Spatial Analysis of Racial Capitalism and COVID-19 in Louisiana

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Louisiana has experienced one of the highest COVID-19 burdens in the United States. This article seeks to investigate the geospatial pattern of COVID-19 in Louisiana using the perspective of racial capitalism. Methods . Using data from the Louisiana Department of Health and American Community Survey, we employed spatial autoregressive models to assess how racial income disparity between White and Black residents connected to COVID-19 cases in Louisiana parishes, controlling for other parish-level characteristics. Results . Greater racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes. A rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity. Conclusions . African Americans from lower socioeconomic backgrounds were likely at a higher risk of COVID-19 in the state of Louisiana. Based on Louisiana's unique historical and sociocultural contexts, implications are further discussed. COVID-19 Louisiana racial capitalism income disparity spatial regression Figures Figure 1 Backgrounds COVID-19 outcomes varied widely between countries, but the United States led the world in COVID-19 cases and deaths (Dong et al., 2020 ). While factors such as age and existing health conditions represented recognized risk factors for cases, hospitalizations, and deaths, social factors also played a crucial role in shaping health outcomes in the United States (Grasselli et al., 2020 ; Hawkins et al., 2020 ; Jones et al., 2022 ). In particular, the Centers for Disease Control and Prevention (CDC) has reported significant racial disparities in the prevalence of COVID-19 and the outcomes related to the disease (Garg et al ., 2020). African Americans, Latinos, and Native American communities are among the groups that have experienced disproportionate impacts of COVID-19 (Kim & Bostwick, 2020 ; Tai et al., 2021 ). For instance, the COVID-19 mortality rate for Blacks was found to be substantially more than double that of Whites (Egbert, 2020). Similarly, racial disparities were evident in both COVID-19 infection and hospitalization rates, with individuals from racial and ethnic minority groups, particularly African Americans and Hispanics, experiencing notably higher rates than Whites (Hooper et al., 2020 ; Romano et al ., 2021). The race-based differences have garnered attention from the U.S. Surgeon General, the public, and recent public health and sociological research publications (Oh, 2023 ; Romano et al ., 2021). Various factors contributed to the uneven risk of COVID-19 exposure by race and ethnicity. For example, racial disparities in occupational risks were associated with limited options to work remotely, inadequate access to personal protective equipment, and difficulties in maintaining social distancing at the workplace (Chen et al., 2021 ). Disparities in environmental exposure between racial groups stemmed from housing conditions, such as overcrowding and multigenerational/multifamily living arrangements (Olayo-Méndez et al., 2021 ; Parolin & Lee, 2022 ). More fundamentally, these racial disparities were a consequence of structural racism and racial capitalism (Bailey et al., 2017 ; Laster Pirtle, 2020 ; McClure et al., 2020 ; Williams & Collins, 2016). The term “racial capitalism” was introduced by Cedric Robinson in his influential book Black Marxism: The Making of the Black Radical Tradition . Much of the recent scholarly discourse on racial capitalism is based on this seminal work, which draws from literature related to the Black experience in North America and the Caribbean (Bledsoe & Wright, 2019 ; Lewis, 2022 ). The discourse emphasizes the fundamental idea that capitalism has been deeply rooted in and reliant on racial distinctions since its emergence from feudalism (Robinson, 2000). Capitalism thrives on inequality, and racism institutionalizes it (Gilmore, 2015 ). Therefore, racial capitalism is not merely a descriptor for the intersection of race and capitalism; it also underscores the convergence between capitalism and racial differentiation, with capitalism and racism coexisting and reinforcing each other (Robinson, 2000). During unprecedented times, such as a pandemic, the social consequences of racial capitalism become particularly pronounced. Amidst the various social repercussions due to racial capitalism, health inequalities are exacerbated within spatial dimensions. Individuals in marginalized racial and ethnic groups often reside in communities marked by racial and economic segregation. These areas frequently feature substandard housing, limited access to safe water, and overcrowded living conditions, hindering proper hand hygiene and self-quarantine (Bailey et al., 2017 ; Laster Pirtle, 2020 ). Moreover, residents in these communities are also disproportionately affected by chronic health conditions such as diabetes, hypertension, and renal diseases that significantly increase their vulnerability to COVID-19-related mortality. Undeniably, such enduring, substantial, and complex links between racism and socioeconomic disparities intensify the risk of adverse health outcomes, including COVID-19 incidence, mortality, and hospitalization (Egbert, 2020; Hooper et al., 2020 ; Oh, 2023 ; Romano et al ., 2021). This article uses insights from existing literature to shed light on the connections between racial capitalism and disparities in neighborhood health, particularly in the context of COVID-19 in Louisiana. In the southeastern part of the country, Louisiana presents a compelling case study of the COVID-19 pandemic due to its unique characteristics. In contrast to the vast majority of U.S. states, its population includes 32.8% Black residents, significantly higher than the national average of 13%. Historically, the city of New Orleans in Louisiana was the largest slave market in the United States. At the same time, other parts of Louisiana were referred to as “Plantation Country,” signifying areas where enslaved Africans and their descendants were forced to labor. The legacy of slavery and racial discrimination has created significant residential segregation in Louisiana, leading to Blacks and other minority populations living in neighborhoods with greater social vulnerability and disadvantages in income, education, transportation, and other socioeconomic conditions (Flanagan et al., 2018 ). This segregation may eventually limit community residents' ability to prepare for and respond to the COVID-19 pandemic. The initial presumptive COVID-19 case was documented in Louisiana in March 2020. During the summer of 2020, Louisiana continued to experience one of the highest COVID-19 burdens in the United States, with 2,495 reported cases per 100,000 individuals and 86 COVID-19-related deaths per 100,000 