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Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records

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Background: It remains unknown whether the frequency and severity of COVID-19 affect women differently than men. Here, we aim to describe the characteristics of COVID-19 patients at disease onset, with special focus on the diagnosis and management of female patients with COVID-19. Methods: We explored the unstructured free text in the electronic health records (EHRs) within the SESCAM Healthcare Network (Castilla La-Mancha, Spain). The study sample comprised the entire population with available EHRs (1,446,452 patients) from January 1st to May 1st, 2020. We extracted patients' clinical information upon diagnosis, progression, and outcome for all COVID-19 cases. Results: A total of 4,780 patients with a test-confirmed diagnosis of COVID-19 were identified. Of these, 2,443 (51%) were female, who were on average 1.5 years younger than males (61.7±19.4 vs. 63.3±18.3, p=0.0025). There were more female COVID-19 cases in the 15-59 yr.-old interval, with the greatest sex ratio (SR; 95% CI) observed in the 30-39 yr.-old interval (1.69; 1.35-2.11). Upon diagnosis, headache, anosmia, and ageusia were significantly more frequent in females than males. Imaging by chest X-ray or blood tests were performed less frequently in females (65.5% vs. 78.3% and 49.5% vs. 63.7%, respectively), all p<0.001. Regarding hospital resource use, females showed less frequency of hospitalization (44.3% vs. 62.0%) and ICU admission (2.8% vs. 6.3%) than males, all p<0.001. Conclusion: Our results indicate important sex-dependent differences in the diagnosis, clinical manifestation, and treatment of patients with COVID-19. These results warrant further research to identify and close the gender gap in the ongoing pandemic.
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1
EVIDENCE OF GENDER BIAS IN THE DIAGNOSIS AND MANAGEMENT OF
COVID-19 PATIENTS: A BIG DATA ANALYSIS OF ELECTRONIC HEALTH
RECORDS
Julio Ancochea, MD 1,2,3, Jose L. Izquierdo, MD4,5, Savana COVID-19 Research
Group*, and Joan B. Soriano, MD 1,2,3 (ORCID 0000-0001-9740-2994)
1 Hospital Universitario de La Princesa, Madrid
2 Universidad Autónoma de Madrid, Madrid
3 Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Instituto
de Salud Carlos III (ISCIII), Madrid;
4 Universidad de Alcalá, Madrid
5 Hospital Universitario de Guadalajara, Guadalajara
all in Spain
*Savana COVID-19 Research Group are: Ignacio H. Medrano, MD; Alberto Porras,
MD, PhD; Marisa Serrano, PhD; Sara Lumbreras, PhD, Universidad Pontificia
Comillas (ORCID: 0000-0002-5506-9027); Carlos Del Rio-Bermudez, PhD (ORCID:
0000-0002-1036-1673); Stephanie Marchesseau, PhD; Ignacio Salcedo; Imanol
Zubizarreta; Yolanda González, PhD.
Corresponding author full contact details:
Dr. Joan B Soriano, MD, PhD, FERS, FCCP
Servicio de Neumología
Hospital Universitario de la Princesa, UAM
Diego de León 62, 28005-Madrid, Spain
Email: jbsoriano2@gmail.com
Cellular: +34 618867769
Word count (Abstract): 250 words
Word count (main text): 2,377 words
Number of references: 42 references
Number of illustrations: 5 tables and 3 figures
Date: July 19, 2020
File name: Big COVIData-Sex_v04.docx
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2
ABSTRACT
Background: It remains unknown whether the frequency and severity of COVID-19
affect women differently than men. Here, we aim to describe the characteristics of
COVID-19 patients at disease onset, with special focus on the diagnosis and
management of female patients with COVID-19.
Methods: We explored the unstructured free text in the electronic health records (EHRs)
within the SESCAM Healthcare Network (Castilla La-Mancha, Spain). The study sample
comprised the entire population with available EHRs (1,446,452 patients) from January
1st to May 1st, 2020. We extracted patients’ clinical information upon diagnosis,
progression, and outcome for all COVID-19 cases.
Results: A total of 4,780 patients with a test-confirmed diagnosis of COVID-19 were
identified. Of these, 2,443 (51%) were female, who were on average 1.5 years younger
than males (61.7±19.4 vs. 63.3±18.3, p=0.0025). There were more female COVID-19
cases in the 15-59 yr.-old interval, with the greatest sex ratio (SR; 95% CI) observed in
the 30-39 yr.-old interval (1.69; 1.35-2.11). Upon diagnosis, headache, anosmia, and
ageusia were significantly more frequent in females than males. Imaging by chest X-ray
or blood tests were performed less frequently in females (65.5% vs. 78.3% and 49.5%
vs. 63.7%, respectively), all p<0.001. Regarding hospital resource use, females showed
less frequency of hospitalization (44.3% vs. 62.0%) and ICU admission (2.8% vs. 6.3%)
than males, all p<0.001.
Conclusion: Our results indicate important sex-dependent differences in the diagnosis,
clinical manifestation, and treatment of patients with COVID-19. These results warrant
further research to identify and close the gender gap in the ongoing pandemic.
Short Title: Gender Bias & COVID-19
Keywords: artificial Intelligence; sex differences; COVID-19; Natural Language
Processing; SARS-CoV-2.
