Original article

Sex disparities in patients with suspected COVID-19 presenting at an emergency department in Switzerland

DOI: https://doi.org/10.4414/smw.2022.w30167
Publication Date: 20.05.2022
Swiss Med Wkly. 2022;152:w30167

Ketina Arslania*, Ceylan Ekena*, Sarah Tschudin-Sutterbc, Caroline E. Gebhardd, Nuria Zellwegerd, Stefano Bassettie, Roland Bingisserf, Maurin Lamparta, Stefan Osswalda, Gabriela M. Kustera**, Raphael Twerenboldagh**, for the COVIVA investigators

a Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland

b Division of Infectious Disease & Hospital Epidemiology, University Hospital Basel, University of Basel, Basel, Switzerland

c Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland

d Department of Intensive Care, University Hospital Basel, University of Basel, Basel, Switzerland

e Department of Internal Medicine, University Hospital Basel, University of Basel, Basel, Switzerland

f Department of Emergency Medicine, University Hospital Basel, University of Basel, Basel, Switzerland

g University Centre of Cardiovascular Science and Department of Cardiology, University Heart and Vascular Centre Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany

h German Centre for Cardiovascular Research (DZHK) Partner Site Hamburg–Kiel–Lübeck, Germany

* Both authors have contributed equally and should be considered first authors.

** Both authors have contributed equally and should be considered senior authors.


AIMS OF THE STUDY: In the global COVID-19 pandemic, female sex is associated with comparable infection rates but better outcome. However, most studies lacked appropriate controls. We investigated whether these sex disparity findings are specific to patients with COVID-19 or generalizable to patients presenting to the emergency room (ER) with similar symptoms but no COVID-19.

METHODS: In this prospective cohort study, consecutive patients presenting with symptoms suggestive of COVID-19 were recruited at the ER of the University Hospital Basel, Switzerland from March to June 2020. Patients were categorized as SARS-CoV-2 positive (cases) or negative (controls) based on nasopharyngeal PCR swab tests. The final clinical diagnosis was determined for all patients. The primary outcome was a composite of intensive care admission, rehospitalization for respiratory distress and all-cause death within 30 days. We used Kaplan–Meier curves and Cox proportional hazards models to explore associations between sex and outcomes.

RESULTS: Among 1,081 consecutive ER patients, 191 (18%) tested positive for SARS-CoV-2, with an even sex distribution (17.9% female vs. 17.5% male, p = 0.855). In COVID-19 patients, female sex was associated with lower risk of hospitalization (51% vs. 66%, p = 0.034), lower necessity of haemodynamic support (8% vs. 20%, p = 0.029), lower rates of intubation (10% vs. 21%, p = 0.037) and the primary outcome (18% vs. 31%, p = 0.045; age-adjusted HR 0.536, 95%CI 0.290–0.989, p = 0.046) compared with male sex. Sex disparities were most prominent in patients ≥55 years (HR for composite primary outcome in women 0.415, 95%CI 0.201–0.855, p = 0.017). In contrast to the COVID-19 patients, no sex-specific differences in outcomes were observed in the unselected overall control group (age-adjusted HR 0.844, 95%CI 0.560–1.273, p = 0.419) or in a subgroup of controls with upper respiratory tract infections or pneumonia (age-adjusted HR 0.840, 95%CI 0.418–1.688, p = 0.624).

CONCLUSION: In this unselected, consecutive cohort study at a tertiary hospital in Switzerland, female sex is associated with better outcome in patients presenting to the ER with COVID-19. These sex disparities seem to be at least partly specific to COVID-19, as they were not observed in comparable controls.


A novel pneumonia-like illness outbreak that started in China in December 2019 resulted in a worldwide pandemic, as declared by the World Health Organization on March 11, 2020. The cause was identified as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a positive-sense, single-stranded RNA virus [1]. Several studies have reported an association between male sex and severe outcome, including intensive care unit (ICU) admission and death, in SARS-CoV-2 infected patients [2–5]. On the other hand, there is little data providing information about sex differences in coronavirus disease 2019 (COVID-19) incidence. Recent global data reported by the Global Health 50/50 research initiative show that the infection rates seem to be comparable for both sexes [2, 6]. Regardless, most of these studies only included patients with confirmed SARS-CoV-2 infections and lacked adequate controls of patients with similar symptoms and disease severity but without SARS-CoV-2 infection. Thus, we aimed to investigate whether sex disparities in outcomes are present in patients with COVID-19, and whether these are specific to COVID-19 or generalizable to all patients with symptoms suggestive of COVID-19 infection upon ER presentation.


