Racial and Ethnic Disparities in Rates of COVID-19–Associated Hospitalization, Intensive Care Unit Admission, and In-Hospital Death in the United States From March 2020 to February 2021

Key Points Question Are rates of COVID-19–associated hospitalization, intensive care unit (ICU) admission, or in-hospital death higher among individuals who belong to racial and ethnic minority groups compared with those who identify as non-Hispanic White? Findings In this cross-sectional study of 143 342 individuals hospitalized with COVID-19, non-Hispanic American Indian or Alaska Native, Hispanic or Latino, non-Hispanic Black, and non-Hispanic Asian or Pacific Islander persons were more likely to have a COVID-19-associated hospitalization, ICU admission, or in-hospital death compared with non-Hispanic White individuals during the first year of the pandemic. Meaning In this study, US residents who belong to racial and ethnic minority groups experienced severe COVID-19–associated outcomes disproportionately; equitable access to preventive measures, such as COVID-19 vaccines, is needed for these populations.


Introduction
The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected racial and ethnic minority populations in the United States, who are at an increased risk of infection, hospitalization, and death. 1,2 During the first 4 months of the pandemic, data from the US Centers for Disease Control and Prevention (CDC) COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) [3][4][5] and other studies demonstrated that non-Hispanic Black persons were disproportionately hospitalized with COVID-19 and that racial and ethnic minority populations, including non-Hispanic Black and Hispanic or Latino persons, had higher rates of hospitalization compared with non-Hispanic White persons. [6][7][8] Nonetheless, data on severe COVID-19 illness among other racial and ethnic minority groups, especially non-Hispanic American Indian or Alaska Native and non-Hispanic Asian or Pacific Islander populations, and longitudinal data across all racial and ethnic groups are limited. Using data from COVID-NET, a large, geographically diverse surveillance network for COVID-19-associated hospitalizations, we describe rates of hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity during the first year of the pandemic.

COVID-NET Surveillance
COVID-NET, which has been previously described, conducts population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among persons of all ages in 99 counties in 14 states (California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah) and represents approximately 10% of the US population. 3 Hospitalized residents in the COVID-NET catchment area who have a positive SARS-CoV-2 molecular or rapid antigen detection test during hospitalization or within 14 days prior to hospital admission are included in surveillance.
Trained surveillance staff identify persons hospitalized with laboratory-confirmed COVID-19 using laboratory, hospital, and reportable condition databases. Using a standardized data collection form, staff abstract medical records on a sample of patients to obtain data on demographic and clinical characteristics, underlying medical conditions, and clinical interventions and outcomes, including ICU admission, invasive mechanical ventilation (IMV), vasopressors, kidney replacement therapy (KRT), median length of stay (LOS) in the hospital, and in-hospital death from all causes.
For this analysis, we categorized race and ethnicity according to the National Center for Health Statistics (NCHS) categories 9 as follows: Hispanic or Latino (Latino), non-Hispanic American Indian or Alaska Native (American Indian or Alaska Native), non-Hispanic Asian or Pacific Islander (Asian or Pacific Islander), non-Hispanic Black (Black), and non-Hispanic White (White). People who identified as more than 1 race and ethnicity or unknown race and ethnicity are captured by surveillance but were not included due to small numbers. If race was unknown but ethnicity was Latino, the person was classified as Latino. If ethnicity was unknown, non-Latino ethnicity was assumed. Race and ethnicity data were obtained from multiple sources, including notifiable disease, laboratory, and hospital databases. In most cases, race and ethnicity are self-reported, but the source could not be confirmed in every case.

COVID-NET Sampling and Weighting Methodology
A minimum data set (including age, sex, race and ethnicity, surveillance site, hospital admission date, and positive SARS-CoV-2 test result and date) is reported for all persons identified by COVID-NET to produce weekly hospitalization rates stratified by age and race and ethnicity. 10 An age-and surveillance site-stratified random sampling scheme is used to collect detailed clinical data for a representative sample of hospitalized adult patients aged at least 18 years; children younger than 18 years are sampled at a rate of 100%. The age strata used for sampling among adults are as follows: 18 to 49 years, 50 to 64 years, and 65 years or older. The sample size is powered to achieve a relative standard error of less than 30% for point estimates with values equal to or greater than approximately 10%, resulting in a 16% sampling rate for adult patients during the analytic time period. Sample weights are calculated as the inverse probability of being selected within each COVID-NET site and age group. These weights are adjusted for nonresponse, raked to adjust the sampled population to the total population using published procedures, 11 and trimmed to reduce variability.

