Yves here. This article contains an important preliminary finding about Covid-19. Higher death rates don’t appear to be the result of disadvantaged access to health care, but to higher infection rates, due to working in crowded “essential” jobs and what the author calls “residential density”. That makes sense since doctors can’t do all that much if someone who contracts Covid-19 gets a severe case. The most effective remedy seems to be administering oxygen.
By John McLaren, Professor of Economics, University of Virginia. Originally published at VoxEU
In the US, COVID-19 tends to magnify inequalities by disproportionately hitting minorities, particularly African Americans, who suffer from higher COVID-19 mortality rates. Higher rates of infection appear to be the cause rather than factors related to treatment. Using an indirect approach, this column uses census data to identify the socioeconomic factors that cause different racial groups to be differentially exposed to the virus. Very strong racial disparities in COVID-19 mortality rates are seen for African-American and First Nations populations. Occupation, income, poverty rates, or access to healthcare insurance appears to matter little. Pre-COVID-19 use of public transport, however, may be a significant factor.
One of the many alarming features of COVID-19 is its tendency to magnify inequalities by hitting minorities disproportionately. African Americans, Latino/Hispanic, and Native American groups have suffered mortality rates that are in many cases much more than double the national average (see, for example, Oppel et al. 2020, Gross et al. 2020, Price-Haywood et al. 2020, and Romero and Healy 2020).
Researchers have two separate contributions to make on this issue. The first is to measure the size of these disparities. The second, likely the more important, is to identify their sources – particularly to try to identify socioeconomic sources that can be addressed by policy.1 This column will provide a sampling of work on both of those tasks.
The most complete data set for measuring COVID-19 racial disparities is that obtained by Oppel et al. (2020), through a Freedom of Information Act suit, of all individual COVID-19 data held by the US Centers for Disease Control and Prevention (CDC) up to 28 May 2020. Even this dataset is very limited: a majority of cases lacked either race or county, and the data are all from fewer than a third of US counties, representing 55% of the national population.
The per-capita COVID-19 case rate in the data for African Americans is 2.7 times that for whites, and 3.2 times for Latino/Hispanics. The ratios tend to be even greater within each age category. These ratios also vary greatly across the country. Gross et al. (2020) obtain age-corrected relative mortality ratios by state for the 28 states whose data is broken down by race. They report a range of African-American-to-white ratios from a sky-high 18 for Wisconsin down to 0.44 for Pennsylvania. Their aggregate estimate for the US is that COVID-19 mortality is 3.57-times greater for African Americans relative to whites, and 1.88-times greater for Hispanic/Latinos relative to whites.
We can thus take it that these disparities are well established in the data and large. Conceptually, one may classify their sources into three categories:
(i) factors that lead to higher rates of minorities becoming infected;
(ii) factors that lead minorities with the virus to require hospitalisation at higher rates; and
(iii) factors that lead to a higher death rate for minority patients once hospitalised.
Category (i) could include the concentration of workers in essential services that cannot be done from home, requiring workers to go to a physical workplace where they will be exposed to other workers or customers (Hooper et al. 2020); differential availability of paid sick leave; differences in reliance on public transit (Harris 2020); differences in access to healthcare insurance;2 and differences in residential density.3
Categories (ii) and (iii) could include differential incidence of pre-existing conditions that can make a COVID-19 infection more dangerous4 or discrimination in treatment by healthcare institutions.5Environmental racism6 may contribute to all three categories.7
The most direct approach to untangling these three categories is employed by Price-Haywood et al. (2020), who obtained confidential data from a large hospital system in Louisiana on 3,481 patients who tested positive for COVID-19 between 1 March and 11 April 2020. For each patient, race, zip code, and medical record were available to the researchers. In total, 70.4% of those who tested positive were Black, while only 31% of the service-area population was Black. About 40% of these patients were hospitalised due to the virus, with 77% of those cases Black; about 9% died in hospital, of which 70.6% were Black.
Since the disparity in death rates is the same as the disparity in infection rates (namely, a Black mortality rate that is 2.28 times the average), the disparity seems to be due to category (i) above, and not (ii) or (iii). Further, the authors also estimate a hazard-rate model for mortality conditional on hospitalisation and find that there is no statistically significant effect of race once comorbidities have been controlled for, which seems to confirm that disparities in care in the hospital are not at work in this case. An earlier but much smaller study in Georgia found similar results (Gold et al. 2020).