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INTRODUCTION
As of July 2020, the World Health Organization (WHO) has declared that the coronavirus
disease 2019 (COVID-19) pandemic is far from controlled. The cumulative number of
confirmed COVID-19 cases across 216 countries, areas, or territories worldwide
amounts to over 11,874,226, and 545,481 confirmed deaths have been reported to date.
1
Record daily numbers of both infections and casualties are seen in many countries, with
many of them already experiencing ‘second waves’ after lockdowns lift.
2
Ever since COVID-19 was initially identified on December 31, 2019 in Wuhan (Hubei
Province, China)
3
, there remain many unknowns regarding the epidemiology, clinical
characteristics, prognosis, and management of the disease.
4
Although substantial efforts
have been aimed at improving our clinical understanding of the disease, less is known
about the gendered impact of the current pandemic. Indeed, investigating sex- and
gender-related issues in healthcare is an ongoing and unmet need,
5
and it is considered
a research priority issue within the WHO’s Sustainable Development Goals, a strategic
opportunity to promote human rights, and achieve health for all.
6
Characterizing the extent to which COVID-19 impacts women and men differently is of
vital importance to better understand the consequences of the pandemic and to design
equitable health policies and effective therapeutic strategies. In this line, recent evidence
suggests that there are indeed sex differences in the clinical outcomes of COVID-19.
7
,
8
,
9
Some hypotheses underscore the influence of hormonal factors,
10
immune response,
11
differential distribution of the ACE-2 receptors, and smoking habits,
12
among others.
13
To further characterize the gendered impact of COVID-19, here we aimed to address
whether the frequency and severity of COVID-19 affect women differently than men. In
addition, we sought to explore the factors underlying these differences. To achieve these
goals, we used big data analytics and artificial intelligence to explore the unstructured,
free-text clinical information captured in the electronic health records (EHRs) of a large
series of test-confirmed COVID-19 cases.
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METHODS
This study is part of the BigCOVIData initiative
14
and was conducted in compliance with
legal and regulatory requirements.
15
This study was classified as a ‘non-post-
authorization study’ (EPA) by the Spanish Agency of Medicines and Health Products
(AEMPS), and was approved by the Research Ethics Committee at the University
Hospital of Guadalajara (Spain). We have followed the STrengthening the Reporting of
OBservational studies in Epidemiology (STROBE) guidance for reporting observational
research.
16
Study design, data source, and patient population
This was a retrospective, multicenter study using secondary free-text data from patients’
EHRs within the SESCAM Healthcare Network in Castilla-La Mancha, Spain. Data was
retrieved from all available departments, including inpatient hospital, outpatient hospital,
and emergency room, for virtually all types of provided services in each participating
hospital. The study period was January 1, 2020 May 1, 2020.
The study database was fully anonymized and aggregated, so it did not contain patients’
personally identifiable information. Given that clinical information was handled in an
aggregate, anonymized, and irreversibly dissociated manner, patient consent
regulations do not apply to the present study.
The study sample included all patients in the source population with test-confirmed
COVID-19 (mainly PCR+ but also IgG/IgM+).
Extracting free-text from EHRs: EHRead®
To meet the study objectives we used EHRead2, a technology developed by SAVANA
that applies Natural Language Processing (NLP), machine learning, and deep learning
to access and analyze the unstructured, free-text information jotted down by health
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professionals in EHRs. The process used for the extraction of clinical data by EHRead
has been previously described.
17
Briefly, all extracted clinical terms are standardized
according to a unique terminology. This custom-made terminology is based on
SNOMED-CT and includes more than 400,000 medical concepts, acronyms, and
laboratory parameters aggregated over the course of five years of free-text mining.
These clinical entities are detected in the unstructured free text are then classified based
on EHRs’ sections using a combination of regular expression rules and machine learning
models. Deep learning (CNN) classification methods, which rely on word embeddings
and context information, are also used to determine whether the clinical information is
expressed in terms of negative, speculative, or affirmative statements.
Internal validation
For particular cases where extra specifications are required (e.g., to differentiate COVID
cases from other mentions of the term related to fear of the disease or potential contact),
the detection output was manually reviewed in more than 5000 reports to avoid any
ambiguity associated with free-text reporting. All NLP deep learning models used here
were validated using the standard training/validation/testing approach; we used a
75/12/13 split ratio in the available annotated data (between 2,000 and 3,000 records,
depending on the model) to ensure efficient generalization on unseen cases. For the
linguistic validation of analyzed variables regarding COVID-19 mentions,
signs/symptoms (e.g., dyspnea, tachypnea, pneumonia), laboratory values (e.g., ferritin,
LDH) and treatments (e.g., hydroxychloroquine, cyclosporine, Lopinavir/Ritonavir) we
obtained F-scores (the harmonic mean between precision and recall) greater than 0.80
in all cases. However, the validation of PCR-confirmed COVID-19’ returned a F-score
of 0.64; although the precision in the identification of this concept was very high (0.90),
the recall value was 0.5. This means that even though our model accurately identifies
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6
PCR+ cases (i.e., very low number of false positives), the prevalence data reported here
may be underestimated.
