Communicating about COVID-19 and Sex Disparities: A Guide for Media, Scientists, Public Health Officials, and Educators

Artist credit: Brian McGowan

Authors: Ann Caroline Danielsen and Nicole Noll

As of June 17, 2020, 53.5% of reported COVID-19 deaths in the United States have been men, although as we show on our US Gender/Sex COVID-19 Data Tracker, there is large variation among US States. With a few notable exceptions, headlines from newspapers around the country highlight the role that biological sex differences, such as hormones and chromosomes, may play in shaping apparent sex disparities in COVID-19 outcomes, only superficially mentioning social and contextual factors that might also be influential.

A focus on biology may seriously underplay the contribution of gender-related factors to COVID-19 disparities, dangerously misdirecting public health efforts. Moreover, as social scientists have amply demonstrated, when people encounter statistics such as those reporting that COVID-19 disproportionately impacts men as a group of people, they are likely to attribute the cause to sex-linked biology, and perhaps assume that men’s greater vulnerability to dying from COVID-19 is inevitable.

In their excellent guide, “Communicating about Racial Inequality and COVID-19,” the Berkeley Media Studies Group explains that without connecting individuals to their social landscape, statistics alone prompt people to think about individual characteristics and behaviors as the source of group disparities. When people view the root cause of disparities as biologically determined, the solutions for containing the pandemic that seem most plausible are altering the biology of men, or potentially asking that men alter their individual behavior to adhere more strictly to social distancing measures.

In short, a serious concern is that decontextualized statistics about sex differences in cases and deaths from COVID-19 can misplace worry and obscure other factors that are potentially as or more relevant than biological sex in shaping vulnerability to COVID-19.

With a hat tip to the Berkeley Media Studies Group, in this guide we offer practical, actionable suggestions for media, researchers, public health officials, and educators for how to responsibly communicate about sex disparities and COVID-19.

“To help people understand the full meaning of data about the gender/sex distribution of COVID-19, communicators must speak with as much specificity as possible and connect sex difference statistics to context every time they are mentioned.”

Boost specificity by understanding and using the relevant terms

Before thinking about how to connect sex difference statistics to relevant context, it is important to consider how the information you want to share is related to sex.

Although “sex” and “gender” are often used interchangeably, they are not synonyms. Being intentional and specific about your language choices is essential for clear understanding. Much has been written about the nature of and distinctions between sex and gender, particularly in a health context. Our aim here is to share a few basic guidelines that you can easily employ to boost specificity.

Sex

The term “sex” refers to biophysical attributes of individuals, typically their genes, gonads, and genitals (the “three g’s” of sex). A sex is typically assigned to an infant at birth by a doctor who bases the decision on visual inspection of the genitals, although these days available technology means a sex is often assigned much earlier than birth. This sounds straightforward, but that is because it is a simplification of the many factors associated with sex. For example, there is an assumption that the three g’s just mentioned all “match.” That is, that an infant with a clitoris and labia is presumed to also have ovaries and a uterus, and XX chromosomes. For many people the g’s do match, but for others they do not: sex is more complex than we usually think of it as being.

There are various other physical characteristics of individuals that tend to be lumped in with sex, such as hormone levels (particularly estrogen and testosterone) and secondary sex characteristics (breasts, body hair, etc.). Assuming that all features we think of as indicating sex match within a given individual is the source of a lot of misunderstanding and can obscure the amazing variability of these physical characteristics and their combinations.

Gender

Gender is an umbrella term that refers to behaviors, roles, appearances, and identities of individuals. As a concept, gender is a way to linguistically acknowledge that these factors that we associate with sex are not synonymous with or determined by it. For example, though nurturing behaviors are more strongly associated with women and competitive behaviors are more strongly associated with men, these are stereotypes. Anyone can care for and/or compete with others, but these behaviors are valued differently in women vs. men and our valuation shapes people’s own behaviors and their perceptions of the behavior of others.