Study design, population and inclusion criteria

The prospective cohort COronaVIrus surviVAl (COVIVA, ClinicalTrials.gov NCT04366765) study included unselected patients aged 18 years or older presenting to the emergency room (ER) of the University Hospital Basel, Switzerland, with clinically suspected or confirmed SARS-CoV-2 infection during the first wave of the COVID-19 pandemic between March and June, 2020. All patients with clinical suspicion of COVID-19 underwent nasopharyngeal SARS-CoV-2 swab tests. Patients were considered SARS-CoV-2 positive (cases) if, in addition to clinical signs and symptoms of COVID-19, one or more SARS-CoV-2 PCR swab tests performed on the day of ER presentation or within 14 days prior to or after the ER presentation were positive. The remaining patients with only negative SARS-CoV-2 swab test results were considered as controls. All participating patients or their legally authorized representatives consented by signing a local general consent form. This study was conducted according to the principles of the Declaration of Helsinki and was approved by the local ethics committee (EKNZ identifier 2020–00566).

The authors designed the study, gathered and analysed the data according to the STROBE guidelines for reporting observational studies [7] (table S1 in the appendix), vouched for the data and analysis, wrote the paper, and decided to submit it for publication.

Clinical assessment

All patients underwent a thorough clinical assessment by the treating ER physician according to local standard operating procedures (SOPs). Vital parameters including heart rate, blood pressure, oxygen saturation and respiratory rate were assessed in every patient. The patients’ management was left to the discretion of the attending physicians in accordance with local SOPs, which did not contain any sex-specific recommendations.

Blood sampling

Blood samples were routinely taken at the time of ER presentation in every patient (both cases and controls). Besides routine laboratory parameters, high-sensitivity troponin T (hs-cTn), natriuretic peptides, D-dimers, procalcitonin and ferritin were measured for every patient as part of the local SOP for suspected COVID-19 patients. The timing and type of subsequent laboratory measurements during hospital stay were left to the discretion of the treating physicians and were not part of this study’s protocol.


Thirty days after discharge, patients were contacted by telephone or in writing by research physicians or study nurses and information about current health, hospitalizations and adverse events were obtained using a predefined set of questions and item checklists. The records of other hospitals/ICUs and primary care physicians, as well as national death registries, were screened for additional information, if applicable.


The primary outcome measure was a composite of ICU admission, rehospitalization due to respiratory complications and all-cause death at 30 days. Secondary outcomes included the components of the composite primary endpoint, case management and length of hospital stay, as well as incidence of intubation, haemodynamic support and acute respiratory distress syndrome during the course of the index hospitalization.

Adjudication of final diagnosis

To determine the final diagnosis that led to the index ER presentation and the clinical suspicion of COVID-19, trained physicians reviewed all medical data available, including 30 days post-discharge follow-up information, and chose from a predefined list the diagnosis which best fitted each patient. The predefined main categories included, but were not limited to, COVID-19, non-SARS-CoV-2 infections (e.g. other respiratory, gastrointestinal or urogenital infections), cardiovascular disease (acute coronary syndrome, rhythm disorder, congestive heart failure, pulmonary embolism), other non-infectious pulmonary disease (e.g. lung tumour, asthma, chronic obstructive pulmonary disease) and neurologic disease (e.g. stroke, seizure).