individuals. These rates represent some of the country's highest incidence and mortality rates (Sun et al., 2020 ). Moreover, they were disproportionately distributed. Although Black residents make up almost 33% of Louisiana’s population, 51% of the state’s COVID-19 mortality occurred in the Black population. Such alarming COVID-19 rates and the disproportionate distributions for race indicate a grim state of fundamental health factors in Louisiana, with the sociohistorical context potentially fueling disparities in the impact of COVID-19 by race. We hypothesized that racial and socioeconomic inequalities rooted in racial capitalism led to the worse COVID-19 outcomes among Black Louisianans. By exploring the links between race, inequality, socioeconomic disparity, and COVID-19 incidence in Louisiana, we aim to provide deeper insights that may help develop strategies to tackle the persistent public health inequalities within the state. Methods To understand the association between racial and socioeconomic inequality and COVID-19 in Louisiana, we used prior studies on the factors contributing to COVID-19 outcomes at the county level (Andersen et al., 2021 ; Oh, 2023 ; Sun et al., 2020 ). Our unit of analysis was the parish, which corresponds to a county in other U.S. states. Louisiana consists of 64 parishes, the boundaries of which often align with church parish lines. Each parish possesses distinct characteristics related to its social and community structure, encompassing demographics and culture. Parish-level variables were defined as follows. From the Louisiana Department of Health, COVID-19 cases were measured as the number of confirmed cases in each Louisiana parish as of December 31, 2021, when the pandemic approached another nationwide peak with the spread of Delta and Omicron variants. To control the effect of population size, we transformed this variable to cases per 1,000 residents per parish. Figure 1 depicts the geographic distribution of COVID-19 cases by parish. There were generally more COVID-19 cases in the north and northeast parishes, with lower case counts in the southern coastal regions. [FIGURE 1 ABOUT HERE] Explanatory variables used in the analysis of COVID-19 cases were from the 2021 American Community Survey: 5-Year Data (2017–2021), available at the National Historical Geographic Information System (NHGIS). The NHGIS publishes U.S. Decennial censuses and other nationwide surveys at various geographic levels, such as block, census tract, county, and others. Given the sociohistorical contexts of Louisiana, the neighborhood-level racial and socioeconomic disparity was measured as the White-Black per capita income ratio . Relatedly, variables for per capita income , which was transformed as the natural logarithm, and the percentage of Black residents were included in our analysis. We used other neighborhood characteristics as control variables. Population density was calculated as the number of residents in a parish per square kilometer. The percentage of older residents indicated the proportion of residents aged 65 or older. To include a parish's social and economic disadvantages, we calculated the poverty rate as the proportion of residents below the poverty line. Lastly, relating to access to medical and healthcare services, the percentage of residents with healthcare professions was included. Note that these variables did not have any missing information. We employed a spatial autoregressive regression (SAR) analysis to ascertain how the parish-level variables explained COVID-19 in Louisiana, highlighting racial income disparity between Whites and Blacks. The SAR approach is broadly used in literature exploring the spatial associations between socioeconomic factors and COVID-19 outcomes across geospatial units (Andersen et al., 2021 ; Cao et al., 2020 ; Oh, 20203; Sannigrahi et al., 2020 ; Sun et al., 2020 ). Stata 18 was used for all statistical analysis. Results Descriptive statistics are reported in Table 1 . Across 64 parishes, the statewide mean of our outcome variable, COVID-19 cases per 1,000 residents as of December 31, 2021, was 186.4 with a standard deviation (SD) of 27.3. The mean of the White-Black income ratio was 2.0 (SD = 0.5), with higher values representing a more significant income gap between White and Black residents. The average per capita annual income was about $ 26,200 (SD = $ 4,900). Notably, the statewide mean of the poverty rate was 21.2, one of the highest levels among all U.S. states. Table 1 Descriptive statistics (n = 64). M S.D. VIF Correlation (1) (2) (3) (4) (5) (6) (7) (8) (1) COVID-19 cases 186.4 27.3 - 1 (2) White-Black income ratio 2.0 0.5 1.51 .338* 1 (3) Per capita income ( $ lk) 26.2 4.9 4.41 − .194 − .262* 1 (4) Percent Black 31.6 14.5 1.69 − .077 .393* − .367* 1 (5) Population density 62.9 139.1 1.69 − .247* .109 .475* .159 1 (6) Percent old 16.4 2.5 1.31 .146 .162 − .479* .232 − .208 1 (7) Poverty 21.2 7.1 4.50 .219 .511* − .785* .579* − .144 .389* 1 (8) Healthcare professions 6.7 1.8 1.06 .199 .085 .117 − .101 .103 .010 − .100 1 * p < .05. [Table 1 ABOUT HERE] For our multivariate analysis using the SAR approach, we created a contiguity weight matrix along with the locations of all parishes and the distances between them. Using this geographically weighted matrix, we found a statistically significant spatial autocorrelation in COVID-19 cases across all Louisiana parishes (Moran’s I = .106, p < .01). This result indicates that our SAR models were methodologically valid in the context of Louisiana. With the geographically weighted matrix, our findings from the SAR models suggest that COVID-19 outcomes in Louisiana were fueled by racial and socioeconomic disparity stemming from historical contexts. Table 2 shows the results from the SAR models estimating COVID-19 cases in Louisiana. The models enabled us to explain how differences in racial income disparity led to spatial variability in COVID-19 cases in 64 Louisiana parishes. Table 2 Results from spatial autoregressive models Model 1 Model 2 Model 3 White-Black income ratio 21.233** 19.926** 612.420† (7.409) (7.352) (353.627) Per capita income 37.587 149.928* (31.555) (71.937) Income × racial income gap -58.718† (35.015) Percent black − .392 − .387† − .340 (.245) (.223) (.210) Population density − .044† − .061* − .047† (.023) (.027) (.028) Percent old .213 .518 1.281 (1.344) (1.370) (1.408) Poverty .437 1.100 1.113 (.605) (.874) (.806) Healthcare professions 2.090 1.880 2.479 (1.669) (1.589) (1.579) (Spatial lag) − .611 − .843 − .980† (.548) (.560) (.569) Log Likelihood -291.06 -29.43 -289.05 N 64 64 64 † p < .10, * p < .05, ** p < .01, *** p < .001. [Table 2 ABOUT HERE] In detail, Model 1 included a variable for White-Black per capita income ratio and other control variables. It revealed that the