Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States

Urban and Rural Patterns of Health Care Utilization Among People With Rheumatoid Arthritis and Osteoarthritis in a Large US Patient Registry

OBJECTIVE

Rural residence has been associated with health disparities in rheumatic diseases and other chronic conditions in the United States. This study aimed to determine if a relationship exists between geographic residence and health care utilization outcomes for people with rheumatoid arthritis (RA) and osteoarthritis (OA) in a US-wide rheumatic disease registry.

METHODS

Participants were in FORWARD, The National Databank for Rheumatic Diseases, a US-wide rheumatic disease longitudinal cohort completing questionnaires between 1999 and 2019. Health care utilization variables (ie, medical visits and diagnostic tests) from six-month questionnaires were analyzed by geographic categories (small rural/isolated, large rural, and urban). Double selection LASSO with Poisson regression was used to assess the best model when examining the association between health care utilization variables and geographic residence.

RESULTS

Among 37,802 participants with RA, urban residents were more likely than small rural residents to use in-person health care by most measures including physician visits and diagnostic tests. Urban residents reported more rheumatologist visits (incidence rate ratio [IRR], 1.22; 95% confidence interval [95% CI], 1.18-1.27) but fewer primary care visits (IRR 0.90; 95% CI 0.85-0.94). Among 8,248 participants with OA, urban residents were also more likely than rural residents to report health care utilization by most measures.

CONCLUSION

Individuals residing in urban areas were more likely than those in rural areas to report in-person health care utilization. Specifically, urban residents with RA were more likely to report rheumatologist visits, but less likely to report primary care visits. Less disparity existed in OA health care utilization, although an urban-rural disparity still existed by most measures.

Bias in mobility datasets drives divergence in modeled outbreak dynamics

BACKGROUND

Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators' geographic coverage, however, may result in biased mobility estimates.

METHODS

We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta's Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources' effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country.

RESULTS

We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources.

CONCLUSIONS

Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.

Still Separate, Still Not Equal: An Ecological Examination of Redlining and Racial Segregation with COVID-19 Vaccination Administration in Washington D.C

Racial residential segregation has been deemed a fundamental cause of health inequities. It is a result of historical and contemporary policies such as redlining that have created a geographic separation of races and corresponds with an inequitable distribution of health-promoting resources. Redlining and racial residential segregation may have contributed to racial inequities in COVID-19 vaccine administration in the early stages of public accessibility. We use data from the National Archives (historical redlining), Home Mortgage Disclosure Act (contemporary redlining), American Community Survey from 1940 (historical racial residential segregation) and 2015-2019 (contemporary racial residential segregation), and Washington D.C. government (COVID-19 vaccination administration) to assess the relationships between redlining, racial residential segregation, and COVID-19 vaccine administration during the early stages of vaccine distribution when a tiered system was in place due to limited supply. Pearson correlation was used to assess whether redlining and racial segregation, measured both historically and contemporarily, were correlated with each other in Washington D.C. Subsequently, linear regression was used to assess whether each of these measures associate with COVID-19 vaccine administration. In both historical and contemporary analyses, there was a positive correlation between redlining and racial residential segregation. Further, redlining and racial residential segregation were each positively associated with administration of the novel COVID-19 vaccine. This study highlights the ongoing ways in which redlining and segregation contribute to racial health inequities. Eliminating racial health inequities in American society requires addressing the root causes that affect access to health-promoting resources.

Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Effects of COVID-19 on coastal and marine environments: Aggravated microplastic pollution, improved air quality, and future perspective

The COVID-19 pandemic during 2020-2023 has wrought adverse impacts on coastal and marine environments. This study conducts a comprehensive review of the collateral effects of COVID-19 on these ecosystems through literature review and bibliometric analysis. According to the output and citation analysis of these publications, researchers from the coastal countries in Asia, Europe, and America payed more attentions to this environmental issue than other continents. Specifically, India, China, and USA were the top three countries in the publications, with the proportion of 19.55%, 18.99%, and 12.01%, respectively. The COVID-19 pandemic significantly aggravated the plastic and microplastic pollution in coastal and marine environments by explosive production and unproper management of personal protective equipment (PPE). During the pandemic, the estimated mismanaged PPE waste ranged from 16.50 t/yr in Sweden to 250,371.39 t/yr in Indonesia. In addition, the PPE density ranged from 1.13 × 10-5 item/m2 to 2.79 item/m2 in the coastal regions worldwide, showing significant geographical variations. Besides, the emerging contaminants released from PPE into the coastal and marine environments cannot be neglected. The positive influence was that the COVID-19 lockdown worldwide reduced the release of air pollutants (e.g., fine particulate matter, NO2, CO, and SO2) and improved the air quality. The study also analyzed the relationships between sustainable development goals (SDGs) and the publications and revealed the dynamic changes of SDGs in different periods the COVID-19 pandemic. In conclusion, the air was cleaner due to the lockdown, but the coastal and marine contamination of plastic, microplastic, and emerging contaminants got worse during the COVID-19 pandemic. Last but not least, the study proposed four strategies to deal with the coastal and marine pollution caused by COVID-19, which were regular marine monitoring, performance of risk assessment, effective regulation of plastic wastes, and close international cooperation.

