scholarly journals SARS-COV-2 Vaccines: What Indicators are Associated with the Worldwide Distribution of the First Doses

2021 ◽  
Vol 58 ◽  
pp. 004695802110601
Author(s):  
Marcos Felipe Falcão Sobral ◽  
Brigitte Renata Bezerra de Oliveira ◽  
Ana Iza Gomes da Penha Sobral ◽  
Marcelo Luiz Monteiro Marinho ◽  
Gisleia Benini Duarte ◽  
...  
Keyword(s):  
Population Density ◽  
Life Expectancy ◽  
Human Development ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
The United States ◽  
Gdp Per Capita ◽  
Reproduction Rate ◽  
Worldwide Distribution ◽  
Per Capita

The present study aimed to identify the factors associated with the distribution of the first doses of the COVID-19 vaccine. In this study, we used 9 variables: human development index (HDI), gross domestic product (GDP per capita), Gini index, population density, extreme poverty, life expectancy, COVID cases, COVID deaths, and reproduction rate. The time period was until February 1, 2021. The variable of interest was the sum of the days after the vaccine arrived in the countries. Pearson’s correlation coefficients were calculated, and t-test was performed between the groups that received and did not receive the immunizer, and finally, a stepwise linear regression model was used. 58 (30.4%) of the 191 countries received the SARS-CoV-2 vaccine. The countries that received the most doses were the United States, China, the United Kingdom, and Israel. Vaccine access in days showed a positive Pearson correlation HDI, GDP, life expectancy, COVID-19 cases, deaths, and reproduction rate. Human development level, COVID-19 deaths, GDP per capita, and population density are able to explain almost 50% of the speed of access to immunizers. Countries with higher HDI and per capita income obtained priority access.

Taxable Wealth and Alcoholic Beverage Consumption in the United States

1994 ◽  
Vol 74 (3) ◽  
pp. 813-814 ◽  
Author(s):  
Thomas J. Young ◽  
Laurence A. French
Keyword(s):  
United States ◽  
Pearson Correlation ◽  
Alcoholic Beverage ◽  
Correlation Coefficients ◽  
Secondary Data ◽  
The United States ◽  
Beverage Consumption ◽  
Distilled Spirits ◽  
Per Capita ◽  
Per Capita Consumption

Analysis of secondary data of the United States yielded significant but small Pearson correlation coefficients between taxable wealth and per capita consumption of wine ( r = .26), beer ( r = .40), and distilled spirits ( r = .30).

scholarly journals Global Discrepancies between Numbers of Available SARS-CoV-2 Genomes and Human Development Indexes at Country Scales

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 775
Author(s):  
Philippe Colson ◽  
Didier Raoult
Keyword(s):  
Life Expectancy ◽  
Human Development ◽  
Human Development Index ◽  
Gdp Per Capita ◽  
Asian Countries ◽  
Genome Sequences ◽  
Microsoft Excel ◽  
Development Index ◽  
The World ◽  
Per Capita

It has now been over a year since SARS-CoV-2 first emerged in China, in December 2019, and it has spread rapidly around the world. Some variants are currently considered of great concern. We aimed to analyze the numbers of SARS-CoV-2 genome sequences obtained in different countries worldwide until January 2021. On 28 January 2021, we downloaded the deposited genome sequence origin from the GISAID database, and from the “Our world in data” website we downloaded numbers of SARS-CoV-2-diagnosed cases, numbers of SARS-CoV-2-associated deaths, population size, life expectancy, gross domestic product (GDP) per capita, and human development index per country. Files were merged and data were analyzed using Microsoft Excel software. A total of 450,968 SARS-CoV-2 genomes originating from 135 countries on the 5 continents were available. When considering the 19 countries for which the number of genomes per 100 deaths was >100, six were in Europe, while eight were in Asia, three were in Oceania and two were in Africa. Six (30%) of these countries are beyond rank 75, regarding the human development index and four (20%) are beyond rank 80 regarding GDP per capita. Moreover, the comparisons of the number of genomes sequenced per 100 deaths to the human development index by country show that some Western European countries have released similar or lower numbers of genomes than many African or Asian countries with a lower human development index. Previous data highlight great discrepancies between the numbers of available SARS-CoV-2 genomes per 100 cases and deaths and the ranking of countries regarding wealth and development.

scholarly journals The Need for the Establishment of Biomedical Engineering (BME) as an Academic and Professional Discipline in the Philippines — A Quantitative Argument

2020 ◽  
Author(s):  
Jamie Ledesma Fermin ◽  
Myles Joshua Tan
Keyword(s):  
Life Expectancy ◽  
Pearson Correlation ◽  
Maternal Mortality Ratio ◽  
The Philippines ◽  
Gdp Per Capita ◽  
Professional Discipline ◽  
Brunei Darussalam ◽  
Life Expectancy At Birth ◽  
Per Capita ◽  
Publication Volume