Estimation of Population-Based Rates of COVID-19-Associated Hospitalization, ICU Admission, and In-Hospital Death
Cumulative and monthly COVID-19-associated hospitalization rates per 100 000 population, stratified by race and ethnicity, were calculated using all hospitalized persons in COVID-NET with known race and ethnicity for the numerator and NCHS vintage 2019 bridged-race population estimates for the denominator. 12 Cumulative rates of ICU admission and in-hospital death were similarly calculated; however, because ICU admission and in-hospital death status were only available for sampled hospitalized patients, weighted frequencies of ICU admission and in-hospital death among sampled patients were used as the numerator. Both crude and age-adjusted rates were estimated. Age-adjusted rates accounted for differences in age distributions within race and ethnicity strata in the COVID-NET catchment area using the following age strata for adjustments: 0 to 17 years, 18 to 49 years, 50 to 64 years, 65 to 74 years, 75 to 84 years, and 85 years and older. Hospitalization, ICU admission, and in-hospital death rate ratios (RRs) for each racial and ethnic group were calculated in comparison with White persons.

Statistical Analysis
Data from all patients hospitalized with COVID-19 during March 1, 2020, to February 28, 2021, were used to describe the demographic characteristics (age, sex, race and ethnicity) of hospitalized patients and hospitalization rates by race and ethnicity. All other analyses were limited to sampled hospitalized patients for whom medical record abstractions were completed and a discharge disposition was known. The weighted distributions of clinical characteristics and outcomes among hospitalized patients were calculated by age group and race and ethnicity; weighted percentages and unweighted case counts are presented throughout.
Data for sampled persons were analyzed using SAS survey procedures to account for sampling weights. P values for cumulative and monthly hospitalization rates, ICU admission, and in-hospital death rates and RRs were calculated using a Z test for the equality of 2 proportions. We calculated 95% CIs around rates and RRs assuming a simple random sample design and a normal distribution using the SAS STDRATE procedure with direct standardization. Statistical significance was set at α = .05, and all tests were 2-tailed. All analyses were performed using SAS version 9.4 (SAS Institute).

Population-Based Rates of Hospitalization, ICU Admission and In-Hospital Mortality
Cumulative age-adjusted hospitalization rates were highest among American Indian or Alaska Native, Latino, and Black persons ( Figure 1A). Compared with White persons, cumulative age-adjusted hospitalization RRs were 3.70 (95% CI, 3.54-3.87) for American Indian or Alaska Native persons, 3.06 (95% CI, 3.01-3.10) for Latino persons, and 2.85 (95% CI, 2.81-2.89) for Black persons ( Figure 1A and Table 1). Although hospitalization rates increased with age across all racial and ethnic groups, the highest hospitalization rates for each age group varied by race and ethnicity ( Figure 1B When examining monthly age-adjusted COVID-19-associated hospitalization rates, 3 distinct peaks were observed, the first in April to May 2020, the second in July 2020, and the third in   4 5 6 7  12 14 16 18 20 22 24 26 28 30 32 34 36 38  Abbreviations: ICU, intensive care unit; NA, not applicable; RR, rate ratio. a Cumulative hospitalization rates per 100 000 population were calculated using all hospitalized persons in COVID-19-Associated Hospitalization Surveillance Network with known race and ethnicity for the numerator and National

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Center for Health Statistics vintage 2019 bridged-race population estimates for the denominator. ICU admission and in-hospital death status were only available for sampled hospitalized patients with known race and ethnicity, complete medical record review, and a known discharge disposition; therefore, cumulative rates of ICU admission and in-hospital death per 100 000 population were calculated using weighted frequencies as the numerator and National Center for Health Statistics vintage 2019 bridged-race population estimates for the denominator. b Rates for all ages combined are age-adjusted. c RR not statistically significant.
December 2020, which had the highest hospitalization rates across all race and ethnicity groups (Figure 2A). During every month, the highest age-adjusted hospitalization rates occurred among Monthly hospitalization RRs for racial and ethnic minority groups compared with White persons peaked from May to June 2020 and then decreased but persisted over time ( Figure 2B and     In-hospital death rate

Frequency of Clinical Interventions and Outcomes Among a Weighted Sample of Hospitalized Patients
Cumulative hospitalization rates per 100 000 population were calculated using all hospitalized persons in the COVID-19-Associated Hospitalization Surveillance Network with known race and ethnicity for the numerator and National Center for Health Statistics vintage 2019 bridged-race population estimates for the denominator. ICU admission and in-hospital death status were only available for sampled hospitalized patients with known race and ethnicity, complete medical record review, and a discharge disposition; therefore, cumulative rates of ICU admission and in-hospital death per 100 000 population were calculated using weighted frequencies as the numerator and National Center for Health Statistics vintage 2019 bridged-race population estimates for the denominator.