greater income inequality between White and Black residents was associated with more COVID-19 cases, indicating that socioeconomic disparity between racial groups was a significant predictor for the epidemiological health issue in Louisiana (b = 21.233, p < .01). Hierarchically, Model 2 added a covariate for per capita income as a regressor after it was transformed into the natural logarithm. With this model, we still found that a widening income gap between racial groups was a significant cause of COVID-19 cases. That is, greater income inequality between White and Black residents yielded higher COVID-19 incidence in the 64 Louisiana parishes (b = 19.926, p < .01). The variable for per capita income was positively associated with COVID-19 cases (b = 37.587), but it was not statistically significant. Model 3 additionally included an interaction term between variables for per capita income and racial income disparity. It showed marginal effects of our explanatory variables, indicating that there were more COVID-19 cases in Louisiana parishes with greater racial income inequality (b = 614.42, p < .10) and a rise in income level (b = 149.928, p < .05). Notably, in this SAR model, despite its marginal significance, the interaction term had a negative influence on COVID-19 cases (b = -58.718, p < .10). This result suggests that the variables for White-Black income ratio and per capita income mitigated each other’s positive effect. In other words, the influence of racial and socioeconomic inequality contributing to COVID-19 cases in Louisiana decreased as income level rose. Discussion Using data from the Louisiana Department of Health and American Community Survey, we sought to explore how racial and socioeconomic disparity was linked to COVID-19 cases in Louisiana. Using a spatial autoregression approach, we found that increased racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes. Furthermore, our SAR models indicated that a rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity. In the unique context of Louisiana, where racial disparities have historically prevailed due to a deep-rooted history of slavery, the lingering effects of racial segregation have continued to remain entrenched. We found that African Americans from lower socioeconomic backgrounds were likely at a higher risk of COVID-19 due to preexisting medical conditions in comparison with their White counterparts from higher socioeconomic backgrounds. Systemic disparities in access to medical insurance, education, and income instability exacerbate racial inequities in risk (Griffith et al., 2017 ). The COVID-19 crisis brought the harsh reality of a system arising from racial capitalism to the forefront. It also highlighted that the wealth and success of the U.S. economy are built upon the labor of Black and Brown communities. Previous research has shown that systemic racial inequalities in both wealth and health are ingrained within the framework of American capitalism (Edwards, 2021 ). Such inequalities, including residential segregation, race-based unequal distribution of resources, housing exclusion, and limited healthcare access, contributed to comorbidities that increased the vulnerability of African Americans and other racialized non-White populations to COVID-19 in the United States (Laster Pirtle, 2020 ). This connection between racial capitalism as a fundamental cause of broad COVID-19 inequities has also been widely explored (Jacobs, 2021 ; McClure et al., 2020 ; Papamichail, 2023 ). This research has significant implications for socioeconomic and racial vulnerability in state public health issues. Income inequality remains a significant issue in Louisiana, which ranks fourth highest in the nation. Racial disparities in Louisiana have increased further in recent years. For instance, research suggests that in 2017, 15.5% of Black residents experienced “deep poverty,” which signifies that their household income was below 50% of the federal poverty line. Thus, African Americans have been consistently prone to experiencing higher levels of fundamental social, economic, and environmental stressors (Johnson et al ., 2023). Higher economic stresses in parishes within Louisiana have also been linked to low median household incomes, elevated child poverty rates, and a substantial number of children inhabiting single-parent households (Johnson et al ., 2023). The combination of these factors, compounded by racial inequalities in resource access, housing, and income, contributed to the increased caseload of COVID-19 in Louisiana. We acknowledge that this study is subject to some limitations. Since our framework was derived from the impetus of explaining COVID-19 using a framework of racial capitalism at a neighborhood level (parish), our modeling strategy did not encompass some individual-level epidemiological factors, including age, gender, and chronic health conditions. This omission may explain why we did not find significant findings with other COVID-19 outcomes, such as hospitalization and death, in our additional analyses. Relatedly, our analysis was cross-sectional at a one-time point, December 31, 2021. We were thus unable to explore temporal variations in COVID-19 cases in Louisiana and how changes in racial and socioeconomic disparity could have affected such fluctuations. We suggest that future research could benefit from utilizing multi-level and longitudinal data to address these issues and explore a more comprehensive geographical range than in our analysis. Understanding the fundamental causes of diseases influences the development of interventions, policies, and programs to address persistent health issues and racial disparities. Central to this understanding is the influence of racial capitalism, which has extensive effects and consequences, as shown in the disproportionate outcomes of COVID-19. Through the framework of racial capitalism, we provide a valuable foundation for predicting these consequences, the effects of which can be multifaceted and far-reaching. In summary, our findings underscore how a history of slavery and systemic racism in Louisiana has made African American communities particularly susceptible to natural disasters, such as the COVID-19 pandemic. Our findings also mirror a pattern seen in other environmental disasters in Louisiana, such as Hurricane Katrina, both historically and contemporary. Further, the consequences of disproportionate COVID-19 outcomes were not limited to Louisiana but extended to the entirety of the southern United States. In this context, a recent study revealed that preexisting social vulnerabilities, combined with racial violence during the pandemic, resulted in pronounced depressive symptoms among racial and ethnic minority communities in the southern United States (Johnson et al ., 