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Sex differences in COVID-19 infections

Könsberoende skillnader i COVID-19 infektioner

Majlinda Spahi

Faculty of Health, Science and Technology

Biology

Bachelor´s thesis, 15 hp

Supervisor: Ted Morrow

Examiner: Larry Greenberg

2020-06-05

Series number: 20:180

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Abstract

The Coronavirus disease 2019 (COVID-19) outbreak have shown that there may be sex-

dependent differences in morbidity and mortality among individuals contracted with the

disease. The aim of the study was to analyse the extent that sex differences appear in COVID-

19 infections and to explore whether any differences are due to intrinsic factors in the sexes that

cause sex-bias in the disease susceptibility and mortality. The study presents an age and sex-

disaggregated analysis of reported cases of total infections, intensive care cases, and deaths

across 13 countries due to the disease. The results demonstrated that there is a general trend

for the disease prevalence, and it shows a female bias among the proportion of individuals

infected with COVID-19. However, males appear to require more intensive care treatment

and higher rates of death when compared to females. The results also show that more

women than men are reportedly infected by the corona virus up to a certain age. After the

age of 60, the proportion of men affected is higher than women, and it is also at this age

that the death rate among men increases significantly. In conclusion, the results of this work

indicate that males could possibly be at a significantly higher risk of severe disease and

death than females, and that the patterns of sex bias in intensive care cases to some extent

follows the expected pattern if sex hormones played a role in influencing the immune

system response to COVID-19.

Keywords: COVID-19, corona virus, sex differences, sex hormones

Sammanfattning

Utbrottet av Coronavirus sjukdomen 2019 (COVID-19) har visat att det kan finnas

könsberoende skillnader i sjuklighet och dödlighet bland individer som drabbats av

sjukdomen. Syftet med studien var att analysera i vilken utsträckning könsskillnader

förekommer i COVID-19-infektioner och att undersöka om skillnaderna beror på inre

faktorer hos könen som möjligtvis orsakar könsfördomar i sjukdomens mottaglighet och

dödlighet. Denna studie presenterar en ålder-och könsfördelad analys av

totala antalet rapporterade fall, intensivvårdsfall och dödsfall i 13 länder till följd av

COVID-19. Resultaten visade att det finns en generell trend för sjukdomens utbredning,

och den visar en högre andel kvinnor än män som har smittats med COVID-19. Men det är

män som är mer i behov av intensivvård och har högre dödsnivåer i jämförelse med

kvinnor. Resultaten visar även att fler kvinnor än män smittas av coronaviruset upp till en viss

ålder. Efter 60-årsåldern är andelen drabbade män högre än kvinnor, det är även vid den här

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åldern som dödsnivån bland män ökar markant. Sammanfattningsvis indikerar resultaten av

denna studie att män eventuellt skulle kunna ha en betydligt högre risk för en allvarligare

sjukdom och död än kvinnor.

Nyckelord: COVID-19, coronavirus, könsskillnader, könshormoner

Introduction

The outbreak of the new strain of coronavirus called severe acute respiratory syndrome

coronavirus 2 (SARS-CoV-2) is known as the main causative for the coronavirus disease 2019

(COVID-19) which was reported in Wuhan, China on 9 January 2020. Since the outbreak there

has been a rapid increase of reported cases nationally and globally (ECDC, 2020). An early

report from Hubei province provided major findings of the disease outbreak in China including

observations and key discoveries. The report explained that SARS-CoV-2 is a new coronavirus

of zoonotic origin that is highly contagious, can spread quickly, and capable of causing

disruption worldwide (WHO, 2020). More interesting findings were the sex differences among

the demographic characteristics, which showed a small male-bias in number of cases and a

higher mortality rate recorded amongst men than women. The report also revealed that the

mortality increased with age (WHO, 2020). Other countries are now observing if they are also

seeing the same pattern while the cases and deaths are increasing worldwide.

Previous research has already shown that other coronaviruses such as SARS (Severe Acute

Respiratory Syndrome) disproportionately affected men more than women during the 2003

SARS outbreak in Hong Kong (Karlberg et al, 2004). Middle Eastern Respiratory Syndrome

(MERS) coronavirus is also known to affect more men than women with a mortality rate

significantly higher among males (Jansen et al, 2015). It is therefore not surprising seeing that

this pattern seems to follow in COVID-19 cases since it is common among other coronaviruses.