Data Analyses
We generated frequency tables to display the information regarding comorbidities,
symptoms, and other categorical variables. Continuous variables (e.g., age) were
described using summary tables containing mean, standard deviation, median, minimum
and maximum values, and quartiles for each variable. To test for possible statistically
significant differences in the distribution of categorical variables between males and
females, we used Yates-corrected chi2 tests for percentages or analysis of variance for
normally distributed continuous variables. Sex ratios and their 95% CIs of several
epidemiological and clinical indicators are presented. To determine whether the sex
ratios of confirmed COVID-19 cases significantly varied across time, we performed linear
regression analyses to test the null hypothesis that the slope is equal to zero. All
statistical inferences were performed at the 5% significance level using 2-sided tests or
95% CIs.
RESULTS
From a source population of 2,045,385 individuals, we extracted and analyzed the
clinical information of 1,446,452 patients with available EHRs from January 1st to May
1st, 2020. Among these, we then retrieved the clinical information upon diagnosis,
progression, and outcome for 4,780 patients with a test-confirmed diagnosis of COVID-
19, of whom 2,443 (51%) were women. The patient flowchart for female and male
patients is depicted in Figure 1.
Isolated COVID-19 cases were already identified in the SESCAM system early in
January and February 2020, yet they were scarce up to the first week of March 2020.
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Shortly after, confirmed cases raised exponentially and reached a daily maximum at the
end of March/early April, 2020. This peak in newly reported cases was followed by a
slow decrease; by early May 2020, confirmed cases went close to near-zero levels
(Figure 2a). As shown in Figure 2b, the proportion of COVID-19 cases in females
remained remarkably constant throughout the beginning of the outbreak up to the
plateau, while it significantly increased by the end of the study period. Our linear
regression analyses showed that the sex ratio of confirmed cases (newly identified cases
in females over new cases in males) significantly increased over time, p < 0.001.
Female COVID-19 patients were on average 1.5 years younger than males (61.7±19.4
vs. 63.3±18.3, p=0.0025). In addition, there were more female patients in the 15-59 yr-
old interval (Figure 3), with the greatest sex ratio (SR; 95% CI) observed in the 30-39
yr.-old interval (1.686; 1.351-2.113) (Table 1).
We did not observe any sex-dependent differences in the number of COVID-19 cases
per 100,000 individuals; the prevalence rates for female and male patients was 239.7
and 227.6, respectively, with a corresponding sex ratio (95% CI) of 1.054 (0.995-1.115),
p=0.0741 (Table 1). The data shown in Table 2 indicates an age-dependent increase
in reported cases in both males and females, being patients aged >79 years the most
affected with rates of 968.1 in men and 689.3 in women, and corresponding sex ratio
(95% CI) of 0.712 (0.632-0.803), p<0.001.
Regarding symptoms upon diagnosis, headache, anosmia, and ageusia were
significantly more frequent in women than men, all p<0.001 (Table 2). Interestingly,
imaging by chest X-ray or blood tests were performed less frequently in females (65.5%
vs. 78.3% and 49.5% vs. 63.7%, respectively), all p<0.001. Regarding hospital resource
use, female COVID-19 patients showed less frequency of hospitalization (44.3% vs.
62.0%) and ICU admission (2.8% vs. 6.3%) than males, all p<0.001.
As expected, comorbidities upon COVID-19 diagnosis were more often reported in men.
Whereas 78.9% of female patients had at least one of the studied comorbidities at
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8
diagnosis, this percentage was 87.4% in males (p=0.0183) (Table 3). However,
depressive disorders and asthma were significantly more frequent in females, with
associated ratios of 2.030 (1.616-2.565) and of 1.743 (1.363-2.241), respectively.
According to the laboratory parameters upon COVID-19 diagnosis, men significantly
suffered more from lymphopenia and worse renal function (as per creatinine and urea
values but not GFR) than women (Table 4). Au contraire, all liver function parameters ,
as well as D-dimer and all acute phase reactants (except for higher CRP levels in men)
were also evenly distributed by sex.
Finally, we explored the treatments received by all COVID-19 patients (Table 5). Our
results indicate that except chloroquine, the sex ratio for all treatments analyzed was <
1. Notably, most of these comparisons were statistically significant against female
patients with COVID-19 (Table 5).
DISCUSSION
Using a big data approach and adopting a population perspective, we have identified
important sex-dependent differences in the clinical manifestation, diagnosis,
management, and hospital resource use associated with COVID-19. Specifically, female
teenagers and young adult women were significantly more affected by COVID-19 than
their male counterparts in the same age ranges; In addition, our results indicate that
headache, as well as ear, nose and throat (ENT) symptoms were significantly more
frequent in female COVID-19 patients. Regarding medical outcomes, both
hospitalization and ICU admission were less frequent outcomes in females than males.
Unfortunately, basic diagnostic tests such as blood tests or imaging were less used in
women.
Our results provide further evidence of the inherent gender bias in the Health System,
which is thought to originate in medical school and impacts all aspects of healthcare.
18
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19
Although this bias is well established the context of cardiovascular
20
, respiratory
21
,
22
,
23
,
and infectious diseases (particularly, STDs
24
), the impact of sex and gender in the
ongoing COVID-19 pandemic is just beginning to be unraveled.
25
,
26
,
27
Beyond
mechanistic and molecular studies,5-9 more subtle and general events may already play
a role in the sex-dependent management of COVID-19 patients.
