Increased Incidence and Mortality of COVID-19 among the U.S. Socially Vulnerable Population

Increased Incidence and Mortality of COVID-19 among the U.S. Socially Vulnerable Population: Findings From the Early Phase of the Pandemic Ibraheem M. Karaye, MD, DrPH, Assistant Professor, Department of Health Professions, School of Health Professions and Human Services, Hofstra University INTRODUCTION The increased incidence and mortality rate of COVID-19 among minority populations in the U.S., particularly African Americans, has captivated significant public attention (Calma, 2020b; Collins, 2020; Eligon et al., 2020). An analysis of available data has shown that African Americans are three times more likely to contract COVID-19 and six times more likely to die from the disease than whites (Thebault, Tran, & Williams, 2020). For example, in Houston, Texas, where African Americans comprise 22.5% of the population, they make up 57% of deaths due to COVID-19 (Bludau, 2020). In Milwaukee, Wisconsin, African Americans 16

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constitute 26% of the population but account for 70% of COVID-19-related deaths (Thebault, Tran, & Williams, 2020). In Louisiana, 70% of deaths from the disease are among African Americans, although they constitute only 32% of the state’s population (Thebault, Tran, & Williams, 2020). Traditionally marginalized populations like racial minorities, persons with disability, the elderly, those living in poverty, and undocumented immigrants have historically borne the brunt of the health impacts of disasters because social factors predispose them to poor health outcomes (Karaye, Ross, & Horney, 2019; Palaiologou et al., 2019;

Rufat et al., 2015). For example, low socioeconomic status is associated with low per capita income, uninsured medical status, and poor access to health care, which may collectively increase the risk of adverse outcomes (Calma, 2020a; Calma, 2020b; Collins, Bhupal, & Doty, 2019; Galea, 2020; Link, 2008; U.S. Department of Health and Human Services (U.S. DHHS), 2019). Specific to COVID-19, residential overcrowding, lack of workplace protections, and inability to telework may limit adherence to physical distancing guidelines among socially vulnerable groups (Perry, Rothwell, & Harshbarger, 2018; Ray, 2020). Poor environmental conditions from historically racial-structured

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redlining of neighborhoods may predispose socially vulnerable individuals to chronic respiratory conditions (e.g., asthma), hypertension, diabetes, and obesity, which may worsen an already poor medical prognosis when exposed to COVID-19 (Barnes et al., 2007; Collins, 2020; Fang, Karakiulakis, & Roth, 2020; U.S. DHHS, 2018). In order to guide public health policy and resource allocation, empirical studies on social vulnerability and COVID-19 are warranted. Using publicly available data on COVID-19 incidence and mortality, and countylevel data on social vulnerability, this study investigates the association of social vulnerability with COVID-19 incidence and mortality in the U.S. during the early phase of the pandemic.

METHODS Data Sources and Software Data from Johns Hopkins University’s Center for Systems Science and Engineering (CSSE) (current as of 04/19/2020) were used to estimate U.S. incidence and mortality for COVID-19. County-level publicly available data on social vulnerability for the lower 48 U.S. states were obtained from the Centers for Disease Control and Prevention’s (CDC, 2020) Agency for Toxic Substances and Disease Registry (ATSDR, 2020). The TIGER file for U.S. county boundaries was downloaded from data.gov (2020). ArcMap 10.4.1 (Redlands, California) was used for the analysis. Social Vulnerability Index Social factors like language, per capita income, housing, transportation, and employment status predict to a great extent the ability of individuals and communities to follow social distancing guidelines issued in response to the COVID-19 pandemic (Bonanno et al., 2007). To enable the identification of

