed high values of overlap with another four studies [7,11,19,21] and moderate
values with another one [12]. Another two studies showed cross-overlap, with the value
reaching 14% [10,14]. Overall, the CCA showed a degree of overlap of 2.05%, which is
considered low.
Biomedicines 2022, 10, 2437 7 of 15
Figure 2. Estimation of the overlap across studies included in the umbrella systematic review.
Regarding the risk estimates for COVID-19 hospitalization, all conditions considered
in the seven meta-analyses investigating this outcome were found to be associated with
increased risk (Table 2 and Figure 3a). For conditions for which we could provide a pOR,
CVD showed the strongest association (pOR = 4.05; 95% CI: 3.20–5.12); the estimated pOR
for CPD, CD DM, hypertension, and cancer ranged from 2.27 to 2.94, and the pOR for
male sex was 1.49. Age, smoking status, obesity, and CKD were studied in <3 meta-anal-
yses, and therefore, we did not calculate a pOR, but all the individual estimations showed
an increased risk, which was higher than 2.5 for age, obesity, and CKD. The estimations
of the individual meta-analyses were in a similar range for male sex, hypertension, and
CVD but were more heterogeneous for cancer, DM, CPD, and CD.
Table 2. Meta-analysis of the different patients’ characteristics and underlying conditions and the
risk of hospitalization due to COVID-19.
Condition
Study
Number of Primary
Studies
Odds Ratio
IC 95%
Male sex
De sole et al.
12
1.22
1.01–1.49
Xinyian Li et al.
41
1.51
1.33–1.71
Booth et al.
66
2.05
1.39–3.04
pOR
119
1.48
1.19–1.85
Age
Dorjee et al.
77
3.60
(Age > 60 years)
2.97–4.36
Booth et al.
66
2.65
(Age > 75 years
1.81–3.90
Smoking
History
Del Sole et al.
12
1.54
1.07–2.22
Obesity
Booth et al.
66
2.57
1.25–5.27
Malignancy
Booth et al.
66
1.46
1.04–2.04
Cheng et al.
22
3.18
2.09–4.82
Yin et al.
41
2.63
1.75–3.93
pOR
129
2.27
1.40–3.33
Systematic reviews
Dorjee et
al
Khan et
al
Zhou et
al
Del Sole et
al
Yang et
al
Ssentongo et
al
Li et
al Booth et
al
Cheng et
al
Honardoost et
al
Yin et al
Kumar et
al
Li et
al
Giri et
al
Fernández et
al
Matsushita et al 1% 2% 2% 6% 15% 2% 0% 1% 3% 4% 2% 3% 7% 2% 1%
Dorjee et
al
2% 2% 8% 16% 4% 2% 1% 4% 4% 2% 4% 8% 2% 2%
Khan et
al
2% 7% 15% 3% 2% 1% 4% 4% 2% 4% 0% 2% 1%
Zhou et
al
7% 8% 14% 3% 0% 1% 0% 0% 2% 3% 8% 2%
Del Sole et
al
13% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Yang et
al
0% 3% 0% 0% 0% 0% 0% 0% 0% 0%
Ssentongo et
al
0% 0% 2% 2% 0% 0% 0% 2% 0%
Li et
al 1% 1% 1% 1% 1% 1% 3% 5%
Booth et
al 4% 4% 4% 4% 4% 3% 3%
Cheng et
al
0% 0% 4% 5% 0% 0%
Honardoost et
al
2% 2% 2% 0% 0%
Yin et al 1% 1% 1% 0%
Kumar et
al
0% 2% 0%
Li et
al
0% 2%
Giri et
al
2%
Biomedicines 2022, 10, 2437 8 of 15
Chronic renal disease
Yin et al.
41
3.60
2.18–5.94
Hypertension
Del sole et al.
12
2.24
1.63–308
Cheng et al.
22
2.79
1.66–4.69
Honardoost et al.
28
2.37
1.80–3.13
Yin et al.
41
2.13
1.81–2.51
pOR
103
2.34
1.95–2.81
Diabetes mellitus
Del Sole et al.
12
2.78
2.09–3.72
Cheng et al.
22
1.64
1.30–2.08
Honadoost et al.
28
3.18
2.09–4.82
pOR
62
2.39
1.56–3.64
Chronic
pulmonary disease
Del Sole et al.
