Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

Evaluation of Rhode Island's Early Geographic COVID-19 Vaccine Prioritization Policy

Objectives. To determine whether geographic prioritization of limited COVID-19 vaccine supply was effective for reducing geographic disparities in case rates. Methods. Rhode Island allocated a portion of the initial COVID-19 vaccine supply to residents of Central Falls, a community already affected by structural policies and inadequate systems that perpetuate health inequities and experiencing disproportionately high COVID-19 morbidity and mortality. The policy was implemented with a culturally and linguistically appropriate community engagement plan and was intended to reduce observed disparities. Using a Bayesian causal analysis with population surveillance data, we evaluated the impact of this prioritization policy on recorded cases over the subsequent 16 weeks. Results. Early geographic prioritization of Central Falls accelerated vaccine uptake, averting an estimated 520 cases (95% confidence interval = 22, 1418) over 16 weeks and reducing cases by approximately 34% during this period (520 averted vs 1519 expected without early prioritization). Conclusions. Early geographic prioritization increased vaccine uptake and reduced cases in Central Falls, thereby reducing geographic disparities. Public Health Implications. Public health institutions should consider geographic prioritization of limited vaccine supply to reduce geographic disparities in case rates. (Am J Public Health. Published online ahead of print August 28, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307741).

Spatial, temporal, and demographic nonstationary dynamics of COVID-19 exposure among older adults in the U.S

This study examines demographic disparities in COVID-19 exposures across older adults age 60-79 and older adults age 80 and over, and explores the factors driving these dynamics in the United States (U.S.) from January 2020 to July 2022. Spatial clusters were identified, and 14 main health determinants were synthesized from 62 pre-existing county-level variables. The study also assessed the correlation between these health determinants and COVID-19 incidence rates for both age groups during the pandemic years. Further examination of incidence rates in relation to health determinants was carried out through statistical and spatial regression models. Results show that individuals aged 80 and over had much higher hospitalization rates, death rates, and case-fatality rates in 2020-2022. Spatial results indicate that the geographical cluster of high incidence rates for both groups shifted from the Midwest at the beginning of the pandemic to the Southwest in 2022. The study revealed marked spatial, temporal, and demographic nonstationary dynamics in COVID-19 exposures, indicating that the health effects of contextual factors vary across age groups. COVID-19 incidence rates in older adults were strongly influenced by race, healthcare access, social capital, environment, household composition, and mobility. Future public health policies and mitigations should further their efforts by considering temporal and demographic nonstationarity as well as local conditions.

Nutritional quality of food purchases during the COVID-19 public health crisis: An analysis of geographic disparities in North Carolina

Objective

To examine geographic disparities in the nutritional quality of food purchases during the COVID-19 public health crisis in North Carolina (NC).

Methods

Using shopper-level longitudinal transaction records between October 2019- and December 2020 from NC's largest grocery retailer, we fit mixed-effect models to examine disparities in the nutritional quality of food purchases among shoppers in counties with different levels of socioeconomic development and how such disparities changed after March 2020, accounting for other observed and contextual factors.

Results

Shoppers in counties with lower development levels purchased a larger share of calories from least healthy foods and a smaller share from healthier foods compared to shoppers in counties with higher development levels. These disparities were slightly attenuated for the least healthy foods and did not change for healthier foods after the onset of the COVID crisis.

Conclusion

Despite existing nutritional disparities among shoppers in counties with different levels of socioeconomic development, we did not observe a large-scale accentuation of inequities in dietary quality during the COVID-19 crisis. This pattern may have resulted from programmatic responses to mitigate the adverse effects of the COVID crisis on vulnerable populations. Future work should further explore the role of such responses.