Statistical analysis

Data are expressed as medians and interquartile ranges (IQR) for continuous variables and as numbers and percentages (%) for categorical variables. All variables were compared using Mann–Whitney U tests for continuous variables and Pearson’s chi-squared or Fisher’s exact test for categorical variables, as appropriate. In this analysis, COVID-19 patients (cases) were compared to the unselected SARS-CoV-2 negative patients (overall control group, table S2 in the appendix), as well to the subgroup of patients with acute respiratory infections but no COVID-19 (respiratory control group, e.g. viral infection of the upper airways, bronchitis, pneumonia). The composite outcome was plotted in Kaplan–Meier curves and the log-rank test was used to assess differences between groups. The association between sex and the composite outcome was assessed using age-adjusted Cox proportional hazards models. In the case of multiple events, the time to the first event within 30 days was considered. Predefined subgroup analyses were performed for patients under and over 55 years of age and with or without cardiac disease, hypertension, diabetes, obesity, smoking and pneumopathy. The cut-off age of 55 years was chosen to assess sex disparities with respect to hormonal changes before and after menopause [8]. In a second multivariable Cox proportional hazards model, we aimed to further investigate the predictive value of sex if adjusted for age and numerous comorbidities using a stepwise-backward approach. The small sample size and number of events meant that this was not possible. Prespecified subgroup comparisons were performed using multivariable Cox models, including treatment with a covariable interaction term, and were summarized using a forest plot.

All hypothesis testing was two-tailed and p-values of less than 0.05 were considered significant. No correction for multiple testing was applied. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY).



From March 2020 to June 2020, 1,086 patients presented at the ER with symptoms suggesting SARS-CoV-2 infection (e.g. dyspnoea, coughing, fever). Follow-up at 30 days was complete in 1,081 patients, meaning 5 patients were excluded from the analysis (none of them with COVID-19, see figure S1 in the appendix). The baseline characteristics of the unselected study population are listed in table S3 in the appendix. Overall, 43.4% were female and 56.6% male (p <0.001). 191 patients (18%) tested positive for SARS-CoV-2, with a median age of 57 years (IQR 44–69 years; table 1). The prevalence of COVID-19 was similar in women (n = 84, 17.9%) and men (n = 107, 17.5%, p = 0.855).

Table 1

Clinical characteristics.

