Ecological Analysis of the Temporal Trends in the Association of Social Vulnerability and Race/Ethnicity with County-Level COVID-19 Incidence and Outcomes in the United States

ABSTRACT Background The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the U.S. initially, but the temporal trends during the year-long pandemic remain unknown. Objective We examined the temporal association between the 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 U.S. in the year starting in March 2020. Methods Counties (n=3091) with > 50 COVID-19 cases by March 6th, 2021 were included in the study. Associations between SVI (and its subcomponents) and county level racial composition with the incidence and death per capita were assessed by fitting a negative-binomial mixed-effects mod-el. This model was also used to examine potential time varying associations between weekly number of cases/deaths and SVI or racial composition. Data was adjusted for percentage of population aged great or equal to 65 years, state level testing rate, comorbidities using the average Hierarchical Condition Category (HCC) score, and environmental factors including average fine particulate matter (PM2.5), temperature and precipitation. Results Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio [IRR] per-10 percentile increase:1.02, (95% CI 1.02, 1.03, p<0.001), and death per capita (1.04, (95% CI 1.04, 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 out-comes. Counties with greater proportion of Black residents also observed similar temporal trends COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis. Conclusion Except for the winter third wave when majority White communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations, experienced worse COVID-19 outcomes.

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Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis

BMJ Open ◽

10.1136/bmjopen-2020-048086 ◽

2021 ◽

Vol 11 (7) ◽

pp. e048086

Author(s):

Shabatun J Islam ◽

Aditi Nayak ◽

Yingtian Hu ◽

Anurag Mehta ◽

Katherine Dieppa ◽

...

Keyword(s):

Social Vulnerability ◽

Negative Binomial ◽

Temporal Trends ◽

Ethnic Composition ◽

Racial Composition ◽

Third Wave ◽

County Level ◽

Incidence And Mortality ◽

The Usa ◽

Per Capita

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.

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Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

Journal of Population Economics ◽

10.1007/s00148-021-00845-2 ◽

2021 ◽

Author(s):

Catalina Amuedo-Dorantes ◽

Neeraj Kaushal ◽

Ashley N. Muchow

Keyword(s):

New York ◽

Political Ideology ◽

Death Rate ◽

The United States ◽

County Level ◽

Level Data ◽

Level Information ◽

Alternative Measures ◽

The U.S ◽

Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

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Disproportionately Overrepresented: Women in Local Elected Offices

State and Local Government Review ◽

10.1177/0160323x18813641 ◽

2018 ◽

Vol 50 (3) ◽

pp. 165-176 ◽

Cited By ~ 1

Author(s):

Ethan M. Bernick ◽

Brianne Heidbreder

Keyword(s):

United States ◽

Maximum Likelihood ◽

Gender Norms ◽

The United States ◽

Census Bureau ◽

National Association ◽

County Level ◽

Political Factors ◽

Using Data ◽

The U.S

This research examines the position of county clerk, where women are numerically disproportionately over-represented. Using data collected from the National Association of Counties and the U.S. Census Bureau, the models estimate the correlation between the county clerk’s sex and county-level demographic, social, and political factors with maximum likelihood logit estimates. This research suggests that while women are better represented in the office of county clerk across the United States, when compared to other elective offices, this representation may be because this office is not seen as attractive to men and its responsibilities fit within the construct of traditional gender norms.

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Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

PLoS ONE ◽

10.1371/journal.pone.0248702 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248702

Author(s):

Brian Neelon ◽

Fedelis Mutiso ◽

Noel T. Mueller ◽

John L. Pearce ◽

Sara E. Benjamin-Neelon

Keyword(s):

Social Vulnerability ◽

Primary Care Physicians ◽

Negative Binomial ◽

Temporal Trends ◽

The United States ◽

Social Vulnerability Index ◽

Crossover Effect ◽

Death Rates ◽

The Impact ◽

Over Time

Background Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. Methods We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. Results At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. Conclusions The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.

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Abstract 3039: Temporal Trends in the Prevalence of Dermatoses in Patients with Stroke and Transient Ischemic Attack in the United States

Stroke ◽

10.1161/str.43.suppl_1.a3039 ◽

2012 ◽

Vol 43 (suppl_1) ◽

Author(s):

Erine A Kupetsky ◽

Mitch Maltenfort ◽

Scott Waldman ◽

Fred Rincon

Keyword(s):