Engaging Communities in Emerging Infectious Disease Mitigation to Improve Public Health and Safety

The COVID-19 pandemic highlighted the need for potent community-based tools to improve preparedness. We developed a community health-safety climate (HSC) measure to assess readiness to adopt health behaviors during a pandemic. We conducted a mixed-methods study incorporating qualitative methods (e.g., focus groups) to generate items for the measure and quantitative data from a February 2021 national survey to test reliability, multilevel construct, and predictive and nomologic validities. The 20-item HSC measure is unidimensional (Cronbach α = 0.87). All communities had strong health-safety climates but with significant differences between communities (F = 10.65; p<0.001), and HSC levels predicted readiness to adopt health-safety behaviors. HSC strength moderated relationships between HSC level and behavioral indicators; higher climate homogeneity demonstrated stronger correlations. The HSC measure can predict community readiness to adopt health-safety behaviors in communities to inform interventions before diseases spread, providing a valuable tool for public health authorities and policymakers during a pandemic.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005-1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024-1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129-1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009-1.0036], percentage of adults aged 45-64 years old [RR = 1.0031, 95% CI 1.0024-1.0039], and particulate matter ( PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083-1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

Risk factors associated with severe COVID-19 outcomes in Jamaica: a cross-sectional study of national surveillance data

Objectives

To describe the characteristics and outcomes of COVID-19 cases in Jamaica and to explore the risk factors associated with severe COVID-19 from 9 March to 31 December 2020.

Methods

A cross-sectional analysis of national surveillance data was conducted using confirmed COVID-19 cases in Jamaica. Definitions of a confirmed case, disease severity, and death were based on World Health Organization guidelines. Chi-square and Fisher exact tests were used to determine association with outcomes. Logistic regression models were used to determine predictors of severe COVID-19.

Results

This analysis included 12 169 cases of COVID-19 (median age, 36 years; 6 744 females [ 55.4%]) of which 512 cases (4.2%) presented with severe disease, and of those, 318 patients (62.1%) died (median age at death, 71.5 years). Severe disease was associated with being male (OR 1.4; 95% CI, 1.2-1.7) and 40 years or older (OR, 6.5; 95% CI, 5.1-8.2). COVID-19 death was also associated with being male (OR, 1.4; 95% CI, 1.1-1.7), age 40 years or older (OR, 17.9; 95% CI, 11.6-27.7), and in the Western versus South East Health Region (OR 1.7; 95% CI, 1.2-2.3).

Conclusions

The findings of this cross-sectional analysis indicate that confirmed cases of COVID-19 in Jamaica were more likely to be female and younger individuals, whereas COVID-19 deaths occurred more frequently in males and older individuals. There is increased risk of poor COVID-19 outcomes beginning at age 40, with males disproportionately affected. COVID-19 death also varied by geographic region. This evidence could be useful to other countries with similar settings and to policymakers charged with managing outbreaks and health.

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.

Educational Intervention to Increase COVID-19 Vaccine Uptake in Rural Patients with Chronic Diseases: Lessons Learned from An Innovative Academic-Community Partnership

BACKGROUND

The pandemic has disproportionately impacted rural communities with a higher burden of chronic disease and COVID-19 infection. West Virginia is a rural state with a high rate of diabetes, hypertension, and COPD, which are known risk factors for severe COVID-19 and long COVID. Yet, there is a significant hesitancy regarding COVID-19 vaccination uptake in the state. The purpose of this study was to use an educational intervention to increase vaccine knowledge and vaccine acceptance in rural patients with chronic disease(s) in West Virginia. This project used an academic-community partnership comprised of researchers, practitioners, community organizations, community-engaged partners, and patient stakeholders to increase COVID-19 health literacy and increase vaccine acceptance among rural West Virginians with chronic conditions.

MATERIALS AND METHODS

A quasi-experimental study design was used to deliver an educational intervention by trained Health Navigators using short videos to increase COVID-19 health literacy and address participants' vaccine concerns. Eligibility included adults (18 years and older) who have at least one chronic condition. A statewide community advisory board (CAB) guided the development of the educational training curriculum and implementation strategies. An adapted version of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework guided the development of the intervention. Health Navigators (n = 45) delivered the educational intervention in their local communities between November 2022 and October 2023 (project implementation is still ongoing). Intervention fidelity checks, an adaptable script, and a flow chart allowed tailoring of brief videos to address participants' specific COVID-19 questions and vaccine concerns. A validated online survey, monitored by an online Research Electronic Data Capture (REDCap) database, assessed participants' knowledge, perceived susceptibility, and vaccine intention.