communities most likely to require support before, during, and after a disaster or emergency, the CDC developed a Social Vulnerability Index (SVI), which includes 15 sociodemographic variables grouped into four themes. The socioeconomic theme is composed of four variables: % below poverty, % unemployed, per capita income, and % with no high school diploma. The household composition and disability theme includes: % aged 65 years or older, % aged 17 years or younger, % civilian with a disability, and % single-parent households. The theme on minority status and language includes two variables: % minority and % speak English “less than well.” Finally, the theme on housing and transportation is made up of five variables: % multiunit structures, % mobile homes, % crowding, % no vehicle, and % group quarters. The SVI is scored from 0 to 1, with larger values denoting higher social vulnerability. Additional information on SVI is provided elsewhere (CDC, 2020; Flanagan et al., 2011). Spatial Statistical Analysis Associations between social vulnerability and incidence and mortality due to COVID-19 were assessed using ordinary least squares and geographically weighted regressions. In the first instance, incidence of COVID-19 was fitted as the outcome variable and social vulnerability as the predictor, while in the second instance, mortality due to COVID-19 was fitted as the outcome variable and social vulnerability as the predictor variable. In an ordinary least squares regression, bivariate relationships are assumed to be uniform across all U.S. counties, as one parameter is estimated for each variable. However, in a geographically weighted regression, local geographic variations in relationships are accounted for by

Using publicly available data on COVID-19 incidence and mortality, and county-level data on social vulnerability, this study investigates the association of social vulnerability with COVID-19 incidence and mortality in the U.S. during the early phase of the pandemic.

estimating parameters for all sample points (i.e., counties) across the U.S. Both models were set at fixed kernel types and Akaike Information Criterion (AIC) bandwidths. To identify spatial locations where the models predicted COVID-19 incidence more accurately, I generated a choropleth map using the local coefficient of determination (R2). Results were summarized using tables and choropleth maps with hot/cold rendering scheme on model residuals.

RESULTS As of April 19, 2020, 726,697 persons have been diagnosed with COVID-19 in the U.S. The highest incidence of the disease (2,815 per 100,000) was in Rockland County, New York, and the lowest (0 cases per 100,000) was in Anderson and Kearny Counties (Kansas); Baraga and Ontonagon Counties (Michigan); Bath, Bland, Dickenson, Falls Church, Grayson, Highland, Martinsville, and Patrick Counties (Virginia); Boundary and Lewis Counties (Idaho); Brule, Butte, and Kingsbury Counties (South Hofstra HORIZONS t Spring 2021

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Dakota); Cedar and Logan Counties (Nebraska); Hancock County (Tennessee); Hidalgo County (New Mexico); Lincoln County (Nevada); Mercer County (North Dakota); Pepin County (Wisconsin); Platte County (Wyoming); and Texas County (Missouri).

Figure 1. Incidence of COVID-19 in U.S. Counties

Overall, the risk of contracting COVID-19 was highest in New York, Georgia, New Jersey, and Louisiana (at least 1,525 per 100,000) (Figure 1). Mortality From COVID-19 In the U.S., 37,601 deaths have been attributed to COVID-19 (current as of April 19, 2020), with the highest mortality in New York City, New York (806 per 100,000). When aggregated by state, mortality was highest in New York, New Jersey, Georgia, and Louisiana (at least 83 per 100,000) (Figure 2). COVID-19 and Social Vulnerability Social vulnerability was associated with increased risk of COVID-19. A single unit increase in SVI was associated with an 8.4% increased risk of COVID-19 (Coef. = 0.084; p-value<0.05). Social vulnerability was also found to predict mortality from COVID-19 (Coef. = 0.005; p-value<0.05). The risk of becoming infected with COVID-19 (due to underlying social vulnerabilities) was found to be highest in New York, New Jersey, Georgia, and Louisiana (n≥7) (Table 1; Figure 3).

Figure 1. Incidence of COVID-19 in U.S. Counties

Figure 2. Distribution of COVID-19 Mortality in U.S. Counties

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Figure 2. Distribution of COVID-19 Mortality in U.S. Counties

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Counties were grouped into two ordinal categories based on the strength of association between social vulnerability and risk of COVID-19 infection. Category I counties have the highest risk of social vulnerability-associated COVID-19 infection since they lie above 2.5 standard deviations from the mean (Table 2); Category II counties lie between 1.5 and 2.5 standard deviations from the mean as shown in Table 3.