28
,
29
One key question is
whether COVID-19 affects women’s reproductive health; in other coronavirus-related
infectious diseases such as the severe acute respiratory syndrome (SARS) and the
middle east respiratory syndrome (MERS), pregnancy has been identified as a risk factor
for developing severe complications.
30
,
31
The increased vulnerability of women to COVID-19 is also associated with occupational
risks. It is well established that most frontline health care professionals are women, which
puts them at a higher risk for infection and negative clinical outcomes.
32
Further, women
are more likely to serve as the primary caregivers within a household, thus becoming
more exposed to the disease. This becomes worrying in disadvantaged populations and
resource-poor communities, as well as countries without the benefits of a universal, free-
for-all healthcare system.
Strengths and limitations
The main strengths of our research include immediacy, large sample size, and direct
access to real-world evidence (RWE). Of note, our methodology ensures absence of any
bias in patient selection as our hypothesis that gender impacts diagnosis and
management of COVID-19 was assessed a posteriori. The observed change in the sex
ratio of confirmed cases at the tail of this first wave of the pandemic should be further
confirmed in other cohorts and geographical locations.
33
Finally, it is unlikely that our
conclusions are impacted by the limitations of pay- or copay-systems, as Spain enjoys
an universal, free-for-all health care system.
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Our results should be interpreted in light of the following limitations. First, given the
variation in COVID-19 severity, it is possible that the free-text information available in
EHRs is not homogeneous across patients seen in different points of care (i.e., primary-
to-tertiary care). For instance, care providers could have been more likely to further
explore (and report more often) milder symptoms in women, who in turn are more likely
to be seen in primary care; on the other hand, the more sever ymptoms reported in men
may be related to the fact that they were more likely to be hospitalized or visit the ICU.
Second, it is possible that women were more likely than men to report ENT symptoms.
34
Third, as indicated in the methods section, our reported COVID-19 prevalence rates are
probably lower than real, as some cases might be missed by the system due to
heterogeneous reporting in EHRs. However, the observed low recall metrics in variables
related to the identification of PCR-confirmed patients do not affect the quality of the
descriptive results since our precision metrics for these concepts were optimal.
Implications for future research
The well-established gender bias in cardiovascular
35
, respiratory
36
,
37
,
38
, and other
diseases should be further investigated in COVID-19 patients. Despite recent regulations
and partial improvements, the attention paid to sex and gender differences in biomedical
and health research is far from optimal.
39
As pointed out in recent reviews, occupational
gender segregation makes women particularly vulnerable to COVID-19 since two-thirds
of the health and social care workforce worldwide are women.
40
Crucially, any gender
bias in the use of diagnostic testing and imaging, as evidenced in our research from a
country with universal, free-for-all healthcare, might be magnified in less privileged
settings.
Conclusion
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11
The biological, behavioral, social, and systemic factors underlying the differences in how
women and men may experience COVID-19 and its consequences cannot be
oversimplified.
41
Regrettably, most research studies are systematically failing to offer
comparisons between women and men, girls and boys, and people with diverse gender
identities.
42
Based on the results presented here, we conclude that women were more
heavily impacted by COVID-19 than men (specifically teenagers and young adults). In
addition, women presented different symptoms at disease onset, clinical outcomes, and
treatment patterns. These results warrant further research to identify and close the
gender gap in the diagnosis and treatment of COVID-19.
Acknowledgments. We thank all the Savaners for helping accelerate health science
with their daily work. We also thank SESCAM (Healthcare Network in Castilla-La
Mancha) for its participation in the study and for supporting the development of cutting-
edge technology in real time.
Author Contribution statement for each author: JA, IHM, JLI and JBS had the original
idea of the study and developed the concept protocol; AP, SL, CDRB, SM, IS and IZ
developed the analytical plan and conducted the statistical analyses; CDRB and JBS
wrote and edited the manuscript; CDRB and IZ are responsible for figures and data
visualization; all authors contributed to drafting and interpretation, and they approved the
final version.
Author Disclosure Statement. The Big COVIData study was funded by Savana.
Savana employees contributed to the design, data analysis, and writing of the present
study. All authors declare there are no other direct or indirect potential conflicts to
disclose.
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FIGURES
Figure 1. Patient flowchart. Flowchart depicting the total number of inhabitants in the
source population, the number (%) of patients with available EHRs analyzed, the number
of patients diagnosed with COVID-19, and of those, the number of hospitalizations and
ICU admissions. = male patients; = female patients.
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Figure 2. Epidemiological curve and sex ratios showing COVID-19 cases within
the study period. (A) Epidemiological curve showing test-confirmed COVID-19 cases
(i.e., PCR+/ IgG/IgM+) across time within the study period in male (blue) and female
(green) patients. (B) Sex Ratios depicting the variation of confirmed COVID-19 cases
over time within the study period, calculated as the number of diagnosed female patients
over male patients. The dotted red line indicates a sex ratio of 1 (that is, equal proportion
of diagnosed male and female patients). As indicated by the linear regression plot, the
sex ratio increases over time, indicating a growing number of diagnosed women (in
relation to men). *p < 0.001 (slope). Shaded gray area indicates CI (95%).
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Figure 3. Age and Sex Distribution of COVID-19 patients. Age distribution of incident
cases of COVID-19 in females (left) and males (right) in the study population for the
period comprised between Jan 1, 2020 and May 1, 2020.
