Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide

Association metrological factors with Covid-19 mortality in Tehran, Iran (2020-2021)

The outbreak of the Coronavirus disease (COVID-19) has raised questions about the potential role of climate and environmental factors in disease transmission. This study examined meteorological and demographic factors to determine their impact on mortality and hospitalization rates in Tehran, Iran from January 1, 2021, to December 31, 2022. Notably, hospitalization cases were positively associated with temperature (P-value: 0.001 in spring, P-value: 0.045 in winter) and pressure (P-value: 0.004 in spring), while being negatively associated with wind speed (P-value: 0.03 in spring, P-value: 0.01 in autumn) and humidity (P-value: 0.001 in autumn) during the spring and autumn seasons. Conversely, mortality was associated with wind speed (P-value: 0.01) and pressure (P-value: 0.02) during winter and spring, respectively. Moreover, temperature was associated with mortality in both spring (P-value: 0.00) and winter (P-value: 0.04). The findings suggest that identifying the environmental factors that contribute to the spread of COVID-19 can help prevent future waves of the pandemic in Tehran.

Impact of social and demographic factors on the spread of the SARS-CoV-2 epidemic in the town of Nice

INTRODUCTION

Socio-demographic factors are known to influence epidemic dynamics. The town of Nice, France, displays major socio-economic inequalities, according to the National Institute of Statistics and Economic Studies (INSEE), 10% of the population is considered to live below the poverty threshold, i.e. 60% of the median standard of living.

OBJECTIVE

To identify socio-economic factors related to the incidence of SARS-CoV-2 in Nice, France.

METHODS

The study included residents of Nice with a first positive SARS-CoV-2 test (January 4-February 14, 2021). Laboratory data were provided by the National information system for Coronavirus Disease (COVID-19) screening (SIDEP) and socio-economic data were obtained from INSEE. Each case's address was allocated to a census block to which we assigned a social deprivation index (French Deprivation index, FDep) divided into 5 categories. For each category, we computed the incidence rate per age and per week and its mean weekly variation. A standardized incidence ratio (SIR) was calculated to investigate a potential excess of cases in the most deprived population category (FDep5), compared to the other categories. Pearson's correlation coefficient was computed and a Generalized Linear Model (GLM) applied to analyse the number of cases and socio-economic variables per census blocks.

RESULTS

We included 10,078 cases. The highest incidence rate was observed in the most socially deprived category (4001/100,000 inhabitants vs 2782/100,000 inhabitants for the other categories of FDep). The number of observed cases in the most social deprivated category (FDep5: N = 2019) was significantly higher than in the others (N = 1384); SIR = 1.46 [95% CI:1.40-1.52; p < 0.001]. Socio-economic variables related to poor housing, harsh working conditions and low income were correlated with the new cases of SARS-CoV-2.

CONCLUSION

Social deprivation was correlated with a higher incidence of SARS-CoV-2 during the 2021 epidemic in Nice. Local surveillance of epidemics provides complementary data to national and regional surveillance. Mapping socio-economic vulnerability indicators at the census block level and correlating these with incidence could prove highly useful to guide political decisions in public health.

Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand

A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran's I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.

Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations

Background

Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.

Methods

In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.

Results

Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.

Conclusion

Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

Ecological Model Explaining the Psychosocial Adaptation to COVID-19

The main objective of this study is to understand and characterize the adoption of an ecological perspective and the physical, psychological, social, and contextual health factors that may influence the adjustment to and mental health experiences during the COVID-19 pandemic. The study included 5479 participants, of which 3710 were female (67.7%), aged between 18 and 90 years old, with a mean age of 48.57 years (SD = 14.29), were considered three age groups: 21.5% up to 35 years old, 61.8% between 36 and 64 years old, and 16.7% 65 years old or more. The mental health and individual adjustment to the COVID-19 situation are explained by socio-demographic factors, health-related factors, lifestyles, attitudes and behaviors, lockdown experience, and place of residence. A better adaptation and mental health are observed among men, people with a higher educational level, people with lower sadness, nervousness, and burnout, and people whose health situation did not worsen with the pandemic. In terms of lifestyle, a better adaptation is related to a better quality of sleep, fewer nightmares, a higher practice of physical activity, and less consumption of processed foods and sweets. A better adaptation is also associated with lower levels of dependence on alcohol, TV, and SN (social networks) and a more positive experience of the lockdown imposed by the pandemic. Gender and age group differences in the described context were studied. Promoting a better adjustment and improved mental health when dealing with the COVID-19 requires an ecological understanding and multitarget interventions, targeting physical, mental, and social health together with the contextual environment.

