The Association Between Neighborhood Social Vulnerability and COVID-19 Testing, Positivity, and Incidence in Alabama and Louisiana

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.

Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 - 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.

Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data

BACKGROUND

COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic.

METHODS

Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC.

RESULTS

As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (β = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (β = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (β: -1.19 and -0.42; p-value is < 0.05 for both).

CONCLUSIONS

County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

Adoption of a National Prophylactic Anticoagulation Guideline for Hospitalized Pregnant Women With COVID-19: Retrospective Cohort Study

BACKGROUND

Both COVID-19 and pregnancy are associated with hypercoagulability. Due to the increased risk for thrombosis, the United States National Institute of Health's recommendation for prophylactic anticoagulant use for pregnant patients has expanded from patients hospitalized for severe COVID-19 manifestation to all patients hospitalized for the manifestation of COVID-19 (no guideline: before December 26, 2020; first update: December 27, 2022; second update: February 24, 2022-present). However, no study has evaluated this recommendation.

OBJECTIVE

The objective of this study was to characterize prophylactic anticoagulant use among hospitalized pregnant people with COVID-19 from March 20, 2020, to October 19, 2022.

METHODS

This was a retrospective cohort study in large US health care systems across 7 states. The cohort of interest was pregnant patients who were hospitalized with COVID-19, without previous coagulopathy or contraindication to anticoagulants (n=2767). The treatment group consisted of patients prescribed prophylactic dose anticoagulation between 2 days before and 14 days after COVID-19 treatment onset (n=191). The control group was patients with no anticoagulant exposure between 14 days before and 60 days after COVID-19 treatment onset (n=2534). We ascertained the use of prophylactic anticoagulants with attention to the updates in guidelines and emerging SARS-CoV-2 variants. We propensity score matched the treatment and control group 1:1 on the most important features contributing to the prophylactic anticoagulant administration status classification. Outcome measures included coagulopathy, bleeding, COVID-19-related complications, and maternal-fetal health outcomes. Additionally, the inpatient anticoagulant administration rate was validated in a nationwide population from Truveta, a collective of 700 hospitals across the United States.

RESULTS

The overall administration rate of prophylactic anticoagulants was 7% (191/2725). It was lowest after the second guideline update (no guideline: 27/262, 10%; first update: 145/1663, 8.72%; second update: 19/811, 2.3%; P<.001) and during the omicron-dominant period (Wild type: 45/549, 8.2%; Alpha: 18/129, 14%; Delta: 81/507, 16%; and Omicron: 47/1551, 3%; P<.001). Models developed on retrospective data showed that the variable most associated with the administration of inpatient prophylactic anticoagulant was comorbidities prior to SARS-CoV-2 infection. The patients who were administered prophylactic anticoagulant were also more likely to receive supplementary oxygen (57/191, 30% vs 9/188, 5%; P<.001). There was no statistical difference in a new diagnosis of coagulopathy, bleeding, or maternal-fetal health outcomes between those who received treatment and the matched control group.

CONCLUSIONS

Most hospitalized pregnant patients with COVID-19 did not receive prophylactic anticoagulants across health care systems as recommended by guidelines. Guideline-recommended treatment was administered more frequently to patients with greater COVID-19 illness severity. Given the low rate of administration and differences between treated and untreated cohorts, efficacy could not be assessed.

Experiences of Health Departments on Community Engagement and Implementation of a COVID-19 Self-testing Program

CONTEXT

Health departments (HDs) work on the front lines to ensure the health of their communities, providing a unique perspective to public health response activities. Say Yes! COVID Test (SYCT) is a US federally funded program providing free COVID-19 self-tests to communities with high COVID-19 transmission, low vaccination rates, and high social vulnerability. The collaboration with 9 HDs was key for the program distribution of 5.8 million COVID-19 self-tests between March 31 and November 30, 2021.

OBJECTIVE

The objective of this study was to gather qualitative in-depth information on the experiences of HDs with the SYCT program to better understand the successes and barriers to implementing community-focused self-testing programs.

DESIGN

Key informant (KI) interviews.

SETTING

Online interviews conducted between November and December 2021.