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TABLES
TABLE 1. Number of COVID-19 cases by age group and sex
Total population*
Age (yr-old)
Female
Male
Female
Male
Sex ratio
IC (95%)
p-value**
Total
1,018,707
1,026,678
239.7
227.6
1.054
0.995-1.115
0.0741
<15
148,133
157,505
11.5
17.1
0.672
0.358-1.225
0.2486
15-29
156,432
168,664
67.1
40.3
1.663
1.229-2.267
0.0012
30-39
128,166
136,230
156.0
92.5
1.686
1.351-2.113
<0.001
40-49
159,660
169,961
217.3
172.4
1.261
1.079-1.474
0.0039
50-59
150,689
157,227
329.2
280.5
1.173
1.032-1.335
0.0159
60-69
108,557
109,862
342.7
409.6
0.837
0.729-0.960
0.0121
70-79
85,197
73,926
400.2
562.7
0.711
0.616-0.821
<0.001
>79
81,970
53,301
689.3
968.1
0.712
0.632-0.803
<0.001
Footnote:*Total population of Castilla La-Mancha (Spain). **p-values from Yates-corrected chi2 test on percentage
difference of female vs. male COVID-19 patients.
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16
TABLE 2. Clinical manifestations of COVID-19 upon diagnosis.
Female, n(%)
(N=2,443)
Male, n(%)
(N=2,337)
TOTAL, n(%)
(N=4,780)
Sex
ratio
95%CI
p-
value*
Age (yr-old)
Mean(SD)
61.7(19.4)
63.3 (18.3)
62.5 (18.9)
0.0025
Signs and Symptoms, n (%)
Patients with no symptoms
705(28.9)
486(20.8)
1191(24.9)
1.387
1.22-1.579
<0.001
Cough
1,094 (44.8)
1,199 (51.3)
2,293 (48.0)
0.873
0.79-0.964
0.0080
Fever
878 (36.0)
1,169 (50.0)
2,047 (42.8)
0.719
0.647-0.797
<0.001
Dyspnoea
759 (31.1)
914 (39.1)
1,673 (35.0)
0.794
0.71-0.888
<0.001
Respiratory crackles
472 (19.3)
627 (26.8)
1,099 (23.0)
0.720
0.631-0.822
<0.001
Diarrhoea
385 (15.8)
350 (15.0)
735 (15.4)
1.052
0.901-1.23
0.5467
Headache
277 (11.3)
166 (7.1)
443 (9.3)
1.596
1.307-1.953
<0.001
Myalgia
230 (9.4)
207 (8.9)
437 (9.1)
1.063
0.874-1.294
0.5757
Lymphopenia
147 (6.0)
186 (8.0)
333 (7.0)
0.756
0.604-0.945
0.0163
Rhonchus
133 (5.4)
179 (7.7)
312 (6.5)
0.711
0.563-0.896
0.0044
Chest pain
158 (6.5)
153 (6.5)
311 (6.5)
0.988
0.785-1.243
0.9635
Anosmia
153 (6.3)
109 (4.7)
262 (5.5)
1.342
1.044-1.731
0.0254
Tachypnoea
74 (3.0)
133 (5.7)
207 (4.3)
0.533
0.397-0.71
<0.001
Wheezing
69 (2.8)
86 (3.7)
155 (3.2)
0.768
0.555-1.059
0.1250
Skin symptoms (*)
39(1.6)
34(1.5)
73(1.5)
1.097
0.689-1.753
0.7834
Rhinitis
24 (1.0)
24 (1.0)
48 (1.0)
0.957
0.538-1.701
0.9938
Ageusia
31(1.3)
15 (0.6)
46 (1.0)
1.966
1.073-3.766
0.0403
Sore throat
27 (1.1)
18 (0.8)
45 (0.9)
1.431
0.789-2.657
0.2993
Dysphagia
12 (0.5)
20 (0.9)
32 (0.7)
0.577
0.272-1.173
0.1746
Neuralgia
16 (0.7)
13 (0.6)
29 (0.6)
1.175
0.561-2.507
0.8025
Hemoptysis
9 (0.4)
12 (0.5)
21 (0.4)
0.721
0.29-1.723
0.5919
Ophthalmologic symptoms (#)
9(0.4)
9(0.4)
18(0.4)
0.957
0.368-2.487
1
Splenomegaly
3 (0.1)
6 (0.3)
9 (0.2)
0.491
0.098-1.924
0.4641
Hepatomegaly
2 (0.1)
5 (0.2)
7 (0.1)
0.400
0.051-1.943
0.4159
Respiratory rate (bpm)
N
249
339
588
Mean(SD)
23.3(14.4)
23.9(12.6)
23.7(13.4)
Patients (n,%) with high RR
(>20)
105(42.3)
167(49.3)
272(46.3)
0.856
0.637-1.148
0.3356
Radiological findings, n (%)
Chest X-ray
1600(65.5)
1829(78.3)
3429(71.7)
0.837
0.766-0.914
<0.001
No abnormalities
552(34.5)
450(24.6)
1002(29.2)
1.402
1.217-1.615
<0.001
Any abnormality
1048(65.5)
1379(75.4)
2427(70.8)
0.869
0.782-0.965
0.0091
Bilateral infiltrates
902(56.4)
1213(66.3)
2115(61.7)
0.850
0.762-0.948
0.0039
Ground-glass opacities
277(17.3)
380(20.8)
657(19.2)
0.833
0.704-0.986
0.0378
Interstitial pattern
152(9.5)
175(9.6)
327(9.5)
0.993
0.790-1.246
0.9972
Alveolar bilateral infiltrates
60(3.8)
88(4.8)
148(4.3)
0.780
0.556-1.088
0.1683
