Racial and Ethnic Disparities in COVID-19: Evidence from Six Large Cities

Limited Awareness of Long COVID Despite Common Experience of Symptoms Among African American/Black, Hispanic/Latino, and Indigenous Adults in Arizona

OBJECTIVES

Communities of color might disproportionately experience long-term consequences of COVID-19, known as Long COVID. We sought to understand the awareness of and experiences with Long COVID among African American/Black (AA/B), Hispanic/Latino (H/L), and Indigenous (Native) adults (18 + years of age) in Arizona who previously tested positive for COVID-19.

METHODS

Between December 2022 and April 2023, the Arizona Community Engagement Alliance (AZCEAL) conducted 12 focus groups and surveys with 65 AA/B, H/L and Native community members. Data from focus groups were analyzed using thematic analysis to identify emerging issues. Survey data provided demographic information about participants and quantitative assessments of Long COVID experiences were used to augment focus group data.

RESULTS

Study participants across all three racial/ethnic groups had limited to no awareness of the term Long COVID, yet many described experiencing or witnessing friends and family endure physical symptoms consistent with Long COVID (e.g., brain fog, loss of memory, fatigue) as well as associated mental health issues (e.g., anxiety, worry, post-traumatic stress disorder). Participants identified a need for Long COVID mental health and other health resources, as well as increased access to Long COVID information.

CONCLUSION

To prevent Long COVID health inequities among AA/B, H/L, and Native adults living in AZ, health-related organizations and providers should increase access to culturally relevant, community-based Long COVID-specific information, mental health services, and other health resources aimed at serving these populations.

The Puerto Rico community engagement alliance (PR-CEAL) against COVID-19 disparities: outreach and research engagement efforts in disproportionately affected communities

In September 2020, the National Institutes of Health acted in response to the COVID-19 pandemic, recognizing the critical need to combat misinformation, particularly in communities disproportionately affected by the crisis. The Community Engagement Alliance (CEAL) emerged as an initiative dedicated to fostering reliable, science-based information, diversity, and inclusion; aiming to implement effective strategies to mitigate the spread of COVID-19 nationwide. One of the teams participating in this initiative is Puerto Rico-CEAL (PR-CEAL). Our whose goal was to raise awareness about the coronavirus disease and advance research, mainly focusing on vulnerable and underserved populations. This concept paper seeks to outline PR-CEAL's infrastructure during its initial two cycles, providing insights into the research and community engagement activities designed to enhance prevention, counter misinformation, and foster awareness and uptake of COVID-19 vaccines. Ultimately, our objective is to reflect on the strengths and challenges encountered thus far as we endeavor to sustain this robust infrastructure, addressing ongoing public health issues with a forward-looking approach.

Social determinants of health and COVID-19 recovery after inpatient rehabilitation

BACKGROUND

Neighborhoods with more social determinants of health (SDOH) risk factors have higher rates of infectivity, morbidity, and mortality from COVID-19. Patients with severe COVID-19 infection can have long-term functional deficits leading to lower quality of life (QoL) and independence measures. Research shows that these patients benefit greatly from inpatient rehabilitation facilities (IRF) admission, but there remains a lack of studies investigating long-term benefits of rehabilitation once patients are returned to their home environment.

OBJECTIVE

To determine SDOH factors related to long-term independence and QoL of COVID-19 patients after IRF stay.

DESIGN

Multisite cross-sectional survey.

SETTING

Two urban IRFs.

MAIN OUTCOME MEASURES

Primary outcome measures were Post-COVID Functional Status Scale (PCFS) and Short Form-36 (SF-36) scores. Secondary outcomes were quality indicator (QI) scores while at IRF and a health care access questionnaire. Results were analyzed using analysis of variance and multivariate logistic regression analyses.

RESULTS

Participants (n = 48) who were greater than 1 year post-IRF stay for severe COVID-19 were enrolled in the study. Higher SF-36 scores were associated with male gender (p = .002), higher income (≥$70,000, p = .004), and living in the city (p = .046). Similarly, patients who were of the male gender (p = .004) and had higher income (≥$70,000, p = .04) had a greater odds of a 0 or 1 on the PCFS. Age was not associated with differences. Women were more likely to seek follow-up care (p = .014). Those who sought follow-up care reported lower SF-36 overall and emotional wellness scores, p = .041 and p = .007, respectively. Commonly reported barriers to health care access were financial and time constraints.

CONCLUSIONS

Patients with SDOH risk factors need to be supported in the outpatient setting to maintain functional gains made during IRF stays. Female gender, income, and urban setting are potential predictors for long-term QoL and independence deficits after rehabilitation for COVID-19 infection. Low emotional wellness is an indicator for patients to seek out care as far out as 1 year from their rehabilitation stay.

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Reasons for Suicide in Black Young Adults: A Latent Class Analysis

BACKGROUND

Suicides have increased among Black youth in the US, though it remains unclear if these trends persist into young adulthood. Further, even less is known about the reasons why individuals begin to consider suicide as a viable option. The current study aims to redress these gaps by identifying specific reasons for suicide among a sample of 264 Black young adults who reported experiencing suicidal thoughts within the past 2 weeks.

METHODS

Participants were recruited from an online panel. Reasons for suicide were measured using eight individual items/indicators. Latent class analysis was used to detect underlying patterns in Black young adults' reasons for considering suicide.

RESULTS

The most commonly reported reason for considering suicide among the entire sample was feeling hopeless about the future. Black women were more likely to report considering suicide because they could not live up to other's expectations and because they felt lonely and sad. Findings for the 3-class model were retained. The first class is described as the "Somewhat hopeless and other reasons" class (n = 85; 32%). The second class is "Accomplished but extremely lonely and sad" (n = 24; 9%). The third class is described as "Pronounced feelings of failure, hopelessness, being overwhelmed, and lack of accomplishment" and includes 59% of the sample (n = 155).

CONCLUSIONS

Culturally grounded clinical treatments and interventions are needed to meet the specific mental health needs of Black young adults. A particular focus on identifying factors that drive feelings of hopelessness and failure is warranted.

Neighborhood Factors Associated with COVID-19 Cases in California

BACKGROUND

There is a need to assess neighborhood-level factors driving COVID-19 disparities across racial and ethnic groups.

OBJECTIVE

To use census tract-level data to investigate neighborhood-level factors contributing to racial and ethnic group-specific COVID-19 case rates in California.