PARTICIPANTS

Sixteen program leads representing 9 HDs were purposefully sampled as KIs. KIs completed 60-minute structured interviews conducted by one trained facilitator and recorded.

MAIN OUTCOME MEASURES

Key themes and lessons learned were identified using grounded theory.

RESULTS

Based on perceptions of KIs, HDs that maximized community partnerships for test distribution were more certain that populations at a higher risk for COVID-19 were reached. Where the HD relied predominantly on direct-to-consumer distribution, KIs were less certain that communities at higher risk were served. Privacy and anonymity in testing were themes linked to higher perceived community acceptance. KIs reported that self-test demand and distribution levels increased during higher COVID-19 transmission levels.

CONCLUSION

HDs that build bridges and engage with community partners and trusted leaders are better prepared to identify and link high-risk populations with health services and resources. When collaborating with trusted community organizations, KIs perceived that the SYCT program overcame barriers such as mistrust of government intervention and desire for privacy and motivated community members to utilize this resource to protect themselves against COVID-19.

Socioeconomic disparities and concentration of the spread of the COVID-19 pandemic in the province of Quebec, Canada

BACKGROUND

Recent studies suggest that the risk of SARS-CoV-2 infection may be greater in more densely populated areas and in cities with a higher proportion of persons who are poor, immigrant, or essential workers. This study examines spatial inequalities in SARS-CoV-2 exposure in a health region of the province of Quebec in Canada.

METHODS

The study was conducted on the 1206 Canadian census dissemination areas in the Capitale-Nationale region of the province of Quebec. The observation period was 21 months (March 2020 to November 2021). The number of cases reported daily in each dissemination area was identified from available administrative databases. The magnitude of inequalities was estimated using Gini and Foster-Greer-Thorbecke (FGT) indices. The association between transmission and socioeconomic deprivation was identified based on the concentration of transmission in socially disadvantaged areas and on nonparametric regressions relating the cumulative incidence rate by area to ecological indicators of spatial disadvantage. Quantification of the association between median family income and degree of exposure of dissemination areas was supplemented by an ordered probit multiple regression model.

RESULTS

Spatial disparities were elevated (Gini = 0.265; 95% CI [0.251, 0.279]). The spread was more limited in the less densely populated areas of the Quebec City agglomeration and outlying municipalities. The mean cumulative incidence in the subsample made up of the areas most exposed to the pandemic was 0.093. The spread of the epidemic was concentrated in the most disadvantaged areas, especially in the densely populated areas. Socioeconomic inequality appeared early and increased with each successive pandemic wave. The models showed that areas with economically disadvantaged populations were three times more likely to be among the areas at highest risk for COVID-19 (RR = 3.55; 95% CI [2.02, 5.08]). In contrast, areas with a higher income population (fifth quintile) were two times less likely to be among the most exposed areas (RR = 0.52; 95% CI [0.32, 0.72]).

CONCLUSION

As with the H1N1 pandemics of 1918 and 2009, the SARS-CoV-2 pandemic revealed social vulnerabilities. Further research is needed to explore the various manifestations of social inequality in relation to the pandemic.

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.

Place, Race, and Case: Examining Racialized Economic Segregation and COVID-19 in Louisiana

Early COVID-19 pandemic data suggested racial/ethnic minority and low-income earning people bore the greatest burden of infection. Structural racism, the reinforcement of racial and ethnic discrimination via policy, provides a framework for understanding disparities in health outcomes like COVID-19 infection. Residential racial and economic segregation is one indicator of structural racism. Little attention has been paid to the relationship of infection to relative overall concentrations of risk (i.e., segregation of the most privileged from the most disadvantaged). We used ordinary least squares and geographically weighted regression models to evaluate the relationship between racial and economic segregation, measured by the Index of Concentration at the Extremes, and COVID-19 cases in Louisiana. We found a significant global association between racial segregation and cumulative COVID-19 case rate in Louisiana and variation across the state during the study period. The northwest and central regions exhibited a strong negative relationship indicating greater risk in areas with high concentrations of Black residents. On the other hand, the southeastern part of the state exhibited more neutral or positive relationships indicating greater risk in areas with high concentrations of White residents. Our findings that the relationship between racial segregation and COVID-19 cases varied within a state further support evidence that social and political determinants, not biological, drive racial disparities. Small area measures and measures of polarization provide localized information better suited to tailoring public health policy according to the dynamics of communities at the census tract level, which may lead to better health outcomes.