2023). Conclusion Using data from the Louisiana Department of Health and American Community Survey, we found that the increased racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes and a rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity. This study underscores the importance of tailored interventions, given the distinct characteristics and varying needs among parishes, even within the same state. To enhance the population’s resilience against COVID-19 and other infectious diseases, efforts should focus on reducing the prevalence of comorbidities, ensuring equitable healthcare access, reducing racial inequities, and addressing critical environmental factors. Declarations Ethics approval and consent to participate : Not applicable. Consent for publication : Not applicable. Availability of data and materials : The datasets and the codes for the current study are available from the corresponding author upon reasonable request. Competing interests : The authors declared no potential conflicts of interest/competing interests with respect to the research and/or authorship of this article. Funding: The authors declared no funding for Authors' contributions : All authors contributed to the study’s conceptualization and design. 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Source: Centers for Disease Control and Prevention. Source: Louisiana Department of Health. This finding resulted from parish-level calculation using American Community Survey. This may not fully match with a population-level estimate from the U.S. Census Bureau of 18.7% due to a difference in a calculation process. In the population-level statistics, Louisiana still showed the second highest level of poverty, followed by Mississippi (19.6%). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4335315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297221000,"identity":"a6e4c386-f3c6-4aa6-885e-b15cd54d0cc6","order_by":0,"name":"Hyunsu Oh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAi0lEQVRIiWNgGAWjYBACAwYexgcPwMwE4rUwGySQqoVNgjQt5uy9xyoS/hxm4GfPMSBOi2XPubQbiW2HGSR73hCpxeBGjtmNxIbDIAYJWgpADrMnSQtDAhvQFgmitZw5YyyR2JbOI3HmWQGRWo73GH748Mdajr89eQNxWmCAhzTlo2AUjIJRMArwAwB3wCtvuUbIAwAAAABJRU5ErkJggg==","orcid":"","institution":"Louisiana State University","correspondingAuthor":true,"prefix":"","firstName":"Hyunsu","middleName":"","lastName":"Oh","suffix":""},{"id":297221003,"identity":"55187ddf-a471-44a7-bf3e-53f445731021","order_by":1,"name":"Shriya Thakkar","email":"","orcid":"","institution":"Louisiana State University","correspondingAuthor":false,"prefix":"","firstName":"Shriya","middleName":"","lastName":"Thakkar","suffix":""}],"badges":[],"createdAt":"2024-04-27 19:38:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4335315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4335315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56132775,"identity":"f11db25d-abcb-425b-bbee-33f0eed6de5f","added_by":"auto","created_at":"2024-05-09 02:18:46","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120660,"visible":true,"origin":"","legend":"\u003cp\u003eCOVID-19 cases in 64 Louisiana Parishes (per 1000 residents, as of 12/31/2021)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4335315/v1/d08fe553e5d9f345d32d0cdb.jpeg"},{"id":56134291,"identity":"30fa6e6d-9010-4bd4-bceb-d30012831255","added_by":"auto","created_at":"2024-05-09 02:34:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":545872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4335315/v1/d6dd7fae-68fc-42a5-a86d-e48ef8f56662.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Analysis of Racial Capitalism and COVID-19 in Louisiana","fulltext":[{"header":"Backgrounds","content":"\u003cp\u003eCOVID-19 outcomes varied widely between countries, but the United States led the world in COVID-19 cases and deaths (Dong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While factors such as age and existing health conditions represented recognized risk factors for cases, hospitalizations, and deaths, social factors also played a crucial role in shaping health outcomes in the United States (Grasselli et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hawkins et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, the Centers for Disease Control and Prevention (CDC) has reported significant racial disparities in the prevalence of COVID-19 and the outcomes related to the disease (Garg \u003cem\u003eet al\u003c/em\u003e., 2020). African Americans, Latinos, and Native American communities are among the groups that have experienced disproportionate impacts of COVID-19 (Kim \u0026amp; Bostwick, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tai et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, the COVID-19 mortality rate for Blacks was found to be substantially more than double that of Whites (Egbert, 2020). Similarly, racial disparities were evident in both COVID-19 infection and hospitalization rates, with individuals from racial and ethnic minority groups, particularly African Americans and Hispanics, experiencing notably higher rates than Whites (Hooper et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Romano \u003cem\u003eet al\u003c/em\u003e., 2021). The race-based differences have garnered attention from the U.S. Surgeon General, the public, and recent public health and sociological research publications (Oh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Romano \u003cem\u003eet al\u003c/em\u003e., 2021).\u003c/p\u003e \u003cp\u003eVarious factors contributed to the uneven risk of COVID-19 exposure by race and ethnicity. For example, racial disparities in occupational risks were associated with limited options to work remotely, inadequate access to personal protective equipment, and difficulties in maintaining social distancing at the workplace (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Disparities in environmental exposure between racial groups stemmed from housing conditions, such as overcrowding and multigenerational/multifamily living arrangements (Olayo-M\u0026eacute;ndez et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Parolin \u0026amp; Lee, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). More fundamentally, these racial disparities were a consequence of structural racism and racial capitalism (Bailey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Laster Pirtle, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McClure et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Williams \u0026amp; Collins, 2016).