RESULTS

Health Navigators delivered the intervention to 1368 West Virginians in 52 counties (59.2% women; 61.8% without a college degree). Participants reported living with an average of 2.1 ± 1.4 chronic conditions. The mean age was 43.5 ± 18.8 years. The majority of participants (81.2%) had received the primary vaccination series, and 63.1% had at least one booster. However, 18% were unvaccinated or did not complete the primary COVID-19 vaccine series. Discussions to improve vaccine literacy focused on how the vaccine was so quickly developed and protects against variants, addressing concerns related to the safety, short- and long-term side effects, and importance of vaccine uptake for immunocompromised individuals. Participants with higher concerns were more likely to be unvaccinated and to have not completed their primary series or boosters (p < 0.001). However, the educational intervention improved the willingness of individuals who were either unvaccinated or did not complete their primary vaccine series to get vaccinated (11.4%).

DISCUSSION

Our findings highlight the importance of vaccine literacy in increasing vaccination rates among rural patients with chronic diseases. Using the EPIS framework allowed us to reflect upon the challenges, ensure resilience during changing local contexts, and plan and implement a promising, cost-effective intervention in rural areas.

CONCLUSIONS

This study provides insights into the need for tailored educational interventions based on disease status, which has implications for public health and patient care in rural and underserved communities. Academic-community partnerships can be useful for successful knowledge transfer for vaccine acceptance to reduce rural health disparities.

Data-driven models for the risk of infection and hospitalization during a pandemic: Case study on COVID-19 in Nepal

The newly emerging pandemic disease often poses unexpected troubles and hazards to the global health system, particularly in low and middle-income countries like Nepal. In this study, we developed mathematical models to estimate the risk of infection and the risk of hospitalization during a pandemic which are critical for allocating resources and planning health policies. We used our models in Nepal's unique data set to explore national and provincial-level risks of infection and risk of hospitalization during the Delta and Omicron surges. Furthermore, we used our model to identify the effectiveness of non-pharmaceutical interventions (NPIs) to mitigate COVID-19 in various groups of people in Nepal. Our analysis shows no significant difference in reproduction numbers in provinces between the Delta and Omicron surge periods, but noticeable inter-provincial disparities in the risk of infection (for example, during Delta (Omicron) surges, the risk of infection of Bagmati province is: ∼ 98.94 (89.62); Madhesh province: ∼ 12.16 (5.1); Karnali province ∼31.16 (3) per hundred thousands). Our estimates show a significantly low level of hospitalization risk during the Omicron surge compared to the Delta surge (hospitalization risk is: ∼10% in Delta and ∼2.5% in Omicron). We also found significant inter-provincial disparities in the hospitalization rate (for example, ∼ 6% in Madhesh province and ∼ 21% in Sudur Paschim) during the Delta surge. Moreover, our results show that closing only schools, colleges, and workplaces reduces the risk of infection by one-third, while a complete lockdown reduces the infections by two-thirds. Our study provides a framework for the computation of the risk of infection and the risk of hospitalization and offers helpful information for controlling the pandemic.

Using wastewater to overcome health disparities among rural residents

The SARS-CoV-2 pandemic highlighted the need for novel tools to promote health equity. There has been a historical legacy around the location and allocation of public facilities (such as health care) focused on efficiency, which is not attainable in rural, low-density, United States areas. Differences in the spread of the disease and outcomes of infections have been observed between urban and rural populations throughout the COVID-19 pandemic. The purpose of this article was to review rural health disparities related to the SARS-CoV-2 pandemic while using evidence to support wastewater surveillance as a potentially innovative tool to address these disparities more widely. The successful implementation of wastewater surveillance in resource-limited settings in South Africa demonstrates the ability to monitor disease in underserved areas. A better surveillance model of disease detection among rural residents will overcome issues around the interactions of a disease and social determinants of health. Wastewater surveillance can be used to promote health equity, particularly in rural and resource-limited areas, and has the potential to identify future global outbreaks of endemic and pandemic viruses.