7-day patterns in Black-White segregation in 49 metropolitan areas

While residential segregation is a persistent attribute of metropolitan areas, recent studies find segregation levels fluctuate throughout the day, reaching their lowest levels during daytime hours. This paper shows hourly variations in Black-White segregation from Monday through Sunday for the top 49 most populated metropolitan areas with Global Positioning System (GPS) data collected from mobile phones from October 2018. I find that segregation levels are higher on average over weekends compared to that of weekdays. I use models to identify the characteristics of neighborhoods with higher levels of segregation on weekends, which include all demographic variables and nearly a third of 35 sectors of businesses and organizations, such as retail, personal care, and religious organizations. I also find more than a third of the sectors are associated with higher levels of segregation during business hours on weekdays, including academic institutions, health care, manufacturing, and financial institutions. Findings from this paper display the significance in the distinction between weekdays and weekends with where people spend their time and how this relates to racial segregation. Specifically, Black people, on average, stay in their home census tracts and visit non-White neighborhoods for organizational resources more so than White people. Significant patterns of associations between racial segregation and the majority of businesses demonstrate the salience of race for more industries than previously understood.

Racial Disparities in Occupational Distribution Among Black and White Adults with Similar Educational Levels: Analysis of Middle-Aged and Older Individuals in the Health and Retirement Study

Background

Occupational classes play a significant role in influencing both individual and population health, serving as a vital conduit through which higher education can lead to better health outcomes. However, the pathway from education to corresponding occupational classes does not apply uniformly across different racial and ethnic groups, hindered by factors such as social stratification, labor market discrimination, and job segregation.

Aims

This study seeks to investigate the relationship between educational attainment and occupational classes among Black, Latino, and White middle-aged and older adults, with a focus on their transition into retirement.

Methods

Using cross-sectional data from the Health and Retirement Study (HRS), this research examines the impact of race/ethnicity, educational attainment, occupational classes, and timing of retirement among middle-aged and older adults. The analysis includes a sample of 7,096 individuals identified as White, Black, or Latino. Through logistic regression, we assess the additive and multiplicative effects of race/ethnicity and education on six defined occupational classes: 1. Managerial and specialty operations, 2. Professional Specialty, 3. Sales, 4. Clerical/administrative support, 5. Services, and 6. Manual labor.

Results

Participants were Black (n = 1,143) or White (n =5,953). This included Latino (N =459) or non-Latino (n = 6,634). Our analysis reveals a skewed distribution of Black and Latino adults in manual and service occupations, in stark contrast to White adults who were more commonly found in clerical/administrative and managerial positions. Educational attainment did not equate to similar occupational outcomes across racial groups. Key findings include: Firstly, Black individuals with a college degree or higher were less likely to occupy clerical and administrative positions compared to their White counterparts. Secondly, holding a General Educational Development (GED) credential or some college education was generally linked to reduced likelihood of being in managerial roles; however, this inverse relationship was less evident among Black middle-aged and older adults than White ones. Thirdly, having a GED reduced the chances of working in sales roles, while having a college degree increased such chances. An interaction between race and some college education revealed that the impact of some college education on sales roles was more significant for Black adults than for White ones. We did not observe any interaction between ethnicity (Latino) and educational attainment on occupational classes. Given the stability of occupational classes, these findings could also apply to the last occupation held prior to retirement.

Conclusion

This study highlights significant racial disparities in occupational classes among individuals with comparable levels of education, underscoring the profound implications for health and wellbeing disparities. Future research should explore strategies to alleviate labor market discrimination and job segregation as ways to close these occupational gaps. Additionally, the influence of social stratification, job segregation, and historical legacies, such as the repercussions of the Jim Crow era, on these disparities merits further investigation. Addressing these issues is crucial for enhancing the health and wellbeing of all populations.