\u003c/p\u003e \u003cp\u003eThe term \u0026ldquo;racial capitalism\u0026rdquo; was introduced by Cedric Robinson in his influential book \u003cem\u003eBlack Marxism: The Making of the Black Radical Tradition\u003c/em\u003e. Much of the recent scholarly discourse on racial capitalism is based on this seminal work, which draws from literature related to the Black experience in North America and the Caribbean (Bledsoe \u0026amp; Wright, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lewis, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The discourse emphasizes the fundamental idea that capitalism has been deeply rooted in and reliant on racial distinctions since its emergence from feudalism (Robinson, 2000). Capitalism thrives on inequality, and racism institutionalizes it (Gilmore, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, racial capitalism is not merely a descriptor for the intersection of race and capitalism; it also underscores the convergence between capitalism and racial differentiation, with capitalism and racism coexisting and reinforcing each other (Robinson, 2000).\u003c/p\u003e \u003cp\u003eDuring unprecedented times, such as a pandemic, the social consequences of racial capitalism become particularly pronounced. Amidst the various social repercussions due to racial capitalism, health inequalities are exacerbated within spatial dimensions. Individuals in marginalized racial and ethnic groups often reside in communities marked by racial and economic segregation. These areas frequently feature substandard housing, limited access to safe water, and overcrowded living conditions, hindering proper hand hygiene and self-quarantine (Bailey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Laster Pirtle, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, residents in these communities are also disproportionately affected by chronic health conditions such as diabetes, hypertension, and renal diseases that significantly increase their vulnerability to COVID-19-related mortality. Undeniably, such enduring, substantial, and complex links between racism and socioeconomic disparities intensify the risk of adverse health outcomes, including COVID-19 incidence, mortality, and hospitalization (Egbert, 2020; Hooper et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Romano \u003cem\u003eet al\u003c/em\u003e., 2021).\u003c/p\u003e \u003cp\u003eThis article uses insights from existing literature to shed light on the connections between racial capitalism and disparities in neighborhood health, particularly in the context of COVID-19 in Louisiana. In the southeastern part of the country, Louisiana presents a compelling case study of the COVID-19 pandemic due to its unique characteristics. In contrast to the vast majority of U.S. states, its population includes 32.8% Black residents, significantly higher than the national average of 13%.\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e Historically, the city of New Orleans in Louisiana was the largest slave market in the United States. At the same time, other parts of Louisiana were referred to as \u0026ldquo;Plantation Country,\u0026rdquo; signifying areas where enslaved Africans and their descendants were forced to labor. The legacy of slavery and racial discrimination has created significant residential segregation in Louisiana, leading to Blacks and other minority populations living in neighborhoods with greater social vulnerability and disadvantages in income, education, transportation, and other socioeconomic conditions (Flanagan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis segregation may eventually limit community residents' ability to prepare for and respond to the COVID-19 pandemic. The initial presumptive COVID-19 case was documented in Louisiana in March 2020. During the summer of 2020, Louisiana continued to experience one of the highest COVID-19 burdens in the United States, with 2,495 reported cases per 100,000 individuals and 86 COVID-19-related deaths per 100,000 individuals.\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e These rates represent some of the country's highest incidence and mortality rates (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, they were disproportionately distributed. Although Black residents make up almost 33% of Louisiana\u0026rsquo;s population, 51% of the state\u0026rsquo;s COVID-19 mortality occurred in the Black population.\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e\u003c/p\u003e \u003cp\u003eSuch alarming COVID-19 rates and the disproportionate distributions for race indicate a grim state of fundamental health factors in Louisiana, with the sociohistorical context potentially fueling disparities in the impact of COVID-19 by race. We hypothesized that racial and socioeconomic inequalities rooted in racial capitalism led to the worse COVID-19 outcomes among Black Louisianans. By exploring the links between race, inequality, socioeconomic disparity, and COVID-19 incidence in Louisiana, we aim to provide deeper insights that may help develop strategies to tackle the persistent public health inequalities within the state.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTo understand the association between racial and socioeconomic inequality and COVID-19 in Louisiana, we used prior studies on the factors contributing to COVID-19 outcomes at the county level (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our unit of analysis was the parish, which corresponds to a county in other U.S. states. Louisiana consists of 64 parishes, the boundaries of which often align with church parish lines. Each parish possesses distinct characteristics related to its social and community structure, encompassing demographics and culture.\u003c/p\u003e \u003cp\u003eParish-level variables were defined as follows. From the Louisiana Department of Health, \u003cem\u003eCOVID-19 cases\u003c/em\u003e were measured as the number of confirmed cases in each Louisiana parish as of December 31, 2021, when the pandemic approached another nationwide peak with the spread of Delta and Omicron variants. To control the effect of population size, we transformed this variable to cases per 1,000 residents per parish. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the geographic distribution of COVID-19 cases by parish. There were generally more COVID-19 cases in the north and northeast parishes, with lower case counts in the southern coastal regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e[FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ABOUT HERE]\u003c/h2\u003e \u003cp\u003eExplanatory variables used in the analysis of COVID-19 cases were from the 2021 American Community Survey: 5-Year Data (2017\u0026ndash;2021), available at the National Historical Geographic Information System (NHGIS). The NHGIS publishes U.S. Decennial censuses and other nationwide surveys at various geographic levels, such as block, census tract, county, and others. Given the sociohistorical contexts of Louisiana, the neighborhood-level racial and socioeconomic disparity was measured as \u003cem\u003ethe White-Black per capita income ratio\u003c/em\u003e. Relatedly, variables for \u003cem\u003eper capita income\u003c/em\u003e, which was transformed as the natural logarithm, and \u003cem\u003ethe percentage of Black residents\u003c/em\u003e were included in our analysis.