Structural Racism and COVID-19 in the USA: a County-Level Empirical Analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Tan SB
PROVIDER: S-EPMC7815192 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundPreliminary studies have suggested a link between socio-economic characteristics and COVID-19 mortality. Such studies have been carried out on particular geographies within the USA or selective data that do not represent the complete experience for 2020.MethodsWe estimated COVID-19 mortality rates, number of years of life lost to SARS-CoV-2 and reduction in life expectancy during each of the three pandemic waves in 2020 for 3144 US counties grouped into five socio-economic status categories, using daily death data from the Johns Hopkins University of Medicine and weekly mortality age structure from the Centers for Disease Control.ResultsDuring March-May 2020, COVID-19 mortality was highest in the most socio-economically advantaged quintile of counties and lowest in the two most-disadvantaged quintiles. The pattern reversed during June-August and widened by September-December, such that COVID-19 mortality rates were 2.58 times higher in the bottom than in the top quintile of counties. Differences in the number of years of life lost followed a similar pattern, ultimately resulting in 1.002 (1.000, 1.004) million years in the middle quintile to 1.381 (1.378, 1.384) million years of life lost in the first (most-disadvantaged) quintile during the whole year.ConclusionsDiverging trajectories of COVID-19 mortality among the poor and affluent counties indicated a progressively higher rate of loss of life among socio-economically disadvantaged communities. Accounting for socio-economic disparities when allocating resources to control the spread of the infection and to reinforce local public health infrastructure would reduce inequities in the mortality burden of the disease.
| S-EPMC8755340 | biostudies-literature
Project description:The basic reproduction number, R0, determines the rate of spread of a communicable disease and therefore gives fundamental information needed to plan public health interventions. Using mortality records, we estimated the rate of spread of COVID-19 among 160 counties and county-aggregates in the USA at the start of the epidemic. We show that most of the high among-county variance is explained by four factors (R2 = 0.70): the timing of outbreak, population size, population density, and spatial location. For predictions of future spread, population density and spatial location are important, and for the latter we show that SARS-CoV-2 strains containing the G614 mutation to the spike gene are associated with higher rates of spread. Finally, the high predictability of R0 allows extending estimates to all 3109 counties in the conterminous 48 states. The high variation of R0 argues for public health policies enacted at the county level for controlling COVID-19.
| S-EPMC7785728 | biostudies-literature
Project description:Black and Hispanic communities are disproportionately affected by both incarceration and COVID-19. The epidemiological relationship between carceral facilities and community health during the COVID-19 pandemic, however, remains largely unexamined. Using data from Cook County Jail, we examine temporal patterns in the relationship between jail cycling (i.e., arrest and processing of individuals through jails before release) and community cases of COVID-19 in Chicago ZIP codes. We use multivariate regression analyses and a machine-learning tool, elastic regression, with 1,706 demographic control variables. We find that for each arrested individual cycled through Cook County Jail in March 2020, five additional cases of COVID-19 in their ZIP code of residence are independently attributable to the jail as of August. A total 86% of this additional disease burden is borne by majority-Black and/or -Hispanic ZIPs, accounting for 17% of cumulative COVID-19 cases in these ZIPs, 6% in majority-White ZIPs, and 13% across all ZIPs. Jail cycling in March alone can independently account for 21% of racial COVID-19 disparities in Chicago as of August 2020. Relative to all demographic variables in our analysis, jail cycling is the strongest predictor of COVID-19 rates, considerably exceeding poverty, race, and population density, for example. Arrest and incarceration policies appear to be increasing COVID-19 incidence in communities. Our data suggest that jails function as infectious disease multipliers and epidemiological pumps that are especially affecting marginalized communities. Given disproportionate policing and incarceration of racialized residents nationally, the criminal punishment system may explain a large proportion of racial COVID-19 disparities noted across the United States.
| S-EPMC8166074 | biostudies-literature
Project description:We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.
| S-EPMC8215509 | biostudies-literature
Project description:BackgroundCoronavirus Disease 2019 (COVID-19) excess deaths refer to increases in mortality over what would normally have been expected in the absence of the COVID-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to COVID-19. In this study, we take advantage of county-level variation in COVID-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to COVID-19 varies across subsets of counties defined by sociodemographic and health characteristics.Methods and findingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct COVID-19 and all-cause mortality occurring in US counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a 10-week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more COVID-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black, and 59.6% non-Hispanic White. A total of 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and COVID-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to COVID-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than COVID-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to COVID-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of COVID-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to COVID-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.ConclusionsIn this study, we found that direct COVID-19 death counts in the US in 2020 substantially underestimated total excess mortality attributable to COVID-19. Racial and socioeconomic inequities in COVID-19 mortality also increased when excess deaths not assigned to COVID-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.
| S-EPMC8136644 | biostudies-literature
Project description:The spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individual's susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy. Using publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context. Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage. The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds. COVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities' attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.
| S-EPMC7276037 | biostudies-literature
Project description:As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.
| S-EPMC7556498 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown.ObjectiveWe examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020.MethodsCounties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 μm, temperature and precipitation.ResultsHigher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented ('third wave'). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis.ConclusionExcept for the winter 'third wave', when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.
| S-EPMC8300549 | biostudies-literature
Project description:BackgroundCovid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to Covid-19. In this study, we take advantage of county-level variation in Covid-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by sociodemographic and health characteristics.Methods and findingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct Covid-19 and all-cause mortality occurring in U.S. counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a ten week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more Covid-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black and 59.6% non-Hispanic White. 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and Covid-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to Covid-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than Covid-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to Covid-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of Covid-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to Covid-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.ConclusionsIn this study, we found that direct Covid-19 death counts in the United States in 2020 substantially underestimated total excess mortality attributable to Covid-19. Racial and socioeconomic inequities in Covid-19 mortality also increased when excess deaths not assigned to Covid-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.
| S-EPMC7480051 | biostudies-literature
Project description:Prior evaluations of the relationship between COVID-19 and weather indicate an inconsistent role of meteorology (weather) in the transmission rate. While some effects due to weather may exist, we found possible misconceptions and biases in the analysis that only consider the impact of meteorological variables alone without considering the urban metabolism and environment. This study highlights that COVID-19 assessments can notably benefit by incorporating factors that account for urban dynamics and environmental exposure. We evaluated the role of weather (considering equivalent temperature that combines the effect of humidity and air temperature) with particular consideration of urban density, mobility, homestay, demographic information, and mask use within communities. Our findings highlighted the importance of considering spatial and temporal scales for interpreting the weather/climate impact on the COVID-19 spread and spatiotemporal lags between the causal processes and effects. On global to regional scales, we found contradictory relationships between weather and the transmission rate, confounded by decentralized policies, weather variability, and the onset of screening for COVID-19, highlighting an unlikely impact of weather alone. At a finer spatial scale, the mobility index (with the relative importance of 34.32%) was found to be the highest contributing factor to the COVID-19 pandemic growth, followed by homestay (26.14%), population (23.86%), and urban density (13.03%). The weather by itself was identified as a noninfluential factor (relative importance < 3%). The findings highlight that the relation between COVID-19 and meteorology needs to consider scale, urban density and mobility areas to improve predictions.
| S-EPMC7663468 | biostudies-literature