 SARS-CoV-2 positive (Cases)SARS-CoV-2 negative (Overall control group)SARS-CoV-2 negative with respiratory infection (Respiratory control group)
All (n = 191)Female (n = 84)Male (n = 107)All (n = 890)Female (n = 385)Male (n = 505)All (n = 323)Female (n = 142)Male (n = 181)
Age – years [IQR]57 [44,69]57 [41,67]58 [46,69]57 [38,74]61 [43,74]57 [44,69]59 [41,74]57 [38,74]61 [43,74]
Age ≥55 years103 (54%)45 (54%)58 (54%)499 (56%)201 (52%)298 (59%)174 (54%)70 (49%)104 (58%)
Risk factors and history
Comorbidity burden – median [IQR]1 [0,3]1 [0,3]2 [0,3]2 [0,3]1 [0,3]2 [1,4]2 [0,3]1 [0,3]2 [1,4]
Cardiac disease38 (20%)11 (13%)27 (25%)261 (29%)85 (22%)176 (35%)92 (29%)30 (21%)62 (34%)
CAD21 (11%)2 (2%)19 (18%)131 (15%)26 (7%)105 (21%)43 (13%)11 (8%)32 (18%)
Hypertension81 (42%)33 (39%)48 (45%)367 (41%)146 (37.9%)221 (44%)142 (44%)58 (41%)84 (46%)
Smoking58 (30%)19 (23%)39 (36%)361 (41%)107 (28%)254 (50%)159 (49%)50 (35%)109 (60%)
COPD9 (5%)2 (2%)7 (7%)111 (12%)36 (9%)75 (15%)58 (18%)20 (14%)38 (21%)
Diabetes34 (18%)12 (14%)22 (21%)137 (15%)41 (11%)96 (19%)51 (16%)18 (13%)33 (18%)
Obesity74 (39%)30 (36%)44 (41%)278 (31%)100 (26%)178 (35%)91 (28%)33 (23%)58 (32%)
Renal insufficiency26 (14%)10 (12%)16 (15%)145 (16.3%)52 (14%)93 (18%)39 (12%)16 (11%)23 (13%)
Stroke/TIA10 (5%)3 (4%)7 (7%)70 (8%)25 (7%)45 (9%)19 (6%)5 (4%)14 (8%)
Beginning of symptoms in days – median [IQR]7 [3,11]7 [3,11]7 [3,11]3 [2,8]3 [2,8]3 [2,7]3 [2,8]3 [2,8]3 [2,7]
Fever104 (55%)42 (50%)62 (58%)353 (40%)155 (40%)198 (39%)147 (46%)67 (47%)80 (44%)
Chills31 (16%)11 (13%)20 (19%)165 (18.5%)84 (22%)81 (16%)68 (21%)38 (27%)30 (17%)
Cough126 (66%)52 (62%)74 (69%)465 (52%)207 (54%)258 (51%)242 (75%)108 (76%)134 (74%)
Dyspnoea81 (42%)38 (45%)43 (40%)438 (49%)192 (50%)246 (49%)185 (57%)82 (58%)103 (57%)
Clinical parameters – median [IQR]
Temperature* in °C37.1 [36.8,38]37.1 [36.8,38]37.2 [36.8,38]37 [36.5,37.7]37 [36.6,37.5]37 [36.5,37.7]37 [36.5,37.7]37 [36.6,37.5]37 [36.5,37.7]
Respiratory rate*20 [16,24]20 [16,23]20 [16,25]18 [16,23]19 [16,23]18 [16,22]18 [16,23]19 [16,23]18 [16,22]
SaO2* in %97 [95,98]97 [96,98]96 [94,98]97 [95,98]97 [96,99]97 [95,98]97 [95,98]97 [96,99]97 [95,98]
Heart rate*89 [80,103]89 [76,101]90 [82,103]88 [75,103]89 [75,102]87 [74,103]88 [75,103]89 [75,102]87 [74,103]
Blood pressure – systolic* in mmHg135 [122,149]128 [116,153]136 [124,148]137 [121,156]135 [120,156]138 [123,155]137 [121,156]135 [120,156]138 [123,155]
Blood pressure – diastolic* in mmHg82 [71,90]80 [71,86]83 [72,90]81 [72,90]80 [70,86]82 [75,91]81 [72,90]80 [70,86]82 [75,91]
BMI in kg/m²29 [25,32]29 [25,33]29 [25,32]26 [23,30]25 [22,31]25 [23,29]26 [23,30]25 [22,31]26 [23,29]
Laboratory parameters – median [IQR]
Leucocytes in *109/l6.27 [4.95,8.34]5.96 [4.53,7.92]6.71 [5.12,8.74]8.48 [6.6,11.1]9.1 [6.91,12.01]8.82 [6.82,11.7]8.82 [6.82,11.7]8.48 [6.6,11.1]9.1 [6.91,12.01]
Lymphocytes – absolute in *109/l1.07 [0.72,1.57]1.3 [0.82,1.77]0.98 [0.69,1.35]1.58 [0.92,2.14]1.42 [0.9,2.02]1.47 [0.9,2.08]1.47 [0.9,2.08]1.58 [0.92,2.14]1.42 [0.9,2.02]
Lymphocytes in %19.2 [11.9,26.9]23.5 [14.8,30.6]17.1 [11.4,22.6]18.9 [10,28.7]16.2 [9.6,25.5]17.2 [9.8,26.6]17.2 [9.8,26.6]18.9 [10,28.7]16.2 [9.6,25.5]
CRP in mg/l28.9 [2.6,73.4]12.1 [1.5,49.1]36.2 [7.8,106.6]5.9 [1,40.6]10.4 [1.3,53.7]7.6 [1.2,47.6]7.6 [1.2,47.6]5.9 [1,40.6]10.4 [1.3,53.7]
Procalcitonin in µg/l0.09 [0.059,0.182]0.065 [0.059,0.146]0.102 [0.059,0.237]0.072 [0.059,0.287]0.103 [0.059,0.332]0.093 [0.059,0.332]0.093 [0.059,0.332]0.072 [0.059,0.287]0.103 [0.059,0.332]
D-dimer in µg/l0.58 [0.35,1.19]0.6 [0.37,1.22]0.57 [0.34,1.18]0.56 [0.29,1.12]0.59 [0.29,1.27]0.58 [0.29,1.19]0.58 [0.29,1.19]0.56 [0.29,1.12]0.59 [0.29,1.27]
Ferritin in ng/ml387 [164,823]193 [84,395]578 [308,1194]121 [62,224]206.5 [116,410]163 [85,329]163 [85,329]121 [62,224]206.5 [116,410]
Creatinine in µmol/l76 [62,95]62 [54,75]85 [74,100]62 [55,77]83 [71,99]75 [61,93]75 [61,93]62 [55,77]83 [71,99]
hs-troponin T in ng/l7 [4,14]5 [3,13]8.5 [5,15]5 [3,16.5]12 [6,26]9 [4,22]9 [4,22]5 [3,16.5]12 [6,26]
NT-proBNP in pg/ml77 [49,242]66 [49,208]82.5 [49,250]110 [49,370]117 [49,569]114.5 [49,462]114.5 [49,461.5]110 [49,370]117 [49,569]

