key: cord-0811574-hrgd78wo
authors: Zelner, Jon; Trangucci, Rob; Naraharisetti, Ramya; Cao, Alex; Malosh, Ryan; Broen, Kelly; Masters, Nina; Delamater, Paul
title: Racial disparities in COVID-19 mortality are driven by unequal infection risks
date: 2020-11-21
journal: Clin Infect Dis
DOI: 10.1093/cid/ciaa1723
sha: cc377a374dea241f6e2018634ed8b42a623768d8
doc_id: 811574
cord_uid: hrgd78wo

BACKGROUND: As of November 1, 2020, there have been more than 230K deaths and 9M confirmed and probable cases attributable to SARS-CoV-2 in the United States. However, this overwhelming toll has not been distributed equally, with geographic, race-ethnic, age, and socioeconomic disparities in exposure and mortality defining features of the U.S. COVID-19 epidemic. METHODS: We used individual-level COVID-19 incidence and mortality data from the U.S. state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. RESULTS: In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than Whites for all groups except Native Americans. Blacks experienced the greatest burden of confirmed and probable COVID-19 infection (Age-standardized incidence = 1,626/100,000 population) and mortality (age-standardized mortality rate 244/100,000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.5 (95% Posterior Credible Interval [CrI] = 5.4, 5.6) and 6.7 (95% CrI = 6.4, 7.1) times higher than Whites, respectively. We found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. CONCLUSIONS: This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as the U.S. state of Michigan, are driven primarily by variation in household, community and workplace exposure rather than case-fatality rates.

As of November 1, 2020, there have been more than 230K deaths and 9M confirmed and probable cases attributable to SARS-CoV-2 in the United States, with these numbers undoubtedly reflecting a substantial underestimate of the true toll. Geographic, race-ethnic, age and socioeconomic disparities in mortality have been key features of the first, second, and ongoing third wave of the U.S. COVID-19 epidemic [1] [2] [3] [4] [5] . However, the extent to which this differential mortality is driven by disparities in rates of infection by age, race, and socioeconomic status, or some combination thereof, remains unknown. Addressing the clear inequities in the toll of death resulting from the COVID-19 pandemic in the U.S. requires disaggregating the relative role of exposure leading to infection from age-specific casefatality rates in drivers of the gaping inequity characteristic of SARS-CoV-2 mortality in the United States.

Analyses of other respiratory viruses, such as RSV and influenza, have documented raceethnic disparities in both rates of infection and case-fatality [6] . This inequality is driven by diverse factors including comorbid conditions that increase susceptibility to infection and disease severity. But it is also a function of structural factors that impact the ability of members of different race/ethnic and socioeconomic groups to avoid infection. Relevant factors include mass incarceration [7, 8] , residential segregation [9, 10] and wealth inequality that facilitates social distancing among the well-off while poorer individuals are more likely to be compelled into "essential work" [11] . A recent cross-national systematic review placed the population average infection fatality ratio (IFR) of COVID-19 infection at 0.75% [12] .

However, demographic factors such as population age structure are key shapers of the such rates and their variation across social groups [13] . While some studies have illustrated the differential impact of SARS-CoV-2 on non-White populations in the U.S. using aggregated data [5] , no existing analyses provide a clear breakdown of these risks by age, sex, and race [14] . In this paper, we aim to partially close this gap using detailed case-level data from the U.S. state of Michigan, which was particularly hard-hit in by SARS-CoV-2 in the winter and spring of 2020, and where the epidemic has been marked by unmistakable racial and socioeconomic inequality. 4 We used data from 73,441 people with PCR-confirmed and probable COVID-19 infections   recorded by the Michigan Disease Surveillance System (MDSS) from March 8, 2020 through July 5, 2020. Probable cases were defined using the criteria outlined in the Michigan State and Local Public Health COVID-19 Standard Operating Procedures [15] . From this dataset, we excluded 25 cases that did not reside in Michigan or were missing a state of residence, 8,613 people for whom race or ethnicity was not recorded, and 27 people who did not have age recorded or had age > 116 years old indicating entry errors. We combined 68 pairs of records that had duplicate patient identification numbers, resulting in 34 fewer cases. Finally, we dropped 28 patients whose sex at birth was unknown, leading to a final dataset of 49,701 people with a confirmed or probable COVID-19 infection, with known age, race or ethnicity, state of residence, sex at birth, and state prisoner status. To mitigate the potential of right censored deaths to erroneously deflate, we truncated the data at the 97.5% quantile of the distribution of times to death from case referral date, which was forty-six days, after which our data comprise 58,428 individuals.

After filtering the case data, we binned age by 10-year intervals to age 80, with ages 80 and above in one bin. We also assigned cases to race/ethnicity categories of Black/African American, Latino, Asian/Pacific Islander, Native American, Other, and White, where Other comprised the census category of "other", and mixed-race individuals. To model per-capita rates of disease we used IPUMS public-use microdata from the 2018 American Community Survey (ACS) [16] to obtain population counts for each age-sex-race stratum. For additional information on data preparation and assignment of cases to race/ethnic categories, see the Supplementary Materials.