12
2.39
1.10–5.19
Cheng et al.
22
1.98
1.26–3.12
Honadoost et al.
28
4.19
2.84–6.19
Yin et al.
41
3.14
2.35–4.19
pOR
103
2.94
2.14–4.04
Cardiovascular disease
Del Sole et al.
12
2.84
1.59–5.10
Cheng et al.
22
1.79
1.08–2.96
Honadoost et al.
28
4.81
3.43–6.74
Yin et al.
41
2.76
2.18–3.49
pOR
103
2.94
2.00–4.33
Cerebrovascular disease
Del Sole et al.
12
3.66
1.73–7.72
Cheng et al.
22
3.92
2.45–6.28
Honardoost et al.
28
4.85
3.11–7.57
Yin et al.
41
3.70
2.51–5.45
pOR
103
4.05
3.20–5.12
OR: pooled odds ratio.
(A)
Biomedicines 2022, 10, 2437 9 of 15
(B)
(C)
Figure 3. Forest plot of pooled odds ratio and 95% confidence intervals of the different patients’
characteristics and underlying conditions for (A) Hospitalization (B) Severe/critical condition, and
(C) Mortality.
For the development of severe/critical COVID-19, the highest estimated pOR was for
CPD (2.82; 95% CI: 1.92–4.14); obesity, cancer, CKD, DM, and CD showed a pOR ranging
from 1.91 to 2.44; CLD showed a pOR of 1.76 (95% CI 1.12–2.78); interestingly, this condi-
tion was the only one for which some individual meta-analyses could not show a signifi-
cant association with the outcome. For male sex and smoking status, a pOR could not be
calculated; individual studies showed a lower OR than for other epidemiological
Biomedicines 2022, 10, 2437 10 of 15
characteristics and comorbidities, in the range of 1.28–1.30 (Table 3 and Figure 3b). The
estimates for each condition in the individual meta-analyses showed some heterogeneity
for all of them.
Table 3. Meta-analysis of the different patients’ characteristics and underlying conditions and the
risk of development of severe/critical COVID-19.
Condition
Study
Number of Primary
Studies
Odds Ratio
IC 95%
Male sex
Dorjee et al.
77
1.30
1.21–1.42
Smoking history
Dorjee et al.
77
1.28
1.06–1.55
Obesity
Zhou et al.
34
1.72
1.04–2.85
Kumar et al.
22
2.84
1.19–6.77
pOR
56
1.95
1.26–3.02
Malignancy
Zhou et al.
34
2.73
1.73–4.21
Ssentongo et al.
25
1.47
1.01–2.14
Kumar et al.
22
2.38
1.25–4.52
Li et al.
12
2.21
1.04–4.72
Giri et al.
41
1.75
1.40–2.18
pOR
134
1.91
1.54–2.37
Chronic renal
Disease
Dorjee et al.
77
2.5
2.09–2.99
Zhou et al.
34
3.02
2.23–4.08
Ssentongo et al.
25
3.25
1.13–9.28
Kumar et al.
22
1.46
1.06–2.02
Fernadez et al.
74
2.5
1.82–3.44
pOR
232
2.35
1.83–3.03
Chronic liver disease
Dorjee et al.
77
2.65
1.88–3.75
Zhou et al.
34
1.54
0.95–2.49
Yin et al.
41
1.32
0.96–1.82
pOR
115
1.76
1.12–2.78
Diabetes mellitus
Dorjee et al.
77
1.5
1.36–1.65
Zhou et al.
34
2.63
2.08–3.33
Ssentongo et al.
25
1.82
1.43–2.23
Kumar et al.
22
2.29
1.56–3.39
Li et al.
12
3.17
2.26–4.45
Giri et al.
41
2.04
1.67–2.50
pOR
211
2.13
1.68–2.70
Chronic
pulmonary disease
Dorjee et al.
77
1.7
1.4–2.0
Zhou et al.
34
3.56
2.87–4.41
Yang et al.
7
2.46
1.76–3.44
Kumar et al.
22
2.92
1.70–5.02
Li et al.
12
5.08
2.68–9.63
pOR
152
2.82
1.92–4.14
Cardiovascular disease
Dorjee et al.
77
2.1
1.82–2.43
Zhou et al.