Inclusion of Race and Ethnicity With Neighborhood Socioeconomic Deprivation When Assessing COVID-19 Hospitalization Risk Among California Veterans Health Administration Users

IMPORTANCE

Despite complexities of racial and ethnic residential segregation (hereinafter referred to as segregation) and neighborhood socioeconomic deprivation, public health studies, including those on COVID-19 racial and ethnic disparities, often rely on composite neighborhood indices that do not account for residential segregation.

OBJECTIVE

To examine the associations by race and ethnicity among California's Healthy Places Index (HPI), Black and Hispanic segregation, Social Vulnerability Index (SVI), and COVID-19-related hospitalization.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included veterans with positive test results for COVID-19 living in California who used Veterans Health Administration services between March 1, 2020, and October 31, 2021.

MAIN OUTCOMES AND MEASURES

Rates of COVID-19-related hospitalization among veterans with COVID-19.

RESULTS

The sample available for analysis included 19 495 veterans with COVID-19 (mean [SD] age, 57.21 [17.68] years), of whom 91.0% were men, 27.7% were Hispanic, 16.1% were non-Hispanic Black, and 45.0% were non-Hispanic White. For Black veterans, living in lower-HPI (ie, less healthy) neighborhoods was associated with higher rates of hospitalization (odds ratio [OR], 1.07 [95% CI, 1.03-1.12]), even after accounting for Black segregation (OR, 1.06 [95% CI, 1.02-1.11]). Among Hispanic veterans, living in lower-HPI neighborhoods was not associated with hospitalization with (OR, 1.04 [95% CI, 0.99-1.09]) and without (OR, 1.03 [95% CI, 1.00-1.08]) Hispanic segregation adjustment. For non-Hispanic White veterans, lower HPI was associated with more frequent hospitalization (OR, 1.03 [95% CI, 1.00-1.06]). The HPI was no longer associated with hospitalization after accounting for Black (OR, 1.02 [95% CI, 0.99-1.05]) or Hispanic (OR, 0.98 [95% CI, 0.95-1.02]) segregation. Hospitalization was higher for White (OR, 4.42 [95% CI, 1.62-12.08]) and Hispanic (OR, 2.90 [95% CI, 1.02-8.23]) veterans living in neighborhoods with greater Black segregation and for White veterans in more Hispanic-segregated neighborhoods (OR, 2.81 [95% CI, 1.96-4.03]), adjusting for HPI. Living in higher SVI (ie, more vulnerable) neighborhoods was associated with greater hospitalization for Black (OR, 1.06 [95% CI, 1.02-1.10]) and non-Hispanic White (OR, 1.04 [95% CI, 1.01-1.06]) veterans.

CONCLUSIONS AND RELEVANCE

In this cohort study of US veterans with COVID-19, HPI captured neighborhood-level risk for COVID-19-related hospitalization for Black, Hispanic, and White veterans comparably with SVI. These findings have implications for the use of HPI and other composite neighborhood deprivation indices that do not explicitly account for segregation. Understanding associations between place and health requires ensuring composite measures accurately account for multiple aspects of neighborhood deprivation and, importantly, variation by race and ethnicity.

