Disaggregated Data and a Real Reflection of COVID-19 Risks
How much can we tell about risk factors for COVID-19 based on people’s sex, gender, age, and other characteristics? A lot, it turns out, if only we have the data. Dyan Mazurana is a research professor at The Fletcher School and at Tufts’ Friedman School of Nutrition Science and Policy. In a conversation with Fletcher’s Office of Communications, PR, and Marketing, she offers her insights into the harm caused by the virus, and the kinds of risk factors we need to be tracking.
Fletcher's Office of Communication's PR, and Marketing: As the Co-Director of the Gender Analysis in International Studies Program at Fletcher, are the skills you teach in your courses relevant to helping us better understand and address the COVID-19 pandemic?
Professor Dyan Mazurana: Absolutely. One thing we quickly learned was that within the COVID-19 pandemic, people are living, suffering and dying in large part because of who they are – by which I mean their sex, gender, age, race/ethnicity, class, ability/disability and so on. This, I’d say, has been a contributing factor to the frustrations that is bringing people out in the protests we’re seeing in America’s streets. Paying attention to these factors is called collecting disaggregated data. Disaggregated data is essential to understanding who is being harmed and why, and should inform our response now and in the future.
COVID-19 was initially thought to be a pathogen that attacked the lungs. Now scientists know it’s capable of damaging almost all major organ systems in the human body and can seriously harm both the old and young.
COVID-19 is also a pretty contagious coronavirus, and as more disaggregated data is coming in, we can now see that it infects women, men, girls and boys at pretty much the same rate. At the same time, it’s now quite clear that it kills particular kinds of people at far greater rates than others.
CPRM: So, who is most at risk for being harmed, and dying, and why?
DM: First, older people. Older people often have a combination of multiple chronic diseases and aged immune systems that don’t communicate as smoothly as younger immune systems. The result is that some, but not all, older people have much greater vulnerability to developing more severe responses to the pathogen.
Second, let’s bring in sex to look at a specific group: men who are older. The data coming in shows that in most countries, men are significantly more likely than women to develop serious responses to, and die from, COVID-19. In new research out of China, researchers concluded that being male was also a significant risk-factor for disease severity, regardless of age, something that was also found for the coronavirus SARS.
In terms of biology, levels of the protein ACE2 on the cells attacked by the virus that causes both COVID-19 and SARS are higher among males than females. In addition, the male sex hormone testosterone has an adverse effect on the ability of the body’s immune system to respond. In men, this results in a weaker immunologic response and more difficulty fighting the virus and resulting diseases. Furthermore, the X chromosome, of which females have two (XX) and males have only one (XY), is encoded with the most “immune-related” genes, which likely contributes to females’ overall stronger and better coordinated immune responses.
Third, we have to consider gender: Sex and gender are closely connected. Worldwide, men are much more likely to smoke and drink alcohol. The harm men do to their lungs, immune systems and bodies overall by smoking is noted as an important factor in explaining the higher COVID-19 death rates among men, as compared to women. Men’s risky behavior, such as excessive drinking, also suppresses their immune systems. Men are also significantly less likely to seek medical care as compared to women. Women are more likely to be the primary providers of caregiving and ensuring the health of their families and they are more likely to engage in health-seeking and health-maintaining activities. All of these gender-related behaviors help shape and explain men’s disadvantage in the current pandemic.
The numbers are stark and speak for themselves. Remember, worldwide infection rates for COVID-19 are pretty equal among people based on sex and age. Yet in Greece, death rates for men over 65 years of age compared to women of that same age range are 7:1. In Pakistan it is 3:1, Peru is 2.75:1 and the Philippines it is 2.70:1. In France, Spain, Australia and Switzerland the rate is 2:1. In the U.S., only partial sex and age disaggregated data is recorded, so we don’t know the real situation there. The U.S. needs to do a much better job of recording disaggregated data so we can best understand who is at risk and why.
Fourth, we have to think about how race/ethnicity and class – or more accurately systematic racism and white supremacy - combine to advantage some people while disadvantaging others. This is where county-level disaggregated data in the U.S., when it is being collected, is proving to be very informative. We see poorer communities, which are predominately made up of racial and ethnic minority groups, record the highest death rates. For example, in Los Angeles County, death rates in low-income neighborhoods are three times higher than in higher-income neighborhoods.
For communities of color, racism, white supremacy, and poverty create conditions that produce more chronic illnesses, poorer health, less ability to work from home or “shelter in place”. Again, it’s factors like these that are part of what’s spurring the protests and unrest across the U.S. in these recent weeks. Understanding that racism, discrimination, and poverty contribute to higher mortality rates for these communities isn’t discussed or examined nearly enough, but it’s something we must consider as we look at these numbers.
CPRM: Is it enough to know who is most at risk from dying from COVID-19, or are there other factors we need to consider in addressing risk?
DM: It’s never enough to only focus on rates of death in any crisis, whether it’s a pandemic, natural disaster, famine or a war.
We need to think about who is most likely to be affected, how, and why. For example, rates of COVID-19 infection are much higher among health care workers, the majority of which are women in most countries around the world. For example, in the U.S., Spain and Italy, over 70% of infected health care workers are female. This has an impact on not only the women but on their families and communities.
We also need to pay attention to the violence that arises from the pandemic. We are seeing dramatically rising rates of domestic abuse, which primarily occurs against women, as well as rising levels of child abuse and abuse of sexual minorities due to increased stress from lockdowns, shelter-in-place orders, and loss of jobs. For example, France and Singapore both reported a 30% increase in domestic violence calls, the U.S. a 35% increase, and Turkey a 38% increase. The UK’s largest domestic abuse charity, Refuge, reported a 700% increase in calls to its helpline in a single day. Reports are showing that abusers are using the COVID-19 pandemic as a means for their abuse, including threatening to withhold necessary items such as hand sanitizers or masks, and giving misinformation to survivors about the pandemic to prevent them from seeking help or to make them think they are sick.
We are also seeing violence used by state forces to enforce shutdowns and suppress civil liberties in countries around the world.
And we need to pay attention to depravations from the pandemic. Hundreds of millions of people live on the edge of severe hunger. Shutdowns of markets, loss of work and lockdowns can increase food insecurity, with potential significant impacts on families and communities of color. And these shutdowns can lead to a negative impact on children’s and pregnant and lactating women’s nutrition and health which can cause cycles of nutritional and developmental problems, again particularly for poorer communities of color.
These are only a few examples of how we need to think about the ways in which harm from the pandemic multiples and spirals. Thinking about and collecting disaggregated data can help us all better understand what is really happening, to whom, when, where, and why, and what we can do about it.