Unilateral infiltrates
7(0.4)
15(0.8)
22(0.6)
0.540
0.203-1.295
0.2393
Arterial blood gases, n (%)
pH
N
476
646
1122
Mean(SD)
7.4(0.1)
7.4(0.1)
7.4(0.1)
Patients (n,%) with pH>7,42
298(62.6)
449(69.5)
747(66.6)
0.901
0.746-1.087
0.2982
pO2 (mmHg)
N
553
778
1331
Mean(SD)
72.1(24.8)
70.5(26.9)
71.2(26.1)
Patients (n,%) with pO2<60
144(26.0)
249(32.0)
393(29.5)
0.814
0.644-1.026
0.0924
pCO2 (mmHg)
N
443
622
1065
Mean(SD)
35.8(7.3)
34.5(7.9)
35.1(7.7)
Patients (n,%) with
pCO2>45
46(10.5)
42(6.8)
88(8.4)
1.538
0.993-2.387
0.0661
O2 Sat (%)
N
1188
1336
2524
Mean(SD)
94.1(5.7)
93.3(5.6)
93.7(5.6)
Patients (n,%) with O2 Sat<94
385(32.4)
528(39.5)
913(36.2)
0.820
0.704-0.955
0.0122
Footnote: *p-values from Yates-corrected chi2 test of difference between percentage of patients (female vs. male) presenting with
the sign/symptom. All tests were performed individually for each variable sign/symptom.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. .https://doi.org/10.1101/2020.07.20.20157735doi: medRxiv preprint
17
TABLE 3. Comorbidities of COVID-19 patients upon diagnosis.
Female
(N=2,443)
Male
(N=2,337)
TOTAL
(N=4,780)
Sex
ratio
95%CI
p-value*
Any comorbidity, n (%)
1,928(78.9)
2,043(87.4)
3,971(83.1)
0.903
0.830-0.982
0.0183
Hypertension
843(34.5)
1,027(43.9)
1,870(39.1)
0.785
0.705-0.874
<0.001
Heart disease
681(27.9)
908(38.9)
1,589(33.2)
0.718
0.640-0.804
<0.001
Ischemic heart disease
72(2.9)
243(10.4)
315(6.6)
0.284
0.215-0.370
<0.001
Heart failure
145(5.9)
151(6.5)
296(6.2)
0.919
0.726-1.162
0.5164
Diabetes
349(14.3)
502(21.5)
851(17.8)
0.665
0.573-0.771
<0.001
Kidney disease
295(12.1)
463(19.8)
758(15.9)
0.610
0.521-0.713
<0.001
Chronic kidney disease
123(5.0)
202(8.6)
325(6.8)
0.583
0.461-0.733
<0.001
Obesity
279(11.4)
244(10.4)
523(10.9)
1.094
0.913-1.311
0.3545
Cancer
200(8.2)
310(13.3)
510(10.7)
0.617
0.512-0.743
<0.001
Haemotologic malignancies
46(1.9)
55(2.4)
101(2.1)
0.801
0.537-1.189
0.3142
Prostate cancer
-
83(3.6)
83(1.7)
-
-
-
Breast cancer
50(2.0)
1(0.0)
51(1.1)
41.852
9.322-974.645
<0.001
Colon cancer
15(0.6)
30(1.3)
45(0.9)
0.481
0.25-0.885
0.0261
Lung cancer
6(0.2)
36(1.5)
42(0.9)
0.163
0.061-0.361
<0.001
Depressive disorder
240(9.8)
113(4.8)
353(7.4)
2.030
1.616-2.565
<0.001
Cerebrovascular disease
152(6.2)
182(7.8)
334(7.0)
0.799
0.639-0.998
0.0545
Ischemic Stroke
68(2.8)
108(4.6)
176(3.7)
0.603
0.441-0.819
0.0015
COPD
64(2.6)
266(11.4)
330(6.9)
0.231
0.173-0.303
<0.001
Asthma
186(7.6)
102(4.4)
288(6.0)
1.743
1.363-2.241
<0.001
Autoimmune disease
111(4.5)
43(1.8)
154(3.2)
2.463
1.737-3.556
<0.001
Obstructive sleep apnea syndrome
42(1.7)
66(2.8)
108(2.3)
0.610
0.409-0.898
0.0157
Alzheimer Disease
48(2.0)
39(1.7)
87(1.8)
1.176
0.768-1.812
0.5201
Epilepsy
30(1.2)
42(1.8)
72(1.5)
0.684
0.423-1.095
0.141
Chronic Liver Disease
26(1.1)
41(1.8)
67(1.4)
0.608
0.366-0.993
0.0605
Parkinson Disease
37(1.5)
27(1.2)
64(1.3)
1.309
0.796-2.18
0.3473
Bronchiectasis
18(0.7)
45(1.9)
63(1.3)
0.385
0.216-0.656
<0.001
Immunodeficiency disorder
29(1.2)
30(1.3)
59(1.2)
0.925
0.55-1.552
0.8668
HIV
6(0.2)
12(0.5)
18(0.4)
0.485
0.165-1.265
0.2042
Footnote: *p-values from Yates-corrected chi2 test of difference between percentage of patients (female vs. male) diagnosed with each
condition or disease. All tests were performed individually for each comorbidity
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. .https://doi.org/10.1101/2020.07.20.20157735doi: medRxiv preprint
18
TABLE 4. Laboratory parameters of COVID-19 patients upon diagnosis.