To calculate age-specific, per-capita rates of COVID-19 infection in each age ( ), sex ( ), race ( ) bin, we fit a Poisson regression model with a population offset term, , where is the size of the population for the -th group from the 2018 ACS. We included age x sex, age x race, and sex x race interaction terms to capture the full spectrum of potential heterogeneity in our outcome data. We denote the observed number of cases in each group as and the per-capita cumulative incidence rate in each bin as . To ensure comparability of incidence and mortality rates across 5 race/ethnic groups, we employed a direct standardization approach to provide age and sexadjusted results where necessary.

Case-fatality rates. Age-specific case fatality rates (CFR) were estimated by fitting a binomial model to the number of deaths ( ) as a proportion of the number of total cases ( ) in each age/sex/race bin. We denote the CFR for each group as , so, inomial . For all analyses of per-capita age-specific incidence rates, we used a log-Gaussian prior distribution with a mean of 0 and standard deviation of 0.1.

Counterfactual analysis of mortality disparities. To understand the relative importance of age-specific incidence versus case-fatality as drivers of race/ethnic disparities as drivers of mortality disparities, we examined a pair of counterfactual scenarios in which: 1) age-and sex-specific COVID-19 incidence rates for each non-White race/ethnic group were replaced by the corresponding age/sex-specific rate among Whites, with original age-and sex-specific case-fatality rates maintained, and 2) the same procedure was repeated for case-fatality rates, keeping race/ethnic incidence rates fixed for non-Whites, while substituting White casefatality for each non-White age/sex bin. We then used posterior simulation to obtain the difference in the number of deaths expected under each scenario to calculate the percent reduction in observed deaths.

Software. All analyses were completed in R 4.0.3, using the rstanarm package [17] for Bayesian regression analysis, the tidybayes package for post-processing [18] and ggplot2 for visualization [19] . Standardized incidence and mortality rates. Table 2 contains age and sex-standardized incidence and mortality rates per 100,000 population, and corresponding between-group rate ratios, by race/ethnic group. Rows of the table are ordered by raw incidence per 100K individuals for comparability with Table 1 . This shows that the general patterns in the raw incidence and mortality hold after adjustment, although the age and sex-adjusted incidence among Latinos increased, reflecting the younger average age of cases identified as Latino.

The provided incidence rate ratios (IRRs) and mortality rate ratios (MRRs) show the enormous disparity in incidence and mortality between Blacks and Whites, with an IRR of 5.5 and an MRR of nearly 7. Again, these IRRs and MRRs reflect the fact that all groups other than Native Americans had higher rates of incidence and mortality than whites and that these differences do not simply reflect the age and sex distribution of cases. For Native Americans, rates were statistically indistinguishable from those for Whites, although this may be due to the very small number of cases and deaths overall in this group in our data. In the following sections, we will examine age-stratified incidence and mortality rates by race/ethnicity for Blacks, Latinos, Asians/Pacific Islanders and Whites. Native Americans are excluded from age-stratified analyses due to a small sample size, as are individuals in the "Other" race/ethic categorization.

Cumulative incidence rates. Figure 1 illustrates the dramatically higher overall and agespecific incidence rates among lacks and individuals in the "Other" race/ethnic category than for whites, particularly at older ages at which individuals are far more likely to die from their infection. In addition, the horizontal dashed line in each panel of Figure 1 shows the raw incidence rate for each group. The extent of these disparities in incidence is clearly in 7 evidence in panel A of Figure 3 , which shows the ratio of the age-specific cumulative incidence rate (incidence rate ratio, IRR) for each race/ethnic group as compared to the comparable rate for Whites. In this case, rates for all non-White groups are significantly higher, with these disparities most pronounced at older ages for Blacks, and younger ages for latinos. The IRR for individuals in the "other" group was fairly consistent across ages, with a small drop in the 20-40 age range.

Case-fatality rates. Figure 2 illustrates a steadily increasing trend in the probability of death among identified cases from age 50 onwards across groups, although there are differences in these rates at younger ages. These are visible in the right-hand panel of Figure 3 , which shows the ratio of the age-specific case-fatality ratio for Blacks, Latinos and those in the "other" group vs. White CFRs. ecause of the small number of deaths among individuals < 20 years of age, these groups are excluded from the figure. For Blacks, all age groups from 30 to 70 years experienced higher case-fatality rates than Whites, with this disparity most pronounced among 40-49 year-olds. However, for Latinos and those in the "other" race/ethnic group, there are no significant differences in age-specific case-fatality rates as compared to

Whites. These results and those in Table 2 suggest that although there are meaningful differences in case-fatality by race and age, the large disparities in COVID-19 mortality cannot be explained by case fatality rates alone.

Counterfactual analysis of mortality disparities. We found that substituting the incidence rates of Whites for those of non-Whites this would result in a decrease of 82%, (95% CrI=81%,84%) of the observed deaths among Blacks, 57%, (95% CrI=47%,66%) among Latinos, and 35% (95% CrI=18%,49%) among Asian/Pacific Islanders. In the second scenario, i.e. when White case-fatality rates were substituted for non-white CFRs but groupspecific incidence rates maintained, we found no significant change in the expected number of deaths for any group except Blacks, who saw a smaller but still meaningful decrease of 19%, (95% CrI = 14%,25%) of deaths.