Many researchers have commented on the pattern and pointed to several possible theories. One

proposed hypothesis explains that the extrinsic factors such as different social norms in gender

behaviors could be the reason for these differences. Men showing higher rates of smoking and

drinking compared to women which is found to be common worldwide. Behaviors like these

are associated with developing comorbidities such as chronic lung and heart diseases, which

are tied to worsen the outcomes if they contract the COVID-19 disease (Purdie al, 2020).

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Another theory states that the intrinsic factors play a role and that our sexes have different

immune responses.

The male and female sex differ not only in their basic physiology, but they can also have a

profound effect on their susceptibility and reaction to various diseases. This would therefore

indicate that there is indeed a sex difference between the sexes. However, much of the research

on sex difference remain poorly understood and far more need to be discovered. The general

conclusion that males are studied much more than females in both animal and human research

limits our knowledge on sex differences (Arnold, 2010).

The sex differences between male and female immune systems are known to contribute to

infectious diseases differently according to research (Purdie et al, 2020). This research explains

that the sex difference in immune responses change throughout life and are also influenced by

age. The sex difference could be because females carry two X chromosomes and men carry

only one X chromosome, that is known to contain immune-related genes and sex hormones

which can cause differential regulation of immune responses between the two sexes (Klein &

Flanagan, 2016; Roved et al. 2016).

This beginning of human adolescence causes a flow of hormones which usually begins by 9-10

years of age in girls and by 10-12 years in boys which is also known as puberty. Pre-pubertal

levels of hormones are otherwise relatively low (Peper & Dahl, 2013). Sex hormones such as

testosterone, estrogen, and progesterone fluctuate in different concentrations between the sexes

throughout life, with lower levels in children and higher levels later in life. Males have

relatively higher levels of testosterone in adult life and low levels of progesterone throughout

life. Adult females often have high levels of estrogen and progesterone at reproductive ages

which are raised substantially during pregnancy their including the testosterone levels (Roberts

et al. 2001). Women are then thought to have a dramatic loss of estrogen and male’s testosterone

level is supposed to be stable up to the age of 60, before gradually declining (Ruggieri, 2018).

Since an individual’s sex hormone levels vary with aging and it should also have effects on the

sex hormones immune response (Klein & Flanagan, 2016; Roberts et al. 2001). The hormone

levels are important and could determine the outcome of infections since testosterone is thought

to have an immunosuppressive effect while estrogen on the other hand is thought to have an

immunoenhancing effect on the immune system (Taneja, 2018). This explanation presents the

following hypothesis: 1) There is no sex-bias in susceptibility or mortality in individuals

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contracted with COVID-19 infection prior to puberty. 2) There is a male-bias in mortality and

severity among the disease infected individuals of reproductive age due to the

immunosuppressive effect of testosterone and protective effect of estrogen. 3) The male bias in

mortality should decline again in the post-reproductive age classes, together with the decline in

testosterone in males and estrogen in females.

The aim of this study was primarily to investigate the extent that sex differences occur in

COVID-19 infections and to explore whether any differences are due to intrinsic factors in the

sexes that causes sex-bias in the disease susceptibility and mortality among individuals

contracted with COVID-19. Extrinsic and intrinsic factors are both likely to play a role in this

discovered pattern among COVID-19 cases. However, this study will primarily focus on the

intrinsic factors of the sexes that could possibly determine the severity and outcome of this

coronavirus. The important characteristics of individuals for this study are sex and age reported

from cases, intensive care cases or deaths from SARS-CoV-2 worldwide, as it is those two

characters that could affect the susceptibility to infection and the severity of the infection in

individuals.

Materials & methods

Data on COVID-19 infections and mortality has been collated from national sources by the

Global Health 5050 initiative that takes action on improving gender equality and contributes to

the 2030 Agenda for Sustainable Development (Globalhealth5050, 2020). The frequency of the

variables: total cases, intensive care cases and deaths was downloaded and updated throughout

7 April- 7 may. To qualify for the analysis data from each country must have reported

cumulative totals of a variable that were disaggregated by both sex and age (in groups spanning

nine-years). The youngest age group (0-9) represented the pre-pubertal years and the second

youngest age group (10-19) represented pubertal years. The middle-aged groups (20-29, 30-39,

40-49, 50-59) represented adult age groups, and the oldest age groups (60-69, 70-79, 80+)

represented the elderly age groups. Data on all variables were sometimes not available for some

countries, with number of intensive care cases being most frequently missing. Every complete

data set for each variable was used for the statistical analysis.