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DISCUSSION Since the identification of a COVID-19 index case on U.S. soil in January 2020, the disease has infected more than 700,000 Americans (as of April 19, 2020), predominantly from disadvantaged communities (Calma (2020a); Calma (2020b); CSSE, 2020; Einhorn, 2020). The findings from this study demonstrate increased incidence of and mortality from COVID-19 among socially vulnerable populations and highlights the need for addressing social justice-related conditions that may be linked to health inequities. Underserved populations are often un- or underinsured and would have to pay out-ofpocket for their medical care, making them less likely to seek COVID-19 screening and treatment (Link, 2008; Collins, Bhupal, & Doty, 2019; Calma (2020a); Calma (2020b); Galea, 2020; U.S. DHHS, 2019). Low income and traditionally marginalized communities are also more likely to reside in crowded neighborhoods and substandard housing created from redlining and structural racism (Ray, 2020; Perry, Rothwell, & Harshbarger, 2018). Such neighborhoods also may have poorer air quality and residents may be more likely to be exposed to environmental health conditions such as mold, asbestos, lead, and pest infestations that are associated with higher prevalence of asthma, obesity, hypertension, chronic respiratory disease, and other comorbidities that increase the likelihood of contracting and dying from COVID-19 (Barnes et al., 2007; Collins, 2020; Fang, Karakiulakis, & Roth, 2020; U.S. DHHS, 2018). Teleworking is an effective arrangement for physical distancing in the workplace, but is infeasible for many socially vulnerable populations who work forward-facing jobs, thus increasing their propensity of contracting and spreading the disease (Gould & Shierholz, 2020). Because of the fear of

Table 1. U.S. States and Counties With Increased Social Vulnerability to COVID-19 (n=89) State

Number of Counties

Counties

Alabama

2

Chambers, Wilcox

Arizona

1

Navajo

Arkansas

2

Cleburne, Lincoln

Colorado

3

Eagle, Gunnison, Morgan

DC

1

District of Columbia

Florida

1

Miami-Dade

Georgia

14

Baker, Clay, Turner, Wilcox, Worth, Calhoun, Dougherty, Early, Lee, Mitchell, Randolph, Sumter, Terrell, Upson

Idaho

1

Blaine

Illinois

1

Cook

Indiana

3

Franklin, Marion, Decatur

Iowa

4

Louisa, Tama, Washington, Muscatine

Kansas

1

Coffey

Kentucky

2

Butler, Jackson

Louisiana

12

Assumption, Bienville, Plaquemines, Caddo, De Soto, Iberville, Jefferson, Orleans, St. Bernard, St. Charles, St. James, St. John the Baptist

Massachusetts

3

Middlesex, Norfolk, Suffolk

Michigan

3

Macomb, Oakland, Wayne

Mississippi

1

Wilkinson

Montana

2

Toole, Golden Valley

Nebraska

1

Hall

New Jersey

10

Bergen, Hudson, Morris, Passaic, Union, Essex, Middlesex, Monmouth, Ocean, Somerset

New Mexico

1

McKinley

New York

7

New York, Orange, Nassau, Putnam, Rockland, Suffolk, Westchester

North Carolina

2

Northampton, Wayne

North Dakota

1

Slope

Ohio

2

Marion, Pickaway

Oklahoma

1

Greer

South Carolina

1

Clarendon

South Dakota

1

Minnehaha

Texas

2

Moore, Donley

Utah

1

Summit

Virginia

1

Harrisonburg City

Washington

1

Yakima

Tennessee

Vermont

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Table 2. U.S. States and Counties With Increased Risk of COVID-19 (GWR Model; n=52; Std. Resid. >2.5 SD) County

State

Incidence (per 100,000)