These results suggest that while differential case-fatality rates can account for some of the disparity in Black vs. White mortality rates, the large majority of COVID-19 deaths among African-Americans in Michigan can be attributed to the large differences in age-specific incidence illustrated in Fig. 1 . Similarly, although Latinos and Asian/Pacific Islanders had similar crude mortality rates to Whites (Table 1) , these results indicate that these rates would be significantly lower if their exposure risks were more similar to their White peers. 8 Sensitivity to case definition. To ensure that our results were not strongly impacted by the combined analysis of probable [15] and PCR-confirmed SARS-CoV-2 cases, we conducted a sensitivity analysis in which all results were re-generated using data from only PCRconfirmed cases. Descriptive analysis showed that younger individuals and Whites were more likely to have a probable infection than older individuals and non-Whites. When these probable cases were excluded, incidence and mortality disparities for younger non-White individuals increased, but our results for older individuals -who experienced the bulk of mortality -remain qualitatively unchanged, as do our population-level conclusions. For full results of this analysis, please see the Supplementary Materials.

Our results highlight yawning gaps in COVID-19 incidence and mortality in Michigan that cannot be explained away by differences in population age and sex composition. Results from our counterfactual analysis suggest that the stark differences in crude and adjusted mortality between Blacks and all other race/ethnic groups shown in Tables 1 & 2 above reported totals. In addition, the damage to health from the pandemic goes beyond the direct impact of infections and deaths from SARS-CoV-2: For example, Woolf et al. [22] showed that 33% of the total excess deaths during the period from March 1-April 25 2020 in 9 Michigan were attributable to non-infectious causes, with the remainder associated with respiratory infections, primarily COVID-19. Though these results are not broken down by race/ethnicity, it is likely that the burden of mortality in them is not equally shared across race/ethnic groups and socioeconomic strata. Beyond delays in healthcare seeking due to the pandemic, it is quite likely that these patterns of excess death reflect underlying disparities in chronic illnesses that predispose individuals to mortality from COVID-19, lack of access to healthcare for Blacks, Latinx individuals and other minority groups, and variable quality of care delivered based on racial-ethnic identity.

When interpreting these and other results illustrating racial disparities in COVID-19

incidence and mortality, it is key not portray race as a risk factor independent of health conditions, wealth, and other potentially modifiable risk factors [23] that may predispose individuals to COVID-19 infection and death. For example, McClure et al. [24] illustrate how a focus on -and adjustment for -individual-level "underlying conditions" obscures the role of racial inequality in shaping the prevalence of these chronic health conditions, and other factors such as residence in multi-generational households, which may increase risk among racial and ethnic minority groups.

A strength of our analysis is the use of detailed case data obtained directly from the Michigan Disease Surveillance System (MDSS). This allowed us to identify age and race-specific risks of COVID-19 infection and death. Nonetheless, there are some limitations that are important to highlight. First, our reliance on census-defined race/ethnicity as a proxy for exposure and mortality risk is necessarily reductive and does not shed light on factors that can be modified to reduce these disparities [25] . Future analyses are necessary using either prospectively collected data inclusive of SES, or spatial analyses that join neighborhood-level information on wealth and other markers of SES with individual-level case data. The set of cases obtained from MDSS during this period is also necessarily incomplete, with large numbers of asymptomatic and less-severe infections undoubtedly missing from this registry.

In addition, although the disparities in our data likely mirror those nationwide, it is important to remember that these results reflect patterns of infection and death in Michigan during the first wave of the COVID-19 pandemic. Although its relatively large population size and socioeconomic and racial composition make Michigan a belwether of many national trends, this analysis should be interpreted relative to its context. Consequently, similar analyses are 10 sorely needed to understand how these outcomes vary across locales and populations. infection and mortality during the current catastrophe -and preventing such inequities in the next one -requires addressing the racialized dismantling of public infrastructure and systematic divestment which has made these disparities in exposure, susceptibility and mortality a foregone conclusion [26] . Accomplishing this necessitates an urgent reorientation around an "epidemiology of consequence" [27] that can identify and attack the structural and practical barriers to health equity before the next disaster strikes.

Author Contributions: JZ, RT conceptualized and completed data analysis; JZ, RT, RN, AC, NM, PD participated in data preparation and cleaning; all authors participated in the writing and editing of the final manuscript. Table 2 . Age and sex-standardized COVID-19 incidence and mortality rates and corresponding rate ratios, by race/ethnic group in Michigan USA March-June 2020. The table shows incidence rates and mortality rates and 95% CrI, as well as corresponding standardized incidence rate ratios and mortality rate ratios. For all ratio measures of association, the incidence and mortality rate among Whites is used as the reference group. (Abbreviations: IRR -incidence rate ratio; MRR -mortality rate ratio; 95% CrI -95 percent posterior credible interval) 

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Control and Prevention. The funding sources had no role in the preparation of this manuscript.None of the authors has any conflicts to disclose 12