Temporal Trends ◽

Progressive Systemic Sclerosis ◽

The United States ◽

Primary Diagnosis ◽

Incidence Rates ◽

Secondary Diagnosis ◽

Cross Sectional ◽

Total Prevalence ◽

Skin Conditions ◽

The U.S

Background. We sought to determine the prevalence of skin conditions traditionally associated with acute ischemic stroke (AIS) and transient ischemic attacks (TIA) in the U.S. Methods. This is a cross-sectional study of data derived from the National Inpatient Sample from 1988-2008. We searched for admissions of patients <18 years, with a primary diagnosis of AIS, TIA, and the following secondary diagnoses (dermatoses): Psoriasis, Behcet’s Disease (BD), Dermatomyositis (DM), Systemic Lupus Eythematosis (SLE), Pseudoxanthoma Elasticum (PXE), Progressive Systemic Sclerosis or Scleroderma (SCD), and Bullous Pemphigoid (BP). Definitions were based on ICD9CM codes, and adjusted incidence rates for the U.S census and prevalence proportions were then calculated. Results. Over the 20-year period, we identified 9,085,147 admissions that corresponded to a primary diagnosis of AIS and TIA of which 53,060 had a secondary diagnosis of dermatoses, for a total prevalence of 0.6%. The adjusted rate of AIS/TIA increased from 71/100,000 in 1988 to 200/100,000 in 2008. Among the secondary diagnosis, the most prevalent condition after AIS/TIA admissions was SLE (54%), psoriasis (34%), SCD (9%), BP (2%), DM (1%), PXE (0.5%), and BD (0.14%). The prevalence of these dermatoses increased from 0.2% in 1988 to 0.8% in 2008 ( Figure 1 ). Conclusion. Despite an overall increase in the prevalence of dermatoses, these skin conditions remain a rare occurrence in AIS/TIA. The over-representation of traditional risk factors for AIS/TIA in patients with these dermatoses, may explain the observed epidemiological phenomenon.

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Temporal Trends in the Microbiological Characteristics of Sepsis in the United States: A Population Based Study

Journal of Intensive Care Medicine ◽

10.1177/08850666211053778 ◽

2021 ◽

pp. 088506662110537

Author(s):

Po-Yang Tsou ◽

Chia-Hung Yo ◽

Yenh-Chen Hsein ◽

Gregory Yungtum ◽

Wan-Ting Hsu ◽

...

Keyword(s):

United States ◽

Temporal Trends ◽

Population Level ◽

The United States ◽

Population Based ◽

Epidemiologic Studies ◽

Annual Increase ◽

Gram Negative ◽

Nationwide Inpatient Sample Database ◽

Incidence And Mortality

Background Epidemiologic studies are needed for monitoring population-level trends in sepsis. This study examines sepsis-causing microorganisms from 2006 to 2014 in the United States using data from the Nationwide Inpatient Sample database. Methods 7 860 686 adults hospitalized with sepsis were identified using a validated ICD-9 coding approach. Associated microorganisms were identified by ICD-9 code and classified by major groups (Gram-positive, Gram-negative, fungi, anaerobes) and specific species for analysis of their incidence and mortality. Results The rate of sepsis incidence has increased for all four major categories of pathogens, while the mortality rate decreased. In 2014, Gram-negative pathogens had a higher incidence than Gram-positives. Anaerobes increased the fastest with an average annual increase of 20.17% (p < 0.001). Fungi had the highest mortality (19.28%) and the slowest annual decrease of mortality (−2.31%, p = 0.006) in 2013, while anaerobic sepsis had the highest hazard of mortality (adjusted HR 1.60, 95% CI 1.53-1.66). Conclusions Gram-negative pathogens have replaced Gram-positives as the leading cause of sepsis in the United States in 2014 during the study period (2006-2014). The incidence of anaerobic sepsis has an annual increase of 20%, while the mortality of fungal sepsis has not decreased at the same rate as other microorganisms. These findings should inform the diagnosis and management of septic patients, as well as the implementation of public health programs.

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COMFORT ZONES

Du Bois Review Social Science Research on Race ◽

10.1017/s1742058x0707004x ◽

2007 ◽

Vol 4 (1) ◽

pp. 41-77 ◽

Cited By ~ 8

Author(s):

Camille Zubrinsky Charles

Keyword(s):