Discussion
Within a large, multisite, US population-based surveillance network with robust methods for case ascertainment and highly complete information on race and ethnicity, we identified racial and ethnic disparities in rates of severe COVID-19 during the first year of the COVID-19 pandemic. American Indian or Alaska Native, Latino, Black, and Asian or Pacific Islander persons were significantly more likely to be hospitalized, receive ICU care, or die with COVID-19-associated illness compared with White persons. These disparities were present across all age groups and persisted during the entire 12-month surveillance period. After peaking in May through July 2020, disparities in monthly hospitalization rates among racial and ethnic minority groups appeared to decrease; however, there was a concerning increase in hospitalization disparities among Black persons in December 2020 to  Kidney replacement therapy 7.6 (5.3-9.9) 9.5 (0.0-19.9) 9.3 (7.1-11.4) 6.9 (2.0-11.9) 4.9 (3.6-6.2) In-hospital death 10 disease in these populations. 4,5,31 Community-level exposure to and incidence of COVID-19 is also likely a large driver of disparities in severe COVID-19 disease. 32,33 Importantly, members of racial and ethnic minority groups face inequity due to structural racism, with its many downstream consequences on overall health, including poor access to health care and economic instability. 34,35 Poverty, unstable housing, lack of transportation, and poor access to quality education, among other social determinants of health, are more common in American Indian or Alaska Native, Asian or Pacific Islander, Latino, and Black populations. [36][37][38] Additionally, these populations are more likely to work in essential industries and live in larger, multigenerational households, increasing the risk of exposure to COVID-19. [38][39][40][41] Other barriers to health care, including lack of health insurance, a primary language other than English, low health literacy, and differing levels of acculturation, are also observed more frequently in American Indian or Alaska Native, Asian or Pacific Islander, Latino, and Black populations. 25,42 Together, these factors may intensify disparities in health outcomes, including the observed rates of severe COVID-19 disease.

Limitations
Several limitations should be considered. COVID-NET relies on clinician-driven or facility-based testing practices to identify cases; rates are likely underestimated, as some patients hospitalized with COVID-19 may not have been tested. While rates are age-adjusted, we were unable to adjust for other important factors, including underlying medical conditions and socioeconomic indicators, as these data were not available for the COVID-NET surveillance population. Future analyses that link COVID-NET data to other sources of population-level health data will be important in understanding the impact of these factors on COVID-19-associated outcomes. Due to the relatively low rates of COVID-19-associated hospitalizations among children, estimates by race and ethnicity within this age group were subject to variability. Although this is among the few analyses to include populationbased data from geographically diverse sites, COVID-NET represents approximately 10% of the US population and findings may not be generalizable to the entire country. While it is reassuring that the COVID-NET racial and ethnic makeup is similar to that of the US population (COVID-NET population: Latino, 14.1%; American Indian or Alaska Native, 0.7%; Asian or Pacific Islander, 8.9%; Black, 17.9%; White, 58.5%; U.S population: Latino, 18.5%; American Indian or Alaska Native, 0.8%; Asian or Pacific Islander, 6.3%; Black, 13.2%; White, 61.2%), findings were likely affected by differences in racial and ethnic distributions and COVID-19 disease incidence across sites. Race and ethnicity classifications were limited to those available through the NCHS, and we could not evaluate specific groups that may be disproportionately affected by COVID-19, such as Pacific Islander persons, or people of more than 1 race and ethnicity. Finally, 5.9% of hospitalizations had missing or unknown ethnicity, which were presumed to be non-Latino; therefore, rates may be underreported for Latino persons.

Conclusions
This longitudinal analysis found that racial and ethnic minority groups have experienced severe COVID-19 outcomes disproportionately in the United States during the first year of the COVID-19 pandemic. Further work is needed to understand the complex relationship between race and ethnicity and COVID-19-associated outcomes. In addition, an emphasis on studying how socioeconomic inequities, structural racism, and cultural differences can result in immediate and long-term barriers to adequate health care for these populations may lead to successful interventions that improve health. Nonetheless, because of the current disproportionately high burden of severe COVID-19 among racial and ethnic minority groups, equitable access to preventive measures, such as vaccination, and treatments should be urgently optimized among these groups.