People often use “gender” to refer to an individual’s assigned sex, which is incorrect. Recently, it has become common to use gender primarily to refer to someone’s gender identity—but it can also refer to other aspects of gender, like roles, behaviors, and social structures. Because gender is a multifaceted construct, it is important to be specific about the aspect of gender to which you are referring.

Gendered behaviors and roles have proven to have explanatory power in the context of infectious disease epidemics. For example, in the 1918 Spanish Flu pandemic men died at a higher rate than women in part due to gender roles, specifically gendered labor norms: working class men and those in the military were not able to follow the recommended precautions due to their jobs, and so were overrepresented among those whose lives were claimed by the flu.

On the continuum between individual behavior and systemic structures, there are many gendered variables that are currently under-examined and that may contribute to shaping the gender/sex distribution of COVID-19 cases and fatalities. These include smoking and drinking rates, caring responsibilities, sex ratios among nursing homes residents, healthcare seeking patterns, access to healthcare and COVID-19 testing, occupational exposures, job security and leave policies, housing status (e.g., private home, incarcerated, homeless, or other institutions), and compliance with hand washing and social distancing recommendations.

Gender/Sex

Although it is conceptually useful, the distinction between “biological” (sex) and “social” (gender) factors is more complicated than commonly acknowledged. Sex and gender are enmeshed, prompting some researchers to use the term “gender/sex” in order to emphasize the continuous and dynamic relationships between biology, behavior, and social structures.

The GenderSci Lab chose to use the term “gender/sex” to describe the disparities we track on our US Gender/Sex COVID-19 Data Tracker, because differences between data tagged “female” and “male” could be sex-driven, gender-driven, or a combination of the two, and we do not currently have the data to disentangle gender and sex and how they interact to shape COVID-19 vulnerabilities. The term gender/sex highlights this, and points to the need to pair sex-disaggregated COVID-19 data with other social and demographic variables. However, we acknowledge that it is a less conventional term and may not be appropriate for all forms of communication.

Question the Binary

Reliance on binary frameworks for assigned sex (i.e., female vs. male) and gender (i.e., feminine vs. masculine) collapses the variability observed within sex and gender categories.

The reporting of COVID-19 cases and deaths in columns of “female” and “male” naturalizes binary, cis-centric conceptualizations of sex and gender. This perpetuates the invisibility of intersex, trans, non-binary, genderqueer, and other individuals who live their lives beyond the binary. This problem is a difficult one to overcome because data collection currently happens within frameworks that rigidly operationalize sex and gender as binary variables. Whenever possible, it should be specified that data refer to COVID-19 among people categorized as female and male and that the nuances of their sex-linked biology and gender identities are not known.

Another strategy for making binary assumptions visible and troubling them is to focus on the specific health challenges, opportunities, and experiences of gender-diverse people. Therefore, when discussing gender/sex disparities in COVID-19 outcomes, consider also highlighting the experiences of gender-diverse individuals/populations with regard to COVID-19.

Which men? Which women? Query how social location influences gender/sex disparities

As the GenderSci Lab US Gender/Sex COVID-19 Data Tracker demonstrates, the gender/sex distribution of COVID-19 deaths varies significantly across localities: as of June 22, 2020, for instance, the percentage of female deaths ranges from 41% in Nevada to 55% in South Dakota. These differences suggest that a person’s risk for COVID-19 is dependent on many factors, some of which may vary based on local contexts. Conveying that these factors interact in ways we do not yet fully understand is critical to keeping our minds open to a variety of explanatory frameworks and to avoiding a rush to prematurely drawn conclusions.

Of course, tracing and explaining all social determinants of health will not be feasible. However, you can bring data and context together by including examples that speak to specific circumstances in your geographic area or population of interest.