This article quantitatively presents the relationship that exists between research endeavors in BME, which was measured in terms of the volume of publications produced in the field of BME from 1990 to 2019 in the 10 member states of the ASEAN, and 12 indicators of the overall and physical health of populations (†) — GDP per capita, HDI value, HAQ index, life expectancy at birth, healthy life expectancy at birth, maternal mortality ratio, neonatal mortality rate, probability of dying from noncommunicable diseases, and incidences of death due to stroke, diabetes mellitus, congenital birth defects, and leukemia. The objective was to show that ASEAN states that recognize BME as an academic and professional discipline have been successful in producing research in the field, and thus, have advanced the provision of high-quality healthcare for their people. The Pearson correlation coefficients (PCCs) between BME publication volume and the 12 healthcare indicators were calculated and were reported in the order previously listed (see †) to be +0.7555, +0.7398, +0.7297, +0.7563, +0.7879, -0.6286, -0.6810, -0.7245, -0.6683, -0.6893, -0.7645, and -0.6827. The PCCs between BME publication volume and the natural logarithm of the same indicators in the same order were calculated and were reported to be +0.7338, +0.7051, +0.7184, +0.7452, +0.7754, -0.7985, -0.7286, -0.7905, -0.7872, -0.9208, -0.9149, and -0.7038. It was also discovered that data from Brunei Darussalam behaved anomalously, as they did not conform with the observed trends. Hence, it was decided that data from Brunei would be removed to check for any improvements in PCC. Indeed, PCCs for all indicators improved. PCCs between BME publication volume and the 12 indicators excluding data from Brunei were reported in the same order as follows: +0.9279, +0.9072, +0.8659, +0.8598, +0.8800, -0.7313, -0.7783, -0.7919, -0.7726, -0.7073, -0.8133, and -0.6907. PCCs between BME publication volume and the natural logarithms of the 12 indicators excluding data from Brunei were reported in the same order as follows: +0.9042, +0.8707, +0.9599, +0.8519, +0.8726, -0.8822, -0.9318, -0.8430, 0.8510, -0.9234, -0.9390, and -0.7069, respectively. These PCCs, many of them with magnitudes above 0.9000, signify especially strong relationships between BME research yield and healthcare quality in a country.Moreover, to best visualize the relationships quantified above, BME publication volume was plotted against GDP per capita, while the remaining 11 indicators were each plotted against BME publication volume. Linear (Lin), logarithmic (Log), and exponential (Exp) regression curves were then overlaid on the datapoints. Coefficients of determination (R2) were calculated to measure the aptness of the fits. R2 values were reported in the same order as above to be: 0.5161 (Log), 0.5708 (Lin), 0.5473 (Lin), 0.5720 (Lin), 0.6207 (Lin), 0.7457 (Log), 0.7517 (Exp), 0.6249 (Exp), 0.6197 (Exp), 0.8469 (Exp), 0.8095 (Log), and 0.4660 (Lin) [incl. Brunei]; 0.9214 (Log), 0.8612 (Lin), 0.8230 (Lin), 0.7393 (Lin), 0.7745 (Lin), 0.9433 (Log), 0.8682 (Exp), 0.7106 (Exp), 0.7242 (Exp), 0.8527 (Exp), 0.8960 (Log), and 0.4771 (Lin) [excl. Brunei].For this reason, we believe that it is certainly time for the Philippines to adopt BME as an academic and professional discipline in its own right, so that it may one day enjoy the benefits brought about by advancements in the provision of healthcare that are experienced by its ASEAN neighbors that have already gone ahead with movements to cultivate the highly essential discipline.

scholarly journals The connenction between global innovation index and economic well-being indexes

2019 ◽  
Vol 13 (3-4) ◽  
pp. 87-92
Author(s):  
Szlobodan Vukoszavlyev
Keyword(s):  
Standard Deviation ◽  
Life Expectancy ◽  
South Asia ◽  
Human Development ◽  
Human Development Index ◽  
Well Being ◽  
Gdp Per Capita ◽  
Development Index ◽  
Global Innovation ◽  
Per Capita

We study the connection of innovation in 126 countries by different well-being indicators and whether there are differences among geographical regions with respect to innovation index score. We approach and define innovation based on Global Innovation Index (GII). The following well-being indicators were emphasized in the research: GDP per capita measured at purchasing power parity, unemployment rate, life expectancy, crude mortality rate, human development index (HDI). Innovation index score was downloaded from the joint publication of 2018 of Cornell University, INSEAD and WIPO, HDI from the website of the UN while we obtained other well-being indicators from the database of the World Bank. Non-parametric hypothesis testing, post-hoc tests and linear regression were used in the study.We concluded that there are differences among regions/continents based on GII. It is scarcely surprising that North America is the best performer followed by Europe (with significant differences among countries). Central and South Asia scored the next places with high standard deviation. The following regions with significant backwardness include North Africa, West Asia, Latin America, the Caribbean Area, Central and South Asia, and sub-Saharan Africa. Regions lagging behind have lower standard deviation, that is, they are more homogeneous therefore there are no significant differences among countries in the particular region.In the regression modelling of the Global Innovation Index, it was concluded that GDP per capita, life expectancy and human development index are significant explanatory indicators. In the multivariable regression analysis, HDI remained the only explanatory variable in the final model. It is due to the fact that there was significant multicollinearity among the explanatory variables and the HDI aggregates several non-economic indicators like GII. JEL Classification: B41, I31, O31, Q55

scholarly journals COVID-19 and socioeconomic development in Africa: The first 6 months (February 2020-August 2020)