A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran

Social vulnerability and society's resilience are two concepts frequently used to examine the capacity of social systems to prepare, absorb, and adapt to environmental hazards and shocks. With the emergence of the COVID-19 pandemic, the role of social vulnerability in dealing with risks has gained renewed attention. Assessing social vulnerability can help managers and planners prioritize budgets, develop prevention programs, and enhance risk preparedness. This study aimed to determine the association between social vulnerability and COVID-19 in the neighborhoods of Ahvaz, Iran. To assess the social vulnerability of Ahvaz neighborhoods, decision-making techniques (best-worst method and weighted aggregated sum product assessment method) and geographic information systems were applied. Moreover, to investigate the relationship between social vulnerability and COVID-19 cases, the Pearson correlation test was used. The results showed that the '20-meteri shahrdari' neighborhood has the highest level of social vulnerability, and the lowest level of social vulnerability among the neighborhoods of Ahvaz belongs to the neighborhood of 'Shahrak Naft'. There is a low inverse association between the integrated index of social vulnerability and the incidence of COVID-19 per 1000 people in Ahvaz. By revealing the most important details at the neighborhood level and levels of vulnerability, the results can inform effective planning actions at the neighborhood level.

A county-level analysis of association between social vulnerability and COVID-19 cases in Khuzestan Province, Iran

Social vulnerability is related to the differential abilities of socio-economic groups to withstand and respond to the adverse impacts of hazards and stressors. COVID-19, as a human risk, is influenced by and contributes to social vulnerability. The purpose of this study was to examine the association between social vulnerability and the prevalence of COVID-19 infection in the counties of Khuzestan province, Iran. To determine the social vulnerability of the counties in the Khuzestan province, decision-making techniques and geographic information systems were employed. Also, the Pearson correlation was used to examine the relationship between the two variables. The findings indicate that Ahvaz county and the province's northeastern counties have the highest levels of social vulnerability. There was no significant link between the social vulnerability index of the counties and the rate of COVID-19 cases (per 1000 persons). We argue that all counties in the province should implement and pursue COVID-19 control programs and policies. This is particularly essential for counties with greater rates of social vulnerability and COVID-19 cases.

Community vulnerability to the COVID-19 pandemic: A narrative synthesis from an ecological perspective

Background

We aimed to conduct a narrative synthesis of components and indicators of community vulnerability to a pandemic and discuss their interrelationships from an ecological perspective.

Methods

We searched from PubMed, Embase, Web of Science, PsycINFO, and Scopus (updated to November 2021) for studies focusing on community vulnerability to a pandemic caused by novel respiratory viruses on a geographic unit basis . Studies that reported the associations of community vulnerability levels with at least one disease morbidity or mortality outcome were included.

Results

Forty-one studies were included. All were about the COVID-19 pandemic. Suitable temperature and humidity environments, advanced social and human development (including high population density and human mobility, connectivity, and occupations), and settings that intensified physical interactions are important indicators of vulnerability to viral exposure. However, the eventual pandemic health impacts are predominant in communities that faced environmental pollution, higher proportions of socioeconomically deprived people, health deprivation, higher proportions of poor-condition households, limited access to preventive health care and urban infrastructure, uneven social and human development, and racism. More stringent social distancing policies were associated with lower COVID-19 morbidity and mortality only in the early pandemic phases. Prolonged social distancing policies can disproportionately burden the socially disadvantaged and racially/ethnically marginalized groups.

Conclusions

Community vulnerability to a pandemic is foremost the vulnerability of the ecological systems shaped by complex interactions between the human and environmental systems.

Registration

PROSPERO (CRD42021266186).

Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.

Association between social vulnerability and COVID-19 vaccination hesitancy and vaccination in pregnant and postpartum individuals

OBJECTIVE

To evaluate the association of community-level social vulnerability with COVID-19 vaccine hesitancy and vaccination among pregnant and postpartum individuals.

METHODS

Prospective cohort study assessing COVID-19 vaccine hesitancy among pregnant and postpartum individuals. We performed a baseline survey on COVID-19 vaccine hesitancy from 03/22/21 to 04/02/21, and a follow-up survey on COVD-19 vaccination status 3- to 6-months later. The primary exposure was the Centers for Disease Control and Prevention SVI (Social Vulnerability Index), measured in quartiles. Higher SVI quartiles indicated greater community-level social vulnerability with the lowest quartile (quartile 1) as the referent group. The primary outcome was COVID-19 vaccine hesitancy on the baseline survey (uncertainty or refusal of the vaccine), and the secondary outcome was self-report of not being vaccinated (unvaccinated) for COVID-19 on the follow-up survey.