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; IQR: interquartile range; CAD: coronary artery disease; COPD: chronic obstructive pulmonary disease; TIA: transient ischaemic attack; SaO2: arterial oxygen saturation; BMI: body mass index; CRP: C-reactive protein; hs-troponin T: high-sensitivity troponin T; NT-proBNP: N-terminal pro brain natriuretic peptide

In the total study population, as well as in those with COVID-19, men had a significantly higher burden of comorbidity, more often had cardiac disease or coronary artery disease (CAD), were more often smokers, and had higher infection parameters (CRP, leucocytes), ferritin and hs-cTn levels compared to women. In contrast, procalcitonin levels were higher only in male SARS-CoV-2 positive patients, which was not the case for controls. Comparable findings were observed in the subgroups of SARS-CoV-2 positive patients <55 and ≥55 years (table S4 in the appendix).

Sex and outcome

SARS-CoV-2 positive female patients were less frequently hospitalized (51% vs. 66%, p = 0.034), intubated (10% vs. 21%, p = 0.037) or received haemodynamic support (8% vs. 20%, p = 0.029) than men. In contrast, no sex differences for the above-mentioned endpoints could be observed in the overall control group or the respiratory control group (table 2).

Table 2


 SARS-CoV-2 positive (Cases)SARS-CoV-2 negative (Overall control group)SARS-CoV-2 negative with respiratory infection (Respiratory control group)
All (n = 191)Female (n = 84)Male (n = 107)p-valueAll (n = 890)Female (n = 385)Male (n = 505)p-valueAll (n = 323)Female (n = 142)Male (n = 181)p-value
Case management
– Outpatient77 (40%)41 (49%)36 (34%)0.034*451 (50%)209 (54%)242 (48%)0.069*186 (57%)87 (61%)99 (54%)0.215*
– Inpatient114 (60%)43 (51%)71 (66%)444 (50%)179 (46%)265 (52%)138 (43%)55 (39%)83 (46%)
Length of stay in days – median [IQR]3 [0,8]0 [0,6]5 [0,11]0.003*0 [0,6]0 [0,5]0 [0,6]0.022*0 [0,6]0 [0,5]0 [0,6]0.045*
Composite endpoint**48 (25%)15 (18%)33 (31%)0.04596 (11%)37 (10%)59 (12%)0.32333 (10%)13 (9%)20 (11%)0.328
– ICU admission40 (21%)13 (16%)27 (25%) 63 (7%)25 (6%)38 (8%) 23 (7%)10 (7%)13 (7%) 
– All-cause death within 30 days16 (8%)5 (6%)11 (10%) 61 (7%)21 (6%)40 (8%) 23 (7%)8 (6%)15 (8%) 
– Rehospitalization for respiratory complications4 (2%)1 (1%)3 (3%) 24 (3%)8 (2%)16 (3%) 10 (3%)4 (3%)6 (3%) 
Intubation30 (16%)8 (10%)22 (21%) 23 (3%)11 (3%)12 (2%) 15 (5%)7 (5%)8 (4%) 
Haemodynamic support28 (15%)7 (8%)21 (20%) 26 (3%)10 (3%)16 (3%) 14 (4%)6 (4%)8 (4%) 
ARDS26 (14%)7 (8%)19 (18%) 6 (1%)2 (1%)4 (1%) 4 (1%)2 (1%)2 (1%) 

* Analyses and p-values are exploratory

** Admission to ICU, rehospitalization for respiratory complication and all-cause death within 30 days

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; IQR: interquartile range; ICU: intensive care unit; ARDS: acute respiratory distress syndrome

The composite primary outcome occurred in 144 of 1,081 (13%) patients, with a significantly higher incidence of 25% (48/191 patients) in COVID-19 cases compared to 11% (96/890 patients) in the overall control group (p<0.001) and 10% (33/323) in the respiratory control group.

