Is COVID-19 an Equal Opportunity Disease?
Updated: Nov 3, 2022
The short answer to this question is no. Data suggest that older people are more affected by COVID-19 than younger ones, men more than women, and people of color affected more than white populations. The graphic below highlights differences in race and ethnicity for COVID-19 cases and deaths, with each group compared to the share of the national population represented by that group.
The chart shows that the share of COVID-19 cases (in orange) is higher than the share of the population for blacks, the multiple-race population, and Hispanics in the U.S. For whites, Asians, and non-Hispanics, the share of COVID-19 cases from that population is smaller than the share of the population for that group. A similar pattern is shown for deaths, shown in grey. But why is this? Some of the answer lies in different levels of exposure, susceptibility to the virus, and the available health care response. But also, a legacy of differential exposure, economics, and access to health care has resulted in rates of underlying health conditions that reinforce the vulnerability to COVID-19 and other diseases.
The current crisis offers an opportunity for us to reevaluate our collective investment in public health and consider a more thoughtful and targeted approach to slowing the incidence of infectious disease in the future. These themes are highlighted below in the context of the COVID-19 pandemic.
1. Exposure at Home The transmission of an infectious disease moves from person to person when we are exposed to each other. Factors that increase exposure levels in the home include:
a. Large number of people in the household.
b. No access to a vehicle.
c. Living in an apartment building.
d. Living in an urban area.
e. Presence of children / unavailability of day care.
There are racial disparities for each of these factors that favor increased transmission among people of color. For example, at 42 percent, a higher proportion of blacks live in urban areas, compared with just 20 percent of whites. Also, the number of persons per household for Hispanics is 3.22 compared to 2.52 for all populations.
2. Exposure Related to Employment. Factors that decrease the ability for workers to stay at home and, therefore, increase exposure in the workplace include:
a. Employment that does not provide the option to work at home.
b. Employment that involves public exposure.
c. Employment with no paid sick leave.
d. Employment with limited potential to take time off.
e. Employment involving a lengthy commute time on public transportation.
For example, consider some of the most hard-hit industries in terms of coronavirus outbreaks. Frontline workers, including grocery store clerks, nurses, cleaners, warehouse workers, bus drivers, and others are over-represented by people of color, and women. Of health care workers who have contracted coronavirus, 21 percent those were black, and 73 percent were women. In the direct care industry, blacks are also over-represented, making up nearly 30 percent of personal care aides, home health aides, and nursing assistants. In the meat processing industry, 27 percent are black, while 47 percent are Hispanic.
3. Susceptibility: Socioeconomic Status Overall income, employment, education-level, and poverty levels are also predictors of lower health status. The federal government uses an index called the Social Vulnerability Index (SVI) to assess which populations will be most vulnerable in times of disasters and disease outbreaks. The index is made up of 15 metrics grouped into housing and transportation (which includes housing, and access to a vehicle), minority and language, household composition including elderly and disabled populations, and socioeconomic status which includes the percent below poverty, employment, and income. Many of these metrics are also correlated with race. For example, poverty rates are shown in the graph below by race and ethnicity with these data showing that blacks, Hispanics, and American Indian/Alaska Natives have higher shares of people below the poverty level.
Poverty Rate by Race and Ethnicity
Evidence suggests that social-distancing is more difficult for lower income workers based on cell-phone data showing that cell phone users from higher income areas consistently decreased movement more than the cell phone owners from lower income zip codes.
4. Susceptibility: Underlying Conditions COVID-19 infection and death is clearly aggravated by underlying health conditions, with over 57 percent of people hospitalized also seen to suffer from hypertension, 50 percent were obese, 41 percent suffered from metabolic disease, and another 31 percent had cardiovascular disease. Over 90 percent of those hospitalized had at least one of those underlying conditions. Just looking at rates of hypertension, it is clear that men more than women, older people more than younger, blacks more than whites, and poorer more than richer populations are all more susceptible to COVID-19 based on hypertension rates alone.