To illuminate potential contributors to the sex distribution of COVID-19 outcomes in a particular population, explore how the sex distribution shifts within and across other socially-relevant demographic variables. In order to do this, consider investigating the following:

• Are sex breakdowns of the data within different race/ethnicity categories available? As of June 13, 2020, it is estimated that 48% of those hospitalized with COVID-19 in the US are women. However, among American Indians/Alaska Natives, women account for 59% of those hospitalized with COVID-19. This shows that interpretations of the gender/sex distribution of COVID-19 that do not take into account age, disability, race/ethnicity, indigeneity, migration status, geographic location, and social class are likely to be grossly misrepresentative.
• What are the occupational risks of exposure in that locality and to what extent do they vary by sex? For example, what are the largest employers in the area and who do they employ? This is particularly relevant for areas where significant employers have remained in operation during stay-at-home orders and where employees work in close proximity to each other. Occupations and workplaces are often dramatically gender-segregated. A particular industry may lend itself to employees working from home. If women are the predominant employees in that industry, while men are the predominant employees in industries that require work to be done in the workplace, this may produce gender/sex disparities in COVID-19 infection and death rates in that particular context.
• What proportion of the population lives below the poverty line and does it vary by sex? In all US states, women are more likely than men to live in poverty, but within a state, proportions vary by county and age group. Take Dukes and Norfolk counties in Massachusetts, for example. In Dukes county, approximately 10% of men aged 75+ are below the poverty line, compared to 9.5% of women in the same age group. In contrast, in Norfolk county, ~8% of women aged 75+ are below the poverty line, compared to ~2% of men. Both age and poverty are relevant to COVID-19 outcomes, so if the distribution of poverty varies by gender/sex within vulnerable strata of society you might anticipate this having an influence on overall COVID-19 trends.
• How might local health surveillance and reporting practices alter the comprehensiveness of the data available and the inferences that can be drawn? For example, the US Centers for Medicare and Medicaid Services did not mandate that COVID-19 deaths be reported to state and local health departments until April 19, 2020. Months later, data from nursing homes are still largely missing. This likely affects our understanding of gender/sex disparities and COVID-19 because the nursing home population in the US is predominantly female. Massachusetts, for instance, has suffered one of the highest COVID-19 death tolls in the United States and nearly 61% of those who died from COVID-19 in MA were nursing home residents. Massachusetts is also one of a handful of states in which women constitute more than half of all COVID-19 deaths, showing how reporting practices around COVID-19 cases and deaths in nursing homes can influence the overall picture of sex distribution of COVID-19 cases and/or deaths.

Contextualize COVID-19 gender/sex disparities within existing gendered patterns of disease, aging, and mortality

Gender/sex disparities are observed in many diseases and conditions that are associated with more severe COVID-19 outcomes. Men also have higher all-cause rates of mortality than women. When communicating about gender/sex disparities in COVID-19, contextualize outcomes within the epidemiologic landscape before the pandemic, including the gendered factors contributing to baseline health disparities between women and men.

Patterns of Mortality

In the US, men had higher mortality rates than women even before the pandemic erupted. Though disparities in COVID-19 deaths between women and men might appear striking, it is possible that death counts are higher among men simply because men’s baseline death rate is higher. Therefore, when reporting sex difference data in COVID-19 fatalities, it is important to differentiate between absolute and relative mortality increases. Even if the absolute number of COVID-19 deaths is higher among men than women, the relative increases in mortality may be comparable in magnitude. Preliminary evidence suggests that in some contexts, such as Massachusetts, this might be the case. As Harvard School of Public Health Professor Nancy Krieger highlighted in a recent Boston Globe article, “I think that COVID-19 is exacerbating the death rates of both men and women, and is doing so relative to what their death rates already are.”

Age-Standardized Mortality Rates

In the current pandemic, statistics such as the raw percentage of deaths that are women or men have become popular proxies to describe the sex distribution of COVID-19. However, such percentages do not reflect the sex structure of different age groups within the region of interest. It is important to take this into account because the number of deaths in a given population is influenced by its age distribution and the sex distribution can vary between age groups.