United States ◽

Los Angeles ◽

English Language ◽

Critical Role ◽

The United States ◽

Ethnic Composition ◽

Racial Composition ◽

Foreign Born ◽

Recent Immigrants ◽

Neighborhood Racial Composition

AbstractThe remarkable increase in immigration from Asia and Latin America requires a rethinking of multiracial analyses of neighborhood racial-composition preferences. This research addresses two interrelated questions: (1) since spatial mobility is so central to social mobility, how do recent Asian and Latino/a immigrants develop ideas about the racial and ethnic composition of the neighborhoods in which they want to live; and (2) what are the implications of processes of immigrant adaptation for the likely dynamics of race and ethnic relations in increasingly diverse communities? Guided by Massey's spatial assimilation model and previous studies of neighborhood racial-composition preferences, this research underscores the critical importance of immigration and assimilation as influences on preferences for same-race, White, and Black neighbors. Data are from the 1993–1994 Los Angeles Survey of Urban Inequality (N = 1921). Results point to the critical role of acculturation—the accumulation of time in the United States and English-language proficiency/use, as well as racial attitudes—in understanding what motivates preferences for these diverse groups, and to the complexities of accurately modeling preferences among largely foreign-born populations. Preferences for both same-race and White neighbors vary by the length of time that immigrants have accumulated in the United States and their ability to communicate effectively in English. English-language fluency is a particularly salient predictor of preferences among recent immigrants. Consistent with prior research on preferences, racial stereotypes stand out as particularly potent predictors of preferences; however, their influence is weakest among the most recent immigrants, coming to resemble those of the native-born with increasing years of U.S. residence.

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Gulf of Mexico Coastal County Resilience to Natural Hazards

Gulf and Caribbean Research ◽

10.18785/gcr.3201.10 ◽

2021 ◽

Vol 32 ◽

pp. 67-78

Author(s):

Kevin Summers ◽

Linda Harwell ◽

Andrea Lamper ◽

Courtney McMillon ◽

Kyle Buck ◽

...

Keyword(s):

United States ◽

Gulf Of Mexico ◽

Natural Hazards ◽

Social Economic ◽

Multiple Scales ◽

The United States ◽

County Level ◽

Coastal County ◽

County Scale ◽

The U.S

Using a Cumulative Resilience Screening Index (CRSI) that was developed to represent resilience to natural hazards at multiple scales for the United States, the U.S. coastal counties of the Gulf of Mexico (GOM) region of the United States are compared for resilience for these types of natural hazards. The assessment compares the domains, indicators and metrics of CRSI, addressing environmental, economic and societal aspects of resilience to natural hazards at county scales. The index was applied at the county scale and aggregated to represent states and two regions of the U.S. GOM coastline. Assessments showed county—level resilience in all GOM counties was low, generally below the U.S. average. Comparisons showed higher levels of resilience in the western GOM region while select counties in Louisiana, Mississippi and Alabama exhibited the lowest resilience (<2.0) to natural hazards. Some coastal counties in Florida and Texas represented the highest levels of resilience seen along the GOM coast. Much of this increased resilience appears to be due to higher levels of governance and broader levels of social, economic and ecological services.

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County-Level Exposures to Greenness and Associations with COVID-19 Incidence and Mortality in the United States

SSRN Electronic Journal ◽

10.2139/ssrn.3670681 ◽

2020 ◽

Author(s):

Jochem O. Klompmaker ◽

Jaime E. Hart ◽

Isabel Holland ◽

M. Benjamin Sabath ◽

Xiao Wu ◽

...

Keyword(s):

United States ◽

The United States ◽

County Level ◽

Incidence And Mortality

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Early Exposure to County Income Mobility and Adult Individual Health in the United States

10.31235/osf.io/5fdzs ◽

2020 ◽

Author(s):

Sebastian Daza ◽

alberto palloni

Keyword(s):

Residential Mobility ◽

Selection Process ◽

Panel Study ◽

The United States ◽

Income Mobility ◽

Childhood And Adolescence ◽

County Level ◽

Adult Health ◽

Adult Individual ◽

The U.S

Despite substantial research, drivers of the widening gap in life expectancy between rich and poor in the U.S. -- the so-called longevity gap -- remain unknown. Recent research has suggested that contextual income mobility (e.g., county-level socioeconomic mobility) may play an essential role in explaining the longevity gap. Previous studies -- based mostly on aggregate and cross-sectional individual data -- show an association between county income mobility and county mortality and individual's health. However, inferring individual effects from aggregate (county-level) data can be problematic (i.e., ecological fallacy), and measuring exposure to income mobility using the county where respondents currently live or die, might overlook the selection process associated with residential mobility. This paper aims to extend previous research by estimating the effect of average exposure to mobility regimes during childhood and adolescence on adult health using longitudinal data and accounting for selection into counties over time (i.e., residential mobility). We use both the National Longitudinal Survey of Youth 1997 (NLSY97) and the Panel Study of Income Dynamics (PSID) with geocoded data to assess the link between county-level income mobility (Chetty's estimates), behaviors (smoking) and health conditions and status (self-reported health, BMI, depressive symptoms). Furthermore, we use cohorts optimally match Chetty's estimates of income mobility in the U.S. (1980-1982) and account for selection and time-varying confounders using marginal structural models (MSM). Overall, we provide a more precise test of the hypothesis that childhood exposure to income mobility regimes may determine health status through behavior (i.e., smoking) later in life and contribute to longevity gaps.

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