\u003c/p\u003e \u003cp\u003eWe used other neighborhood characteristics as control variables. \u003cem\u003ePopulation density\u003c/em\u003e was calculated as the number of residents in a parish per square kilometer. \u003cem\u003eThe percentage of older residents\u003c/em\u003e indicated the proportion of residents aged 65 or older. To include a parish's social and economic disadvantages, we calculated \u003cem\u003ethe poverty rate\u003c/em\u003e as the proportion of residents below the poverty line. Lastly, relating to access to medical and healthcare services, \u003cem\u003ethe percentage of residents with healthcare professions\u003c/em\u003e was included. Note that these variables did not have any missing information.\u003c/p\u003e \u003cp\u003eWe employed a spatial autoregressive regression (SAR) analysis to ascertain how the parish-level variables explained COVID-19 in Louisiana, highlighting racial income disparity between Whites and Blacks. The SAR approach is broadly used in literature exploring the spatial associations between socioeconomic factors and COVID-19 outcomes across geospatial units (Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oh, 20203; Sannigrahi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Stata 18 was used for all statistical analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e Descriptive statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Across 64 parishes, the statewide mean of our outcome variable, COVID-19 cases per 1,000 residents as of December 31, 2021, was 186.4 with a standard deviation (SD) of 27.3. The mean of the White-Black income ratio was 2.0 (SD\u0026thinsp;=\u0026thinsp;0.5), with higher values representing a more significant income gap between White and Black residents. The average per capita annual income was about \u003cspan\u003e$\u003c/span\u003e26,200 (SD = \u003cspan\u003e$\u003c/span\u003e4,900). Notably, the statewide mean of the poverty rate was 21.2,\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e one of the highest levels among all U.S. states.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics (n\u0026thinsp;=\u0026thinsp;64).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.D.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCorrelation\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) COVID-19 cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) White-Black income ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.338*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) Per capita income (\u003cspan\u003e$\u003c/span\u003elk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.262*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) Percent Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.393*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.367*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(5) Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.247*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.475*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(6) Percent old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.479*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(7) Poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.511*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.785*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.579*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.389*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(8) Healthcare professions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e* \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003cp\u003e[Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ABOUT HERE]\u003c/h2\u003e \u003cp\u003eFor our multivariate analysis using the SAR approach, we created a contiguity weight matrix along with the locations of all parishes and the distances between them. Using this geographically weighted matrix, we found a statistically significant spatial autocorrelation in COVID-19 cases across all Louisiana parishes (Moran\u0026rsquo;s \u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.106, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). This result indicates that our SAR models were methodologically valid in the context of Louisiana.\u003c/p\u003e \u003cp\u003eWith the geographically weighted matrix, our findings from the SAR models suggest that COVID-19 outcomes in Louisiana were fueled by racial and socioeconomic disparity stemming from historical contexts. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results from the SAR models estimating COVID-19 cases in Louisiana. The models enabled us to explain how differences in racial income disparity led to spatial variability in COVID-19 cases in 64 Louisiana parishes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from spatial autoregressive models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite-Black income ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.233**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.926**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e612.420\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(7.352)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(353.627)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer capita income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.928*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(31.555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(71.937)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome \u0026times; racial income gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-58.718\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(35.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.387\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.210)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.044\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.061*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.047\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.370)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.408)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.806)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare professions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.589)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.579)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Spatial lag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.980\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(.548)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(.560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.569)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog Likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-291.