34
3.13
2.65–3.70
Ssentongo et al.
25
2.25
1.60–3.17
Kumar et al.
22
1.61
1.31–1.98
Li et al.
12
2.66
1.71–4.15
Giri et al.
41
2.78
2.00–3.86
Fernández et al.
34
3.20
2.29–4.48
pOR
245
2.44
1.97–3.01
Biomedicines 2022, 10, 2437 11 of 15
Cerebrovascular disease
Zhou et al.
34
2.74
1.59–4.74
pOR: pooled odds ratio.
Regarding mortality, three conditions showed a pOR > 3 (CKD, CVD, CD); pOR
ranged from 2.24 to 2.52 for CPD, hypertension, cancer, and DM. A pOR could not be
calculated for male sex, smoking status, and obesity; the OR from individual studies was
in the range of 1.40 to 1.89 for these conditions (Table 4 and Figure 3c). Overall, the esti-
mated strength of association of the different conditions with mortality was quite homo-
geneous across studies.
Table 4. Meta-analysis of the different patients’ characteristics and underlying conditions and the
risk of mortality due to COVID-19.
Condition
Study
Number of Primary
Studies
Odds Ratio
IC 95%
Male sex
Matsushita et al.
25
1.73
1.50–2.01
Smoking history
Xinyang et al.
41
1.40
1.06–1.85
Obesity
Xinyang et al.
41
1.89
1.44–2.46
Malignancy
Kahn et al.
41
2.22
1.63–3.03
Xinyang et al.
41
2.60
2.00–3.40
pOR
82
2.43
1.99–2.97
Chronic kidney
Disease
Khan et al.
41
3.02
2.60–3.51
Li et al.
41
2.97
1.63–5.41
pOR
82
3.02
2.61–3.49
Hypertension
Matsushita et al.
25
2.87
2.09–3.93
Li et al.
41
2.42
2.03–2.88
pOR
66
2.52
2.16–2.94
Chronic liver disease
Kahn et al.
41
2.35
1.50–3.6
Li et al.
41
1.51
1.06–2.17
pOR
82
1.85
1.20–2.85
Diabetes mellitus
Matsushita et al.
25
3.20
2.26–4.53
Kahn et al.
41
2.46
2.03–2.85
Li et al.
41
2.40
1.98–2.91
pOR
107
2.52
2.22–2.85
Chronic
pulmonary disease
Khan et al.
41
1.94
1.72–2.19
Li et al.
41
2.88
1.89–4.38
pOR
82
2.24
1.54–3.25
Cardiovascular disease
Matsushita et al.
25
4.97
2.76–6.58
Ali Khan et al.
41
3.42
2.86–4.09
Yang et al.
7
3.41
1.88–6.22
Li et al.
41
2.87
2.22–3.71
pOR
114
3.59
2.83–4.56
Cerebrovascular disease
Khan et al.
41
4.12
3.04–5.58
Li et al.
41
2.47
1.54–3.97
Giri et al.
41
2.68
1.29–5.57
Fernández et al.
75
2.70
1.74–4.19
pOR
198
3.11
2.36–4.11
pOR: pooled odds ratio.
Biomedicines 2022, 10, 2437 12 of 15
Overall, the exclusion of studies with higher degrees of overlap did not change the
results. Twelve of the sixteen selected systematic reviews and meta-analyses had signifi-
cant heterogeneity, and eleven systematic reviews had I2 > 50%. Individual studies in each
meta-analysis differed in terms of geographic location, ethnicity of the selected subjects,
frequency of diagnosis of the determinant condition, method of diagnosis, COVID-19 clas-
sification, duration of follow-up, and outcome assessment. These studies did not publish
the heterogeneity of the primary studies included in the specific risk comparison.
We were unable to establish the possible publication bias according to Egger’s re-
gression test. The test was repeated in 10 studies of meta-analyses because the remaining
had insufficient data. Of the ones we reanalyzed, five systematic reviews had statistical
evidence of publication bias. For the meta-analyses that could not be reanalyzed, none
reported significant publication bias or did not perform or publish any statistical test of
publication bias for the specific exposure comparison.