Given that the COVID-19 disparities seem to be more rooted in rates of exposure to the virus than in subsequent treatment, a natural focus is to hunt down socioeconomic reasons for the differential exposure. Here data problems are a major obstacle. Ideally, one would have a large sample of patients and full information about their employment, education, occupation, income, and so on, as well as medical outcomes for each individual, but that is not available. Researcher Merlin Chowkwanyun has commented: “When a patient comes to a clinic, they can self-report their race, but how do they self-report socioeconomic status? How do you actually gather that information?” (Wood 2020).
One imperfect workaround can be described as follows. Recall that the mortality data are not broken down by race for most counties; however, population figures are. Suppose that mortality rates for each minority do not vary much across the country. Imagine we regress county mortality rates on county minority-population shares. Then if the population share of minority group i has a positive coefficient, that indicates that group i has a higher-than-average mortality rate, and the ratio of its mortality rate to the average can be backed out with a simple calculation. If, then, the disparity in mortality rates thus measured disappears when we control for additional socioeconomic variables, that can be taken as evidence that those variables are the underlying source of the disparity. This is the approach I take in McLaren (2020).
This indirect approach is applied to four census demographic groups: African Americans, Hispanic/Latinos, Asian Americans, and the census category ‘American Indian and Alaska Native’, abbreviated here as ‘First Nations’. The control variables are pre-COVID-19 county features, averaged over 2013–8 and taken from the American Community Survey of the Census Bureau. They include median household income, poverty rate, education, uninsured fraction, the occupational profile of the county, and the fraction who use public transit to get to work.
The main findings are:
(i) This approach confirms very strong racial disparities in mortality rates, not far out of line with the estimates provided by direct measures.
(ii) For Latino/Hispanic and Asian populations, those disparities mostly disappear once education and occupation are controlled for.
(iii) By contrast, for African-American and First Nations populations, the disparity is very robust. Surprisingly, it is barely affected by controlling for occupation, income, poverty rates, or – importantly – even access to healthcare insurance, so those factors do not seem to be an important source of the disparity.
(iv) One factor that does seem to be significant for the African-American disparity is the pre-COVID-19 use of public transport – at least for April, when the partial correlation between mortality and public transit is very strong. Harris (2020) builds a strong case that the subway was a crucial element in the early spread of COVID-19 in New York City, but that the effect dissipated as ridership dropped, and these results are consistent with that (even when New York City is dropped).
These negative results for African Americans and First Nations people can at least help narrow down the search for the source of the disparities. Important candidates that cannot be ruled out at present include:
(i) Disproportional employment in ‘essential’ occupations that cannot be done from home. Despite the insignificant effect of occupations in the regressions above, these occupational effects could still be a reason for the disparity because the county occupational categories in the American Community Survey are very crude;8 this may be the reason occupational variables show no significant results (even though they were very significant for the other two minorities).
(ii) Residential density. Emeruwa et al. (2020) study every woman admitted for labour and delivery at two New York hospitals over several weeks. As required by law, every one of these patients was tested for COVID-19, making this an ideal sample for studying the correlates of positivity. The authors found that the strongest predictor of COVID-19 positivity was whether or not the patient lived in a neighbourhood with a high average number of people per dwelling. This variable mattered far more than neighbourhood income or population density. Unfortunately, the data do not include race, so it is an open question whether this is a source of the disparity or not.
(iii) Environmental racism. Wu et al. (2020) study the effect of county-level particulate matter in the air on COVID-19 death rates and find alarmingly strong effects. Together with the abundant evidence on environmental hazards disproportionately located in minority neighbourhoods, this seems as if it could be a reason for the disparities. However, the study separately controls for the Black population share and finds that it has just as strong an effect as the studies that do not control for pollution.9 This seems to rule out particulate matter as the underlying source of the disparity in death rates, but there are many other forms of environmental harm that could be explored in a similar manner.
The inequities of COVID-19 are grotesque. Nailing down which mechanisms are most at fault can help focus attention on which policies are needed to address them. So far, the exact mechanisms have been elusive.
See original post for references