The age structure of populations across countries plays a determining role in the number of

cases and deaths and must be considered in the analyses. The population size for each age class

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and sex was collected from each country’s official statistical service sources (see Appendix A).

These data were used to calculate the numbers per 100.000 of the population present for that

country, age class and sex. This to give the rate of infections, intensive care cases and deaths

per 100,000 residents. This enables a more direct comparison to be made between data points.

Reports were translated using Google translate if they were not in English or Swedish. All

processed data were exported from a Microsoft Excel spreadsheet to a SPSS (IMB SPSS

statistics 26 for Windows) spreadsheet for statistical analyses. Initially, the sex-bias in the data

were explored by subtracting the number of cases per 100,000 of the population in males from

the number in females, giving an index of how sex biased each dependent variable was. This

would indicate a negative value for female bias. The raw count data on the three dependent

variables were then modeled using a generalized linear model with errors estimated following

a Poisson distribution the factors sex, country and age class were fitted as main effects as well

as all 2-way interactions. The 3-way interaction was not modelled due to a lack of replication

in the dataset.

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Results

As of 15 May 2020, 213 countries and territories around the world have been affected by the

new coronavirus (Worldometer, 2020). Each country had been affected differently with some

countries far more severe outcomes and some showing a higher sex difference compared to

others in general (Table 1). According to Globalhealth5050 (2020), only 40 countries had

reported sex-specific data (18.8%). Other countries either had partial sex-specific data or none.

Some of the sex-specific data were not specifically sex-disaggregated but only described as

total cases in all males and females with no age distribution which did complicate the data

collection. In this study there were 13 countries (6.1%) affected with COVID-19 that provided

the data in the format that was required for the analysis – sex and age disaggregated for at least

one of the dependent variables.

Table 1. Grand total of reported cases, intensive care cases, and deaths across countries where sex-and age-

disaggregated data were available and used for the analysis. Countries are ordered by highest total deaths and is

displayed by sums of females and males.

Country

Cases Intensive care cases Deaths

Males Females Males Females Males Females

England and Wales

15953 11377

Italy 94138 104817

15662 9553

Spain 100430 131661 5348 2361 10532 7889

France

9757 6668

Germany

4042 3218

The Netherlands

3040 2400

Switzerland 13815 16409

893 645

Portugal 10429 15095

525 538

Denmark 4197 5741 217 81 281 209

Norway 3989 4045

118 95

Sweden

1725 578

Belgium 19318 33486

Canada 7133 8213

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The number of infection cases across all 8 countries and 9 different age-classes (72 different

values in total) showed a general female bias, although with a high variance (mean =-38.08,

standard deviation = 134,59, n = 72; Figure 1a). In contrast, both number of intensive care cases

and number of deaths were both male biased on average, and with a highly skewed distribution

towards males (intensive care cases: mean = 17,76, standard deviation = 21,19, n = 23; Figure

1b) (deaths: mean = 26,01, standard deviation =57,57, n = 83; Figure 1c). There is a general

trend then for the COVID-19 infections to be appear initially as generally female biased, but

for more serious infections to be male biased, and mortality to be even more male-biased,

although there is much less data on the number of intensive care cases available.

Figure 1a: Frequency histogram shows the distribution of sex difference rates in mean and variation in all cases

per 100k. A negative value indicating a female bias and positive value indicating a male bias.

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Figure 1b: Frequency of sex difference rates in all intensive care cases per 100k of the population. A negative

value indicating a female bias and positive value indicating a male bias.

Figure 1c: Frequency of sex difference rates in all deaths per 100k of the population. A negative value indicating

a female bias and positive value indicating a male bias.