Spatial Variation in COVID-19 Mortality in New York City and Its Association with Neighborhood Race, Ethnicity, and Nativity Status

We examined the association between variation in COVID-19 deaths and spatial differences in the racial, ethnic, and nativity-status composition of New York City neighborhoods, which has received little scholarly attention. Using COVID-19 mortality data (through 31 May 2021) and socioeconomic and demographic data from the American Community Survey at the Zip Code Tabulation Area level as well as United-Hospital-Fund-level neighborhood data from the Community Health Survey of the New York City Department of Health and Mental Hygiene, we employed multivariable Poisson generalized estimating equation models and assessed the association between COVID-19 mortality, racial/ethnic/nativity-status composition, and other ecological factors. Our results showed an association between neighborhood-level racial and ethnic composition and COVID-19 mortality rates that is contingent upon the neighborhood-level nativity-status composition. After multivariable adjustment, ZCTAs with large shares of native-born Blacks and foreign-born Hispanics and Asians were more likely to have higher COVID-19 mortality rates than areas with large shares of native-born Whites. Areas with more older adults and essential workers, higher levels of household crowding, and population with diabetes were also at high risk. Small-area analyses of COVID-19 mortality can inform health policy responses to neighborhood inequalities on the basis of race, ethnicity, and immigration status.

Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR)

During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.

The COVID-19, racism, and violence syndemic: Evidence from a qualitative study with Black residents of Chicago

The COVID-19 pandemic emerged in the United States in the shadows of a vast history of structural racism and community and police violence that disproportionately affect Black communities. Collectively, they have created a syndemic, wherein COVID-19, racism, and violence are mutually reinforcing to produce adverse health outcomes. The purpose of this study was to understand the COVID-19, racism, and violence syndemic and examine how structural racism and violence contributed to the disproportionate impact of COVID-19 on Black communities. In early 2021, we conducted phenomenological qualitative interviews with 50 Black residents of Chicago. Interview transcripts were coded and analyzed using thematic analysis. We identified four primary themes in our analyses: 1) the intersection of racism and violence in Chicago; 2) longstanding inequities were laid bare by COVID-19; 3) the pervasiveness of racism and violence contributes to poor mental health; 4) and COVID-19, racism and violence emerged as a syndemic. Our findings underscore the importance of addressing social and structural factors in remediating the health and social consequences brought about by COVID-19.

County-Level Segregation and Racial Disparities in COVID-19 Outcomes

CONTEXT

Segregation has been linked to unequal life chances. Individuals from marginalized communities experience more crime, higher levels of poverty, poorer health, and less civic engagement. In addition, segregated metropolitan regions have been found to display inequality in access to basic services. This article builds on these findings by linking segregation to infection and deaths from COVID-19.

METHODS

Using census data matched to COVID infection and death statistics at the county level, this article offers a theoretical basis for the researchers' choice of segregation measures and predictions for different racial groups. It analyzes the relationship between two dimensions of segregation-racial isolation and racial unevenness-and COVID outcomes for different racial and ethnic groups.

FINDINGS

In counties where Black and Latino residents lived in more racially isolated neighborhoods, they were much more likely to contract COVID-19. This pattern was exacerbated in counties with a high proportion of frontline workers. In addition, racial segregation increased COVID-19 death rates for Black, Latino, and white residents.

CONCLUSIONS

These findings suggest that devastating outcomes of the coronavirus pandemic were linked to a long history of racial marginalization and entrenched discrimination produced by structural inequalities embedded in our geographies. This knowledge should be used to inform public health planning.

Understanding mobility change in response to COVID-19: A Los Angeles case study

The COVID-19 pandemic has affected people's lives throughout the world. Governments have imposed restrictions on business and social activities to reduce the spread of the virus. In the US, the pandemic response has been largely left to state and local governments, resulting in a patchwork of policies that frequently changed. We examine travel behavior across income and race/ethnic groups in Los Angeles County over several stages of the pandemic. We use a difference-in-difference model based on mobile device data to compare mobility patterns before and during the various stages of the pandemic. We find a strong relationship between income/ethnicity and mobility. Residents of low-income and ethnic minority neighborhoods reduced travel less than residents of middle- and high-income neighborhoods during the shelter-in-place order, consistent with having to travel for work or other essential purposes. As public health rules were relaxed and COVID vaccines became available, residents of high-income and White neighborhoods increased travel more than other groups, suggesting more discretionary travel. Our trip purpose model results show that residents of low-income and ethnic minority neighborhoods reduced work and shopping travel less than those of White and high-income neighborhoods during the shelter-in-place order. Results are consistent with higher-income workers more likely being able to work at home than lower-income workers. In contrast, low-income/minorities apparently have more constraints associated with work or household care. The consequence is less capacity to avoid virus risk. Race and socioeconomic disparities are revealed in mobility patterns observed during the COVID-19 pandemic.