DESIGN

Quasi-Poisson generalized linear models were used to identify neighborhood-level factors associated with COVID-19 cases. In separate sequential models for Hispanic, Black, and Asian, we characterized the associations between neighborhood factors on neighborhood COVID-19 cases. Subanalyses were conducted on neighborhoods with majority Hispanic, Black, and Asian residents to identify factors that might be unique to these neighborhoods. Geographically weighted regression using a quasi-Poisson model was conducted to identify regional differences.

MAIN MEASURES

All COVID-19 cases and tests reported through January 31, 2021, to the California Department of Public Health. Neighborhood-level data from census tracts were obtained from American Community Survey 5-year estimates (2015-2019), United States Census (2010), and United States Department of Housing and Urban Development.

KEY RESULTS

The neighborhood factors associated with COVID-19 case rate were racial and ethnic composition, age, limited English proficiency (LEP), income, household size, and population density. LEP had the largest influence on the positive association between proportion of Hispanic residents and COVID-19 cases (- 2.1% change). This was also true for proportion of Asian residents (- 1.8% change), but not for the proportion of Black residents (- 0.1% change). The influence of LEP was strongest in areas of the Bay Area, Los Angeles, and San Diego.

CONCLUSION

Neighborhood-level contextual drivers of COVID-19 burden differ across racial and ethnic groups.

Black in the pandemic: Comparing experiences of mistrust, anxiety, and the COVID-19 vaccine among Black adults in the U.S

COVID-19 vaccine decisions are shaped by many factors including historical and contemporary patterns of medical mistreatment of marginalized communities. In attending to these concerns, we measured whether fear of COVID-19, general feelings of mistrust, and race-specific mistrust of the government and healthcare providers are indirectly associated with COVID-19 vaccination status via anxiety among Black Americans. We analyzed responses from 996 Black adults who participated in the AmeriSpeak panel - a nationally representative probability-based sample recruited from the National Opinion Research Center from April-June 2022. We used multiple-group structural equation modeling to compare outcomes among those who lost a loved one to COVID-19 to those who did not. Results indicate that fear of COVID-19 was associated with a greater probability of being fully vaccinated for those who lost a family member/friend. Race-specific mistrust was positively associated with anxiety, but was negatively associated with being fully vaccinated for bereaved Black Americans. Targeted efforts are needed to specifically reach those who lost a loved one to COVID-19. More within-group evaluations are needed to identify barriers to COVID-19 vaccination that are specific to Black Americans living with loss and grief.

The effects of COVID-19 on African American communities in Baltimore's health enterprise zones: a mixed-methods examination

BACKGROUND

The CoVID-19 pandemic underscored effects of community resources on the built environment, health and health outcomes. The purpose of this study was to conduct community-engaged research and examine aspects of health, and access to healthcare from the voices of community members, as a foundation for improving health equity through the built environment.

METHODS

This study utilized a convergent mixed methods design that included surveys and semi-structured interviews conducted from July 2021 to August 2022 to examine the impact of limited community resources, such as community health clinics on participants during the CoVID-19 pandemic. A convenient sample of 345 male and female African American participants represented five zip codes (21215, 21216, 21217, 21223, and 21229) in with the highest impact from CoVID 19, in Baltimore, Maryland. Quantitative and qualitative data were integrated to describe how the two types supported one another in health, healthcare and healthcare access.

RESULTS

More than half of all participants reported satisfaction with overall health, quality of healthcare provided and access to health care services. However, results indicated extreme differences in factors related to health and wellness after, as comparted to before the onset of the pandemic, Semi-structured interviews, expanded on overall community health, highlighting that overall satisfaction with health does not equal satisfaction with health-related resources and suggested participants felt frustrated and left out of much-needed community health resources to improve health and mental health services for all ages, nutrition services and community activities that make communities thrive. Data integration provided a more realistic view of what participants really experience, due to the expanded analysis of semi-structured interviews, and indicated quantitative and qualitative data did not always support each other.

CONCLUSIONS

Future research to improve the built environment, and to address historic health inequities, will require ongoing community engagement to better understand community needs. This study results encourage ongoing research to expand resources for community-engaged research and interventions. Researchers must remain cognoscente of changing needs, and persistent disparities that can only be addressed if policies, supported by these results, are introduced to make equitable investments to forge an environment where healthy communities thrive.

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 Insurance, Race and Ethnicity, and COVID-19 Hospitalization, Beyond Underlying Health Conditions: A Retrospective Cohort Study

Introduction

: People of lower socioeconomic position (SEP) and people of color (POC) experience higher risks of severe COVID-19, but understanding of these associations beyond the effect of underlying health conditions (UHCs) is limited. Moreover, few studies have focused on young adults, who have had the highest incidence of COVID-19 during much of the pandemic.

Methods

: We conducted a retrospective cohort study using electronic health record data from the University of Washington Medicine healthcare system. Our study population included individuals aged 18-39 years who tested positive for SARS-CoV-2 from February 2020 to March 2021. Using regression modeling, we estimated adjusted risk ratios (aRRs) and differences (aRDs) of COVID-19 hospitalization by SEP (using health insurance as a proxy) and race and ethnicity. We adjusted for any UHC to examine these associations beyond the effect of UHCs. Results: Among 3,101 individuals, the uninsured/publicly insured had a 1.9-fold higher risk of hospitalization (aRR [95% CI]=1.9 [1.0, 3.6]) and 9 additional hospitalizations per 1,000 SARS-CoV-2 positive persons (aRD [95% CI]=9 [-1, 20]) compared to the privately insured. Hispanic or Latine, non-Hispanic (NH) Asian, NH Black, and NH Native Hawaiian or Pacific Islander patients had a 1.5-, 2.7-, 1.4-, and 2.1-fold-higher risk of hospitalization (aRR [95% CI]=1.5 [0.7, 3.1]; 2.7 [1.1, 6.5]; 1.4 [0.6, 3.3]; 2.1 [0.5, 9.1]), respectively, compared to NH White patients. Conclusions: Though they should be interpreted with caution given low precision, our findings suggest the increased risk of COVID-19 hospitalization among young adults of lower SEP and young adults of color may be driven by forces other than UHCs, including social determinants of health.