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The overall pattern of female bias in the number of infected appears to be highly dependent

upon age and country (Figure 2). There is very little difference in rates between males and

females in the two youngest age classes. The infection rate seems to be female biased up to the

ages of 50-59 and generally male biased in elderly age groups (Figure 2), although there is a

clear switch towards being female biased again in the oldest age group, especially for data from

Belgium. Data from some countries appear to show very little variation across different age

classes, including Norway and Canada.

Figure 2: A line graph that displays the mean sex difference in the rates of number of cases of COVID-19

prevalence adjusted by age class and country per 100k of the population. Each single line represents an individual

country. A negative value indicating a female bias and positive value indicating a male bias.

The three countries with complete intensive care cases dataset seem to follow the same profile

as each other but with different variance. There is a clear male-bias in all elderly age groups,

but again the oldest age-group shows a tendency to be far less male-biased (Figure 3).

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Figure 3: A line graph that displays the mean sex difference in the rates of COVID-19 intensive care cases adjusted

by age class and country per 100k of the population. Each single line represents an individual country. (Negative

values indicate a female-bias and positive value indicate a male-bias.)

The number of deaths per 100,000 of the population across countries again shows that there is

no sex difference in death rates until the age class 50-59 and onwards. It is in fact males that

are dying more frequently in all countries (figure 4), although the change across age classes

show very large differences across countries and there was a clear trend for greater and greater

male bias with increasing age, in contrast to the patterns seen for cases and intensive care cases

(Figures 3 and 4). ), with no decline in male bias in the oldest age class being present for the

mortality data.

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Figure 4: a line graph that displays the mean sex difference in the rates of COVID-19 death rates adjusted by age

class and country per 100k of the population. Each single line represents an individual country (Negative values

indicate a female-bias and positive value indicate a male-bias.)

Overall, the generalized linear models generally confirmed the patterns in sex bias observed

across age classes and countries presented in Figures 2, 3 and 4. For each of the three dependent

variables investigated the fitted models were all found to be significant (Table 2).

Table 2. Omnibus test results for three generalized linear models for each dependent variable and shows their

variation.

The following tables presented each factor in the model that was tested whether it had any effect

for chosen dependent variable. The table for Cases per 100k indicated that all three factors had

an independent significant effect on the variation, with age class and country showing a greater

proportion of the variation. All interactions between the factors were also showing a significant

effect on the variation (Table 3).

Dependent Variable

Likelihood Ratio

Chi-Square df P-value

Cases_100k 47475,081 87 <0,001

Intensive_100k 1112,776 33 <0,001

Deaths_100k 23167,143 101 <0,001

Omnibus Test

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Table 3. Refers to the generalized linear model for the dependent variable Cases per 100k.

Tests of Model Effects

Source

Type III

Wald Chi-

Square Df P-value

(Intercept) 311443,648 1 <0,001

Country 2578,352 7 <0,001

Sex 63,313 1 <0,001

Age_Class 8328,745 8 <0,001

Country * Sex 192,963 7 <0,001

Country * Age_Class 3428,001 56 <0,001

Sex * Age_Class 797,179 8 <0,001

Dependent Variable: cases_100k

Model: (Intercept), Country, Sex, Age_Class, Country * Sex, Country *

Age_Class, Sex * Age_Class

The table for Intensive care cases per 100k indicated that age class was showing the greater

proportion of the variation and all three factors had an independent significant effect on the

variation. The interaction between countries in different age classes were also showing a

significant effect on the variation. However, the other interactions were not showing a

significant effect since p > 0,05 (Table 4).

Table 4. Refers to the generalized linear model for the dependent variable Intensive care cases per 100k.

Tests of Model Effects

Source

Type III

Wald Chi-

Square df P-value

(Intercept) 314,076 1 <0,001

Country 76,199 2 <0,001

Sex 12,353 1 <0,001

Age_Class 239,657 8 <0,001

Country * Sex 2,047 2 0,359

Country * Age_Class 63,327 10 <0,001

Sex * Age_Class 7,283 7 0,400

Dependent Variable: intensive_100k

Model: (Intercept), Country, Sex, Age_Class, Country * Sex, Country *

Age_Class, Sex * Age_Class

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The table for number of deaths per 100k showed that all three factors had an independent

significant effect on the variation, with age class showing a greater proportion of the variation.

All interactions between the factors were also showing a significant effect on the variation. The

interaction between country and sex showed a larger p-value than the other interactions but still

had a significant effect on the variation (Table 5).