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-289.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u0026dagger; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.10, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003cp\u003e[Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ABOUT HERE]\u003c/h2\u003e \u003cp\u003eIn detail, Model 1 included a variable for White-Black per capita income ratio and other control variables. It revealed that the greater income inequality between White and Black residents was associated with more COVID-19 cases, indicating that socioeconomic disparity between racial groups was a significant predictor for the epidemiological health issue in Louisiana (b\u0026thinsp;=\u0026thinsp;21.233, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e \u003cp\u003eHierarchically, Model 2 added a covariate for per capita income as a regressor after it was transformed into the natural logarithm. With this model, we still found that a widening income gap between racial groups was a significant cause of COVID-19 cases. That is, greater income inequality between White and Black residents yielded higher COVID-19 incidence in the 64 Louisiana parishes (b\u0026thinsp;=\u0026thinsp;19.926, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). The variable for per capita income was positively associated with COVID-19 cases (b\u0026thinsp;=\u0026thinsp;37.587), but it was not statistically significant.\u003c/p\u003e \u003cp\u003eModel 3 additionally included an interaction term between variables for per capita income and racial income disparity. It showed marginal effects of our explanatory variables, indicating that there were more COVID-19 cases in Louisiana parishes with greater racial income inequality (b\u0026thinsp;=\u0026thinsp;614.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.10) and a rise in income level (b\u0026thinsp;=\u0026thinsp;149.928, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Notably, in this SAR model, despite its marginal significance, the interaction term had a negative influence on COVID-19 cases (b = -58.718, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.10). This result suggests that the variables for White-Black income ratio and per capita income mitigated each other\u0026rsquo;s positive effect. In other words, the influence of racial and socioeconomic inequality contributing to COVID-19 cases in Louisiana decreased as income level rose.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing data from the Louisiana Department of Health and American Community Survey, we sought to explore how racial and socioeconomic disparity was linked to COVID-19 cases in Louisiana. Using a spatial autoregression approach, we found that increased racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes. Furthermore, our SAR models indicated that a rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity.\u003c/p\u003e \u003cp\u003eIn the unique context of Louisiana, where racial disparities have historically prevailed due to a deep-rooted history of slavery, the lingering effects of racial segregation have continued to remain entrenched. We found that African Americans from lower socioeconomic backgrounds were likely at a higher risk of COVID-19 due to preexisting medical conditions in comparison with their White counterparts from higher socioeconomic backgrounds. Systemic disparities in access to medical insurance, education, and income instability exacerbate racial inequities in risk (Griffith et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The COVID-19 crisis brought the harsh reality of a system arising from racial capitalism to the forefront. It also highlighted that the wealth and success of the U.S. economy are built upon the labor of Black and Brown communities. Previous research has shown that systemic racial inequalities in both wealth and health are ingrained within the framework of American capitalism (Edwards, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such inequalities, including residential segregation, race-based unequal distribution of resources, housing exclusion, and limited healthcare access, contributed to comorbidities that increased the vulnerability of African Americans and other racialized non-White populations to COVID-19 in the United States (Laster Pirtle, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This connection between racial capitalism as a fundamental cause of broad COVID-19 inequities has also been widely explored (Jacobs, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McClure et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Papamichail, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research has significant implications for socioeconomic and racial vulnerability in state public health issues. Income inequality remains a significant issue in Louisiana, which ranks fourth highest in the nation. Racial disparities in Louisiana have increased further in recent years. For instance, research suggests that in 2017, 15.5% of Black residents experienced \u0026ldquo;deep poverty,\u0026rdquo; which signifies that their household income was below 50% of the federal poverty line. Thus, African Americans have been consistently prone to experiencing higher levels of fundamental social, economic, and environmental stressors (Johnson \u003cem\u003eet al\u003c/em\u003e., 2023). Higher economic stresses in parishes within Louisiana have also been linked to low median household incomes, elevated child poverty rates, and a substantial number of children inhabiting single-parent households (Johnson \u003cem\u003eet al\u003c/em\u003e., 2023). The combination of these factors, compounded by racial inequalities in resource access, housing, and income, contributed to the increased caseload of COVID-19 in Louisiana.\u003c/p\u003e \u003cp\u003eWe acknowledge that this study is subject to some limitations. Since our framework was derived from the impetus of explaining COVID-19 using a framework of racial capitalism at a neighborhood level (parish), our modeling strategy did not encompass some individual-level epidemiological factors, including age, gender, and chronic health conditions. This omission may explain why we did not find significant findings with other COVID-19 outcomes, such as hospitalization and death, in our additional analyses. Relatedly, our analysis was cross-sectional at a one-time point, December 31, 2021. We were thus unable to explore temporal variations in COVID-19 cases in Louisiana and how changes in racial and socioeconomic disparity could have affected such fluctuations. We suggest that future research could benefit from utilizing multi-level and longitudinal data to address these issues and explore a more comprehensive geographical range than in our analysis.