COVID-19 deaths show black female bias
Among COVID-19 deaths in the United States, minorities are over-represented (Yancy 2020) while women are under-represented (Peckam et al. 2020). Looking at the intersection between race and gender, we discover a black female bias: while black men are affected as much as white men, black women are more affected than white women, and this is due to their status. lower socio-economic. The first and hardest hit by the pandemic were black women employed as frontline workers who commute by public transport from historically red blocs.
In a new article (Bertocchi and Dimico 2021), we take advantage of extremely detailed, individual-level and georeferenced data on daily deaths in the United States from COVID-19 and other causes provided by the County Medical Examiner of Cook, Illinois, the county that includes the Chicago metro area. The information includes race and ethnicity among a wide range of other individual characteristics such as gender, age, pre-existing conditions and georeferenced home address. This analysis is based on data up to September 15, 2020, covering the first wave of the outbreak in Cook County. Figure 1 shows the spatial distribution of COVID-19 deaths recorded since March 16, 2020, the day the first COVID-19 death was recorded. We overlay the boundaries of the census block groups on the map.
Figure 1 COVID-19 Deaths in Cook County, March 16 to September 15, 2020
We combine data on deaths with US Census data on occupation by sector, use of public transportation, household overcrowding, and access to health insurance – down to the level of block group disaggregation. . Given that the county comprises nearly 4,000 block groups, this represents a major advantage over other analyzes of the racially differentiated impact of the pandemic (Almagro and Orane-Hutchinson 2020, McLaren 2020) that have been conducted in State, County, or at best Postal Code Level (there are only 164 for Cook County). The resulting unique data set allows us to jointly study the racial and gendered impact of COVID-19, its timing, determinants, and geography.
The black feminine bias
Our dataset allows us to focus on the potential intersection between race and other demographic characteristics, including gender. Preliminary correlative evidence suggests that, even after controlling for age and co-morbidities, the likelihood of dying from COVID-19 was particularly high for black women, while black men were not significantly more likely to die from COVID-19. disease than white men.
To establish our main results, we use information on all deaths (from COVID-19 and any other cause reported by the medical examiner) recorded from January 1 to September 15 in 2020 and 2019 and build a panel at the cell level. , with cells aggregated to race, census block group, week, and year level. The primary outcome of interest is a measure of the excess deaths for each breed in a given block group and week in 2020, compared to the same race, same block group, and same week in 2019. Using an event study approach, we capture differential patterns of deaths. between years, pre and post COVID-19 weeks and races. In Figure 2, we compare these differential trends for women and men.
Figure 2 Sex-disaggregated excess death for blacks and whites and the excess death differential between blacks and whites
To note: The graphs show the coefficients of the fixed-effects regressions where the dependent variables are excess deaths for Blacks and Whites, by sex (women in the upper left panel, men in the upper right panel) and the Black-White differential in excess deaths, by sex (females in the lower left panel, males in the lower right panel). Vertical lines represent 95% confidence intervals. Data refer to deaths, regardless of cause, reported between January 1 and September 15, 2020 and 2019. Event time 0 is the week of March 11.
The top two panels in Figure 2 show excess deaths approaching zero, as expected, in the weeks leading up to the start of the epidemic. They grow in the second half of March 2020, at the start of the epidemic, and are more numerous in males regardless of race. However, we also observe that black women outnumber white women (top left panel), while among men the racial differences are much less pronounced (top right).
The bottom two panels confirm that the racial differential for excess deaths is larger and more prolonged for females (bottom left). This means that the racial disadvantage is largely attributable to black women, who are affected by the epidemic earlier and more seriously. In other words, a male bias is only present within the white population while, strikingly, within the black population we do not observe any significant gender differences. To quantify, during the critical week of April 8, 2020, the black-white differential in excess death was 3 percentage points and was entirely attributable to black women.
What drives black female bias?
The emergence of a black female bias exposes an interplay between race and gender that had heretofore been overlooked. What explains it? A comparison between the groups of blocks reveals that it is motivated by those with the highest share of the population in poverty. Differences in poverty rates absorb differences in the proportions of people aged 65 and over with pre-existing illnesses. This suggests that socio-economic disparities, rather than demographic and biological differences, are at the heart of black women’s greatest vulnerability. But what, among the socio-economic disparities, can channel higher viral transmission and mortality?
We look at four potential and not mutually exclusive channels: employment, use of public transport, housing overcrowding, and health insurance coverage. The first and second reflect the risk of contracting the virus in the workplace and on the way to work; the third can amplify transmission rates within the household; and the last affects access to medical care once the contagion has occurred.
In order to assess whether the higher risk of contracting the virus in the workplace may explain the bias of black women in deaths, we calculate the share of women and men employed in 20 industries, at the block group level. Dividing the sample into clusters of blocks with shares above and below the median shows that the black female mortality gap is explained by female employment in two key high-exposure frontline sectors: health care and transportation / storage. These are sectors where black women are overrepresented and pay lower wages (Bertocchi 2020, Ross and Bateman 2019). Other highly exposed and poorly paid jobs, for example in restaurants, where again black women are strongly represented, do not explain the differences in mortality, probably because the closure of the food sector has protected their health, despite massive layoffs (Albanesi and Kim 2021, Alon et al. 2020).
A second contributing channel is the intensity of use of public transport, which we measure with the share of people who use it and the length of the journey to work (Caselli et al. 2020). On the other hand, we find no explanatory power for the overcrowding of housing, the spread of multigenerational families, or even the absence of health insurance. Finally, using the georeferenced home address of the deceased, we overlay the death map on the redlining maps created in the 1930s in order to assess the risk of mortgage default (Bertocchi and Dimico 2020). We find that the diminished resilience of black women is geographically concentrated in once low-level blocs, revealing a lingering influence of historic racial segregation.
Using a single data source, we have established that the COVID-19 death toll in Cook County has been disproportionately imposed on black women employed in high-risk front-line jobs in the healthcare and healthcare industries. transport, which they reach by public transport. historically poor neighborhoods where they reside.
Since we are dealing with the second most populous county in the United States, which contains the third largest metropolitan area in the country, our results are more relevant. They also highlight the need for granular data combining COVID-19 results by race and gender with socio-economic information. It is only with this data that scientists can generate evidence that can inform effective policy responses, including prioritization strategies for immunization campaigns, even after the emergency is over.
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