Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Inequality in COVID-19 mortality in Quebec associated with neighbourhood-level vulnerability domains

OBJECTIVES

We measured disparities in COVID-19 mortality associated with increasing vulnerability to severe outcomes of infectious disease at the neighbourhood level to identify domains for prioritization of public interventions.

METHODS

In this retrospective ecological study, we calculated COVID-19 mortality rate ratios (RR) comparing neighbourhoods with the greatest vulnerability relative to lowest vulnerability using the five domains from the COVID-19 vulnerability index for Quebec using hospital data from the first year of the pandemic and vulnerability levels from 13,182 neighbourhoods. We estimated the attributable fraction to assess disparities in COVID-19 mortality associated with vulnerability. Domains covered biological susceptibility, sociocultural characteristics, socioeconomic characteristics, and indoor and outdoor risk factors for exposure to SARS-CoV-2.

RESULTS

Vulnerable neighbourhoods accounted for 60.7% of COVID-19 deaths between March 2020 and February 2021. Neighbourhoods with biological susceptibility accounted for 46.1% and indoor exposure for 44.6% of deaths. Neighbourhoods with socioeconomic vulnerability experienced 23.5%, outdoor exposure 14.6%, and sociocultural vulnerability 9.0% of deaths. Neighbourhoods with high relative vulnerability had 4.66 times greater risk of COVID-19 mortality compared with those with low vulnerability (95%CI 3.82-5.67). High vulnerability in the biological (RR 3.33; 95%CI 2.71-4.09), sociocultural (RR 1.50; 95%CI 1.27-1.77), socioeconomic (RR 2.08; 95%CI 1.75-2.48), and indoor (RR 3.21; 95%CI 2.74-3.76) exposure domains were associated with elevated risks of mortality compared with the least vulnerable neighbourhoods. Outdoor exposure was unassociated with mortality (RR 1.17; 95%CI 0.96-2.43).

CONCLUSION

Public intervention to protect vulnerable populations should be adapted to focus on domains most associated with COVID-19 mortality to ensure addressing local needs.

Is there a relationship between internet access and COVID-19 mortality? Evidence from Nigeria based on a spatial analysis

With over 6.5 million deaths due to COVID-19, it has become an issue of global health concern. Early findings have identified several social determinants of deaths from COVID-19. However, very few studies have been done on the relationship between internet access and COVID-19 mortality in the context of developing countries. Using geospatial methods, this study examines the relationship between internet access and COVID-19 mortality disparity in Nigeria. In contrast to the widely reported relationship in the literature that internet access lowers the risk of COVID-19 mortality, the current study finds that geographical locations with the highest internet access are the hotspots of COVID-19 mortality in Nigeria, especially some parts of southwest Nigeria. In addition, findings show that population density and unemployment are risk factors of COVID-19 mortality. The study recommends educating the population on the use of online health information and the need to adhere strictly to non-pharmaceutical and vaccination interventions to reduce the number of deaths caused by the virus.

Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model

The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.

Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review

Objectives: International evidence of socioeconomic inequalities in COVID-19 outcomes is extensive and growing, but less is known about the temporal dynamics of these inequalities over the course of the pandemic.

Methods: We systematically searched the Embase and Scopus databases. Additionally, several relevant journals and the reference lists of all included articles were hand-searched. This study follows the PRISMA guidelines for scoping reviews.

Results: Forty-six studies were included. Of all analyses, 91.4% showed stable or increasing socioeconomic inequalities in COVID-19 outcomes over the course of the pandemic, with socioeconomically disadvantaged populations being most affected. Furthermore, the study results showed temporal dynamics in socioeconomic inequalities in COVID-19, frequently initiated through higher COVID-19 incidence and mortality rates in better-off populations and subsequent crossover dynamics to higher rates in socioeconomically disadvantaged populations (41.9% of all analyses).

Conclusion: The identified temporal dynamics of socioeconomic inequalities in COVID-19 outcomes have relevant public health implications. Socioeconomic inequalities should be monitored over time to enable the adaption of prevention and interventions according to the social particularities of specific pandemic phases.

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.

Forest Area, CO2 Emission, and COVID-19 Case-Fatality Rate: A Worldwide Ecological Study Using Spatial Regression Analysis

Spatial analysis is essential to understand the spreading of the COVID-19 pandemic. Due to numerous factors of multi-disciplines involved, the current pandemic is yet fully known. Hence, the current study aimed to expand the knowledge on the pandemic by exploring the roles of forests and CO2 emission in the COVID-19 case-fatality rate (CFR) at the global level. Data were captured on the forest coverage rate and CO2 emission per capita from 237 countries. Meanwhile, extra demographic and socioeconomic variables were also included to adjust for potential confounding. Associations between the forest coverage rate and CO2 emission per capita and the COVID-19 CFR were assessed using spatial regression analysis, and the results were further stratified by country income levels. Although no distinct association between the COVID-19 CFR and forest coverage rate or CO2 emission per capita was found worldwide, we found that a 10% increase in forest coverage rates was associated with a 2.37‰ (95%CI: 3.12, 1.62) decrease in COVID-19 CFRs in low-income countries; and a 10% increase in CO2 emission per capita was associated with a 0.94‰ (95%CI: 1.46, 0.42) decrease in COVID-19 CFRs in low-middle-income countries. Since a strong correlation was observed between the CO2 emission per capita and GDP per capita (r = 0.89), we replaced CO2 emission with GDP and obtained similar results. Our findings suggest a higher forest coverage may be a protective factor in low-income countries, which may be related to their low urbanization levels and high forest accessibilities. On the other hand, CO2 can be a surrogate of GDP, which may be a critical factor likely to decrease the COVID-19 CFR in lower-middle-income countries.