Racial residential segregation and COVID-19 vaccine uptake: an analysis of Georgia USA county-level data

BACKGROUND

Foundational literature demonstrates that racial residential segregation results in poorer health outcomes for Black people than white people due to a variety of social determinants of health. COVID-19 vaccine uptake is important for better health outcomes, regardless of race. The COVID-19 pandemic has elevated concerns about racial health disparities but with little discussion of racial residential segregation as a predictor of disparate health outcomes. This paper investigates the relationship between racial residential segregation and COVID-19 vaccine uptake using county level data from the State of Georgia (USA).

METHODS

Using publicly available data, regression analysis is conducted for 138 of the 159 counties in Georgia USA, using a dissimilarity index that describes county level differences in racial residential segregation. The primary independent variable is Black-white differences in vaccine uptake at the county level. The analytic methods focus on a spatial analysis to support information for county level health departments as the basis for health policy and resource allocation.

RESULTS

Constructing a variable of the difference in vaccination rates between Black and white residents we find that Black-white differences in COVID-19 vaccination are most notable in the 69 most segregated of the 159 counties in Georgia. A ten-point lower segregation index is associated with an improvement in the Black-white vaccination gap of 1.5 percentage points (95% CI -0.31, -0.00). Income inequality and access to health care resources, such as access to a primary care physician, also predict Black-white differences in vaccination rates at the county level. Suggested mapping approaches of publicly available data at a state county level, provides a resource for local policy makers to address future challenges for epidemic and pandemic situations.

CONCLUSION

County level and geospatial data analysis can inform policy makers addressing the impact of racial residential segregation on local health outcomes, even for pandemic and epidemic issues.

Age-based spatial disparities of COVID-19 incidence rates in the United States counties

COVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and community), various contextual variables, multiple COVID-19 outcomes, and different geographic contextual elements. The model assumes the existence of age nonstationarity effects on health determinants, suggesting that health effects of contextual variables vary among place and age groups. Based on this conceptual model and theory, the study selected 62 county-level variables for 1,748 U.S. counties during the pandemic, and created an Adjustable COVID-19 Potential Exposure Index (ACOVIDPEI) using principal component analysis (PCA). The validation was done with 71,521,009 COVID-19 patients in the U.S. from January 2020 through June 2022, with high incidence rates shifting from the Midwest, South Carolina, North Carolina, Arizona, and Tennessee to the West and East coasts. This study corroborates the age nonstationarity effect of health determinants on COVID-19 exposures. These results empirically identify the geographic disparities of COVID-19 incidence rates among age groups and provide the evidentiary guide for targeting pandemic recovery, mitigation, and preparedness in communities.

Did COVID-19 enlarge spatial disparities in population dynamics? A comparative, multivariate approach for Italy

A short-term issue that has been occasionally investigated in the current literature is if (and, eventually, how) population dynamics (directly or indirectly) driven by COVID-19 pandemic have contributed to enlarge regional divides in specific demographic processes and dimensions. To verify this assumption, our study run an exploratory multivariate analysis of ten indicators representative of different demographic phenomena (fertility, mortality, nuptiality, internal and international migration) and the related population outcomes (natural balance, migration balance, total growth). We developed a descriptive analysis of the statistical distribution of the ten demographic indicators using eight metrics that assess formation (and consolidation) of spatial divides, controlling for shifts over time in both central tendency, dispersion, and distributional shape regimes. All indicators were made available over 20 years (2002-2021) at a relatively detailed spatial scale (107 NUTS-3 provinces) in Italy. COVID-19 pandemic exerted an impact on Italian population because of intrinsic (e.g. a particularly older population age structure compared with other advanced economies) and extrinsic (e.g. the early start of the pandemic spread compared with the neighboring European countries) factors. For such reasons, Italy may represent a sort of 'worst' demographic scenario for other countries affected by COVID-19 and the results of this empirical study can be informative when delineating policy measures (with both economic and social impact) able to mitigate the effect of pandemics on demographic balance and improve the adaptation capacity of local societies to future pandemic's crises.

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

Higher hospitalization and mortality rates among SARS-CoV-2-infected persons in rural America

PURPOSE

Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality.

METHODS

This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression.

FINDINGS

Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients.

CONCLUSIONS

Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

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).