Female
(N=2,443)
Male
(N=2,337)
TOTAL
(N=4,780)
Sex
ratio
95%CI
p-value*
Patients with blood test n(%)
1,210(49.5)
1,489(63.7)
2,699(56.5)
0.777
0.707-0.855
<0.001
Hematology
White blood cell (x10e3/mm3)
N
749
939
1688
Mean(SD)
11.2(45.3)
11.2(49.0)
11.2(47.4)
Patients (n,%) with high white
blood cell count (>9.5 males /
>11.1 females)
178(23.8)
249(26.5)
427(25.3)
0.896
0.722-1.111
0.3448
Neutrophil (x10e3/mm3)
N
350
443
793
Mean(SD)
5.6(3.0)
5.7(2.9)
5.7(2.9)
Patients (n,%) with high neutrophil
count (>6.1 males / >7.5 females)
218(37.3)
314(41.9)
532(39.9)
0.892
0.727-1.093
0.2930
Lymphocyte (x10e3/mm3)
N
520
692
1212
Mean(SD)
1.5(1.5)
1.5(1.8)
1.5(1.6)
Patients (n,%) with low
lymphocyte count (<1.1)
375(46.0)
578(57.0)
953(52.1)
0.807
0.688-0.947
0.0094
Liver Function
Bilirubin (mg/dl)
N
352
497
849
Mean(SD)
0.7(0.8)
0.8(0.8)
0.8(0.8)
Patients (n,%) with high levels
(>1.2)
23(6.5)
61(12.3)
84(9.9)
0.535
0.318-0.870
0.0167
ALT (u/l)
N
913
1171
2084
Mean(SD)
40.3(102.0)
52.1(57.5)
46.9(80.3)
Patients (n,%) with high levels
(>55 male / >53 female)
162(17.7)
305(26.0)
467(22.4)
0.682
0.552-0.839
<0.001
AST (u/l)
N
735
922
1657
Mean(SD)
49.9(247.2)
52.8(45.6)
51.5(168.0)
Patients (n,%) with high levels
(>40 male / >37 female)
275(37.4)
454(49.2)
729(44.0)
0.760
0.635-0.908
0.0029
GGT (u/l)
N
198
315
513
Mean(SD)
74.1(82.2)
112.7(156.4)
97.8(134.0)
Patients (n,%) with high levels
(>64 male / >36 female)
124(62.6)
154(48.9)
278(54.2)
1.281
0.952-1.722
0.1173
Renal Function
Creatinine (mg/dl)
N
1015
1280
2295
Mean(SD)
1.0(0.8)
1.2(1.3)
1.1(1.1)
Patients (n,%) with high levels
(>1.3)
142(14.0)
285(22.3)
427(18.6)
0.629
0.505-0.780
<0.001
Urea (mg/dl)
N
879
1129
2008
Mean(SD)
50.8(47.4)
53.6(38.8)
52.4(42.8)
Patients (n,%) with low levels
(<20)
75(8.5)
33(2.9)
108(5.4)
Patients (n,%) with high levels
(>48)
265(30.1)
422(37.4)
687(34.2)
0.807
0.675-0.963
0.0195
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. .https://doi.org/10.1101/2020.07.20.20157735doi: medRxiv preprint
19
Female
(N=2,443)
Male
(N=2,337)
TOTAL
(N=4,780)
Sex
ratio
95%CI
p-value*
Glomerular Filtration Rate
(ml/min/1.73m2)
N
304
372
676
Mean(SD)
60.2(30.7)
62.6(30.8)
61.5(30.7)
Patients (n,%) with low rate (<60)
111(36.5)
125(33.6)
236(34.9)
1.087
0.807-1.463
0.6368
Coagulation, Inflammatory and
tissue damage markers
D-Dimer (mg/l)
N
831
1006
1837
Mean(SD)
461(773.2)
492(851.2)
478(816.7)
Median(Min-Max)
14.1(0-4860)
7.2(0-4976)
8.9(0-4976)
(Q1-Q3)
0.6-649
0.7-737.8
0.7-691
Patients (n,%) with high levels
(>0,49)
683(82.2)
843(83.8)
1526(83.1)
0.981
0.856-1.124
0.8078
C-Reactive Protein (mg/l)
N
1173
1439
2612
Mean(SD)
51.4(77.3)
70.6(92.0)
62(86.2)
Median(Min-Max)
18(0-524)
29(0-690)
22.9(0-690)
(Q1-Q3)
4.7-64.8
8.0-96.8
6.0-79.8
Patients (n,%) with high levels
(>8)
763(65.0)
1071(74.4)
1834(70.2)
0.874
0.775-0.986
0.031
Ferritin (ng/ml)
N
365
470
835
Mean(SD)
520(762.5)
1037.2(1211.7)
811.1(1070.2)
Median(Min-Max)
362(4-9559)
745(11-19391)
524.7(4-19391)
(Q1-Q3)
164.0-606.0
434.8-1299.2
271-1054.5
Patients (n,%) with high levels
(>250 male / >120 female)
298(81.6)
417(88.7)
715(85.6)
0.920
0.752-1.126
0.45
LDH (u/l)
N
885
1133
2018
Mean(SD)
373.1(484.1)
402.8(259.1)
389.8(374.9)
Median(Min-Max)