4. Discussion
In this umbrella review, which used restrictive criteria for the inclusion of studies,
we found that male sex, age >60 years, being a smoker, and suffering from hypertension,
DM, cancer, CD, CPD, CLD, CVD, and CKD are associated with significantly higher risk
of hospitalization, severe disease, and death due to COVID-19. The estimations for the
risks in the individual meta-analyses showed some differences but were more homogene-
ous for the mortality predictors.
Previous umbrella reviews also evaluated the impact of underlying conditions on
COVID-19 outcomes; however, these studies had important differences with this one, the
most important being that the criteria used to select the studies were less restrictive than
in ours. Two of them evaluated only one underlying disease or a group of conditions. In
a study on obesity, Kristensen et al. found a similar risk estimate as in our study [23].
Kastora et al. studied the impact of DM on COVID-19 outcomes [24]; the risks estimated
for ICU admission and mortality in patients with DM in that study were 1.56 (95% CI:
1.28–1.89) and 1.82 (96% CI 1.65–2.02), respectively, which was somewhat lower than in
our study. Harrison et al. studied the impact of cardiovascular risk factors on COVID-19
severity [25]; overall, the risk estimates in that study were similar to ours. We found one
umbrella review including all the underlying conditions, focusing on whether there were
geographical differences [26]. Interestingly, the authors found some regional heterogene-
ity in the risk estimates.
Some of the chronic conditions associated with worse outcomes in COVID-19, such
as hypertension, obesity, DM, CPD, CKD, CLD, and CD, share some characteristics, in-
cluding chronic proinflammatory states and innate or adaptive immunity dysfunction,
which might facilitate a dysregulated immune response against SARS-CoV-2 [27,28].
Some of these conditions and smoking can also increase the expression of ACE2, the viral
receptor in respiratory tract cells [29]. Age-related immune system changes (immunose-
nescence and inflammageing) have been associated with the increased risk of complica-
tions and mortality in older persons with COVID-19 [30]. However, from a physiopatho-
genic perspective, the confounding or modifying effect of age on the underlying condi-
tions and vice versa must be considered when evaluating their independent risk esti-
mates. As an example, male sex is also associated with severity and mortality in patients
with COVID-19; although this may be related to the confounding effect of some comor-
bidities that might be more frequent among men, it might as well be a consequence of the
sexual dimorphism in the immune response [31] and lower circulating concentration of
ACE in females compared to males [32]. Regarding the smoking status, although the po-
tential confounding effect of some associated comorbidities may play a role in the associ-
ation found, smoking is known to alter mucosal innate immunity patterns and can in-
crease the expression of ACE2 [33].
Biomedicines 2022, 10, 2437 13 of 15
The association of cancer with deleterious outcomes in COVID-19 patients found in
our study would need some considerations. Early in the pandemic, it was suggested that
only patients who had recently received cancer treatment were at increased risk of death,
which could be linked to treatment-related immunosuppression [34]. Additionally, pa-
tients with active hematologic malignancies seem to be at increased risk of mortality [35].
However, depending on the treatment received, some subsets of patients receiving im-
munomodulatory drugs, which may help avoiding the deleterious dysregulated immune
response in COVID-19, might even have a better prognosis [36]. When interpreting the
results of this study, it should be noted that we could only analyze the underlying condi-
tions included in the systematic reviews detected by our strategy. Therefore, we could not
evaluate the potential impact of less frequent diseases, including autoimmune conditions
being treated with immunosuppressive drugs [37] or rare diseases with neuromuscular
involvement [38], among others.
Our study has some limitations; because of the nature of an umbrella review, it was
not possible to provide data on specific subgroups within each comorbid condition eval-
uated, for which the risk may be substantially different. Additionally, the heterogeneity
of the systematic reviews and meta-analyses included must be considered when interpret-
ing the data. The possible causes of that heterogeneity are differences in the designs, pop-
ulations included, and methodological approaches. We could not provide the estimations
for publication bias using Egger’s test, since the cumulative assessment did not exceed 10
studies. We included meta-analyses with adjusted and unadjusted estimations. We did
not consider the geographical differences in the impact of the conditions. Most of the stud-
ies did not include information on in-hospital therapy nor on early treatment. Finally,
most of the studies included were performed before vaccines were available and the omi-
cron variant was predominant. However, these results could be useful in case of a rise of
new virulent variants with immune-escape capacity. The strengths include studies evalu-
ating several comorbidities and the large number of patients represented in the meta-anal-
ysis reported.
