The Prevalence of COVID-19 in Europe by the End of November 2022: A Cross-Sectional Study.

Background The world has been affected differently by the coronavirus disease 2019 (COVID-19), and Europe reaped the largest number of cases and deaths. Moreover, COVID-19 statistics are dynamic. Objectives The current study aimed to use COVID-19 data to examine the COVID-19 prevalence in Europe by the end of November 2022 and compare the findings globally. Methods The primary data on COVID-19 for each European country were obtained from the "Worldometer" website. The data include the cumulative incidence of COVID-19 per country, the cumulative number of deaths, the total number of tests performed, the number of cases per million population, the number of deaths per million, the number of tests per million, and the total population. The case-fatality rate was calculated (number of deaths/number of cases). In addition, the median age and the vaccination coverage rate (people who received two doses) for each European country were extracted from the "United Nations" website and the "Our World in Data" website, respectively. To compare European countries to the globe, COVID-19 data for each continent were obtained. The analysis of variance (ANOVA) test was used to compare variances across the means of the four parts of Europe based on the geographic division. An independent sample t-test was also used to compare the means between the European Union (EU) states and non-EU states. The Spearman correlation coefficient was used to determine the relationship between different variables across Europe. Results As of December 1, 2022, about 648 million COVID-19 cases and 6.6 million deaths have been recorded worldwide. Europe accounted for nearly 36.8% and 29.5% of all cases and deaths, respectively. Based on the number of deaths per million, Europe was the most affected continent after South America. Nearly 6.8 billion tests have been conducted worldwide, 41% done in Europe; 43 European countries have performed tests more than their population. However, COVID-19 statistics were inconsistent across the four parts of Europe. A significant difference was noticed between Eastern Europe and others, especially Northern Europe and Western Europe. By affiliation with the EU, there was no significant difference. For global comparison, the mean deaths per million, the mean cases per million, and the mean tests per population for European countries were higher than those of the world's countries, although they recorded a lower mean case-fatality rate (CFR). Thirteen European countries were among the 15 most affected countries worldwide based on the number of deaths per million, most located in Eastern and Southern Europe. The number of cases and the number of deaths were significantly proportional to the number of tests performed. Conclusions By the end of November 2022, Europe had the most cases of COVID-19, the most deaths, and the most tests performed, even though it accounts for 9.4% of the world's population. However, COVID-19 data were inconsistent across the four parts of Europe, especially between Eastern Europe and others. Given the natural immunity acquired during the three years and the excellent vaccine coverage in Europe, it is essential to reconsider the definition of a suspected case and establish more specific criteria for testing.

Global COVID-19 case fatality rates influenced by inequalities in human development and vaccination rates.

Aim: COVID-19 has exerted distress on virtually every aspect of human life with disproportionate mortality burdens on older individuals and those with underlying medical conditions. Variations in COVID-19 incidence and case fatality rates (CFRs) across countries have incited a growing research interest regarding the effect of social factors on COVID-19 case-loads and fatality rates. We investigated the effect of population median age, inequalities in human development, healthcare capacity, and pandemic mitigation indicators on country-specific COVID-19 CFRs across countries and regions. Subject and methods: Using population secondary data from multiple sources, we conducted a cross-sectional study and used regional analysis to compare regional differences in COVID-19 CFRs as influenced by the selected indicators. Results: The analysis revealed wide variations in COVID-19 CFRs and the selected indicators across countries and regions. Mean CFR was highest for South America at 1.973% (± 0.742) and lowest for Oceania at 0.264% (± 0.107), while the Africa sub-region recorded the lowest scores for pandemic preparedness, vaccination rate, and other indicators. Population Median Age [0.073 (0.033 0.113)], Vaccination Rate [-3.3389 (-5.570.033 -1.208)], and Inequality-Adjusted Human Development Index (IHDI) [-0.014 (-0.023 -0.004)] emerged as statistically significant predictors of COVID-19 CFR, with directions indicating increasing Population Median Age, higher inequalities in human development and low vaccination rate are predictive of higher fatalities from COVID-19. Conclusion: Regional differences in COVID-19 CFR may be influenced by underlying differences in sociodemographic and pandemic mitigation indicators. Populations with wide social inequalities, increased population Median Age and low vaccination rates are more likely to suffer higher fatalities from COVID-19.

All-cause mortality during the COVID-19 pandemic in Peru.

There was heterogeneity in the median age of all-cause deaths in Peru during different waves of the coronavirus disease 2019 (COVID-19) pandemic. Before predominance of the Omicron variant, the median age of deaths was lower than normal during the peaks of daily all-cause mortality. However, this increased above normal when the Omicron variant was predominant. The daily patterns of cause-specific deaths related directly and indirectly to COVID-19 in Peru were also investigated. Most excess deaths indirectly related to COVID-19 were caused primarily by diseases of the circulatory system, possibly due to disruption of medical services, and the majority of excess deaths directly related to COVID-19 were caused primarily by COVID-19 and diseases of the respiratory system.

The impact of civil liberties, global health security, median age and population size on the spread of COVID-19 (SARS-CoV-2)

Purpose>The purpose of this paper is to investigate the impact of civil liberties, global health security, median age and population size on the spread of COVID-19 across the globe.Design/methodology/approach>This study was done by taking data from 166 different countries from the Economist Intelligence Unit, European Centre for Disease Prevention and Control, World Bank, Johns Hopkins University and United Nations Population Division (UNPD). After conducting all the necessary standard econometric tests, the study was analyzed using the ordinary least squares (OLS) regression.Findings>The finding of the study indicated that COVID-19 tests per million people (LTT/PM), Population Size (LPOP), Civil Liberty Index (CLI) are statistically significant and positively affect the number of confirmed COVID-19 cases;on the other hand, the Health Security Index (HSI) negatively affects the number of confirmed COVID-19 cases.Practical implications>In emergency circumstances, the government ought to have a special responsibility to align civil rights with the protection of public health cautiously. However, measures to restrict civil liberties must be proportionate.Originality/value>Besides other variables, the study included and considered civil liberties as a significant factor to affect the spread of COVID-19, which is a new contribution to the existing body of knowledge in the field.