302(1-13260)
336(16-4276)
319(1-13260)
(Q1-Q3)
230-434
243-494
236-466
Patients (n,%) with high levels
(>243)
619(69.9)
847(74.8)
1466(72.6)
0.936
0.817-1.072
0.3548
Fibrinogen (mg/dl)
N
394
499
893
Mean(SD)
544.8(201.2)
593.4(235.3)
571.9(222.1)
Median(Min-Max)
530.5(24-1496)
577.9(156-1579)
548(24-1579)
(Q1-Q3)
370-674.8
370-763
370-720
Patients (n,%) with high levels
(>400)
276(70.1)
343(68.7)
619(69.3)
1.019
0.829-1.253
0.8988
Procalcitonin (ng/ml)
N
404
543
947
Mean(SD)
0.7(4.5)
0.9(3.5)
0.8(4)
Median(Min-Max)
0.1(0-71.2)
0.1(0-50.1)
0.1(0-71.2)
(Q1-Q3)
0.1-0.2
0.1-0.3
0.1-0.2
Patients (n,%) with high levels
(>0,05)
308(76.2)
485(89.3)
793(83.7)
0.854
0.704-1.035
0.1174
Footnote: *p-values from Yates-corrected chi2 test of difference between percentage of patients (female vs. male) in either outcome group
(high levels). All tests were performed individually for each parameter.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. .https://doi.org/10.1101/2020.07.20.20157735doi: medRxiv preprint
20
TABLE 5. Treatments used in Covid-19 patients.
Female,N(%)
(N=2,443)
Male, N(%)
(N=2,337)
TOTAL(%)
(N=4,780)
Sex Ratio
95%CI
p-value*
Antibacterials
Azithromycin
1340(54.9)
1465(62.7)
2805(58.7)
0.875
0.797-0.961
0.0054
azithromycin +
hydroxychloroquine
1015(41.6)
1220(52.2)
2235(46.8)
0.796
0.720-0.880
<0.001
Ceftriaxone
736(30.1)
1033(44.2)
1769(37.0)
0.682
0.610-0.761
<0.001
Levofloxacin
205(8.4)
298(12.8)
503(10.5)
0.658
0.546-0.793
<0.001
amoxicillin
169(6.9)
209(8.9)
378(7.9)
0.774
0.626-0.955
0.0192
clarithromycin
35(1.4)
36(1.5)
71(1.5)
0.930
0.580-1.491
0.8542
doxycycline
10(0.4)
22(0.9)
32(0.7)
0.439
0.197-0.909
0.0392
Antithrombotic agents
1459(59.7)
1662(71.1)
3121(65.3)
0.840
0.767-0.919
<0.001
Vitamin K antagonists
125(5.1)
151(6.5)
276(5.8)
0.792
0.620-1.010
0.069
Heparins
776(31.8)
1032(44.2)
1808(37.8)
0.719
0.645-0.802
<0.001
Platelet aggregation inhibitors
313(12.8)
519(22.2)
832(17.4)
0.577
0.496-0.671
<0.001
Direct factor Xa inhibitors
81(3.3)
93(4.0)
174(3.6)
0.833
0.614-1.129
0.2696
Direct thrombin inhibitors
7(0.3)
18(0.8)
25(0.5)
0.377
0.145-0.874
0.0353
Enzymes
3(0.1)
6(0.3)
9(0.2)
0.491
0.098-1.924
0.4641
Antimalarials
hydroxychloroquine
1207(49.4)
1478(63.2)
2685(56.2)
0.781
0.71-0.859
<0.001
chloroquine
33(1.4)
29(1.2)
62(1.3)
1.088
0.657-1.81
0.8388
Antivirals
ritonavir
439(18.0)
656(28.1)
1095(22.9)
0.640
0.560-0.731
<0.001
darunavir and cobicistat
24(1.0)
31(1.3)
55(1.2)
0.742
0.429-1.267
0.3337
darunavir
0(0)
5(0.2)
5(0.1)
-
Mucolytics
acetylcysteine
572(23.4)
626(26.8)
1199(25.1)
0.876
0.771-0.994
0.0431
Immunosuppresants
glucocorticoids
682(27.9)
1019(43.6)
1701(35.6)
0.640
0.572-0.716
<0.001
tozilizumab
37(1.5)
89(3.8)
126(2.6)
0.399
0.267-0.583
<0.001
Selective
immunosuppressants
25(1.0)
54(2.3)
79(1.7)
0.444
0.271-0.709
<0.001
ciclosporin
1(0)
6(0.3)
7(0.1)
0.178
0.007-1.081
0.1166
Immunostimulants
Interferon beta 1b
40(1.6)
60(2.6)
100(2.1)
0.639
0.423-0.954
0.0359
Footnote: *p-values from Yates-corrected chi2 test of difference between percentage of patients prescribed with the therapeutic agents
(male vs. female). All tests were performed individually for each treatment.
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