Barriers and facilitators to COVID-19 testing among staff and parents from San Diego schools

COVID-19 testing is an important risk mitigation strategy for COVID-19 prevention in school settings, where the virus continues to pose a public health challenge for in-person learning. Socially vulnerable school communities with the highest proportion of low-income, minority, and non-English speaking families have the least testing access despite shouldering a disproportionate burden of COVID-19 morbidity and mortality. Through the Safer at School Early Alert (SASEA) program, we investigated community perceptions of testing in San Diego County schools, with a focus on barriers and facilitators from the perspective of socially vulnerable parents and school staff. Using a mixed-methods approach, we administered a community survey and conducted focus group discussions (FGDs) with staff and parents from SASEA-affiliated schools and childcares. We recruited 299 survey respondents and 42 FGD participants. Protecting one's family (96.6%) and protecting one's community (96.6%) were marked as key motivators to testing uptake. School staff in particular reported that the reassurance of a negative status mitigated concerns about COVID-19 infection in schools. Participants expressed that COVID-19-related stigma, loss of income as a result of isolation/quarantine requirements, and lack of multilingual materials were the most significant barriers to testing. Our findings suggest that the testing barriers faced by school community members are predominantly structural. Testing uptake efforts must provide support and resources to manage the social and financial consequences of testing while continuously communicating its benefits. There is a clear need to continue to incorporate testing as a strategy to maintain school safety and facilitate access for vulnerable community members.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

Cognitive and emotional factors related to COVID-19 among high-risk ethnically diverse adults at the onset of the New York City outbreak: A cross-sectional survey

A cross-sectional survey was conducted among high-risk, racially/ethnically diverse adults at the point in time when New York City (NYC) became the COVID-19 pandemic's global epicenter. The study objective was to assess the threat and coping appraisals (cognitive factors known to correspond with people's willingness to adopt behaviorally focused interventions) and levels of distress, anxiety, and intolerance for uncertainty (emotional factors). Survey respondents were recruited in April 2020 using an online survey with unpaid recruitment on the GetHealthyHeights.org community-oriented website. We also recruited participants that engaged in previous research studies to gain survey responses from community members at higher risk for COVID-19 complications due to comorbidities compared to the general population. Analysis was performed to test for differences in survey responses by comorbidities, age, race, ethnicity, and employment status. Results show that the devastating effects of the pandemic appear to have uniquely impacted minority respondents, who reported significantly higher levels of anxiety and were significantly more likely to report having little control over whether they will get COVID-19 compared with White/non-Hispanic respondents. Minority respondents also had significantly higher mean scores on the behaviorally focused dimension of the intolerance of uncertainty (IU) scale, which measures avoidance and paralysis in the face of uncertainty. In multivariate analysis, IU predicted anxiety levels, and this association was not mediated by cognitive factors (threat and coping appraisals). By conducting this survey early in the pandemic, our study uniquely evaluated cognitive and emotional factors among a racially/ethnically diverse group of NYC residents during the height of the COVID-19 pandemic. Our findings suggest the need to acknowledge the disparities that appear to exist in pandemic response and for culturally tailored messaging and interventions. Few studies have reported differences by race and ethnicity during pandemic exposure. Therefore, further research on factors that may influence pandemic response among minority populations is needed.

Supporting COVID-19 Disparity Investigations with Dynamically Adjusting Case Reporting Policies

Data access limitations have stifled COVID-19 disparity investigations in the United States. Though federal and state legislation permits publicly disseminating de-identified data, methods for de-identification, including a recently proposed dynamic policy approach to pandemic data sharing, remain unproved in their ability to support pandemic disparity studies. Thus, in this paper, we evaluate how such an approach enables timely, accurate, and fair disparity detection, with respect to potential adversaries with varying prior knowledge about the population. We show that, when considering reasonably enabled adversaries, dynamic policies support up to three times earlier disparity detection in partially synthetic data than data sharing policies derived from two current, public datasets. Using real-world COVID-19 data, we also show how granular date information, which dynamic policies were designed to share, improves disparity characterization. Our results highlight the potential of the dynamic policy approach to publish data that supports disparity investigations in current and future pandemics.

Non-pharmaceutical Interventions and COVID-19: Do County- and State-Level Policies Predict the Spread of COVID-19?

This study examines the impact of county- and state-level policies on the spread and severity of COVID-19 in communities in the USA during the first wave of the COVID-19 pandemic. We use county-level COVID-19 death and case data to examine the impact of county- and state-level mandates and non-pharmaceutical interventions (NPIs) on the spread and severity of COVID-19. Following previous work by Amuendo-Dorantes et al. (2020), we utilize a strategy that incorporates the duration of NPI implementation within a county. Specifically, we examine aggregated measures of mask mandates, daycare closures, stay-at-home orders, and restaurant and bar closures. In addition to the implementation and duration of NPI policy, we examine the role of pre-existing factors that contribute to social determinants of health in a locality. We incorporate information on the incidence of prior health conditions, socio-economic factors, and demographics including racial and ethnic composition, share of immigrant population of counties, and state governance in our estimations. To alleviate the possible endogeneity of COVID-19 outcomes and NPIs, we use instrumental variable estimation and our results show that collectively NPIs decreased the intensity of the pandemic by decreasing the total deaths and cases. Furthermore, we find the magnitude of the impact of NPIs increases the longer they are implemented. We also estimate a specification that allows for heterogeneity of NPI impact based on the racial and ethnic composition of counties. Our results suggest that NPIs have a non-uniform impact in counties with different racial and ethnic compositions.

Racial and Ethnic Disparities in Violent Penetrating Injuries and Long-Term Adverse Outcomes

Violent injury is known to be a chronic, recurrent issue, with high rates of recidivism following initial injury. While the burden of violence is disproportionately felt among young Black men and in communities of color, examination of distinct risk factors and long-term outcomes for other racial and ethnic groups could lead to improved violence intervention strategies. In this study, we examined the risk of violent penetrating injury and long-term adverse outcomes by race and ethnicity. This retrospective study was performed using a cohort of patients presenting to the Boston Medical Center emergency department for a violent penetrating injury between 2006 and 2016. Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (95%CI) for the risk of all-cause mortality and violent re-injury at one and 3 years after surviving a penetrating injury.Of the 4191 victims of violent injury, 12% were White, 18% were Hispanic, and the remaining 70% self-identified as Black. Within 3 years after initial injury, Black patients were at the greatest risk of all-cause violent re-injury (vs. Hispanic: HR = 1.46, 95%CI[1.15,1.85], p = 0.002; vs. White: HR = 1.89, 95%CI[1.40,2.57], p < 0.0001), particularly by gunshot wound (vs. Hispanic: HR = 2.04, 95%CI[1.29,3.22] p = 0.002; vs. White: HR = 2.34, 95%CI[1.19,4.60], p = 0.01). At 3-years following initial injury, White patients were at 2.03 times the risk for all-cause mortality, likely due to a 4.96 times greater risk of death by drug or alcohol overdose for White patients compared to Black patients (HR = 4.96, 95%CI[2.25,10.96], p < 0.0001). In conclusion, Black survivors of violent injury have a significantly higher risk of violent re-injury, particularly by gun violence, while White patients are at the highest risk for mortality due to the incidence of drug and alcohol overdose. Violence intervention programs with similar patient populations should explore options to collaborate with drug treatment programs to reach this vulnerable population.