Prevalence of Hypertension by Demographic Characteristic
5. Susceptibility and Severity – Access to Health Care The same vulnerable groups tend to also have lower (on average) rates of uninsured among them. Compared to about 11 percent of the population in the U.S. overall, the rate of uninsured is 12.2 percent for men and 9.9 percent for women. Low income groups have even less coverage, with 20.2 percent of those living in households with incomes less than $35,000 reporting no insurance. Black Americans have an uninsured rate of 12.2 percent, while the rate for white Americans is 10.9 percent.
Another factor that affects access to care – even for the insured – is cost. When asked if they have ever foregone health care, or delayed health care due to cost, 4.7 percent said they had foregone health care for cost reasons, and 7.2 percent of the nation had delayed visiting a health care provider due to cost. Men actually answer yes with less frequency than the national average, while women answered with higher frequency. These disparities in terms of health care access are relevant to the current COVID-19 outbreak because they may be influencing the outcome of the disease. Some researchers have raised concerns about patterns of testing for the coronavirus and how black and brown people may not be receiving testing at the same rates as white people.
6. What Else May be Causing the Disparities? A significant amount of research on the topic of inequalities in health care outcomes has shown that despite the many explanatory variables that shed light on the root causes of the disparities, there are still things we don’t understand. For example, numerous studies that show health disparities persist across racial lines even after correcting for income, education, gender, age, and health behavior such as smoking, diet, activity, and access to health care. It may come down to cumulative risk factors that are not yet explained by anything better than race. Recent research has called for a better approach for understanding cumulative risk, especially with respect to respiratory ailments and susceptibility to air pollutants.
And no discussion of inequity in health in the U.S. would be complete without reference to Dr. Robert Bullard, thought of as the father of the environmental justice movement. His seminal research in 1987 addressed health risks and exposures to hazardous waste in the south. In 2007, Bullard and others revisited the research and demonstrated that even after 20 years, the neighborhoods within three kilometers of the nation’s 413 commercial hazardous waste facilities, were made up of 56 percent people of color, whereas the rest of the country was just 30 percent people of color.
7. International Implications The concerns identified above point out how COVID-19 may disproportionately infect populations based on income, housing, employment, age, race, access to health care, and other factors in the U.S. But, translating this to the broader international situation is not encouraging, since many countries have lower incomes, inadequate health care facilities, less capacity to shelter-in-place, and worse underlying health conditions. In addition, the current disruption to global food supplies is presenting the risk of severe hunger that could hit on top of the COVID-19 pandemic.
8. Poor Health Outcomes for Some Mean Greater Risk for All The CDC has a Global Health Security Agenda that recognizes the need to support an interconnected global network to “limit the spread of infectious disease outbreaks in humans and animals, mitigate human suffering and the loss of human life, and reduce economic impact.“ But this pandemic is underscoring the need for a more targeted response to the populations where the disease is more likely to flourish in the attempt to mitigate the suffering. This point is summarized nicely in the following quote from a 2014 journal article from Biosecurity and Bioterrorism: Biodefence Strategy Practice and Science,
Historical accounts of influenza pandemics and contemporary reports on infectious diseases clearly demonstrate that poverty, inequality, and social determinants of health create conditions for the transmission of infectious diseases, and existing health disparities or inequalities can further contribute to unequal burdens of morbidity and mortality.
Despite all of this evidence, maybe the virus (a biochemical phenomenon) truly is color-blind, age-blind, and sex-blind; an equal opportunity disease. But in economics, the risk of an event is made up of two factors: the probability of the event and the consequences of that event. Without a doubt, different demographic groups have greater exposure to the virus, which increases the probability that they contract it. The consequences of the virus will also vary depending on access to health care, and underlying factors. So, while the virus may blindly seek its human hosts, different people face different levels of risk. Ultimately, a greater understanding of the complicated and overlapping demography of COVID-19 risk may be an essential clue to mitigating the human suffering, loss of life, and economic damage of the disease.
Acknowledgments: Greene Economics thanks Dr. Deborah McGrath, from the University of the South; Max Kunetz, from the University of Washington; and Beth Umland, from Mercer, Inc. for their thoughtful reviews and comments.
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