Relationship Between the COVID-19 Pandemic and Ecological, Economic, and Social Conditions.

The COVID-19 pandemic had huge impacts on the global world, with both a negative impact on society and economy but a positive one on nature. But this universal effect resulted in different infection rates from country to country. We analyzed the relationship between the pandemic and ecological, economic, and social conditions. All of these data were collected in 140 countries at six time points. Correlations were studied using univariate and multivariate regression models. The world was interpreted as a single global ecosystem consisting of ecosystem units representing countries. We first studied 140 countries around the world together, and infection rates were related to per capita GDP, Ecological Footprint, median age, urban population, and Biological Capacity, globally. We then ranked the 140 countries according to infection rates. We created four groups with 35 countries each. In the first group of countries, the infection rate was very high and correlated with the Ecological Footprint (consumption) and GDP per capita (production). This group is dominated by developed countries, and their ecological conditions have proved to be particularly significant. In country groups 2, 3, and 4, infection rates were high, medium, and low, respectively, and were mainly related to median age and urban population. In the scientific discussion, we have interpreted why infection rates are very high in developed countries. Sustainable ecosystems are balanced, unlike the ecosystems of developed countries. The resilience and the health of both natural ecosystems and humans are closely linked to the world of microbial communities, the microbiomes of the biosphere. It is clear that both the economy and society need to be in harmony with nature, creating sustainable ecosystems in developed countries as well.

Association between ageing population, median age, life expectancy and mortality in coronavirus disease (COVID-19).

As of May 5, 2020, the number of confirmed coronavirus disease (COVID-19) cases has been more than 3.5 million with 243,540 deaths. We aimed to determine the associations between ageing population, median age, life expectancy at birth and COVID-19 mortality. The numbers of COVID-19 cases and deaths in the European region were obtained from the World Health Organization database. The data on percentage of the population aged 65 and over, median age and life expectancy at birth were extracted from the World Factbook of Central Intelligence Agency. A total of 56 countries/areas in the Europe reported COVID-19 cases and deaths (1,121,853 cases and 100,938 deaths) on April 20, 2020. The results showed significant positive associations between COVID-19 mortality and ageing population (r =0.274; P =0.021), median age (r =0.255; P=0.029), male median age (r =0.284; P =0.017), female median age (r =0.224; P=0.049), life expectancy at birth (r =0.336; P=0.006), male life expectancy at birth (r =0.342; P=0.005), female life expectancy at birth (r =0.312; P=0.01) in the 56 European countries/areas. This study illustrated that COVID-19 mortality was positively associated with ageing population, median age, and life expectancy at birth.

Lifestyle Acquired Immunity, Decentralized Intelligent Infrastructures, and Revised Healthcare Expenditures May Limit Pandemic Catastrophe: A Lesson From COVID-19.

Throughout history, the human race has often faced pandemics with substantial numbers of fatalities. As the COVID-19 pandemic has now affected the whole planet, even countries with moderate to strong healthcare support and expenditure have struggled to contain disease transmission and casualties. Countries affected by COVID-19 have different demographics, socioeconomic, and lifestyle health indicators. In this context, it is important to find out to what extent these parametric variations are modulating disease outcomes. To answer this, this study selected demographic, socioeconomic, and health indicators e.g., population density, percentage of the urban population, median age, health expenditure per capita, obesity, diabetes prevalence, alcohol intake, tobacco use, case fatality of non-communicable diseases (NCDs) as independent variables. Countries were grouped according to these variables and influence on dependent variables e.g., COVID-19 positive tests, case fatality, and case recovery rates were statistically analyzed. The results suggested that countries with variable median age had a significantly different outcome on positive test rate (P < 0.01). Both the median age (P = 0.0397) and health expenditure per capita (P = 0.0041) showed a positive relation with case recovery. An increasing number of tests per 100 K of the population showed a positive and negative relationship with the number of positives per 100 K population (P = 0.0001) and the percentage of positive tests (P < 0.0001), respectively. Alcohol intake per capita in liter (P = 0.0046), diabetes prevalence (P = 0.0389), and NCDs mortalities (P = 0.0477) also showed a statistical relation to the case fatality rate. Further analysis revealed that countries with high healthcare expenditure along with high median age and increased urban population showed more case fatality but also had a better recovery rate. Investment in the health sector alone is insufficient in controlling the severity of the pandemic. Intelligent and sustainable healthcare both in urban and rural settings and healthy lifestyle acquired immunity may reduce disease transmission and comorbidity induced fatalities, respectively.

The impact of temperature, population size and median age on COVID-19 (SARS-CoV-2) outbreak.

OBJECTIVES: This study aims to explore the association between the spread of COVID-19 and external parameters. In this regard, temperature, population size, median age, and health care facilities of 58 different countries are considered as external factors. METHODS: A negative binomial regression model was fitted to identify the associations between the factors and cases of COVID-19 during the study periods. RESULTS: The temperature, population size, and median age are positively associated with the spreading rate of COVID-19. There is no evidence supporting that case counts of COVID-19 could decline in countries with better health care facilities. Also, an empirical model was presented to estimate the number of cases within a country using the external parameters. CONCLUSIONS: It is impossible to express the change in the number of cases for a unit increase in each of the variables because a change in a single variable depends on different values of other variables. However, the findings of this study provide useful implications for the authorities and decision-makers to take specific precautionary measures.