5. Conclusions
This umbrella review provides a comprehensive summary of meta-analyses examin-
ing the impact of patients’ characteristics on COVID-19 outcomes. Elderly patients and
those with cardiovascular, cerebrovascular, and chronic renal diseases should be priori-
tized for pre-exposure and post-exposure prophylaxis and early treatment.
Supplementary Materials: The following supporting information can be downloaded at:
www.mdpi.com/article/10.3390/biomedicines10102437/s1, Table S1: Search strategy, and Table S2:
AMSTAR-2 Checklist.
Author Contributions: The protocol was prepared by E.R.-V., M.G.C., N.M., E.T., M. G., J.R.-B., and
Z.R.P.-B. The literature search and data extraction were performed by E.R.-V., M.G.C., N.M., and
P.O. and reviewed by Z.R.P.-B. and J.R.-B. The analyses were performed by E.R.-V. and reviewed
by all authors (N.C., C.B., B.T., M.G., E.C.). E.R.-V., J.R.-B., and Z.R.P.-B. drafted the manuscript,
which was reviewed for scientific content by all authors. E.R.-V. is the guarantor of the review. All
authors have read and agreed to the published version of the manuscript.
Funding: This systematic review was developed as part of the ORCHESTRA project (Connecting
European Cohorts to increase common and effective SARS-CoV-2 Response), which was funded by
the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no.
101016167).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Biomedicines 2022, 10, 2437 14 of 15
References
1. World Health Organization. COVID-19 Weekly Epidemiological Update. 2022. Available online: https://www.who.int/publica-
tions/m/item/weekly-epidemiological-update-on-covid-19---27-april-2022 (accessed on 19 April 2022).
2. Cevik, M.; Bamford, C.G.G.; Ho, A. COVID-19 pandemic-a focused review for clinicians. Clin. Microbiol. Infect. 2020, 26, 842–847.
3. Moola, S.; Munn, Z.; Sears, K.; Sfetcu, R.; Currie, M.; Lisy, K.; Tufanaru, C.; Qureshi, R.; Mattis, P.; Mu, P. Conducting systematic
reviews of association (etiology): The Joanna Briggs Institute’s approach. Int. J. Evid.-Based Healthc. 2015, 13, 163–169.
4. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan,
S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med. 2021, 18, e1003583.
5. Shea, B.J.; Reeves, B.C.; Wells, G.; Thuku, M.; Hamel, C.; Moran, J.; Moher, D.; Tugwell, P.; Welch, V.; Kristjansson, E.; et al.
AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare
interventions, or both. BMJ 2017, 358, j4008.
6. Pieper, D.; Antoine, S.L.; Mathes, T.; Neugebauer, E.A.; Eikermann, M. Systematic review finds overlapping reviews were not
mentioned in every other overview. J. Clin. Epidemiol. 2014, 67, 368–375.8.
7. Del Sole, F.; Farcomeni, A.; Loffredo, L.; Carnevale, R.; Menichelli, D.; Vicario, T.; Pignatelli, P.; Pastori, D. Features of severe
COVID-19: A systematic review and meta-analysis. Eur. J. Clin. Investig. 2020, 50, e13378.
8. Cheng, S.; Zhao, Y.; Wang, F.; Chen, Y.; Kaminga, A.C.; Xu, H. Comorbidities’ potential impacts on severe and non-severe
patients with COVID-19: A systematic review and meta-analysis. Medicine 2021, 100, e24971.
9. Honardoost, M.; Janani, L.; Aghili, R.; Emami, Z.; Khamseh, M. The Association between Presence of Comorbidities and COVID-
19 Severity: A Systematic Review and Meta-Analysis. Cerebrovasc. Dis. 2021, 50, 132–140.
10. Yin, T.; Li, Y.; Ying, Y.; Luo, Z. Prevalence of comorbidity in Chinese patients with COVID-19: Systematic review and meta-
analysis of risk factors. BMC Infect. Dis. 2021, 21, 200.
11. Dorjee, K.; Kim, H.; Bonomo, E.; Dolma, R. Prevalence and predictors of death and severe disease in patients hospitalized due to
COVID-19: A comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients. PLoS ONE 2020, 15, e0243191.