36119

Westchester

New York

2395.747

517.8931

01017

Chambers

Alabama

721.7177

Louisiana

553.4719

05079

Lincoln

Arkansas

852.2727

Iberville

Louisiana

830.4881

08037

Eagle

Colorado

876.1587

22051

Jefferson

Louisiana

1281.639

08051

Gunnison

Colorado

652.8462

22071

Orleans

Louisiana

1525.847

13037

Calhoun

Georgia

920.9889

22087

St. Bernard

Louisiana

865.7184

13095

Dougherty

Georgia

1601.937

22089

St. Charles

Louisiana

917.1375

13099

Early

Georgia

1619.235

22093

St. James

Louisiana

1028.631

13177

Lee

Georgia

906.9085

22095

St. John the Baptist

Louisiana

1487.032

13205

Mitchell

Georgia

969.6748

26125

Oakland

Michigan

478.7752

13243

Randolph

Georgia

2080.26

26163

Wayne

Michigan

770.0605

13261

Sumter

Georgia

1063.542

28157

Wilkinson

Mississippi

637.3117

13273

Terrell

Georgia

1711.405

30101

Toole

Montana

612.3311

13293

Upson

Georgia

645.8967

31079

Hall

Nebraska

643.8153

16013

Blaine

Idaho

2063.333

34003

Bergen

New Jersey

1304.76

18031

Decatur

Indiana

625.0235

34017

Hudson

New Jersey

1500.615

19115

Louisa

Iowa

1603.987

34027

Morris

New Jersey

810.0113

19171

Tama

Iowa

729.7971

34031

Passaic

New Jersey

1581.425

19183

Washington

Iowa

514.4548

34039

Union

New Jersey

1610.343

20031

Coffey

Kansas

562.4159

36059

Nassau

New York

2150.452

39101

Marion

Ohio

1510.147

36061

New York

New York

1625.644

39129

Pickaway

Ohio

516.6191

County

State

Incidence (per 100,000)

21031

Butler

Kentucky

481.4038

22017

Caddo

Louisiana

22031

De Soto

22047

FIPS

FIPS

36071

Orange

New York

1692.212

40055

Greer

Oklahoma

962.8852

36079

Putnam

New York

791.2937

46099

Minnehaha

South Dakota

660.6812

36087

Rockland

New York

2815.012

48129

Donley

Texas

701.6473

36103

Suffolk

New York

1815.25

49043

Summit

Utah

749.7924

Std. Resid.: Standard Residual; GWR: Geographically Weighted Regression

deportation and retaliation, undocumented immigrants, who may live in segregated neighborhoods and share the social attributes previously described, may also be less likely to present themselves to healthcare facilities for COVID-19 testing or treatment. In the event they are diagnosed with the disease, they are also less likely to isolate and observe physical distancing as they have to continue earning wages to support their families or are employed in essential 20

Hofstra HORIZONS t Spring 2021

service industries. In essence, racial and ethnic minorities and those who are socially vulnerable in other ways are more likely to contract and die from COVID-19 due to prevailing social conditions, which predispose them to poor health. To improve the health and well-being of minorities in America, interventions must address the social determinants of health, which make minorities particularly vulnerable to natural and

technological disasters and public health emergencies (Galea, 2020; Pavlos, Bartlett, & Galea, 2020). Recently, the Families First Coronavirus Response Act (H.R. 6201) (2020) provided some appropriations, including free COVID-19 testing, temporary paid emergency leave and unemployment insurance, food security initiatives, and increased Medicaid funding to states. The Department of Labor has issued guidance on state unemployment insurance programs,

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permitting the provision of COVID-19-related unemployment benefits (116th Congress, 2020). The Department of Agriculture has agreed to the continued provision of summer meals during school closure (116th Congress, 2020).