Importantly, within the SARS-CoV-2 positive population the incidence of the composite outcome was significantly lower in women (18%, 15/84 patients) than in men (31%, 33/107 patients, log-rank p = 0.045; age-adjusted HR for female sex 0.536, 95%CI 0.290–0.989, p = 0.046). Both age and sex were independent predictors of the composite outcome in COVID-19 positive patients. In contrast, no sex-related differences were observed among the SARS-CoV-2 negative patients, neither in the overall control group (age-adjusted HR 0.844, 95%CI 0.560–1.273, p = 0.419) nor in the respiratory control group (age-adjusted HR 0.840, 95%CI 0.418–1.688, p = 0.624). Figure 1 shows the Kaplan–Meier curves for the occurrence of the composite outcome within 30 days for men and women in the SARS-CoV-2 positive and negative groups.

Figure 1

Composite outcome of admission to ICU, rehospitalization for respiratory complication and death within 30 days in COVID-19 cases and overall control; depicted hazard ratios (HR) are age-corrected.

Figure S2 in the appendix shows the same for the respiratory control group.

When stratified by age, in COVID-19 patients under the age of 55 years, no sex differences in the primary outcome could be observed (HR 1.275, 95%CI 0.369–4.405, p = 0.701, figure 2).

Figure 2

Composite outcome of admission to ICU, rehospitalization for respiratory complication and death within 30 days in SARS-CoV-2 positive patients under and over the age of 55 years.

In contrast, in COVID-19 patients ≥55 years, female sex was associated with a significantly superior event-free survival (HR 0.415, 95%CI 0.201–0.855, p = 0.017).

In additional subgroup analyses, a consistent trend towards better outcomes in women could be observed in the majority of subgroups without reaching the level of significance (figure 3).

Figure 3

Forest plot showing age-adjusted hazard ratios (HR) and their corresponding 95% confidence intervals to quantify the impact of sex on the composite primary outcome in predefined subgroups within the COVID-19 cases (first column) and the controls (second column).


In this prospective cohort study of consecutive patients presenting with suspected SARS-CoV-2 infection to the ER of the University Hospital in Basel, Switzerland, we explored the impact of sex on clinical outcome in COVID-19 patients and in comparable controls.

First, the prevalence of confirmed COVID-19 infections in patients presenting to the ER was comparable between women and men. Second, the incidence of adverse clinical outcomes, defined as the composite of ICU admission, rehospitalization for respiratory distress or death within 30 days, was almost halved in women compared to in men with COVID-19, regardless of age. In addition, female patients with SARS-CoV-2 infection were less often hospitalized, intubated or in need of haemodynamic support. Third, sex-related differences in outcome were most prominent in patients aged 55 years and older and were seen across a range of additional subgroup analyses. Fourth, the observed sex disparities were consistently associated with COVID-19 infections and were not found in SARS-CoV-2 negative patients, nor in patients with other upper respiratory infections or pneumonia.

Among patients presenting to the ER with suspected COVID-19, male sex was more prevalent, as had already been observed in patients with similar symptoms before the pandemic [9]. However, in line with the most recent data, our results showed that the prevalence of SARS-CoV-2 infection was similar in men and women [2, 6, 10, 11].

Male sex and older age have been described as predictors of adverse outcome [4, 12–14]. However, whether this is specific to COVID-19 has not yet been clearly established. To the best of our knowledge, this is one of the few studies recruiting consecutive patients presenting to the ER with symptoms suggesting SARS-CoV-2 infection and therefore with parallel enrolment of an adequate control group. Importantly, we could demonstrate that the highest risk was observed in male COVID-19 patients over the age of 55, whose risk was substantially higher compared to their age-matched female counterparts.

