A spatial analysis of the effect of neighborhood contexts on cumulative number of confirmed cases of COVID-19 in U.S. Counties through October 20 2020

Area-Level Social Vulnerability and Severe COVID-19: A Case-Control Study Using Electronic Health Records from Multiple Health Systems in the Southeastern Pennsylvania Region

Knowledge about neighborhood characteristics that predict disease burden can be used to guide equity-based public health interventions or targeted social services. We used a case-control design to examine the association between area-level social vulnerability and severe COVID-19 using electronic health records (EHR) from a regional health information hub in the greater Philadelphia region. Severe COVID-19 cases (n = 15,464 unique patients) were defined as those with an inpatient admission and a diagnosis of COVID-19 in 2020. Controls (n = 78,600; 5:1 control-case ratio) were a random sample of individuals who did not have a COVID-19 diagnosis from the same geographic area. Retrospective data on comorbidities and demographic variables were extracted from EHR and linked to area-level social vulnerability index (SVI) data using ZIP codes. Models adjusted for different sets of covariates showed incidence rate ratios (IRR) ranging from 1.15 (95% CI, 1.13-1.17) in the model adjusted for individual-level age, sex, and marital status to 1.09 (95% CI, 1.08-1.11) in the fully adjusted model, which included individual-level comorbidities and race/ethnicity. The fully adjusted model indicates that a 10% higher area-level SVI was associated with a 9% higher risk of severe COVID-19. Individuals in neighborhoods with high social vulnerability were more likely to have severe COVID-19 after accounting for comorbidities and demographic characteristics. Our findings support initiatives incorporating neighborhood-level social determinants of health when planning interventions and allocating resources to mitigate epidemic respiratory diseases, including other coronavirus or influenza viruses.

A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana

The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.

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.

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis

OBJECTIVES

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic.

METHODS

Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well.

RESULTS

The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns.

CONCLUSION

Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

Unveiling Spatial Associations between COVID-19 Severe Health Index, Racial/Ethnic Composition, and Community Factors in the United States

There are limited efforts to incorporate different predisposing factors into prediction models that account for population racial/ethnic composition in exploring the burden of high COVID-19 Severe Health Risk Index (COVID-19 SHRI) scores. This index quantifies the risk of severe COVID-19 symptoms among a county's population depending on the presence of some chronic conditions. These conditions, as identified by the Centers for Disease Control and Prevention (CDC), include Chronic Obstructive Pulmonary Disease (COPD), heart disease, high blood pressure, diabetes, and obesity. Therefore, the objectives of this study were (1) to investigate potential population risk factors preceding the COVID-19 pandemic that are associated with the COVID-19 SHRI utilizing non-spatial regression models and (2) to evaluate the performance of spatial regression models in comparison to non-spatial regression models. The study used county-level data for 3107 United States counties, utilizing publicly available datasets. Analyses were carried out by constructing spatial and non-spatial regression models. Majority White and majority Hispanic counties showed lower COVID-19 SHRI scores when compared to majority Black counties. Counties with an older population, low income, high smoking, high reported insufficient sleep, and a high percentage of preventable hospitalizations had higher COVID-19 SHRI scores. Counties with better health access and internet coverage had lower COVID-19 SHRI scores. This study helped to identify the county-level characteristics of risk populations to help guide resource allocation efforts. Also, the study showed that the spatial regression models outperformed the non-spatial regression models. Racial/ethnic inequalities were associated with disparities in the burden of high COVID-19 SHRI scores. Therefore, addressing these factors is essential to decrease inequalities in health outcomes. This work provides the baseline typology to further explore many social, health, economic, and political factors that contribute to different health outcomes.

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.

Epidemiology and Transmission Dynamics of COVID-19 in an Urban Pediatric US Population

OBJECTIVE

This analysis summarizes observational epidemiologic data and transmission dynamics of SARS-CoV-2 among people aged <18 years to better characterize the pediatric COVID-19 pandemic.

METHODS

We conducted a retrospective study of public health surveillance data among children in Denver, Colorado, who were reported to have COVID-19 from March 1, 2020, through September 30, 2021. We used descriptive statistics and bivariate rate ratios (RRs) to describe demographic and clinical characteristics, transmission dynamics, case trends, and ecological associations.

RESULTS

A total of 9815 children and adolescents who had COVID-19 were reported during the study period. Adolescents aged 14-17 years had the highest incidence rate (IR) per 1000 people (IR = 107.5; 3021 of 28 108). Hispanic/Latino children had a 1.6 times higher rate of infection than non-Hispanic White children (RR = 1.57; 95% CI, 1.50-1.65; P < .001). Few hospitalizations (n = 138, 1.4%) and deaths (n = 3, 0%) occurred. Most children were symptomatic (4487 of 5499, 81.6%). Within household clusters, a large proportion of pediatric cases (n = 6136) were a secondary case (n = 3959, 64.5%), followed by index case (n = 1170, 19.1%) and co-index case (n = 1007, 16.4%). Non-Hispanic White children had an increased risk of being an index or co-index case (RR = 1.14; 95% CI, 1.06-1.23; P < .001), while Hispanic/Latino children had an increased risk of being a secondary case (RR = 1.07; 95% CI, 1.03-1.11; P < .001). From 2020 to 2021, the association between pediatric case rates and neighborhoods with higher poverty and households with ≥3 people decreased.

CONCLUSIONS

Older children and those identifying as Hispanic/Latino had a disproportionate incidence of disease. A sizable proportion of children were considered index cases or co-index cases. Pediatric prevention strategies, especially vaccinations, are vital for pandemic control.

Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil

OBJECTIVES

To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil.

METHODS

Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates.

RESULTS

Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19.

CONCLUSIONS

Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.

Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data

Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.

Determinants of COVID-19 Case Fatality Rate in the United States: Spatial Analysis Over One Year of the Pandemic

Background: The United States continues to account for the highest proportion of the global Coronavirus Disease-2019 (COVID-19) cases and deaths. Currently, it is important to contextualize COVID-19 fatality to guide mitigation efforts. Objectives: The objective of this study was to assess the ecological factors (policy, health behaviors, socio-economic, physical environment, and clinical care) associated with COVID-19 case fatality rate (CFR) in the United States. Methods: Data from the New York Times' COVID-19 repository and the Centers for Disease Control and Prevention Data (01/21/2020 - 02/27/2021) were used. County-level CFR was modeled using the Spatial Durbin model (SDM). The SDM estimates were decomposed into direct and indirect impacts. Results: The study found percent positive for COVID-19 (0.057% point), stringency index (0.014% point), percent diabetic (0.011% point), long-term care beds (log) (0.010% point), premature age-adjusted mortality (log) (0.702 % point), income inequality ratio (0.078% point), social association rate (log) (0.014% point), percent 65 years old and over (0.055% point), and percent African Americans (0.016% point) in a given county were positively associated with its COVID-19 CFR. The study also found food insecurity, long-term beds (log), mental health-care provider (log), workforce in construction, social association rate (log), and percent diabetic of a given county as well as neighboring county were associated with given county's COVID-19 CFR, indicating significant externalities. Conclusion: The spatial models identified percent positive for COVID-19, stringency index, elderly, college education, race/ethnicity, residential segregation, premature mortality, income inequality, workforce composition, and rurality as important ecological determinants of the geographic disparities in COVID-19 CFR.