Older Black Americans During COVID-19: Race and Age Double Jeopardy.

The concept of "double jeopardy"-being both older and Black-describes how racism and ageism together shape higher risks for coronavirus exposure, COVID-19 disease, and poor health outcomes for older Black adults. Black people and older adults are the two groups most affected by COVID-19 morbidity and mortality. Double jeopardy, as a race- and age-informed analysis, demonstrates how Black race and older age are associated with practices and policies that shape key life circumstances (e.g., racial residential segregation, family and household composition) and resources in ways that embody elevated risk for COVID-19. The concept of double jeopardy underscores long-standing race- and age-based inequities and social vulnerabilities that produce devastating COVID-19 related deaths and injuries for older Black adults. Developing policies and actions that address race- and age-based inequities and social vulnerabilities can lower risks and enhance protective factors to ensure the health of older Black Americans during the COVID-19 pandemic.

Association Between Racial Segregation and COVID-19 Vaccination Rates.

OBJECTIVE: To examine the association between county-level Black-White residential segregation and COVID-19 vaccination rates. DESIGN: Observational cross-sectional study using multivariable generalized linear models with state fixed effects to estimate the average marginal effects of segregation on vaccination rates. SETTING: National analysis of county-level vaccination rates. MAIN OUTCOME MEASURE: County-level vaccination rates across the United States. RESULTS: We found an overall positive association between county-level segregation and the proportion population fully vaccinated, with a 6.8, 11.3, and 12.8 percentage point increase in the proportion fully vaccinated by May 3, September 27, and December 6, 2021, respectively. Effects were muted after adjustment for sociodemographic variables. Furthermore, in analyses including an interaction term between the county proportion of Black residents and the county dissimilarity index, the association between segregation and vaccination is positive in counties with a lower proportion of Black residents (ie, 5%) but negative in counties with the highest proportions of Black residents (ie, 70%). CONCLUSIONS: Findings highlight the importance of methodological decisions when modeling disparities in COVID-19 vaccinations. Researchers should consider mediating and moderating factors and examine interaction effects and stratified analyses taking racial group distributions into account. Results can inform policies around the prioritization of vaccine distribution and outreach.

Association of urban inequality and income segregation with COVID-19 mortality in Brazil.

Socioeconomic factors have exacerbated the impact of COVID-19 worldwide. Brazil, already marked by significant economic inequalities, is one of the most affected countries, with one of the highest mortality rates. Understanding how inequality and income segregation contribute to excess mortality by COVID-19 in Brazilian cities is essential for designing public health policies to mitigate the impact of the disease. This paper aims to fill in this gap by analyzing the effect of income inequality and income segregation on COVID-19 mortality in large urban centers in Brazil. We compiled weekly COVID-19 mortality rates from March 2020 to February 2021 in a longitudinal ecological design, aggregating data at the city level for 152 Brazilian cities. Mortality rates from COVID-19 were compared across weeks, cities and states using mixed linear models. We estimated the associations between COVID-19 mortality rates with income inequality and income segregation using mixed negative binomial models including city and week-level random intercepts. We measured income inequality using the Gini index and income segregation using the dissimilarity index using data from the 2010 Brazilian demographic census. We found that 88.2% of COVID-19 mortality rates variability was between weeks, 8.5% between cities, and 3.3% between states. Higher-income inequality and higher-income segregation values were associated with higher COVID-19 mortality rates before and after accounting for all adjustment factors. In our main adjusted model, rate ratios (RR) per 1 SD increases in income inequality and income segregation were associated with 17% (95% CI 9% to 26%) and 11% (95% CI 4% to 19%) higher mortality. Income inequality and income segregation are long-standing hallmarks of large Brazilian cities. Risk factors related to the socioeconomic context affected the course of the pandemic in the country and contributed to high mortality rates. Pre-existing social vulnerabilities were critical factors in the aggravation of COVID-19, as supported by the observed associations in this study.

A Bibliometric Analysis on Research Regarding Residential Segregation and Health Based on CiteSpace.

Considerable scholarly attention has been directed to the adverse health effects caused by residential segregation. We aimed to visualize the state-of-the-art residential segregation and health research to provide a reference for follow-up studies. Employing the CiteSpace software, we uncovered popular themes, research hotspots, and frontiers based on an analysis of 1211 English-language publications, including articles and reviews retrieved from the Web of Science Core Collection database from 1998 to 2022. The results revealed: (1) The Social Science & Medicine journal has published the most studies. Roland J. Thorpe, Thomas A. LaVeist, Darrell J. Gaskin, David R. Williams, and others are the leading scholars in residential segregation and health research. The University of Michigan, Columbia University, Harvard University, the Johns Hopkins School of Public Health, and the University of North Carolina play the most important role in current research. The U.S. is the main publishing country with significant academic influence. (2) Structural racism, COVID-19, mortality, multilevel modelling, and environmental justice are the top five topic clusters. (3) The research frontier of residential segregation and health has significantly shifted from focusing on community, poverty, infant mortality, and social class to residential environmental exposure, structural racism, and health care. We recommend strengthening comparative research on the health-related effects of residential segregation on minority groups in different socio-economic and cultural contexts.

Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces.

The relationship between the social structure of urban spaces and the evolution of the COVID-19 pandemic is becoming increasingly evident. Analyzing the socio-spatial structure in relation to cases may be one of the keys to explaining the ways in which this contagious disease and its variants spread. The aim of this study is to propose a set of variables selected from the social context and the spatial structure and to evaluate the temporal spread of infections and their different degrees of intensity according to social areas. We define a model to represent the relationship between the socio-spatial structure of the urban space and the spatial distribution of pandemic cases. We draw on the theory of social area analysis and apply multivariate analysis techniques to check the results in the urban space of the city of Malaga (Spain). The proposed model should be considered capable of explaining the functioning of the relationships between societal structure, socio-spatial segregation, and the spread of the pandemic. In this paper, the study of the origins and consequences of COVID-19 from different scientific perspectives is considered a necessary approach to understanding this phenomenon. The personal and social consequences of the pandemic have been exceptional and have changed many aspects of social life in urban spaces, where it has also had a greater impact. We propose a geostatistical analysis model that can explain the functioning of the relationships between societal structure, socio-spatial segregation, and the temporal evolution of the pandemic. Rather than an aprioristic theory, this paper is a study by the authors to interpret the disparity in the spread of the pandemic as shown by the infection data.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

How education and racial segregation intersect in neighborhoods with persistently low COVID-19 vaccination rates in Philadelphia.

BACKGROUND: COVID-19 infection has disproportionately affected socially disadvantaged neighborhoods. Despite this disproportionate burden of infection, these neighborhoods have also lagged in COVID-19 vaccinations. To date, we have little understanding of the ways that various types of social conditions intersect to explain the complex causes of lower COVID-19 vaccination rates in neighborhoods. METHODS: We used configurational comparative methods (CCMs) to study COVID-19 vaccination rates in Philadelphia by neighborhood (proxied by zip code tabulation areas). Specifically, we identified neighborhoods where COVID-19 vaccination rates (per 10,000) were persistently low from March 2021 - May 2021. We then assessed how different combinations of social conditions (pathways) uniquely distinguished neighborhoods with persistently low vaccination rates from the other neighborhoods in the city. Social conditions included measures of economic inequities, racial segregation, education, overcrowding, service employment, public transit use, health insurance and limited English proficiency. RESULTS: Two factors consistently distinguished neighborhoods with persistently low COVID-19 vaccination rates from the others: college education and concentrated racial privilege. Two factor values together - low college education AND low/medium concentrated racial privilege - identified persistently low COVID-19 vaccination rates in neighborhoods, with high consistency (0.92) and high coverage (0.86). Different values for education and concentrated racial privilege - medium/high college education OR high concentrated racial privilege - were each sufficient by themselves to explain neighborhoods where COVID-19 vaccination rates were not persistently low, likewise with high consistency (0.93) and high coverage (0.97). CONCLUSIONS: Pairing CCMs with geospatial mapping can help identify complex relationships between social conditions linked to low COVID-19 vaccination rates. Understanding how neighborhood conditions combine to create inequities in communities could inform the design of interventions tailored to address COVID-19 vaccination disparities.

Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic.

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).

Racial/Ethnic Segregation and Access to COVID-19 Testing: Spatial Distribution of COVID-19 Testing Sites in the Four Largest Highly Segregated Cities in the United States.

Objectives. To quantify the relationship between the segregation of Black, Indigenous, and Latinx communities and COVID-19 testing sites in populous US cities. Methods. We mapped testing sites as of June 2020 in New York City; Chicago, Illinois; Los Angeles, California; and Houston, Texas; we applied Bayesian methods to estimate the association between testing site location and the proportion of the population that is Black, Latinx, or Indigenous per block group, the smallest unit for which the US Census collects sociodemographic data. Results. In New York City, Chicago, and Houston, the expected number of testing sites decreased by 1.29%, 3.05%, and 1.06%, respectively, for each percentage point increase in the Black population. In Chicago, Houston, and Los Angeles, testing sites decreased by 5.64%, 1.95%, and 1.69%, respectively, for each percentage point increase in the Latinx population. Conclusions. In the largest highly segregated US cities, neighborhoods with more Black and Latinx residents had fewer COVID-19 testing sites, likely limiting these communities' participation in the early response to COVID-19. Public Health Implications. In light of conversations on the ethics of racial vaccine prioritization, authorities should consider structural barriers to COVID-19 control efforts. (Am J Public Health. 2022;112(3):518-526. https://doi.org/10.2105/AJPH.2021.306558).