Impacts of Individual Patient Language and Neighborhood Ethnic Enclave on COVID-19 Test Positivity Among Hispanic/Latinx Patients in San Francisco

BACKGROUND

Given the known disparities in COVID-19 within the Hispanic/Latinx community, we sought to examine the interaction between individual-level and neighborhood-level social determinants of health using linked electronic health record data.

METHODS

We examined electronic health record data linked to neighborhood data among Hispanic/Latinx patients tested for COVID-19 between March 1, 2020, and February 28, 2021, from 2 large health care systems in San Francisco. Hispanic/Latinx ethnic enclave is measured using an index of census-tract level indicators of ethnicity, nativity, and language. Multilevel logistic regression models examined associations between ethnic enclave and COVID-19 positivity (COVID-19+), adjusting for patient-level sociodemographic and clinical characteristics and health system. Cross-level interactions were used to test whether associations between ethnic enclave and COVID-19+ differed by patient language preference.

RESULTS

Among 26,871 patients, mean age was 37 years, 56% had Spanish-language preference, and 21% were COVID-19+. In unadjusted models, patients living in the highest versus lowest Hispanic/Latinx enclave had 3.2 higher odds of COVID-19+ (95% CI, 2.45-4.24). Adjusted, the relationship between ethnic enclave and COVID-19+ was attenuated, but not eliminated (odds ratio: 1.4; 95% CI, 1.13-1.17). Our results demonstrated a significant cross-level interaction, such that the influence of ethnic enclave was modified by patient language preference. For individuals with Spanish-language preference, risk of COVID-19+ was high regardless of neighborhood context, whereas for those with English preference, neighborhood ethnic enclave more than doubled the odds of infection.

CONCLUSIONS

Findings suggest that a multilevel and intersectional approach to the study of COVID-19 inequities may illuminate dimensions of health inequity that affect marginalized communities and offer insights for targeted clinical and community-based interventions.

Impact of COVID-19 on Families with Children: Examining Sociodemographic Differences

OBJECTIVE

The COVID-19 pandemic has exacerbated differences related to employment and family psychological health. However, empirical evidence examining COVID-19-linked differences concerning children and families remains scant. This study addresses this gap by examining sociodemographic differences associated with COVID-19 on family access to resources and family psychological health.

METHOD

A telephone survey of 600 caregivers living in Mississippi was conducted from August 2020 to April 2021. Caregivers answered questions about levels of worry regarding themselves or their child contracting COVID-19 and impact of the pandemic on household income, access to resources, and family psychological health.

RESULTS

Multivariate models demonstrated that Black caregivers (n = 273; 45.5%) had increased odds of agreeing that they worry about contracting COVID-19 (odds ratio [OR] = 2.57). Furthermore, as caregiver reported household annual income decreased, caregivers had increased odds of agreeing that they worry about contracting COVID-19 (OR = 1.16), lost job-related income (OR = 1.14), and had a hard time obtaining resources (OR = 1.16) because of the pandemic. No significant differences related to rural or urban residence were observed.

CONCLUSION

The findings highlight the need for pragmatic responses that are attuned to differences by providing more equitable access to resources for families. The findings suggest that strategies addressing family worry, obtaining job-related income support, and helping families obtain tangible resources may positively affect family psychological health. As population changes in vaccination rates and COVID variants emerge, reassessment of family and community impact seems indicated. Limitations and future research directions are discussed.

Considering social inequalities in health in COVID-19 response: insights from a French case study

The COVID-19 pandemic highlighted the impact of social inequalities in health (SIH). Various studies have shown significant inequalities in mortality and morbidity associated with COVID-19 and the influence of social determinants of health. The objective of this qualitative case study was to analyze the consideration of SIH in the design of two key COVID-19 prevention and control interventions in France: testing and contact tracing. Interviews were conducted with 36 key informants involved in the design of the intervention and/or the government response to the pandemic as well as relevant documents (n = 15) were reviewed. We applied data triangulation and a hybrid deductive and inductive analysis to analyze the data. Findings revealed the divergent understandings and perspectives about SIH, as well as the challenges associated with consideration for these at the beginning stages of the pandemic. Despite a shared concern for SIH between the participants, an epidemiological frame of reference dominated the design of the intervention. It resulted in a model in which consideration for SIH appeared as a complement, with a clinical goal of the intervention: breaking the chain of COVID-19 transmission. Although the COVID-19 health crisis highlighted the importance of SIH, it did not appear to be an opportunity to further their consideration in response efforts. This article provides original insights into consideration for SIH in the design of testing and contact-tracing interventions based upon a qualitative investigation.

Racial inequality in COVID-treatment and in-hospital length of stay in the US over time

Introduction

Demonstrated health inequalities persist in the United States. SARS-CoV-2 (COVID) has been no exception, with access to treatment and hospitalization differing across race or ethnic groups. Here, we aim to assess differences in treatment with remdesivir and hospital length of stay across the four waves of the pandemic.

Materials and methods

Using a subset of the Truveta data, we examine the odds ratio (OR) of in-hospital remdesivir treatment and risk ratio (RR) of in-hospital length of stay between Black or African American (Black) to White patients. We adjusted for confounding factors, such as age, sex, and comorbidity status.

Results

There were statistically significant lower rates of remdesivir treatment and longer in-hospital length of stay comparing Black patients to White patients early in the pandemic (OR for treatment: 0.88, 95% confidence interval [CI]: 0.80, 0.96; RR for length of stay: 1.17, CI: 1.06, 1.21). Rates became close to parity between groups as the pandemic progressed.

Conclusion

While inpatient remdesivir treatment rates increased and length of stay decreased over the beginning course of the pandemic, there are still inequalities in patient care.