Figure 3. Association Between Social Vulnerability and COVID-19 in U.S. Counties

There are several limitations to this study. First, the hierarchical model of the SVI is associated with lower precision and weaker internal validity (Rufat et al., 2019) and is sensitive to the weighting scheme chosen (Tate, 2012). However, I chose the index because of its higher accuracy compared to other models (Bakkensen, Fox-Lent, Read & Linkov, 2017; Tate, 2012). It is also publicly Figure 3. Association Between Social Vulnerability and COVID-19 in U.S. Counties available and has been cited insights. Proceedings of the American CONCLUSION Thoracic Society, 4(1), 58-68. https://doi. more than 400 times in the literature Because of underlying social org/10.1513/pats.200607-146JG (Flanagan et al., 2011). Second, the vulnerabilities, minorities are often Benfer, E.A., & Wiley, L.F. (2020) Health bivariate relationships between disproportionately affected by disasters justice strategies to combat COVID-19: COVID-19 incidence or mortality with and emergencies. Using publicly Protecting vulnerable communities during SVI were assessed without recourse to available data on COVID-19 incidence, a pandemic. Health Affairs. Retrieved potential confounding variables. In my mortality, and social vulnerability, from https://www.healthaffairs.org/ assessment, a majority of the potential I found increased incidence and do/10.1377/hblog20200319.757883/full/ confounders were already included as mortality from COVID-19 among Bludau, J. (2020). Is COVID-19 part of the 15 variables that constitute socially vulnerable populations in the disproportionately impacting black the SVI, and further adjustment may U.S. during the early phase of the residents in Houston? Here’s what experts result in multi-collinearity. To account pandemic. Strategies to mitigate the say. KHOU 11. Retrieved from https:// for this assumption, I added countydisparate burden of the disease on www.khou.com/article/news/health/ level socio-demographic covariates to coronavirus/is-covid-19traditionally marginalized communities disproportionately-killing-blackthe model (e.g., percent persons aged must consider social factors like americans-in-houston-heres-what-experts65 years or older, percent civilians with housing quality, socioeconomic status, 21 say/285-a4044110-d8e9-44bf-b5c3disability, population density) and transportation, and income that produce 511752b387f6 found results similar to the original poor health. Bonanno, G. A., Galea, S., Bucciarelli, A., bivariate model. Finally, the data from & Vlahov, D. (2007). What predicts References which this study was conducted were psychological resilience after disaster? Bakkensen, L. A., Fox-Lent, C., Read, L. downloaded on April 19, 2020. With The role of demographics, resources, and K., & Linkov, I. (2017). Validating continuing rise in incidence of the life stress. Journal of Consulting and resilience and vulnerability indices in the disease, periodic revision of the Clinical Psychology, 75(5), 671. context of natural disasters. Risk Analysis. analysis may be warranted to identify, 37(5):982-1004. Calma, J. (2020a). Air pollution could in real time, counties where socially Barnes, K. C., Grant, A. V., Hansel, N. N., make the COVID-19 pandemic worse for vulnerable communities are at the Gao, P., & Dunston, G. M. (2007). some people: Pollution piles on top of highest risk. African Americans with asthma: Genetic other risk factors. The Verge. Retrieved

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Table 3. U.S. States and Counties With Increased Risk of COVID-19 (n=37; Std. Resid. 1.5<SD<2.5) FIPS

County

State

Incidence (per 100,000)