Global variations in the burden of SARS-CoV-2 infection and its outcomes in pregnant women by geographical region and country’s income status: a meta-analysis

Introduction

The prevalence of COVID-19 and its impact varied between countries and regions. Pregnant women are at high risk of COVID-19 complications compared with non-pregnant women. The magnitude of variations, if any, in SARS-CoV-2 infection rates and its health outcomes among pregnant women by geographical regions and country’s income level is not known.

Methods

We performed a random-effects meta-analysis as part of the ongoing PregCOV-19 living systematic review (December 2019 to April 2021). We included cohort studies on pregnant women with COVID-19 reporting maternal (mortality, intensive care admission and preterm birth) and offspring (mortality, stillbirth, neonatal intensive care admission) outcomes and grouped them by World Bank geographical region and income level. We reported results as proportions with 95% confidence intervals (CI).

Results

We included 311 studies (2 003 724 pregnant women, 57 countries). The rates of SARS-CoV-2 infection in pregnant women varied significantly by region (p<0.001) and income level (p<0.001), with the highest rates observed in Latin America and the Caribbean (19%, 95% CI 12% to 27%; 13 studies, 38 748 women) and lower-middle-income countries (13%, 95% CI 6% to 23%; 25 studies, 100 080 women). We found significant differences in maternal and offspring outcomes by region and income level. Lower-middle-income countries reported significantly higher rates of maternal mortality (0.68%, 95% CI 0.24% to 1.27%; 3 studies, 31 136 women), intensive care admission (4.53%, 95% CI 2.57% to 6.91%; 54 studies, 23 420 women) and stillbirths (1.09%, 95% CI 0.48% to 1.88%; 41 studies, 4724 women) than high-income countries. COVID-19 complications disproportionately affected South Asia, which had the highest maternal mortality rate (0.88%, 95% CI 0.16% to 1.95%; 17 studies, 2023 women); Latin America and the Caribbean had the highest stillbirth rates (1.97%, 95% CI 0.9% to 3.33%; 10 studies, 1750 women).

Conclusion

The rates of SARS-CoV-2 infection in pregnant women vary globally, and its health outcomes mirror the COVID-19 burden and global maternal and offspring inequalities.

PROSPERO registration number

CRD42020178076.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

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.

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.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization

Background

Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR).

Methods

In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software.

Results

During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males.

Conclusions

A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13649-0.

Medical avoidance among marginalized groups: the impact of the COVID-19 pandemic

Medical avoidance is common among U.S. adults, and may be emphasized among members of marginalized communities due to discrimination concerns. In the current study, we investigated whether this disparity in avoidance was maintained or exacerbated during the onset of the COVID-19 pandemic. We assessed the likelihood of avoiding medical care due to general-, discrimination-, and COVID-19-related concerns in an online sample (N = 471). As hypothesized, marginalized groups (i.e., non-White race, Latinx/e ethnicity, non-heterosexual sexual orientation, high BMI) endorsed more general- and discrimination-related medical avoidance than majoritized groups. However, marginalized groups were equally likely to seek COVID-19 treatment as majoritized groups. Implications for reducing medical avoidance among marginalized groups are discussed.

Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging three-dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy-makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.

Spatial-temporal differences of COVID-19 vaccinations in the U.S

Although the disparities in COVID-19 outcomes have been proved, they have not been explicitly associated with COVID-19 full vaccinations. This paper examines the spatial and temporal patterns of the county-level COVID-19 case rates, fatality rates, and full vaccination rates in the United States from December 24, 2020 through September 30, 2021. Statistical and geospatial analyses show clear temporal and spatial patterns of the progression of COVID-19 outcomes and vaccinations. In the relationship between two time series, the fatality rates series was positively related to past lags of the case rates series. At the same time, case rates series and fatality rates series were negatively related to past lags of the full vaccination rates series. The lag level varies across urban and rural areas. The results of partial correlation, ordinary least squares (OLS) and Geographically Weighted Regression (GWR) also confirmed that the existing COVID-19 infections and different sets of socioeconomic, healthcare access, health conditions, and environmental characteristics were independently associated with COVID-19 vaccinations over time and space. These results empirically identify the geographic health disparities with COVID-19 vaccinations and outcomes and provide the evidentiary basis for targeting pandemic recovery and public health mitigation actions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44212-022-00019-9.