Table 5. Refers to the generalized linear model for the dependent variable Deaths per 100k.

Tests of Model Effects

Source

Type III

Wald Chi-

Square df P-value

(Intercept) 265,831 1 <0,001

Country 220,252 9 <0,001

Sex 11,132 1 0,001

Age_Class 3380,548 6 <0,001

Country * Sex 17,417 9 0,043

Country * Age_Class 79,192 38 <0,001

Sex * Age_Class 32,634 6 <0,001

Dependent Variable: deaths_100k

Model: (Intercept), Country, Sex, Age_Class, Country * Sex, Country *

Age_Class, Sex * Age_Class

Discussion

This study was based on tracking differences in COVID-19 infections and deaths among

women and men. The lack of published sex-disaggregated data on cases, intensive care cases,

and deaths made it more difficult and challenging to move forward with the study.

The results presented shows that there was very much variation among the variables and factors.

There was a country difference for all variables, and the interactions effects submitted showed

that there the sex differences varied in different countries. The variety of age classes also

showed different effects in different countries.

Even though earlier reports from Hubei province have shown that males are more susceptible

to the COVID-19 disease (WHO, 2020), my findings showed otherwise. This study indicated

that adult females in the ages between 20-59 years were affected by the virus the most. A shift

occurs after age ≥60 and it is elderly males that appears to have the highest rate of COVID-19

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infections. A possible reason for this could be the fact that woman dominate the health care

occupation, for example holding 76% of all health care jobs in The United States (U.S. Census

Bureau, 2019). Females working as caregivers in hospices and nursing homes, is also one of

the most female-dominated occupations in Sweden, with 91 percent women and only 9 percent

men (SCB, 2020). With more women on the frontline helping the patients infected with

COVID-19 puts them at greater risk of contracting the virus and spreading it to other colleagues

in their workplaces. Moreover, it is also these critical key workers that are being prioritized and

are receiving a limited COVID-19 testing to ensure they do not spread the virus while

caregiving (GOV.UK, 2020; Folkhälsomyndigheten, 2020). This should also be taken into

consideration since this could be a possible explanation for the results that women are showing

a higher infection rate.

The female rate of infection seems to decline around 60 years of age which is usually a normal

age for retirement, that could mean that females after the ages of 60 are not contracting the virus

as frequently as younger females since they are not as exposed to the virus any longer. It could

also justify the rate shift to male bias around age ≥60, which means it is not as based on infection

rates from exposure in workplaces. This could possibly mean that men and women are equally

susceptible to the infection, or that men are more susceptible if they were equally exposed to

the disease as much as women. The no sex bias at youngest age group 0-9 suggests that there

are no major extrinsic or intrinsic factors that are important for those age groups.

The severity of the disease displayed as the rate of intensive care cases appears to incline to

male bias gradually right around the age of ≥40 and inclines with advancing age and plummets

down to a significantly lower rate once reached age ≥80 but remains male bias in all three

countries. The lack of sex bias in pre-pubertal years and the male bias in the middle-aged years

and the plunge that demonstrates the reduced male bias in elderly age groups - are all consistent

with the idea that sex hormones may be determining the outcome of infections. It appears as

immunosuppressive effects of testosterone and immunoenhancing effects of estrogen were

acting, implying that pre-pubertal children and elderly people do not have high levels of sex

hormones and middle-aged adults do.

The tests showed that the age distributions of male and female death cases were significantly

different with a p-value of <0,0001. The fatality rate in this study appears to strongly incline to

male bias right around the age of ≥50 in all countries and the sex difference increases

significantly with age in half of the countries. The demonstrated results in this study support

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my hypotheses that there is no sex-bias in susceptibility or mortality in individuals contracted

with COVID-19 infection prior to puberty as well as there is a male-bias in mortality and

severity among the disease infected individuals of reproductive age.

It has been documented that people over 65 years of age are generally at a greater risk of

complications resulting from influenza or other infections than younger adults. Elderly people

often have weaker immune systems, which can turn the slightest infection fatal. This implies

that elderly people are more likely to suffer more severe from infectious diseases like COVID-

19 (CDC, 2019). However, it does not state why older men are dying more frequently than

women.