Psychology Meets Biology in COVID-19: What We Know and Why It Matters for Public Health

Psychosocial factors are related to immune, viral, and vaccination outcomes. Yet, this knowledge has been poorly represented in public health initiatives during the COVID-19 pandemic. This review provides an overview of biopsychosocial links relevant to COVID-19 outcomes by describing seminal evidence about these associations known prepandemic as well as contemporary research conducted during the pandemic. This focuses on the negative impact of the pandemic on psychosocial health and how this in turn has likely consequences for critically relevant viral and vaccination outcomes. We end by looking forward, highlighting the potential of psychosocial interventions that could be leveraged to support all people in navigating a postpandemic world and how a biopsychosocial approach to health could be incorporated into public health responses to future pandemics.

Race, employment, and the pandemic: An exploration of covariate explanations of COVID-19 case fatality rate variance

We derive a simple asymptotic approximation for the long-run case fatality rate of COVID-19 (alpha and delta variants) and show that these estimations are highly correlated to the interaction between US State median age and projected US unemployment rate (Adj. r2 = 60%). We contrast this to the high level of correlation between point (instantaneous) estimates of per state case fatality rates and the interaction of median age, population density and current unemployment rates (Adj. r2 = 50.2%). To determine whether this is caused by a "race effect," we then analyze unemployment, race, median age and population density across US states and show that adding the interaction of African American population and unemployment explains 53.5% of the variance in COVID case fatality rates for the alpha and delta variants when considering instantaneous case fatality rate. Interestingly, when the asymptotic case fatality rate is used, the dependence on the African American population disappears, which is consistent with the fact that in the long-run COVID does not discriminate on race, but may discriminate on access to medical care which is highly correlated to employment in the US. The results provide further evidence of the impact inequality can have on case fatality rates in COVID-19 and the impact complex social, health and economic factors can have on patient survival.

Racial and ethnic disparities in excess mortality among U.S. veterans during the COVID-19 pandemic

OBJECTIVE

The COVID-19 pandemic disproportionately affected racial and ethnic minorities among the general population in the United States; however, little is known regarding its impact on U.S. military Veterans. In this study, our objectives were to identify the extent to which Veterans experienced increased all-cause mortality during the COVID-19 pandemic, stratified by race and ethnicity.

DATA SOURCES

Administrative data from the Veterans Health Administration's Corporate Data Warehouse.

STUDY DESIGN

We use pre-pandemic data to estimate mortality risk models using five-fold cross-validation and quasi-Poisson regression. Models were stratified by a combined race-ethnicity variable and included controls for major comorbidities, demographic characteristics, and county fixed effects.

DATA COLLECTION

We queried data for all Veterans residing in the 50 states plus Washington D.C. during 2016-2020. Veterans were excluded from analyses if they were missing county of residence or race-ethnicity data. Data were then aggregated to the county-year level and stratified by race-ethnicity.

PRINCIPAL FINDINGS

Overall, Veterans' mortality rates were 16% above normal during March-December 2020 which equates to 42,348 excess deaths. However, there was substantial variation by racial and ethnic group. Non-Hispanic White Veterans experienced the smallest relative increase in mortality (17%, 95% CI 11%-24%), while Native American Veterans had the highest increase (40%, 95% CI 17%-73%). Black Veterans (32%, 95% CI 27%-39%) and Hispanic Veterans (26%, 95% CI 17%-36%) had somewhat lower excess mortality, although these changes were significantly higher compared to White Veterans. Disparities were smaller than in the general population.

CONCLUSIONS

Minoritized Veterans experienced higher rates excess of mortality during the COVID-19 pandemic compared to White Veterans, though with smaller differences than the general population. This is likely due in part to the long-standing history of structural racism in the United States that has negatively affected the health of minoritized communities via several pathways including health care access, economic, and occupational inequities.