For example, we know that older age is a risk factor for COVID-19 death. If a state reports that 55% of COVID-19 deaths were elderly men, we do not know if it is because elderly men in that state are particularly numerous compared to elderly women, or if there are similar numbers of elderly women and men and the percentage reflects the disease hitting men harder. One way to overcome this problem is to use weighted averages that reflect the age distribution of the population, which are called age-standardized mortality rates. While we await more precise intersectional data linking age and sex, the GenderSci Lab has computed COVID-19 indirect age-standardized mortality rates for US states and published them on the US Gender/Sex COVID-19 Data Tracker.

Pre-existing Conditions

Pre-existing conditions such as heart disease, asthma, chronic obstructive pulmonary disease, diabetes, kidney disease, and liver disease are risk factors for severe cases of COVID-19. Pre-existing conditions are another realm where gender/sex shapes complex relationships between individual biology and the social environment. Patterns of pre-existing conditions among women and men can vary significantly between localities or depending on social conditions.

Localities: Diabetes is more common among men in the US, but in South Africa it is more common among women. Similarly, asthma in the US is more prevalent among women, but in Italy it is more prevalent among men. When writing about sex disparities in COVID-19, note that the distribution of relevant pre-existing conditions is shaped by local circumstances, structures, and gender norms in ways that can consequentially shape the gender/sex distribution of COVID-19 cases and deaths. In order to highlight this, contextualize COVID-19 data points in a given context with the corresponding chronic disease statistics.

Social conditions: Gender/sex interacts with other markers of social experience (such as race/ethnicity, sexual orientation, and class) to further nuance patterns of pre-existing conditions. For instance, in the US, gender/sex interacts with race/ethnicity in relation to heart disease prevalence. Heart disease is a leading risk factor for severe COVID-19. Although the prevalence of acute coronary syndrome (a form of heart disease) among American men aged 60 or older is more than double that among women, breakdowns by gender/sex and race/ethnicity show that Black men top the charts for myocardial infarctions, followed by Black women, then white men and white women, respectively. These patterns in the prevalence of heart disease are influenced by social conditions including social marginalization, historical and ongoing racism and discrimination, insurance statuspoverty, which interact with gender/sex.

In sum, patterns of pre-existing conditions among women and men can vary significantly between localities or depending on social conditions, and it is critical to acknowledge them as potential explanatory factors when commenting on gender/sex disparities in COVID-19.

Take-homes

• Use precise terms: “Sex” and “gender” are not synonyms, nor are they as independent of one another as people tend to believe. You can use the term “gender/sex” to emphasize the continuous and dynamic relationships between biology, behavior, and social structures.
• Question the binary: Intersex, trans, non-binary, genderqueer, and other gender-diverse individuals live their lives beyond the binary. Profile their experiences and point out that COVID-19 data refer to people categorized as female and male.
• Ask questions about how social variables interact with gender/sex: There are many socially-relevant variables that may influence the sex distribution of COVID-19 outcomes, such as age, disability, race/ethnicity, indigeneity, migration status, geographic location, occupation, and social class. Investigate these and explain them to your audience.
• Report age- and population-adjusted statistics and relative, not absolute mortality: Raw counts are not sufficient. Always contextualize COVID-19 gender/sex disparities within existing gendered and sexed patterns of disease, aging, and mortality.

Recommended Citation:

Danielsen, Ann Caroline, & Noll, Nicole E. “Communicating about COVID-19 and Sex Disparities: A Guide for Media, Scientists, Public Health Officials, and Educators,” GenderSci Blog, June 24, 2020, https://www.genderscilab.org/blog/covid-communication

Statement of Intellectual Labor:

Danielsen and Noll conceptualized the piece, drew up the initial draft, and integrated lab member comments and edits during the revision process. Both authors equally provided substantive contributions to the ideas expressed in this blog post and participated in the preparation of the post.

Contact:

Questions, interested in collaborating with the GenderSci Lab, or media inquiry? Email us at: genderlab@fas.harvard.edu.