21109

Jackson

Kentucky

322.6048466

22007

Assumption

Louisiana

653.2364899

22013

Bienville

Louisiana

490.8994789

22075

Plaquemines

Louisiana

672.5007544

25017

Middlesex

Massachusetts

514.7983588

25021

Norfolk

Massachusetts

517.7036539

25025

Suffolk

Massachusetts

957.3246657

26099

Macomb

Michigan

486.400022

30037

Golden Valley

Montana

365.408039

34013

Essex

New Jersey

1239.838543

34023

Middlesex

New Jersey

924.0517682

34025

Monmouth

New Jersey

731.744762

34029

Ocean

New Jersey

749.0291278

34035

Somerset

New Jersey

694.0602066

35031

McKinley

New Mexico

498.8299915

37131

Northampton

North Carolina

359.287584

37191

Wayne

North Carolina

306.9901162

01131

Wilcox

Alabama

433.8185674

04017

Navajo

Arizona

407.4862068

05023

Cleburne

Arkansas

280.9101489

08087

Morgan

Colorado

505.7107472

11001

District of Columbia

District of Columbia

377.7546975

12086

Miami-Dade

Florida

332.9112899

13007

Baker

Georgia

526.6622778

13061

Clay

Georgia

705.716302

13287

Turner

Georgia

638.6975579

13315

Wilcox

Georgia

486.3925883

13321

Worth

Georgia

587.7413938

17031

Cook

Illinois

396.0015013

18047

Franklin

Indiana

391.0712716

18097

Marion

Indiana

375.7067828

19139

Muscatine

Iowa

412.5257829

38087

Slope

North Dakota

400

45027

Clarendon

South Carolina

323.0108164

48341

Moore

Texas

472.7793696

51660

Harrisonburg City

Virginia

439.4899653

53077

Yakima

Washington

320.4808808

Std. Resid.: Standard Residual; GWR: Geographically Weighted Regression 22

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from https://www.theverge.com/2020/3/19/ 21186653/coronavirus-covid-19-air-pollutionvulnerable-lung-disease-pandemic Calma, J. (2020b). America set up black communities to be harder hit by COVID-19. Science. Retrieved from https://www. theverge.com/2020/ 4/8/21213974/african-americans-covid-19coronavirus-race-disparities Centers for Disease Control and Prevention (CDC). (2020). CDC SVI 2016 Documentation. Retrieved from https:// svi.cdc.gov/Documents/Data/2016_SVI_ Data/SVI2016Documentation.pdf Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry. (2020). Social Vulnerability Index. Retrieved from https://svi.cdc.gov/data-andtools-download.html Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/Geospatial Research, Analysis, and Service Program. Social Vulnerability Index (2020). Retrieved from https://svi.cdc.gov/ data-and-tools-download.html Center for Systems Science and Engineering (CSSE). (2020). COVID-19 case counts. Johns Hopkins University. Retrieved from https://www.arcgis.com/home/item.html?id =628578697fb24d8ea4c32fa0c5ae1843 Collins, S. (2020). The Trump administration blames Covid-19 black mortality rates on poor health. It should blame its policies. Vox. Retrieved from https://www.vox.com/policy-andpolitics/2020/4/8/21213383/coronavirusblack-americans-trump-administration -high-covid-19-death-rate Collins, S., Bhupal, H., & Doty, M. (2019). Health insurance coverage eight years after the ACA: Fewer uninsured Americans and shorter coverage gaps, but more underinsured. Commonwealth Fund. Retrieved from https://doi.org/10.26099/ penv-q932 Data.gov. (2019). U.S. county boundaries. U.S. Department of Homeland Security. Retrieved from https://catalog.data.gov/ dataset/u-s-county-boundaries Einhorn, E. (2020). African Americans may be dying from COVID-19 at a higher rate. Better data is essential, experts say. NBC News. Retrieved from https://www. nbcnews.com/news/nbcblk/africanamericans-may-be-dying-covid-19-higherrate-better-n1178011

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Eligon, J., Burch, A. D., Searcey, D., & Oppel Jr, R. A. (2020). Black Americans face alarming rates of coronavirus infection in some states. The New York Times, 7. Fang, L., Karakiulakis, G., & Roth, M. (2020). Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? The Lancet. Respiratory Medicine. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8(1). Galea, S. (2020). Closing the health gap to fight coronavirus. U.S. News and World Report. Retrieved from https://www.usnews.com/news/elections/ articles/2020-03-20/payments-toamericans-help-close-the-health-gapand-fight-coronavirus Gould, E., & Shierholz, H. (2020). Not everybody can work from home: Black and Hispanic workers are less likely to be able to telework. Economic Policy Institute. Retrieved from https://www.epi. org/blog/black-and-hispanic-workersare-much-less-likely-to-be-able-to-workfrom-home/ Karaye, I. M., Ross, A. D., & Horney, J. A. (2019). Self-rated mental and physical health of U.S. Gulf Coast residents. Journal of Community Health, 1-8.