A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R0 and social distancing behaviour

We investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R₀ for each CSA. Values of R₀ ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index c_flatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found c_flatten to be more influential in the clustering process than R₀. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R₀ itself.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy

An accurate estimator of the real-time fatality rate is warranted to monitor the progress of ongoing epidemics, hence facilitating the policy-making process. However, most of the existing estimators fail to capture the time-varying nature of the fatality rate and are often biased in practice. A simple real-time fatality rate estimator with adjustment for reporting delays is proposed in this paper using the fused lasso technique. This approach is easy to use and can be broadly applied to public health practice as only basic epidemiological data are required. A large-scale simulation study suggests that the proposed estimator is a reliable benchmark for formulating public health policies during an epidemic with high accuracy and sensitivity in capturing the changes in the fatality rate over time, while the other two commonly-used case fatality rate estimators may convey delayed or even misleading signals of the true situation. The application to the COVID-19 data in Germany between January 2020 and January 2022 demonstrates the importance of the social restrictions in the early phase of the pandemic when vaccines were not available, and the beneficial effects of vaccination in suppressing the fatality rate to a low level since August 2021 irrespective of the rebound in infections driven by the more infectious Delta and Omicron variants during the fourth wave.

Moderating Effects of Racial Segregation on the Associations of Cardiovascular Outcomes with Walkability in Chicago Metropolitan Area

Cardiovascular diseases (CVDs), as the leading cause of death in the U.S., pose a disproportionate burden to racial/ethnic minorities. Walkability, as a key concept of the built environment, reflecting walking and physical activity, is associated with health behaviors that help to reduce CVDs risk. While the unequal social variation and spatial distribution inequality of the CVDs and the role of walkability in preventing CVDs have been explored, the moderating factors through which walkability affects CVDs have not been quantitatively analyzed. In this paper, the spatial statistical techniques combined with the regression model are conducted to study the distribution of the CVDs' health outcomes and factors influencing their variation in the Chicago metropolitan area. The spatial statistical results for the CVDs' health outcomes reveal that clusters of low-value incidence are concentrated in the suburban rural areas and areas on the north side of the city, while the high-value clusters are concentrated in the west and south sides of the city and areas extending beyond the western and southern city boundaries. The regression results indicate that racial segregation reduced the positive association between health outcomes and walkability, although both racial segregation and walkability factors were positively associated with CVDs' health outcomes.

Racial/Ethnic Differences in Prosocial Beliefs and Prevention Behavior During the COVID-19 Pandemic

Controlling the COVID-19 pandemic has required communities to engage in prosocial action, including behaviors that may inconvenience individuals, but protect the collective (e.g., mask wearing, social distancing). The purpose of this study was to understand to what extent COVID-19 prosocial beliefs and behavior differ by race/ethnicity and why this might be the case. A US nationally representative sample of 410 adults completed a survey about COVID-19 beliefs and prevention behaviors between June 12 and 18, 2020. Compared to White respondents, Black respondents perceived the risk of COVID-19 to be greater to the US population; and both Black and Latinx respondents thought it was more important to protect a variety of non-close others (e.g., people in their city or state). Black and Latinx respondents engaged in several prevention behaviors, including social distancing, to a greater extent than White respondents. There were indirect effects of Black vs. White race on engaging in protective behaviors through greater perceived risk to others and beliefs in the importance of protecting distal others. Results indicate that targeted messages promoting prevention, including vaccination with pro-social messages, may resonate with communities of color. They also suggest that lower levels of prosocial beliefs among White people have likely hindered the US response to the epidemic.