Urban density and COVID-19: understanding the US experience

This paper revisits the debate around the link between population density and the severity of COVID-19 spread in the USA. We do so by conducting an empirical analysis based on graphical evidence, regression analysis and instrumental variable strategies borrowed from the agglomeration literature. Studying the period between the start of the epidemic and the beginning of the vaccination campaign at the end of 2020, we find that the cross-sectional relationship between density and COVID-19 deaths changed as the year evolved. Initially, denser counties experienced more COVID-19 deaths. Yet, by December, the relationship between COVID deaths and urban density was completely flat. This is consistent with evidence indicating density affected the timing of the outbreak-with denser locations more likely to have an early outbreak-yet had no influence on time-adjusted COVID-19 cases and deaths. Using data from Google, Facebook, the US Census and other sources, we investigate potential mechanisms behind these findings.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

This article aims to provide a better understanding of the associations between groups of socioeconomic variables and confirmed cases of COVID-19. The focus is on cross-continental differences of reported positive, negative, unclear, or no associations. A systematic review of the literature is conducted on the Web of Science and SCOPUS databases. Our search identifies 314 eligible studies published on or before 31 December 2021. We detect nine groups of frequently used socioeconomic variables and results are presented by region of the world (Africa, Asia, Europe, Middle East, North American and South America). The review expands to describe the most used statistical and modelling techniques as well as inclusion of additional dimensions such as demographic, healthcare weather and mobility. Meanwhile findings agree on the generalized positive impact of population density, per capita GDP and urban areas on transmission of infections, contradictory results have been found concerning to educational level and income.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Speaking COVID-19: supporting COVID-19 communication and engagement efforts with people from culturally and linguistically diverse communities

Background

Since the emergence of COVID-19, issues have been raised regarding the approach used to engage with Culturally and Linguistically Diverse (CaLD) communities during this public health crisis. This study aimed to understand the factors impacting communication and engagement efforts during the COVID-19 pandemic from the perspective of crucial CaLD community stakeholders and opinion leaders.

Methods

Forty-six semi-structured telephone interviews were undertaken with key stakeholders who have an active role (established before the pandemic) in delivering services and other social support to CaLD communities in Australia.

Results

Seven key themes emerged: (1) the digital divide and how to connect with people; (2) information voids being filled by international material; (3) Differentiating established with new and emerging communities’ needs; (4) speaking COVID-19; (5) ineffectiveness of direct translations of English language resources; (6) coordination is needed to avoid duplication and address gaps and (7) recognising the improvements in governments’ approach.

Conclusion

Alliances must be set up that can be activated in the future to reduce issues around resource development, translation, and dissemination of messages to minimise gaps in the response. Financial assistance must be provided in a timely way to community organisations to support the development and dissemination of culturally appropriate communication materials.

Were the socio-economic determinants of municipalities relevant to the increment of COVID-19 related deaths in Brazil in 2020?

BACKGROUND

The COVID-19 pandemic in Brazil has been showing a pattern of distribution of related deaths associated with individual socioeconomic status (SES). However, little is known about the role of SES in the distribution of the mortality rate in different population, from an ecological perspective.

OBJECTIVE

The objective of this study was to evaluate the role of socioeconomic factors in the distribution of the COVID-19-related mortality rate among Brazilian municipalities in 2020.

METHODS

We conducted a retrospective, cross-sectional, observational, population-wide, and ecological study, using data of COVID-19-related deaths from the Influenza Epidemiological Surveillance Information System database (SIVEP-Gripe) and SES from the Social Vulnerability Index (SVI), the Human Development Index (HDI), the Geographic Index of the Socioeconomic Context and Social Studies (GeoSES), and 2010 Demographic Census (IBGE/Brazil). We computed crude, age- and sex-standardized, and the latter offset by the time of exposure to the epidemic mortality rates. To determine socioeconomic factors associated with mortality rates we used log-linear models with state codes as a random effect and Haversine variance-covariance matrix.

RESULTS

191,528 deaths were related to COVID-19 and distributed in 4,928 (88.55%) Brazilian municipalities. Whatever the socioeconomic indexes used, the R2 were very small to explain SMRT. Consistent across all socioeconomic indexes used, high-income, more educated, and well infrastructure municipalities generally had higher mortality rates.

CONCLUSION

Excluding the effect of demographic structure and pandemic timing from mortality rates, the contribution of SES to explain differences in COVID-19-related mortality rates among municipalities in Brazil became very low. The impact of SES on COVID-19-related mortality may vary across levels of aggregation. Urban infrastructure, which includes mobility structures, more complex economic activities and connections, may have influenced the average municipal death rate.

The COVID-19 Pandemic as a Threat Multiplier for Childhood Health Disparities: Evidence from St. Louis, MO

The COVID-19 pandemic has highlighted socioeconomic and racial health disparities in the USA. In this study, we examined the COVID-19 pandemic as a threat multiplier for childhood health disparities by evaluating health behavior changes among urban St. Louis, MO, children (ages 6-14) during the COVID-19 pandemic. From 27 October to 10 December 2020, 122 parents/guardians reported on their children's health behaviors (Eating, Sleeping, Physical activity, Time outside, Time with friends in-person, Time with friends remotely, Time using media for educational proposes, Time using media for non-educational proposes, and Social connectedness) prior to and during the COVID-19 pandemic. We ran K-means cluster analyses to identify distinct health behavior cluster profiles. Relative risks were determined to evaluate behavioral differences between the two clusters. Two distinct cluster profiles were identified: a High Impact profile (n = 49) and a Moderate Impact profile (n = 73). Children in the High Impact cluster had a greater risk of being diagnosed with COVID-19, developed worsened eating habits (RR = 2.10; 95% CI = 1.50-2.93), spent less time sleeping, and spent less time outdoors (RR = 1.55; 95% CI = 1.03-2.43) than the Moderate Impact cluster. The High Impact cluster was more likely to include Black children and children from single-adult households than the Moderate Impact cluster (both p < 0.05). Our findings suggest that the COVID-19 pandemic may be a threat multiplier for childhood health disparities. Further research is needed to better understand the long-term effects of the COVID-19 pandemic on children's health.

Occupation and Educational Attainment Characteristics Associated With COVID-19 Mortality by Race and Ethnicity in California

IMPORTANCE

Racial and ethnic inequities in COVID-19 mortality may be driven by occupation and education, but limited evidence has assessed these mechanisms.

OBJECTIVE

To estimate whether occupational characteristics or educational attainment explained the associations between race and ethnicity and COVID-19 mortality.

DESIGN, SETTING, AND PARTICIPANTS

This population-based retrospective cohort study of Californians aged 18 to 65 years linked COVID-19 deaths to population estimates within strata defined by race and ethnicity, gender, age, nativity in the US, region of residence, education, and occupation. Analysis was conducted from September 2020 to February 2022.