The Association Between Neighborhood Social Vulnerability and Cardiovascular Health Risk Among Black/African American Women in the InterGEN Study.

BACKGROUND: Black/African American women in the United States are more likely to live in neighborhoods with higher social vulnerability than other racial/ethnic groups, even when adjusting for personal income. Social vulnerability, defined as the degree to which the social conditions of a community affect its ability to prevent loss and suffering in the event of disaster, has been used in research as an objective measure of neighborhood social vulnerability. Black/African American women also have the highest rates of hypertension and obesity in the United States. OBJECTIVES: The purpose of this study was to examine the relationship between neighborhood social vulnerability and cardiovascular risk (hypertension and obesity) among Black/African American women. METHODS: We conducted a secondary analysis of data from the InterGEN Study that enrolled Black/African American women in the Northeast United States. Participants' addresses were geocoded to ascertain neighborhood vulnerability using the Centers for Disease Control and Prevention's Social Vulnerability Index at the census tract level. We used multivariable regression models to examine associations between objective measures of neighborhood quality and indicators of structural racism and systolic and diastolic blood pressure and obesity (body mass index > 24.9) and to test psychological stress, coping, and depression as potential moderators of these relationships. RESULTS: Seventy-four percent of participating Black/African American women lived in neighborhoods in the top quartile for social vulnerability nationally. Women living in the top 10% of most socially vulnerable neighborhoods in our sample had more than a threefold greater likelihood of hypertension when compared to those living in less vulnerable neighborhoods. Objective neighborhood measures of structural racism (percentage of poverty, percentage of unemployment, percentage of residents >25 years old without a high school diploma, and percentage of residents without access to a vehicle) were significantly associated with elevated diastolic blood pressure and obesity in adjusted models. Psychological stress had a significant moderating effect on the associations between neighborhood vulnerability and cardiovascular risk. DISCUSSION: We identified important associations between structural racism, the neighborhood environment, and cardiovascular health among Black/African American women. These findings add to a critical body of evidence documenting the role of structural racism in perpetuating health inequities and highlight the need for a multifaceted approach to policy, research, and interventions to address racial health inequities.

Identifying US County-level characteristics associated with high COVID-19 burden.

BACKGROUND: Identifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems. METHODS: Synthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends. RESULTS: Rural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates. CONCLUSIONS: These county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.

An ecological study of the association between neighborhood racial and economic residential segregation with COVID-19 vulnerability in the United States' capital city.

PURPOSE: The COVID-19 pandemic has had a profound impact on American life. However, the burden of the pandemic has not been distributed equally. The purpose of this study was to investigate whether racial and economic residential segregation were associated with COVID-19 related factors in the nation's capital, Washington D.C., during the first year of the pandemic. METHODS: Racial, economic, and racialized economic segregation were assessed using the Index of Concentration at the Extremes measure and data from the 2014-2018 American Community Survey. COVID-19 related factors (i.e., incidence, testing rate, and percent positivity) were assessed using data from the Washington D.C. government. Spearman rank correlation was used to assess the relationship between each segregation measure and each COVID-19 related factor. RESULTS: Washington D.C. neighborhoods with a higher concentration of African Americans, lower income residents, and African Americans with low income had a higher incidence of COVID-19 and greater percent positivity, but lower testing rates compared to their counterparts. CONCLUSIONS: There is a geographic mismatch between neighborhoods most vulnerable to COVID-19 and the neighborhoods where the testing resources are being used. More resources should be allocated to the most vulnerable neighborhoods to address the COVID-19 pandemic in an equitable manner.

The Association of Social Determinants of Health With COVID-19 Mortality in Rural and Urban Counties.

PURPOSE: To identify the county-level effects of social determinants of health (SDoH) on COVID-19 (corona virus disease 2019) mortality rates by rural-urban residence and estimate county-level exceedance probabilities for detecting clusters. METHODS: The county-level data on COVID-19 death counts as of October 23, 2020, were obtained from the Johns Hopkins University. SDoH data were collected from the County Health Ranking and Roadmaps, the US Department of Agriculture, and the Bureau of Labor Statistics. Semiparametric negative binomial regressions with expected counts based on standardized mortality rates as offset variables were fitted using integrated Laplace approximation. Bayesian significance was assessed by 95% credible intervals (CrI) of risk ratios (RR). County-level mortality hotspots were identified by exceedance probabilities. FINDINGS: The COVID-19 mortality rates per 100,000 were 65.43 for the urban and 50.78 for the rural counties. Percent of Blacks, HIV, and diabetes rates were significantly associated with higher mortality in rural and urban counties, whereas the unemployment rate (adjusted RR = 1.479, CrI = 1.171, 1.867) and residential segregation (adjusted RR = 1.034, CrI = 1.019, 1.050) were associated with increased mortality in urban counties. Counties with a higher percentage of college or associate degrees had lower COVID-19 mortality rates. CONCLUSIONS: SDoH plays an important role in explaining differential COVID-19 mortality rates and should be considered for resource allocations and policy decisions on operational needs for businesses and schools at county levels.

COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020.

Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data.Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics.Results. At the surge peak (week 16, April 15-21), mortality rate ratios (comparing 2020 vs 2015-2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0).Conclusions. The COVID-19 mortality surge exhibited large inequities.Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.

Socioeconomic and Racial Segregation and COVID-19: Concentrated Disadvantage and Black Concentration in Association with COVID-19 Deaths in the USA.

INTRODUCTION: This study's objective was to examine the association of the percentage of county population residing in concentrated disadvantage and Black-concentrated census tracts with county-level confirmed COVID-19 deaths in the USA, concentrated disadvantage and Black concentration at census tract-level measure socioeconomic segregation and racial segregation, respectively. METHODS: We performed secondary data analysis using tract (N = 73,056) and county (N = 3142) level data from the US Census Bureau and other sources for the USA. Confirmed COVID-19 deaths per 100,000 population was our outcome measure. We performed mixed-effect negative binomial regression to examine the association of county population's percentage residing in concentrated disadvantage and Black-concentrated tracts with COVID-19 deaths while controlling for several other characteristics. RESULTS: For every 10% increase in the percentage of county population residing in concentrated disadvantage and Black-concentrated tracts, the rate for confirmed COVID-19 deaths per 100,000 population increases by a factor of 1.14 (mortality rate ratio [MMR] = 1.14; 95% confidence interval [CI]:1.11, 1.18) and 1.11 (MMR = 1.11; 95% CI:1.08, 1.14), respectively. These relations stayed significant in all models in further sensitivity analyses. Moreover, a joint increase in the percentage of county population residing in racial and socioeconomic segregation was associated with a much greater increase in COVID-19 deaths. CONCLUSIONS: It appears that people living in socioeconomically and racially segregated neighborhoods may be disproportionately impacted by COVID-19 deaths. Future multilevel and longitudinal studies with data at both individual and aggregated tract level can help isolate the potential impacts of the individual-level characteristics and neighborhood-level socioeconomic and racial segregation with more precision and confidence.

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.

This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30, 2020. The analysis focuses on the effects of racial residential segregation on mortality and infection rates for the overall population and on racial and ethnic mortality gaps. To account for potential confounding, I assemble a dataset that includes 50 county-level factors that are potentially related to residential segregation and COVID-19 infection and mortality rates. These factors are grouped into eight categories: demographics, density and potential for public interaction, social capital, health risk factors, capacity of the health care system, air pollution, employment in essential businesses, and political views. I use double-lasso regression, a machine learning method for model selection and inference, to select the most important controls in a statistically principled manner. Counties that are 1 SD above the racial segregation mean have experienced mortality and infection rates that are 8% and 5% higher than the mean. These differences represent an average of four additional deaths and 105 additional infections for each 100,000 residents in the county. The analysis of mortality gaps shows that, in counties that are 1 SD above the Black-White segregation mean, the Black mortality rate is 8% higher than the White mortality rate. Sensitivity analyses show that an unmeasured confounder that would overturn these findings is outside the range of plausible covariates.

Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA.

The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

White Counties Stand Apart: The Primacy of Residential Segregation in COVID-19 and HIV Diagnoses.

Emerging epidemiological data suggest that white Americans have a lower risk of acquiring COVID-19. Although many studies have pointed to the role of systemic racism in COVID-19 racial/ethnic disparities, few studies have examined the contribution of racial segregation. Residential segregation is associated with differing health outcomes by race/ethnicity for various diseases, including HIV. This commentary documents differing HIV and COVID-19 outcomes and service delivery by race/ethnicity and the crucial role of racial segregation. Using publicly available Census data, we divide US counties into quintiles by percentage of non-Hispanic white residents and examine HIV diagnoses and COVID-19 per 100,000 population. HIV diagnoses decrease as the proportion of white residents increase across US counties. COVID-19 diagnoses follow a similar pattern: Counties with the highest proportion of white residents have the fewest cases of COVID-19 irrespective of geographic region or state political party inclination (i.e., red or blue states). Moreover, comparatively fewer COVID-19 diagnoses have occurred in primarily white counties throughout the duration of the US COVID-19 pandemic. Systemic drivers place racial minorities at greater risk for COVID-19 and HIV. Individual-level characteristics (e.g., underlying health conditions for COVID-19 or risk behavior for HIV) do not fully explain excess disease burden in racial minority communities. Corresponding interventions must use structural- and policy-level solutions to address racial and ethnic health disparities.