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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
3 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).
4 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
5 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
6 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
8 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.
9 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.
10 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).
11 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.
12 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
13 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
14 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
15 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
16 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
17 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
