By Mimi Tarrant
Since the first case of COVID-19 was identified in China in January, 2020, multiple studies have been conducted to understand the various factors that influence an individual’s risk to the disease. One of the first of these analyses, released by the Chinese Center for Disease Control and Prevention a month after the first reported case of COVID-19, reported various social and demographic socially-relevant variables that appeared to have influence over case and fatality rates, such as age, occupation, sex and comorbidities. Notably, there was a large disparity in fatality rates between males and females, with 2.8% of cases ending in a fatality for males compared to only 1.7% of female cases resulting in fatality. This gender/sex disparity was soon shown to exist beyond China as the coronavirus spread across the globe; from America to Europe, the distribution of men among COVID-19 fatalities has been disproportionately high, suggesting that gender/sex may be a significant risk factor for COVID-19.
Gender/sex as an epidemiologically relevant variable is by no means a new concept; the SARS outbreak of 2003 also had a greater fatality rate for males compared to females. However, little was ultimately understood about the direct mechanisms behind the gender/sex disparity among SARS cases. As COVID-19 has wielded similar gender/sex disparities, we remain equally as oblivious to its gender/sex-linked mechanisms.
Similarly, little has been done in the time between SARS and COVID-19 to improve the state of epidemiological reporting for gender and/or sex. As of the current reporting status, gender and sex are indistinguishable as socially-relevant variables for COVID-19, despite being two separate and distinct characteristics. On the broadest level (and for the purposes of this blog post), sex is a biologically determined variable, often dictated by the (socially approved) 3 Gs criteria: gametes, gonads and genitals. In contrast, gender is a socially determined variable, relating to presentation, attitudes and activities of and towards an individual based on their assumed sex category. Crucially, an individual’s sex may not align heteronormatively with their gender, and thus current COVID-19 statistics on sex rates do not necessarily accurately portray the rates for gender. In this way, the line between sex and gender has been blurred by COVID-19 reports (explaining why the use of “gender/sex” is necessary to describe statistics), with significant consequences for our understanding of the disease.
Following the initial reports of gender/sex disparities in COVID-19 outcomes, mainstream media outlets were quick to jump on the sex-hype wagon that began to take speed. Initial reports highlighted the higher vulnerability among men, and emotively broadcast the message that men were most at risk; COVID-19 was even being labelled as the “global mankiller”. Inevitably, this led to an immediate analysis of the biological differences between males and females, with studies conducting hormone testing under the assumption that women's higher levels of progesterone and estrogen were providing their apparent immunity. Whilst press coverage extensively covered these early attempts at biological explanations, little has been heard about these studies since.
Meanwhile, other factors that could be causal to the gender/sex disparities, such as social factors, were swept to the wayside, subsequently receiving little of the discussion they demand. Both science and media didn’t hesitate to promote the idea of fundamental, biologically determined differences between males and females, whilst they continue to be unwilling to engage with the more nuanced and potentially more controversial social factors that gender entails. Not only does this mean that they are failing to address a large proportion of arguments explaining for gender/sex disparities, but by emphasising these supposedly fundamental biological differences a dangerous platform is created that can help promote extremist voices and opinions. A new stream of conversation surrounding “X versus Y”, alluding to one sex being stronger than the other, is dangerous territory to be wading into; polarising males and females by pitting them against one another not only fragments our society, but also isolates intersex and trans individuals, who may not ‘neatly fit’ into one of these dichotomous categories. For this reason, it is imperative that the conversation moves away from only stating the biological differences by developing a better understanding of, and acknowledging, the social factors at play.
An example of a key social variable when discussing gender/sex rates of COVID-19 is occupation; 90% of nurses in the NHS are women, a reflection of the extensive higher representation of women in the healthcare sector. This could allow for the fact that case fatality rates for women are lower than that for men: more young, healthy women who continue to work in healthcare throughout the pandemic are contracting COVID-19 in the first place, and so the number of women contracting COVID-19 is disproportionately high, and the number dying from it is disproportionately low (as those who are contracting are young, healthy, with no other risk factors, explaining why they are still working). A reluctance to acknowledge this occupational gender mismatch is a failure to appreciate the reliance of our country’s healthcare system on women, stemmed from a society that appears to value money-making individuals more than those who save lives.
Ultimately, epidemiological studies fall short in understanding the demographics of COVID-19 in a multitude of ways; not only are social variables such as occupation not taken into account in data reporting, but there remains a clear lack of intersectional data for COVID-19 cases and fatalities. Only once variables such as age, gender, race/ethnicity and comorbidities are interacted can an individuals’ risk be understood - in isolation, these variables only paint a small part of the larger picture. For example, looking at state-level data from the US gives us insights into the importance of interactional data - for Georgia, as of July 9th, 51.4% of cases were female. However, when interacting race with gender/sex, it is shown that females make up 51.9% of White cases, yet make up 58.4% of Black cases. This disproportionate impact on females for different races shows the nuance required to understand individual risk; simply labelling differences as a result of biological factors between males and females falls dramatically short of the required analysis.
Hopefully, COVID-19 will be a turning point in approaching epidemiology and public health from a more gender-focused lens. Identifying social factors as an influence to an individual’s risk of a disease not only helps mitigate the immediate spread of the disease, but can help in diminishing the long-term socio-economic effects for individuals most at risk. As we enter into the next phase of the COVID-19 pandemic, we can only hope that our failure to acknowledge the social factors that disproportionately impact women does not translate to a failure to support those who need our help most in the ‘new normal’. We’ve already failed our women once in this pandemic by belittling their individual risk to COVID-19, and refusing to realise their risk in the upcoming economic downturn would mean failing them once again.