12. Zhou, Y.; Yang, Q.; Chi, J.; Dong, B.; Lv, W.; Shen, L.; Wang, Y. Comorbidities and the risk of severe or fatal outcomes associated
with coronavirus disease 2019: A systematic review and meta-analysis. Int. J. Infect. Dis. 2020, 99, 47–56.
13. Yang, J.; Zheng, Y.; Gou, X.; Pu, K.; Chen, Z.; Guo, Q.; Ji, R.; Wang, H.; Wang, Y.; Zhou, Y. Prevalence of comorbidities and its
effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int. J. Infect. Dis. 2020, 94, 91–95.
14. Ssentongo, P.; Ssentongo, A.E.; Heilbrunn, E.S.; Ba, D.M.; Chinchilli, V. Association of cardiovascular disease and 10 other pre-
existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PLoS ONE 2020, 15, e0238215.
15. Sahu, A.K.; Mathew, R.; Aggarwal, P.; Nayer, J.; Bhoi, S.; Satapathy, S.; Ekka, M. Clinical Determinants of Severe COVID-19
Disease—A Systematic Review and Meta-Analysis. J. Glob. Infect. Dis. 2021, 13, 13–19.
16. Li, J.; He, X.; Yuan, Y.; Zhang, W.; Li, X.; Zhang, Y.; Li, S.; Guan, C.; Gao, Z.; Dong, G. Meta-analysis investigating the relationship
between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumo-
nia. Am. J. Infect. Control 2021, 49, 82–89.
17. Giri, M.; Puri, A.; Wang, T.; Guo, S. Comparison of clinical manifestations, pre-existing comorbidities, complications and treat-
ment modalities in severe and non-severe COVID-19 patients: A systemic review and meta-analysis. Sci. Prog. 2021, 104,
368504211000906.
18. Fernández Villalobos, N.V.; Ott, J.J.; Klett-Tammen, C.J.; Bockey, A.; Vanella, P.; Krause, G.; Lange, B. Effect modification of the
association between comorbidities and severe course of COVID-19 disease by age of study participants: A systematic review
and meta-analysis. Syst. Rev. 2021, 10, 194.
19. Matsushita, K.; Ding, N.; Kou, M.H.; Hu, X.; Chen, M.K.; Gao, Y.; Honda, Y.M.; Zhao, D.; Dowdy, D.; Mok, Y.; et al. The Rela-
tionship of COVID-19 Severity with Cardiovascular Disease and Its Traditional Risk Factors: A Systematic Review and Meta-
Analysis. Glob. Heart 2020, 15, 64.
20. Li, X.; Zhong, X.; Wang, Y.; Zeng, X.; Luo, T.; Liu, Q. Clinical determinants of the severity of COVID-19: A systematic review
and meta-analysis. PLoS ONE 2021, 16, e0250602.
21. Khan, M.M.A.; Khan, M.N.; Mustagir, M.G.; Rana, J.; Islam, M.S.; Kabir, M. Effects of underlying morbidities on the occurrence
of deaths in COVID-19 patients: A systematic review and meta-analysis. J. Glob. Health 2020, 10, 020503.
22. Booth, A.; Reed, A.B.; Ponzo, S.; Yassaee, A.; Aral, M.; Plans, D.; Labrique, A.; Mohan, D. Population risk factors for severe
disease and mortality in COVID-19: A global systematic review and meta-analysis. PLoS ONE 2021, 16, e0247461.
23. Kristensen, N.M.; Gribsholt, S.B.; Andersen, A.L.; Richelsen, B.; Bruun, J.M. Obesity augments the disease burden in COVID-
19: Updated data from an umbrella review. Clin. Obes. 2022, 12, e12508.
24. Kastora, S.; Patel, M.; Carter, B.; Delibegovic, M.; Myint, P.K. Impact of diabetes on COVID-19 mortality and hospital outcomes
from a global perspective: An umbrella systematic review and meta-analysis. Endocrinol. Diabetes Metab. 2022, 5, e00338.
25. Harrison, S.L.; Buckley, B.J.R.; Rivera-Caravaca, J.M.; Zhang, J.; Lip, G.Y.H. Cardiovascular risk factors, cardiovascular disease,
and COVID-19: An umbrella review of systematic reviews. Eur. Heart J.-Qual. Care Clin. Outcomes 2021, 7, 330–339.