Heterogeneity of the COVID-19 Pandemic in the United States of America: A Geo-Epidemiological Perspective

The spread of the COVID-19 pandemic has shown great heterogeneity between regions of countries, e. g., in the United States of America (USA). With the growing of the worldwide COVID-19 pandemic, there is a need to better highlight the variability in the trajectory of this disease in different worldwide geographic areas. Indeed, the epidemic trends across areas can display completely different evolution at a given time. Geo-epidemiological analyses using data, that are publicly available, could be a major topic to help governments and public administrations to implement health policies. Geo-epidemiological analyses could provide a basis for the implementation of relevant public health policies. With the COVID-19 pandemic, geo-epidemiological analyses can be readily utilized by policy interventions and USA public health authorities to highlight geographic areas of particular concern and enhance the allocation of resources.

The association between the initial outcomes of COVID-19 and the human development index: An ecological study

BACKGROUND & OBJECTIVE: Outcomes of the pandemic COVID-19 varied from one country to another. We aimed to describe the association between the global recovery and mortality rates of COVID-19 cases in different countries and the Human Development Index (HDI) as a socioeconomic indicator. METHODS: A correlational (ecological) study design is used. The analysis used data from 173 countries. Poisson regression models were applied to study the relationship between HDI and pandemic recovery and mortality rates, adjusting for country median age and country male to female sex ratio. RESULTS: During the first three months, the global pooled recovery rate was 32.4%(95%CI 32.3%–32.5%), and the pooled mortality rate was 6.95%(95%CI 6.94%–6.99%). Regression models revealed that HDI was positively associated with recovery β= 1.37, p = 0.016. HDI was also positively associated with the mortality outcome β= 1.79, p = 0.016. CONCLUSIONS: Our findings imply that the positive association between the HDI and recovery rates is reflective of the pandemics’ preparedness. The positive association between the HDI and mortality rates points to vulnerabilities in approaches to tackle health crises. It is critical to better understand the connection between nations’ socioeconomic factors and their readiness for future pandemics in order to strengthen public health policies.

The socio-economic determinants of COVID-19: A spatial analysis of German county level data

The study explores the association of socioeconomic, demographic, and health-related variables at the regional level with COVID-19 related cases and deaths in Germany during the so-called first wave through mid-June 2020. Multivariate spatial models include the 401 counties in Germany to account for regional interrelations and possible spillover effects. The case and death numbers are, for example, significantly positively associated with early cases from the beginning of the epidemic, the average age, the population density and the share of people employed in elderly care. By contrast, they are significantly negatively associated with the share of schoolchildren and children in day care as well as physician density. In addition, significant spillover effects on the case numbers of neighbouring regions were identified for certain variables, with a different sign than the overall effects, giving rise to further future analyses of the regional mechanisms of action of COVID-19 infection. The results complement the knowledge about COVID-19 infection beyond the clinical risk factors discussed so far by a socio-economic perspective at the ecological level.

The socio-economic determinants of COVID-19: A spatial analysis of German county level data

The study explores the association of socioeconomic, demographic, and health-related variables at the regional level with COVID-19 related cases and deaths in Germany during the so-called first wave through mid-June 2020. Multivariate spatial models include the 401 counties in Germany to account for regional interrelations and possible spillover effects. The case and death numbers are, for example, significantly positively associated with early cases from the beginning of the epidemic, the average age, the population density and the share of people employed in elderly care. By contrast, they are significantly negatively associated with the share of schoolchildren and children in day care as well as physician density. In addition, significant spillover effects on the case numbers of neighbouring regions were identified for certain variables, with a different sign than the overall effects, giving rise to further future analyses of the regional mechanisms of action of COVID-19 infection. The results complement the knowledge about COVID-19 infection beyond the clinical risk factors discussed so far by a socio-economic perspective at the ecological level.

Geographically varying relationships of COVID-19 mortality with different factors in India

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found.

Independent association of meteorological characteristics with initial spread of Covid-19 in India

Whether weather plays a part in the transmissibility of the novel Coronavirus Disease-19 (COVID-19) is still not established. We tested the hypothesis that meteorological factors (air temperature, relative humidity, air pressure, wind speed and rainfall) are independently associated with transmissibility of COVID-19 quantified using the basic reproduction rate (R0). We used publicly available datasets on daily COVID-19 case counts (total n = 108,308), three-hourly meteorological data and community mobility data over a three-month period. Estimated R0 varied between 1.15 and 1.28. Mean daily air temperature (inversely), wind speed (positively) and countrywide lockdown (inversely) were significantly associated with time dependent R0, but the contribution of countrywide lockdown to variability in R0 was over three times stronger as compared to that of temperature and wind speed combined. Thus, abating temperatures and easing lockdown may concur with increased transmissibility of COVID-19 in India.