\u003c/p\u003e \u003cp\u003eUnderstanding the fundamental causes of diseases influences the development of interventions, policies, and programs to address persistent health issues and racial disparities. Central to this understanding is the influence of racial capitalism, which has extensive effects and consequences, as shown in the disproportionate outcomes of COVID-19. Through the framework of racial capitalism, we provide a valuable foundation for predicting these consequences, the effects of which can be multifaceted and far-reaching. In summary, our findings underscore how a history of slavery and systemic racism in Louisiana has made African American communities particularly susceptible to natural disasters, such as the COVID-19 pandemic. Our findings also mirror a pattern seen in other environmental disasters in Louisiana, such as Hurricane Katrina, both historically and contemporary. Further, the consequences of disproportionate COVID-19 outcomes were not limited to Louisiana but extended to the entirety of the southern United States. In this context, a recent study revealed that preexisting social vulnerabilities, combined with racial violence during the pandemic, resulted in pronounced depressive symptoms among racial and ethnic minority communities in the southern United States (Johnson \u003cem\u003eet al\u003c/em\u003e., 2023).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing data from the Louisiana Department of Health and American Community Survey, we found that the increased racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes and a rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity. This study underscores the importance of tailored interventions, given the distinct characteristics and varying needs among parishes, even within the same state. To enhance the population\u0026rsquo;s resilience against COVID-19 and other infectious diseases, efforts should focus on reducing the prevalence of comorbidities, ensuring equitable healthcare access, reducing racial inequities, and addressing critical environmental factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets and the codes for the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declared no potential conflicts of interest/competing interests with respect to the research and/or authorship of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declared no funding for\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: All authors contributed to the study\u0026rsquo;s conceptualization and design. \u003cem\u003eHO\u003c/em\u003e acquired and analyzed the data and drafted and revised the manuscript; S\u003cem\u003eT\u003c/em\u003e validated and drafted the findings. All contributors have reviewed and approved the submitted version.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen LM, Harden SR, Sugg MM, Runkle JD, Lundquist TE. Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci Total Environ. 2021;754:142396. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.142396\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2020.142396\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBailey ZD, Krieger N, Ag\u0026eacute;nor M, Graves J, Linos N, Bassett MT. 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Community venue exposure risk estimator for the COVID-19 pandemic. Health Place. 2020;66:102450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.healthplace.2020.102450\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2020.102450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTai DB, Shah A, Doubeni CA, Sia IG, Wieland ML. The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clin Infect Dis. 2021;72(4):703\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cid/ciaa815\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciaa815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001 Sep;1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/phr/116.5.40\u003c/span\u003e\u003cspan address=\"10.1093/phr/116.5.40\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Source: U.S. Census Bureau.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Source: Centers for Disease Control and Prevention.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Source: Louisiana Department of Health.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e \u003cspan\u003e This finding resulted from parish-level calculation using American Community Survey. This may not fully match with a population-level estimate from the U.S. Census Bureau of 18.7% due to a difference in a calculation process. In the population-level statistics, Louisiana still showed the second highest level of poverty, followed by Mississippi (19.6%).\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Louisiana, racial capitalism, income disparity, spatial regression","lastPublishedDoi":"10.21203/rs.3.rs-4335315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4335315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e. Louisiana has experienced one of the highest COVID-19 burdens in the United States. This article seeks to investigate the geospatial pattern of COVID-19 in Louisiana using the perspective of racial capitalism.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e. Using data from the Louisiana Department of Health and American Community Survey, we employed spatial autoregressive models to assess how racial income disparity between White and Black residents connected to COVID-19 cases in Louisiana parishes, controlling for other parish-level characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e. Greater racial income disparity between White and Black residents yielded more COVID-19 cases in Louisiana parishes. A rise in income had a buffering effect on the role of racial income disparity aggravating COVID-19 severity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e. African Americans from lower socioeconomic backgrounds were likely at a higher risk of COVID-19 in the state of Louisiana. Based on Louisiana's unique historical and sociocultural contexts, implications are further discussed.\u003c/p\u003e","manuscriptTitle":"Spatial Analysis of Racial Capitalism and COVID-19 in Louisiana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 02:18:30","doi":"10.21203/rs.3.rs-4335315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"809e3380-9583-4ac7-9838-6714dea83fc0","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T02:18:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 02:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4335315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4335315","identity":"rs-4335315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}