Thus, a postulated protective effect of female sex hormones, which are presumably present in patients <55 years, seems to be neglectable regarding disease severity and outcome in symptomatic COVID-19 patients [15, 16]. In contrast, some data in mouse models even suggest a stronger immunological response driven by oestrogen. The sex disparities observed in this analysis could be explained by a plethora of possible reasons, such as differences in cardiovascular comorbidities, genetic factors and immune functions [15–17]. Some influencing factors could also possibly be attributed to gender, which is defined by social and cultural norms, roles and behaviours. As an example, women tend to take over the role of the primary caregiver not only at home, but also in the health care system. Based on our exploratory subgroup analyses, cardiovascular comorbidities and risk factors do not themselves seem to the primary factors accounting for the sex differences observed in COVID-19, as a numerical advantage for women was observed across most risk categories. The innate and adaptive immune response to infections is generally stronger in women than in men [18, 19]. Moreover, some sex-related immunological aspects change according to age, which leads to men being potentially more susceptible to infections and therefore having worse outcomes [15]. Thus, sex differences in immune composition and response may be another reason for the observed sex differences in SARS-CoV-2 infection outcome. The key finding of this study is that the observed sex disparities in COVID-19 could not be seen in either the control group of unselected patients presenting with symptoms suggestive of COVID-19 but testing negative for SARS-CoV-2 or in a subgroup of patients with other upper respiratory infections and pneumonia. Accordingly, the sex disparities seem not to be generalizable to all patients presenting to the ER with symptoms suggestive of COVID-19, but may be related to disease-specific mechanisms involved in COVID-19.


The main limitation of our study is its single centre design, with all data coming from one Swiss tertiary centre. However, during the current COVID-19 pandemic the number of affected patients, disease severity and the overloading of limited health care resources have differed widely between regions and health care systems. While most of the data reported are from severely affected regions, our data represent a typical Central European setting with a health care system that was not overloaded. Accordingly, event rates observed in this analysis are lower than the ones reported from North America or China [20, 21]. Second, the study may be insufficiently powered for multivariable analysis due to the limited number of SARS-CoV-2 positive patients, and also, only adjusting for age could have led to substantial residual confounding. Accordingly, no direct causalities can be extrapolated from our findings, as our data only allow us to explore associations. Nevertheless, given the design of this study (i.e. consecutive inclusion of unselected control patients presenting with comparable symptoms in the same time period), the observed results still add valuable information to the literature. In particular, they allow us to put findings observed in COVID-19 patients into perspective when compared with other acute conditions, including respiratory infections from other causes. Our findings need to be validated in larger future studies, as residual confounding could be substantial. Third, the patients for this analysis were recruited if they had symptoms suggesting COVID-19, leading to their triage to the ER during the first wave of the pandemic. Therefore, our findings cannot be extrapolated to the general population or to asymptomatic patients with SARS-CoV-2 infection.


Despite similar prevalences of SARS-CoV-2 infection in women and men, female sex is associated with better outcome in symptomatic COVID-19 patients presenting to the ER, particularly in patients aged  years. Sex disparities seem to be specific to COVID-19, as they were not observed in comparable controls. Further studies are needed in order to explain the underlying mechanisms.


We thank the clinical staff for their valuable help during the study period and all the patients for their participation in the study.

Financial disclosure

The COVIVA study was supported by the Swiss Heart Foundation, the Cardiovascular Research Foundation Basel and an unrestricted grant by Roche Diagnostics.

Potential competing interests

Dr. Arslani has received a research grant from the Swiss Academy of Medical Sciences and the Bangerter Foundation (YTCR 09/19) and the Swiss National Science Foundation (P500PM_202963).

Dr. Twerenbold reports research support from the Swiss National Science Foundation (Grant No P300PB_167803), the Swiss Heart Foundation, the Swiss Society of Cardiology, the Cardiovascular Research Foundation Basel, the University of Basel and the University Hospital Basel, as well as speaker honoraria/consulting honoraria from Abbott, Amgen, Astra Zeneca, Roche, Siemens, Singulex and Thermo Scientific BRAHMS.

Dr. Kuster reports research support from the Swiss National Science Foundation (Grant No IZCOZ0_189877) and the Cardiovascular Research Foundation Basel, Switzerland, that are unrelated to this work, and speaker honoraria/consulting honoraria from Janssen-Cilag.

Dr. Gebhard reports funding from the Swiss National Science Foundation (Grant No 31CA30196140), the University of Basel and the University Hospital of Basel.

Authors not named here have disclosed no conflicts of interest.


Header image: © Bogdan Lazar | Dreamstime.com


Dr. Raphael Twerenbold, MD

Clinic of Cardiology

University Hospital Basel

Petersgraben 4

CH-4031 Basel



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Appendix: Supplementary data

The appendix is available in the pdf version of the article.

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