26. Treskova-Schwarzbach, M.; Haas, L.; Reda, S.; Pilic, A.; Borodova, A.; Karimi, K.; Koch, J.; Nygren, T.; Scholz, S.; Schönfeld, V.;
et al. Pre-existing health conditions and severe COVID-19 outcomes: An umbrella review approach and meta-analysis of global
evidence. BMC Med. 2021, 19, 212.
Biomedicines 2022, 10, 2437 15 of 15
27. Hameed, I.; Masoodi, S.R.; Mir, S.A.; Nabi, M.; Ghazanfar, K.; Ganai, B. Type 2 diabetes mellitus: From a metabolic disorder to
an inflammatory condition. World J. Diabetes 2015, 6, 598–612.
28. Blanco-Melo, D.; Nilsson-Payant, B.E.; Liu, W.C.; Uhl, S.; Hoagland, D.; Møller, R.; Jordan, T.X.; Oishi, K.; Panis, M.; Sachs, D.;
et al. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 2020, 181, 1036–1045.e9.
29. Smith, J.C.; Sausville, E.L.; Girish, V.; Yuan, M.L.; Vasudevan, A.; John, K.M.; Sheltzer, J.M. Cigarette Smoke Exposure and Inflamma-
tory Signaling Increase the Expression of the SARS-CoV-2 Receptor ACE2 in the Respiratory Tract. Dev. Cell 2020, 53, 514–529.e3.
30. Witkowski, J.M.; Fulop, T.; Bryl, E. Immunosenescence and COVID-19. Mech. Ageing Dev. 2022, 204, 111672.
31. Shepherd, R.; Cheung, A.S.; Pang, K.; Saffery, R.; Novakovic, B. Sexual Dimorphism in Innate Immunity: The Role of Sex Hor-
mones and Epigenetics. Front. Immunol. 2021, 11, 604000.
32. Sama, I.; Ravera, A.; Santema, B.T.; Van Goor, H.; Ter Maaten, J.M.; Cleland, J.G.F.; Rienstra, M.; Friedrich, A.W.; Samani, N.J.;
Ng, L.L.; et al. Circulating plasma concentrations of angiotensin-converting enzyme 2 in men and women with heart failure
and effects of renin-angiotensin-aldosterone inhibitors. Eur. Heart J. 2020, 41, 1810–1817.
33. Brake, S.J.; Barnsley, K.; Lu, W.; McAlinden, K.D.; Eapen, M.S.; Sohal, S. Smoking Upregulates Angiotensin-Converting En-
zyme-2 Receptor: A Potential Adhesion Site for Novel Coronavirus SARS-CoV-2 (COVID-19). J. Clin. Med. 2020, 9, 841.
34. Chavez-MacGregor, M.; Lei, X.; Zhao, H.; Scheet, P.; Giordano, S.H. Evaluation of COVID-19 Mortality and Adverse Outcomes
in US Patients with or without Cancer. JAMA Oncol. 2022, 8, 69–78.
35. Naimi, A.; Yashmi, I.; Jebeleh, R.; Mofrad, M.I.; Abhar, S.A.; Jannesar, Y.; Heidary, M.; Pakzad, R. Comorbidities and mortality rate in
COVID-19 patients with hematological malignancies: A systematic review and meta-analysis. J. Clin. Lab. Anal. 2022, 36, e24387.
36. Martynowicz, H.; Jodkowska, A.; Poręba, R.; Mazur, G.; Więckiewicz, M. Demographic, clinical, laboratory, and genetic risk
factors associated with COVID-19 severity in adults: A narrative review. Dent. Med. Probl. 2021, 58, 115–121.
37. Akiyama, S.; Hamdeh, S.; Micic, D.; Sakuraba, A. Prevalence and clinical outcomes of COVID-19 in patients with autoimmune
diseases: A systematic review and meta-analysis. Ann. Rheum. Dis. 2021, 80, 384–391.
38. Di Stefano, V.; Battaglia, G.; Giustino, V.; Gagliardo, A.; D’Aleo, M.; Giannini, O.; Palma, A.; Brighina, F. Significant reduction
of physical activity in patients with neuromuscular disease during COVID-19 pandemic: The long-term consequences of quar-
antine. J. Neurol. 2021, 268, 20–26.