EXPOSURES

Education and occupational characteristics associated with COVID-19 exposure (essential sector, telework option, wages).

MAIN OUTCOMES AND MEASURES

All confirmed COVID-19 deaths in California through February 12, 2021. The study estimated what COVID-19 mortality would have been if each racial and ethnic group had (1) the COVID-19 mortality risk associated with the education and occupation distribution of White people and (2) the COVID-19 mortality risk associated with the lowest-risk educational and occupational positions.

RESULTS

Of 25 235 092 participants (mean [SD] age, 40 [14] years; 12 730 395 [50%] men), 14 783 died of COVID-19, 8 125 565 (32%) had a Bachelor's degree or higher, 13 345 829 (53%) worked in essential sectors, 11 783 017 (47%) could not telework, and 12 812 095 (51%) had annual wages under $51 700. COVID-19 mortality ranged from 15 deaths per 100 000 for White women and Asian women to 139 deaths per 100 000 for Latinx men. Accounting for differences in age, nativity, and region of residence, if all races and ethnicities had the COVID-19 mortality associated with the occupational characteristics of White people (sector, telework, wages), COVID-19 mortality would be reduced by 10% (95% CI, 6% to 14%) for Latinx men, but increased by 5% (95% CI, -8% to 17%) for Black men. If all working-age Californians had the COVID-19 mortality associated with the lowest-risk educational and occupational position (Bachelor's degree, nonessential, telework, and highest wage quintile), there would have been 43% fewer COVID-19 deaths among working-age adults (8441 fewer deaths; 95% CI, 32%-54%), with the largest absolute risk reductions for Latinx men (3755 deaths averted; 95% CI, 3304-4255 deaths) and Latinx women (2329 deaths averted; 95% CI, 2038-2621 deaths).

CONCLUSIONS AND RELEVANCE

In this population-based cohort study of working-age California adults, occupational disadvantage was associated with excess COVID-19 mortality for Latinx men. For all racial and ethnic groups, excess risk associated with low-education, essential, on-site, and low-wage jobs accounted for a substantial fraction of COVID-19 mortality.

Fair and diverse allocation of scarce resources

We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and create a synergistic intervention strategy that gives vulnerable populations higher priority in medical resource distribution. This prevention-centered strategy seeks a balance between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation.

The effect of social deprivation on the dynamic of SARS-CoV-2 infection in France: a population-based analysis

BACKGROUND

Data on health inequalities related to the dynamic of SARS-CoV-2 infection in France are scarce. The aim of this study was to analyse the association between an area-based deprivation indicator and SARS-CoV-2 incidence, positivity, and testing rates between May 2020 and April 2021.

METHODS

We analysed data reported to the Système d'Information de Dépistage Populationnel surveillance system between May 14, 2020 and April 29, 2021, which records the results of all SARS-CoV-2 tests in France. Residential addresses of tested individuals were geocoded to retrieve the associated aggregated units for the statistical information (IRIS) scale, corresponding to an area comprising 2000 inhabitants relatively homogenous in terms of socioeconomic characteristics. A social deprivation score was assigned to each area using the European Deprivation Index (EDI). We fitted negative binomial generalised additive models to model the age-standardised and sex-standardised ratios for SARS-CoV-2 incidence, positivity rates, and testing rates, and to estimate incidence rate ratios (IRRs) and 95% CIs of their association with EDI quintiles, using the first quintile (least deprived) as the reference category, adjusted for week, population density, and region.

FINDINGS

Analyses were based on 70 990 478 SARS-CoV-2 tests, of which 5 000 972 were positive. SARS-CoV-2 incidence was higher in the most deprived areas than the least deprived areas (IRR 1·148 [95% CI 1·138-1·158]) and positivity rates were also higher (IRR 1·283 [1·273-1·294]), whereas testing rates were lower in the most deprived areas than the least deprived areas (IRR 0·905 [0·904-0·907]). SARS-CoV-2 incidence and positivity rates remained higher in the most deprived areas than the least deprived areas during the second and third national lockdowns, and variation in testing rate was observed according to population density.

INTERPRETATION

Our results highlight a positive social gradient between deprivation and the risk of testing positive for SARS-CoV-2, with the highest risk among individuals living in the most deprived areas and a negative social gradient for testing rate. These findings might reflect structural barriers to health-care access in France and lower capacity of deprived populations to benefit from protective measures.

FUNDING

None.

Inequality in Life and Death

We argue that the COVID epidemic disproportionately affected the economic well-being and health of poor people. To disentangle the forces that generated this outcome, we construct a model that is consistent with the heterogeneous impact of the COVID recession on low- and high-income people. According to our model, two-thirds of the inequality in COVID deaths reflect preexisting inequality in comorbidity rates and access to quality health care. The remaining third stems from the fact that low-income people work in occupations where the risk of infection is high. Our model also implies that the rise in income inequality generated by the COVID epidemic reflects the nature of the goods that low-income people produce. Finally, we assess the health–income trade-offs associated with fiscal transfers to the poor and mandatory containment policies.

Single-cell immunology of SARS-CoV-2 infection

Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.

Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States

Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.

Did COVID-19 change life insurance offerings?

The profitability of life insurance offerings is contingent on accurate projections and pricing of mortality risk. The COVID-19 pandemic created significant uncertainty, with dire mortality predictions from early forecasts resulting in widespread government intervention and greater individual precaution that reduced the projected death toll. We analyze how life insurance companies changed pricing and offerings in response to COVID-19 using monthly data on term life insurance policies from Compulife. We estimate event-study models that exploit well-established variation in the COVID-19 mortality rate based on age and underlying health status. Despite the increase in mortality risk and significant uncertainty, the results generally indicate that life insurance companies did not increase premiums or decrease policy offerings due to COVID-19. Nonetheless, we find some evidence that premiums differentially increased for individuals with very high risk and that some policies were removed for the oldest of the old.

Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs' visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents' responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

COVID-19 among African Americans and Hispanics: Does gastrointestinal symptoms impact the outcome?

BACKGROUND

The coronavirus disease 2019 (COVID-19) disproportionately affected African Americans (AA) and Hispanics (HSP).

AIM

To analyze the significant effectors of outcome in African American patient population and make special emphasis on gastrointestinal (GI) symptoms, laboratory values and comorbidities.

METHODS

We retrospectively evaluated the medical records of 386 COVID-19 positive patients admitted at Howard University Hospital between March and May 2020. We assessed the symptoms, including the GI manifestations, comorbidities, and mortality, using logistic regression analysis.