As mentioned earlier in the introduction the recent outbreak of COVID-19 appears to follow

the same pattern as previous corona viruses SARS and MERS, where there is a clear male bias

in the number of deaths (Karlberg et al, 2004; Jansen et al 2015). A previous study concerning

MERS are hormones such as testosterone discussed as a possible explanation for this gender

bias pattern. It is said that a high level of testosterone has a suppressive effect on immune

responses and consequently, men with a lower level of testosterone may have improved immune

responses to viral infections (Alghamdi, 2014). Whether or not sex-associated hormones

determine different immune responses in COVID-19 infections is not clear, however, some

scientists are convinced it does determine different immune responses and outcomes in SARS

infections based on experimental research on mice (Channappanavar, 2017).

The experiment was performed by injecting male and female mice of different age groups with

SARS-CoV and observing their reaction to the infection. Their result showed that male mice

were more susceptible to the infection compared to age-matched females when given the same

dose of injection with the virus. It also showed that the degree of sex bias to the infection

increased with advancing age, with younger mice showing less sex differences than older mice.

Some observed characteristics among vulnerable male mice were high virus titers, increased

vascular leakage, and alveolar edema. These effects were associated with elevated

proinflammatory cytokine levels which cause a buildup of inflammatory monocyte

macrophages and neutrophils in the lungs of male mice. A reduction of the proinflammatory

cytokine levels partially protected these mice from dying. Another interesting finding in the

study was that they performed ovariectomies on female mice which resulted in an increased

fatality (Channappanavar, 2017). This could perhaps mean testosterone have a suppressive

immunity effect for males and estrogen a protective effect for females with viral infections.

However, much is unknown if sex hormones levels correlate with cytokine production, but it is

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clear cytokine production does have an impact on immune responses and the outcome of an

infection.

Another experimental study on mice based on sex differences in susceptibility to respiratory

and pneumococcal diseases also shows interesting conclusions (Kadioglu, 2011). It

demonstrates that highly increased levels of proinflammatory cytokines and prolonged

recruitment of neutrophils play a key role in the development of infection and persistence in

male hosts. Female mice, which do not produce high-level proinflammatory cytokines, had a

slower neutrophil recruitment rate in the early stages after being infected, survived while

clearing their respiratory infection over time. The same was done for male mice and it showed

that they responded quickly and vigorously to bacterial infection in the crucial early stages of

infection. This early and strong response is highly proinflammatory and damaging to the host

which seem to correlate with the mortality rate amongst males (Kadioglu, 2011).

This could most likely be the case for individuals contracted with COVID-19 since it causes a

severe respiratory infection. Where the immune system works as a double-edged sword and

instead of protecting you it overworks and destroys tissues. This in combination with a weaker

immune system can turn fatal in worst cases. There is no way of saying there is only one cause

to the severity of the virus, rather multiple factors that contributes to it.

There are limitations to this analysis which must be considered since much of the essential data

were not available because not all countries submitted data in every category. This study only

provides an analysis of the early available data to create initial hypotheses about sex-specific

differences for the COVID-19 outbreak across the world.

Acknowledgments

I wish to thank my supervisor Ted Morrow for much useful guidance and his support. I would

also like to thank my family and friends for the moral support during this period of writing my

thesis.

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Appendices

Appendix A.

Country Source for population

Spain https://www.ine.es/jaxiT3/Tabla.htm?t=31304

France https://www.insee.fr/fr/statistiques/1892086?sommaire=1912926

Italy http://dati.istat.it/Index.aspx?QueryId=42869

Switzerland https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do

England and

Wales

https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmi

gration/populationestimates/datasets/populationestimatesforukenglandand

walesscotlandandnorthernireland

The

Netherlands

https://opendata.cbs.nl/statline/#/CBS/en/dataset/03743eng/table?ts=1589

977467518

Germany https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do

Norway https://www.ssb.no/en/statbank/table/07459/

Sweden http://www.statistikdatabasen.scb.se/pxweb/sv/ssd/START__BE__BE010

1__BE0101A/BefolkningR1860/

Belgium https://statbel.fgov.be/en/themes/population/structure-population#figures

Canada https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000501

Portugal https://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do

Denmark https://www.statbank.dk/10021