Inequities in spatial accessibility to COVID-19 testing in 30 large US cities

Testing for SARS-CoV-2 infection has been a key strategy to mitigate and control the COVID-19 pandemic. Wide spatial and racial/ethnic disparities in COVID-19 outcomes have emerged in US cities. Previous research has highlighted the role of unequal access to testing as a potential driver of these disparities. We described inequities in spatial accessibility to COVID-19 testing locations in 30 large US cities. We used location data from Castlight Health Inc corresponding to October 2021. We created an accessibility metric at the level of the census block group (CBG) based on the number of sites per population in a 15-minute walkshed around the centroid of each CBG. We also calculated spatial accessibility using only testing sites without restrictions, i.e., no requirement for an appointment or a physician order prior to testing. We measured the association between the social vulnerability index (SVI) and spatial accessibility using a multilevel negative binomial model with random city intercepts and random SVI slopes. Among the 27,195 CBG analyzed, 53% had at least one testing site within a 15-minute walkshed, and 36% had at least one site without restrictions. On average, a 1-decile increase in the SVI was associated with a 3% (95% Confidence Interval: 2% - 4%) lower accessibility. Spatial inequities were similar across various components of the SVI and for sites with no restrictions. Despite this general pattern, several cities had inverted inequity, i.e., better accessibility in more vulnerable areas, which indicates that some cities may be on the right track when it comes to promoting equity in COVID-19 testing. Testing is a key component of the strategy to mitigate transmission of SARS-CoV-2 and efforts should be made to improve accessibility to testing, particularly as new and more contagious variants become dominant.

Actual Racial/Ethnic Disparities in COVID-19 Mortality for the Non-Hispanic Black Compared to Non-Hispanic White Population in 353 US Counties and Their Association with Structural Racism

INTRODUCTION

Although disparities in COVID-19 mortality have been documented at the national and state levels, no previous study has quantified such disparities at the county level by explicitly measuring race-specific COVID-19 death rates. In this paper, we quantify the racial/ethnic disparities in COVID-19 mortality between the non-Hispanic Black and non-Hispanic White populations at the county level by estimating age-adjusted, race-specific death rates.

METHODS

Using COVID-19 case data from the Centers for Disease Control and Prevention, we calculated crude and indirect age-adjusted COVID-19 mortality rates for the non-Hispanic White and non-Hispanic Black populations in each of 353 counties for the period February 2, 2020, through January 30, 2021. Using linear regression analysis, we examined the relationship between several county-level measures of structural racism and the observed differences in racial disparities in COVID-19 mortality across counties.

RESULTS

Ninety-three percent of the counties in our study experienced higher death rates among the Black compared to the White population, with an average ratio of Black to White death rates of 1.9 and a 17.5-fold difference between the disparity in the lowest and highest counties. Three traditional measures of structural racism were significantly related to the magnitude of the Black-White racial disparity in COVID-19 mortality rates across counties.

CONCLUSIONS

There are large disparities in COVID-19 mortality rates between the Black and White populations at the county level, there are profound differences in the level of these disparities, and those differences are directly related to the level of structural racism in a given county.

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.

Environmental Factors Influencing COVID-19 Incidence and Severity

Emerging evidence supports a link between environmental factors-including air pollution and chemical exposures, climate, and the built environment-and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment-COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities.

Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

County-level societal predictors of COVID-19 cases and deaths changed through time in the United States: A longitudinal ecological study

People of different racial/ethnic backgrounds, demographics, health, and socioeconomic characteristics have experienced disproportionate rates of infection and death due to COVID-19. This study tests if and how county-level rates of infection and death have changed in relation to societal county characteristics through time as the pandemic progressed. This longitudinal study sampled monthly county-level COVID-19 case and death data per 100,000 residents from April 2020 to March 2022, and studied the relationships of these variables with racial/ethnic, demographic, health, and socioeconomic characteristics for 3125 or 97.0% of U.S. counties, accounting for 96.4% of the U.S. population. The association of all county-level characteristics with COVID-19 case and death rates changed significantly through time, and showed different patterns. For example, counties with higher population proportions of Black, Native American, foreign-born non-citizen, elderly residents, households in poverty, or higher income inequality suffered disproportionately higher COVID-19 case and death rates at the beginning of the pandemic, followed by reversed, attenuated or fluctuating patterns, depending on the variable. Patterns for counties with higher White versus Black population proportions showed somewhat inverse patterns. Counties with higher female population proportions initially had lower case rates but higher death rates, and case and death rates become more coupled and fluctuated later in the pandemic. Counties with higher population densities had fluctuating case and death rates, with peaks coinciding with new variants of COVID-19. Counties with a greater proportion of university-educated residents had lower case and death rates throughout the pandemic, although the strength of this relationship fluctuated through time. This research clearly shows that how different segments of society are affected by a pandemic changes through time. Therefore, targeted policies and interventions that change as a pandemic unfolds are necessary to mitigate its disproportionate effects on vulnerable populations, particularly during the first six months of a pandemic.

Spatial Clustering of County-Level COVID-19 Rates in the U.S

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

OBJECTIVE

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

MATERIALS AND METHODS

We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

RESULTS

In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

DISCUSSION

Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

CONCLUSION

There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.