RESULTS

Of these 386 COVID-19 positive patients, 257 (63.7%) were AAs, 102 (25.3%) HSP, and 26 (6.45%) Whites. There were 257 (63.7%) AA, 102 (25.3%) HSP, 26 (6.45%) Whites. The mean age was 55.6 years (SD = 18.5). However, the mean age of HSP was the lowest (43.7 years vs 61.2 for Whites vs 60 for AAs). The mortality rate was highest among the AAs (20.6%) and lowest among HSP (6.9%). Patients with shortness of breath (SOB) (OR2 = 3.64, CI = 1.73-7.65) and elevated AST (OR2 = 8.01, CI = 3.79-16.9) elevated Procalcitonin (OR2 = 8.27, CI = 3.95-17.3), AST (OR2 = 8.01, CI = 3.79-16.9), ferritin (OR2 = 2.69, CI = 1.24-5.82), and Lymphopenia (OR2 = 2.77, CI = 1.41-5.45) had a high mortality rate. Cough and fever were common but unrelated to the outcome. Hypertension and diabetes mellitus were the most common comorbidities. Glucocorticoid treatment was associated with higher mortality (OR2 = 5.40, CI = 2.72-10.7). Diarrhea was prevalent (18.8%), and GI symptoms did not affect the outcome.

CONCLUSION

African Americans in our study had the highest mortality as they consisted of an older population and comorbidities. Age is the most important factor along with SOB in determining the mortality rate. Overall, elevated liver enzymes, ferritin, procalcitonin and C-reactive protein were associated with poor prognosis. GI symptoms did not affect the outcome. Glucocorticoids should be used judiciously, considering the poor outcomes associated with it. Attention should also be paid to monitor liver function during COVID-19, especially in AA and HSP patients with higher disease severity.

New Jersey COVID-19 municipal dataset

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.

Spatial spillover and COVID-19 spread in the U.S

BACKGROUND

This research estimates the effects of vulnerability on the spread of COVID-19 cases across U.S. counties. Vulnerability factors (Socioeconomic Status, Minority Status & Language, Housing type, Transportation, Household Composition & Disability, Epidemiological Factors, Healthcare system Factors, High-risk Environments, and Population density) do not only influence an individual's likelihood of getting infected but also influence the likelihood of his/her neighbors getting infected. Thus, spatial interactions occurring among individuals are likely to lead to spillover effects which may cause further virus transmission.

METHODS

This research uses the COVID-19 community index (CCVI), which defines communities likely vulnerable to the impact of the pandemic and captures the multi-dimensionality of vulnerability. The spatial Durbin model was used to estimate the spillover effects of vulnerability to COVID-19 in U.S. counties, from May 1 to December 15, 2020.

RESULTS

The findings confirm the existence of spatial spillover effects; with indirect effects (from neighboring counties) dominating the direct effects (from county-own vulnerability level). This not only validates social distancing as a strategy to contain the spread of the pandemic but also calls for comprehensive and coordinated approach to fight its effects. By keeping vulnerability factors constant but varying the number of reported infected cases every 2 weeks, we found that marginal effects of vulnerability vary significantly across counties. This might be the reflection of both the changing intensity of the pandemic itself but also the lack of consistency in the measures implemented to combat it.

CONCLUSION

Overall, the results indicate that high vulnerability in Minority, Epidemiological factors, Healthcare System Factors, and High-Risk Environments in each county and adjacent counties leads to an increase in COVID-19 confirmed cases.

The Lines That Held Us: Assessing Racial and Socioeconomic Disparities in SARS-CoV-2 Testing

BACKGROUND

Racial disparities in SARS-CoV-2 prevalence are apparent. Race is a sociocultural construct, necessitating investigation into how sociocultural factors contribute.

METHODS

This cross-sectional study linked laboratory data of adult patients between February 29 and May 15, 2020 with socio-demographics variables from the 2018 American Community Survey (ACS). Medical sites included healthcare organizations in Michigan, New York, North Carolina, California, Florida, Pennsylvania, and Washington. Race was treated as a proxy for racism and not biological essentialism. Laboratory data included patient age, sex, race, ethnicity, test result, test location, and residential ZIP code. ACS data included economic and educational variables contributing to an SES Index, population density, proportion Medicaid, and racial composition for corresponding ZIP code. Associations between race/socioeconomic variables and test results were examined using odds ratios (OR).

RESULTS

Of 126 452 patients [mean (SD) age 51.9 (18.4) years; 52 747 (41.7%) men; 68 856 (54.5%) White and 27 805 (22.0%) Black], 18 905 (15.0%) tested positive. Of positive tests, 5238 (SD 27.7%) were White and 7223 (SD 38.2%) were Black. Black race increased the odds of a positive test; this finding was consistent across sites [OR 2.11 (95% CI 1.95-2.29)]. When subset by race, higher SES increased the odds of a positive test for White patients [OR 1.10 (95% CI 1.05-1.16)] but decreased the odds for Black patients [OR 0.92 (95% CI 0.86-0.99)]. Black patients, but not White patients, who tested positive overwhelmingly resided in more densely populated areas.

CONCLUSIONS

Black race was associated with SARS-CoV-2 positivity and the relationship between SES and test positivity differed by race, suggesting the impact of socioeconomic status on test positivity is race-specific.

COVID-19, Race/Ethnicity, and Age: the Role of Telemedicine to Close the Gaps on Health Disparities

The novel COVID-19 outbreak is a major public health challenge that quickly turned into an economic recession of great proportions. This pandemic poses a trade-off between health and the economy where social distancing, quarantines, and isolation shut down demand and supply chains across the USA. This paper analyzes the impact of COVID-19 on illness and death among older adults and communities of color with low socioeconomic status in New York City. To achieve this goal, fractional logit models are used to capture changes in the novel virus' morbidity and mortality rates at the neighborhood level. Median income, race/ethnicity, age, household crowding, and socially interactive employment explained the disproportionate exposure and fatalities across the city. We also employ a variable related to telehealth/telemedicine to sustain that technology goods along with government intervention as a provider of social goods can ameliorate existing health disparities. There is a need for evidence-based data on the economic costs and social benefits of COVID-19 relief programs.

Stress and Sleep Across the Onset of the COVID-19 Pandemic: Impact of Distance Learning on U.S. College Students' Health Trajectories

Study Objectives

This study examined associations between average and intraindividual trajectories of stress, sleep duration, and sleep quality in college students before, during, and after transitioning to online learning due to the COVID-19 pandemic.