RESULTS

Of 456 assessed individuals, 46% reported COVID-19 vaccine hesitancy on the baseline survey; and of 290 individuals (290/456, 64%) who completed the follow-up survey, 48% (140/290) were unvaccinated. The frequency of baseline vaccine hesitancy ranged from 25% in quartile 1 (low SVI) to 68% in quartile 4 (high SVI), and being unvaccinated at follow-up ranged from 29% in quartile 1 to 77% in quartile 4. As social vulnerability increased, the risk of COVID-19 vaccine hesitancy at baseline increased (quartile 2 aRR (adjusted relative risk): 1.46; 95% CI:0.98 to 2.19; quartile 3 aRR: 1.86; 95% CI:1.28 to 2.71; and quartile 4 aRR: 2.24; 95% CI:1.56 to 3.21), as did the risk of being unvaccinated at follow-up (quartile 2 aRR: 1.00; 95% CI:0.66 to 1.51; quartile 3 aRR: 1.68; 95% CI:1.17 to 2.41; and quartile 4 aRR: 1.82; 95% CI:1.30 to 2.56).

CONCLUSIONS

Pregnant and postpartum individuals living in an area with higher community-level social vulnerability were more likely to report COVID-19 vaccine hesitancy and subsequently to be unvaccinated at follow-up.

Exploring spatial distribution of social vulnerability and its relationship with the Coronavirus disease 2019: the Capital region of South Korea

Background & objective

The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to cause death and socioeconomic problems worldwide. This study examined the spatial distribution of social vulnerability to COVID-19 and its relationship with the number of confirmed COVID-19 cases in 2020, focusing on the Capital region of South Korea.

Methods

A traditional social vulnerability index (SVI), healthy SVI, and the difference of each SVI were constructed in 2015 and 2019. The traditional SVI was constructed across five domains: age, socioeconomic disadvantage, housing, income, and environment. The healthy SVI domains were: prevention, health-related habits, chronic disease, healthcare infrastructure, and mortality. The spatial distribution of the traditional SVI, healthy SVI, and confirmed cases of COVID-19 was explored using ArcGIS 10.5. Pearson correlation was used to identify the relationship between confirmed COVID-19 cases and the two SVIs and their changes between 2015 and 2019. Four multiple linear regression models were used to identify the impact of the changes of the two SVIs on the confirmed COVID-19 cases for the three episodes and total period with control of population using STATA/MP 16.1.

Results

Confirmed COVID-19 cases were concentrated in a specific area of the Capital region. The traditional SVI was more vulnerable in the outer regions of the Capital region, and some central, western, and eastern areas reflected an increase in vulnerability. Healthy SVI was more vulnerable in the northern part of the Capital region, and increase in vulnerability showed in some central areas above Seoul. By multiple regression with the population controlled, the difference of the traditional SVI between 2015 and 2019 showed a positive relationship with the confirmed COVID-19 cases in all models at a significance level of 0.05, and the 2019 integrated SVI showed a negative relationship with confirmed COVID-19 cases in all models.

Conclusions

The results of this study showed that the confirmed COVID-19 cases are associated with increased traditional SVI vulnerability between 2015 and 2019 and have a high positive relationship with the spread of COVID-19. Policy efforts are needed to reduce confirmed COVID-19 cases among the vulnerable in regions with relatively increased traditional SVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14212-7.

What Makes Urban Communities More Resilient to COVID-19? A Systematic Review of Current Evidence

It has been more than two years since the outbreak of the COVID-19 epidemic at the end of 2019. Many scholars have introduced the "resilience" concept into COVID-19 prevention and control to make up for the deficiencies in traditional community governance. This study analyzed the progress in research on social resilience, which is an important component of community resilience, focusing on the current literature on the impact of social resilience on COVID-19, and proposed a generalized dimension to integrated previous relevant literature. Then, VOSviewer was used to visualize and analyze the current progress of research on social resilience. The PRISMA method was used to collate studies on social resilience to the pandemic. The result showed that many current policies are effective in controlling COVID-19, but some key factors, such as vulnerable groups, social assistance, and socioeconomics, affect proper social functioning. Some scholars have proposed effective solutions to improve social resilience, such as establishing an assessment framework, identifying priority inoculation groups, and improving access to technology and cultural communication. Social resilience to COVID-19 can be enhanced by both external interventions and internal regulation. Social resilience requires these two aspects to be coordinated to strengthen community and urban pandemic resilience.

