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1.
Nurs Res ; 70(5S Suppl 1): S3-S12, 2021.
Article in English | MEDLINE | ID: covidwho-1429365

ABSTRACT

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.


Subject(s)
African Continental Ancestry Group/ethnology , Heart Disease Risk Factors , Social Segregation/psychology , Adult , African Continental Ancestry Group/psychology , African Continental Ancestry Group/statistics & numerical data , COVID-19/prevention & control , COVID-19/psychology , Female , Humans , Middle Aged , Ohio , Socioeconomic Factors
2.
Am J Respir Crit Care Med ; 204(5): 496-498, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1416751
3.
Ann Surg ; 273(1): 10-12, 2021 01 01.
Article in English | MEDLINE | ID: covidwho-1327423
4.
BMC Public Health ; 21(1): 1007, 2021 05 28.
Article in English | MEDLINE | ID: covidwho-1247583

ABSTRACT

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.


Subject(s)
COVID-19 , Social Segregation , Humans , Pandemics , Rural Population , SARS-CoV-2 , United States/epidemiology
5.
Ann Epidemiol ; 59: 33-36, 2021 07.
Article in English | MEDLINE | ID: covidwho-1198610

ABSTRACT

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.


Subject(s)
COVID-19 , Social Segregation , Humans , Pandemics , Residence Characteristics , SARS-CoV-2 , United States/epidemiology , Washington/epidemiology
6.
J Rural Health ; 37(2): 278-286, 2021 03.
Article in English | MEDLINE | ID: covidwho-1160529

ABSTRACT

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.


Subject(s)
COVID-19/mortality , Rural Population/statistics & numerical data , Social Determinants of Health , Urban Population/statistics & numerical data , African Continental Ancestry Group/statistics & numerical data , Diabetes Mellitus/epidemiology , Female , HIV Infections/epidemiology , Humans , Male , Social Segregation , Unemployment/statistics & numerical data , United States/epidemiology
7.
Am J Public Health ; 110(12): 1850-1852, 2020 12.
Article in English | MEDLINE | ID: covidwho-1067488

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Poverty/statistics & numerical data , COVID-19/mortality , Continental Population Groups/statistics & numerical data , Female , Humans , Male , Massachusetts , Pandemics , Residence Characteristics , SARS-CoV-2 , Social Segregation , Socioeconomic Factors
8.
Proc Natl Acad Sci U S A ; 118(7)2021 02 16.
Article in English | MEDLINE | ID: covidwho-1061188

ABSTRACT

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.


Subject(s)
COVID-19/mortality , Machine Learning , Social Segregation , COVID-19/ethnology , COVID-19/virology , Ethnic Groups/statistics & numerical data , Humans , Mortality , Regression Analysis , Risk Factors , SARS-CoV-2/isolation & purification , Socioeconomic Factors , United States/epidemiology
9.
Int J Environ Res Public Health ; 17(24)2020 12 19.
Article in English | MEDLINE | ID: covidwho-1011501

ABSTRACT

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.


Subject(s)
COVID-19/ethnology , Health Services Accessibility , Social Segregation , African Americans , Health Status Disparities , Hispanic Americans , Humans , Incidence , Massachusetts/epidemiology
10.
Health Educ Behav ; 47(6): 855-860, 2020 12.
Article in English | MEDLINE | ID: covidwho-885954

ABSTRACT

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.


Subject(s)
African Americans/statistics & numerical data , Coronavirus Infections/ethnology , Health Status Disparities , Pneumonia, Viral/ethnology , Age Factors , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Coronavirus Infections/mortality , Homes for the Aged/standards , Humans , Male , Middle Aged , Nursing Homes/standards , Pandemics , Pneumonia, Viral/mortality , Religion , SARS-CoV-2 , Social Isolation , Social Segregation/trends , Socioeconomic Factors
11.
AIDS Patient Care STDS ; 34(10): 417-424, 2020 10.
Article in English | MEDLINE | ID: covidwho-729065

ABSTRACT

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.


Subject(s)
Coronavirus Infections/ethnology , Ethnic Groups/statistics & numerical data , HIV Infections/ethnology , Health Status Disparities , Healthcare Disparities/statistics & numerical data , Pandemics , Pneumonia, Viral/ethnology , Residence Characteristics/statistics & numerical data , Social Segregation , Betacoronavirus , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , HIV Infections/diagnosis , HIV Infections/epidemiology , Healthcare Disparities/ethnology , Humans , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , SARS-CoV-2 , United States
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