Methods

One hundred and sixty-four first-year college students answered twice-weekly questionnaires assessing stress exposure and perception, sleep duration, and sleep quality from January until May, 2020 (N=4,269 unique observations).

Results

Multilevel growth modeling revealed that prior to distance learning, student stress was increasing and sleep duration and quality were decreasing. After transitioning online, students’ stress exposure and perception trajectories immediately and continuously decreased; sleep quality initially increased but decreased over time; and sleep duration increased but then plateaued for the remainder of the semester. Days with higher stress exposure than typical for that student were associated with lower sleep quality, and both higher stress exposure and perception at the transition were linked with simultaneous lower sleep quality. Specific groups (e.g., females) were identified as at-risk for stress and sleep problems.

Conclusion

Although transitioning to remote learning initially alleviated college students’ stress and improved sleep, these effects plateaued, and greater exposure to academic, financial, and interpersonal stressors predicted worse sleep quality on both daily and average levels. Environmental stressors may particularly dictate sleep quality during times of transition, but adaptations in learning modalities may help mitigate short-term detrimental health outcomes during global emergencies, even during a developmental period with considerable stress vulnerability. Future studies should examine longer-term implications of these trajectories on mental and physical health.

Pandemics, economic freedom, and institutional trade-offs

We argue that institutions are bundles that involve trade-offs in the government's ability to provide public goods that affect public health. We hypothesize that the institutions underlying economic freedom affect the mix of diseases by reducing diseases of poverty relative to diseases of commerce (those associated with free movement of people, such as smallpox or COVID-19). We focus on smallpox and typhoid fever in the late nineteenth century and early twentieth century in order to build on recent work that make arguments similar to ours, especially the framework Werner Troesken sets forth in The Pox of Liberty. Our evidence shows that economic freedom, in multiple periods of time and settings prior to the eradication of smallpox in the second half of the twentieth century, reduced typhoid mortality but had no effect on smallpox deaths. The implication for COVID-19 is that the trade-off between fighting the pandemic and preserving economic freedom may not be too severe in the short run. However, in the long run, the wealth benefits from economic freedom are likely to be crucial in reducing vulnerability to diseases of commerce primarily from their impact on comorbidities (such as diabetes and heart disease). Thus, economic freedom is on balance good for public health, which suggests that it, while requiring trade-offs, might be the best institutional bundle for dealing with pandemics.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10657-021-09704-7.

The Disparities on Loss of Employment Income by US Households During the COVID-19 Pandemic

This paper examines the role of the COVID-19 pandemic on the loss of employment income on different ethnic groups in the USA using weekly Household Pulse Survey (HPS) data from the US Census Bureau from August 19 to November 9, 2020. This study is significant for two reasons. First, it documents the loss of employment income on various households in the USA during the COVID-19 pandemic period from March 13, 2020, to November 9, 2020. Second, it examines the effect on the different ethnic groups based on demographic and socioeconomic status of these households. We specifically examine the role of income, employment, education, location, access to technology, and health insurance among the different age groups, race/ethnicity, and gender. We employ multivariate logistic regression analysis for the study. The study also employs Oaxaca-Blinder decomposition analysis to investigate the source of disparities in loss of employment income on the different racial/ethnic groups. The multivariate regression examines the effects of income, employment, education, location, health insurance, access to technology, different age groups, race/ethnicity, and gender. This method enables us to estimate the level of differences in loss of employment income outcomes among the various race/ethnic groups based on their socioeconomic status. Our a priori expectation is that loss of employment income and household income, educational status, and employment will be positively correlated. However, we have no a priori expectation of the correlation with location, race/ethnicity, and gender. Our results show that Hispanics, Blacks, Other, and Asians experienced a loss of employment income of 35.6%, 25.3%, 31.2%, and 6.2% higher than Whites, respectively. Equally important is that 45.9%, 40.3%, and 25.2% of the differences are unexplained or attributed to discrimination for Hispanics, Other, and Blacks, respectively.

Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California

With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5 percent White, 39.1 percent Latino, 5.3 percent Black, and 14.4 percent Asian) experienced 59,258 COVID-19 deaths through April 14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October 2020, combining data from 15.4 million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6 percent versus 2.9 percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.

Shifting US Patterns of COVID-19 Mortality by Race and Ethnicity From June-December 2020

OBJECTIVES

The COVID-19 pandemic has disproportionately affected racial and ethnic minorities in the United States and has been devastating for residents of nursing homes (NHs). However, evidence on racial and ethnic disparities in COVID-19-related mortality rates within NHs and how that has changed over time has been limited. This study examines the impact of a high proportion of minority residents in NHs on COVID-19-related mortality rates over a 30-week period.

DESIGN

Longitudinal study.

SETTING AND PARTICIPANTS

Centers for Medicare & Medicaid Services Nursing Home COVID-19 Public Use File data from 50 states from June 1, 2020, to December 27, 2020.

METHODS

We linked data from 11,718 NHs to (1) Nursing Home Compare data, (2) the Long-Term Care: Facts on Care in the U.S., and (3) US county-level data on COVID cases and deaths. Our primary independent variable was proportion of minority residents (blacks and Hispanics) in NHs and its association with mortality rate over time.

RESULTS

During the first 6 weeks from June 1, 2020, NHs with a higher proportion of black residents reported more COVID-19 deaths per 1000 followed by NHs with a higher proportion of Hispanic residents. Between 7 and 12 weeks, NHs with a higher proportion of Hispanic residents reported more deaths per 1000, followed by NHs with a higher proportion of black residents. However, after 23 weeks (mid-November 2020), NHs serving a higher proportion of white residents reported more deaths per 1000 than NHs serving a high proportion of black and Hispanic residents.

CONCLUSIONS AND IMPLICATIONS

The disparities in COVID-19-related mortality for nursing homes serving minority residents is evident for the first 12 weeks of our study period. Policy interventions and the equitable distribution of vaccine are required to mitigate the impact of systemic racial injustice on health outcomes of people of color residing in NHs.

Food Hardship during the COVID-19 Pandemic and Great Recession

I compare the extent of food hardships in the United States among adults and seniors before and during the COVID-19 pandemic. Food insufficiency increased threefold compared to 2019, and more than doubled relative to the Great Recession. Food insufficiency among seniors increased 75% during the COVID period, but more than doubled when including reduced intake of food varieties. Receipt of charitable foods among disadvantaged adults spiked 50% in the COVID period, but the initial response among seniors was a sharp reduction, before rising. These patterns are consistent with strong social distancing measures enacted in response to the pandemic.