Disaggregating disparities: A case study of heterogenous COVID-19 disparities across waves, geographies, social vulnerability, and political lean in Louisiana

While the first wave of COVID-19 primarily impacted urban areas, subsequent waves were more widespread. Most analysis of Covid-19 rates examine state or metropolitan areas, ignoring potential heterogeneity within states and metro areas, over time, and between populations with differing contextual and compositional features. In this study, we compare spatial and temporal trends in Covid-19 cases and deaths in Louisiana, USA, over time and across populations and geographies (New Orleans, other urban areas, suburban, rural) and parish-level political lean. We employ publicly available longitudinal census tract and parish-level Covid-19 data reported from February 27th, 2020 to October 27th, 2021. We find that incidence and mortality rates were initially highest in New Orleans and Democratic areas and higher in other geographies and more conservative areas during subsequent waves. We also find wide relative disparities during the first wave, where increased social vulnerability was associated with increased positivity and incidence across geographies and political contexts. However, relative disparities diverged by geography and political lean and outcome across the remaining waves. This work draws attention to the differential rates of Covid-19 cases and deaths by geography, time, and population throughout the pandemic, and importance of political and geographic boundaries for rates of Covid-19.

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes

OBJECTIVE

This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.

METHODS

Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.

RESULTS

A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).

CONCLUSIONS

Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

Optimization of a new adaptive intervention using the SMART Design to increase COVID-19 testing among people at high risk in an urban community

Background

COVID-19 has impacted the health and social fabric of individuals and families living across the USA, and it has disproportionately affected people living in urban communities with co-morbidities, those working in high-risk settings, refusing or unable to adhere to CDC guidelines, and more. Social determinants of health (SDH), such as stigmatization, incarceration, and poverty, have been associated with increased exposure to COVID-19 and increased deaths. While vaccines and booster shots are available, it will take time to reach herd immunity, and it is unclear how long newly developed vaccines provide protection and how effective they are against emerging variants. Therefore, prevention methods recommended by the Centers for Disease and Control (CDC)—i.e., testing, hand-washing, social distancing, contact tracing, vaccination and booster shots, and quarantine—are essential to reduce the rates of COVID-19 in marginalized communities. This project will adapt and test evidence-based HIV interventions along the prevention and treatment cascade to help address COVID-19 prevention needs.

Methods

The study aims to (1) optimize an adaptive intervention that will increase rates of testing and adherence to New Jersey State COVID-19 recommendations (testing, social distancing, quarantine, hospitalization, contact tracing, and acceptance of COVID-19 vaccination and booster shots) among high-risk populations and (2) identify predictors of testing completion and adherence to New Jersey recommendations. This study follows Community Based Participatory Research (CBPR) principles to conduct a Sequential, Multiple Assignment Randomized Trial (SMART) with 670 COVID-19 medically/socially vulnerable people. Participants will be recruited using a variety of strategies including advertisements on social media, posting fliers in public places, street outreach, facility-based, and snowball sampling. Participants complete a baseline survey and are randomized to receive navigation services or an electronic brochure. They then complete a follow-up 7 days after baseline and are randomized again to either continue with their original assignment or switch to the other intervention or critical dialog or brief counseling. Participants then complete a 5-week post-baseline follow-up. Guided by the COVID-19 Continuum of Prevention, Care, and Treatment, the analysis will explore the factors associated with COVID-19 testing within 7 days of the intervention.

Discussion

This paper describes the protocol of the first study to use SMART following CBPR to adapt evidence-based HIV prevention interventions to COVID-19. The findings will inform the development of an effective and scalable adaptive intervention to increase COVID-19 testing and adherence to public health recommendations, including vaccination and booster shots, among a marginalized and difficult-to-engage population.

Trial registration

ClinicalTrials.govNCT04757298. Registered on February 17, 2021.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal-geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.