Link, B. G. (2008). Epidemiological sociology and the social shaping of population health. Journal of Health and Social Behavior, 49(4), 367-384. Palaiologou, P., Ager, A. A., NielsenPincus, M., Evers, C. R., & Day, M. A. (2019). Social vulnerability to large wildfires in the western USA. Landscape and Urban Planning, 189, 99-116. Pavlos, C. Bartlett, C., Galea, S. (2020). Slowing the spread of COVID-19 the right way Common Wealth. Retrieved from https://commonwealthmagazine. org/opinion/slowing-the-spread-of-covid19-9-the-right-way/. Accessed 17th Jun 2020 Perry, A. M., Rothwell, J., & Harshbarger, D. (2018). The devaluation of assets in black neighborhoods. Brookings. Retrieved from https://www.brookings. edu/research/devaluation-of-assets-inblack-neighborhoods/ Ray, R. (2020). Why are blacks dying at higher rates from COVID-19? Brookings. Retrieved from https://www.brookings. edu/blog/fixgov/2020/04/09/why-areblacks-dying-at-higher-rates-fromcovid-19/ Rufat, S., Eric, T., Emrich, C. T., & Antolini, F. (2019). How valid are social vulnerability models? Ann Am Assoc Geogr, 1-23.

implications for measurement. International Journal of Disaster Risk Reduction, 14, 470-486. Tate, E. (2012). Social vulnerability indices: A comparative assessment using uncertainty and sensitivity analysis. Natural Hazards, 63(2):325-347 Thebault, R., Tran, A. B., & Williams, V. (2020). The coronavirus is infecting and killing black Americans at an alarmingly high rate. The Washington Post. Retrieved from https://www.washingtonpost.com/ nation/2020/04/07/coronavirus-isinfecting-killing-black-americans-analarmingly-high-rate-post-analysisshows/?arc404=true U.S. Department of Health and Human Services, Office for Minority Health. (2018). Asthma and African Americans. Retrieved from https://minorityhealth.hhs. gov/omh/browse.aspx?lvl=4&lvlid=15 U.S. Department of Health and Human Services, Office for Minority Health. (2019). Profile: Black/African Americans. Retrieved from https:// minorityhealth.hhs.gov/omh/browse. aspx?lvl=3&lvlid=61 116th Congress. (2020). Families First Coronavirus Response Act (H.R. 6201). Retrieved from https://www.congress. gov/bill/116th-congress/house-bill/ 6201/text

Rufat, S., Tate, E., Burton, C. G., & Maroof, A. S. (2015). Social vulnerability to floods: Review of case studies and

Ibraheem M. Karaye is assistant professor in the Department of Health Professions at Hofstra University. Previously, he was a postdoctoral researcher and adjunct assistant professor in the epidemiology program at the University of Delaware. His research uses novel statistical and spatial methods to study disaster epidemiology, the health effects of mass trauma and conflict, health disparities, and global health. Projects have included an exploration of the physical and mental health of a large representative sample of the entire U.S. Gulf Coast population, as well as studies assessing risks to environmental contamination and barriers to essential medical care such as dialysis after disasters. Recently, Dr. Karaye utilized the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry’s Social Vulnerability Index (SVI) to assess the impact of social vulnerability on the coronavirus pandemic. Dr. Karaye attended medical school at Bayero University Kano, Nigeria. He holds a Master of Public Health (epidemiology) and a Doctor of Public Health (epidemiology and environmental health) from Texas A&M University, where his research leveraged spatial statistical methods to describe hurricane evacuation shelter distribution and capacity in the Houston-Galveston Metropolitan Statistical Area after Hurricane Harvey. Hofstra HORIZONS t Spring 2021

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