2020 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
M. Michel Garenne
Keyword(s):  
Population Density ◽  
Age Structure ◽  
Socioeconomic Development ◽  
Case Fatality ◽  
World Health ◽  
Gdp Per Capita ◽  
Reproduction Rate ◽  
African Countries ◽  
The World ◽  
Per Capita

The study covers the first 6 months of the coronavirus disease 2019 (COVID-19) epidemics in 56 African countries (February 2020-August 2020). It links epidemiological parameters (incidence, case fatality) with demographic parameters (population density, urbanization, population concentration, fertility, mortality, and age structure), with economic parameters (gross domestic product [GDP] per capita, air transport), and with public health parameters (medical density). Epidemiological data are cases and deaths reported to the World Health Organization, and other variables come from databases of the United Nations agencies. Results show that COVID-19 spread fairly rapidly in Africa, although slower than in the rest of the world: In 3 months, all countries were affected, and in 6 months, approximately 1.1 million people (0.1% of the population) were diagnosed positive for COVID-19. The dynamics of the epidemic were fairly regular between April and July, with a net reproduction rate R0 = 1.35, but tended to slow down afterward, when R0 fell below 1.0 at the end of July. Differences in incidence were very large between countries and were correlated primarily with population density and urbanization, and to a lesser extent, with GDP per capita and population age structure. Differences in case fatality were smaller and correlated primarily with mortality level. Overall, Africa appeared very heterogeneous, with some countries severely affected while others very little.

scholarly journals 209Novel indicator of air pollution control efficacy: analysis of 196 countries from 2000 to 2016

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Chunlei Han
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Co2 Emission ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Gdp Per Capita ◽  
Air Pollution Control ◽  
Control Efficacy ◽  
Per Capita ◽  
Pm2.5 Concentration

Abstract Background PM2.5 concentration is different with the same CO2 emission across countries, which might because of different air pollution control efficacy. But there is no indicator to reflect the level of air pollution control efficacy in previous studies. We aimed to develop such an indicator, and to evaluate its global and temporal distribution and its association with country-level health metrics. Methods A novel indicator, PM2.5 concentration per unit per capita CO2 emission (PC), was developed to show the air pollution control efficacy. We estimated and mapped the global average distribution of PC and PC changes during 2000-2016 of 196 countries for the first time. Gini coefficient was used to show the inequity of PC among different countries. Pearson correlation coefficients and Generalized Additive Mixed Model (GAMM) were used to evaluate the relationship between PC and health metrics. Results PC varied by country with an inverse association with GDP per capita. PC showed a declining trend globally from 2000 to 2016. The most remarkable decreases were observed for countries in Central Africa like Chad, Democratic Republic of Congo and Niger, then China and India. The international inequality of PC has also decreased. The Pearson correlation coefficients between PC and life expectancy at birth (LE), Infant-mortality rate (IMR), Under-five mortality rate (U5MR) and logarithm of GDP per capita(LPGDP) were -0.566, 0.646, 0.659,-0.585 respectively(all P-values <0.05). Compared with PM2.5 and CO2, PC could explain more variation of LE, IMR and U5MR. Conclusions PC might be a good indicator of air pollution control efficacy and was related to important health indicators. Our findings provide a new way to interpret health equity across the globe from the point of air pollution control efficacy. Key messages air pollution, climate change, health equity, air pollution control efficacy our study developed a novel air pollution control efficacy indicator named PM2.5 concentration per unit per capita CO2 emission (PC). In the context of global climate change, PC is a good indicator to deal with air pollution for policymakers.

scholarly journals Air pollution control efficacy and health impacts: A global observed study from 2000 to 2016

2020 ◽  
Author(s):  
Chunlei Han ◽  
Rongbin Xu ◽  
Yajuan Zhang ◽  
Wenhua Yu ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Mortality Rate ◽  
Pollution Control ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Infant Mortality Rate ◽  
Gdp Per Capita ◽  
Air Pollution Control ◽  
Control Efficacy ◽  
Per Capita

AbstractBackgroundPM2.5 concentrations vary between countries with similar CO2 emissions, possibly due to differences in air pollution control efficacy. However, no indicator of the level of air pollution control efficacy has yet been developed. We aimed to develop such an indicator, and to evaluate its global and temporal distribution and its association with country-level health metrics.MethodA novel indicator, ground level population-weighted average PM2.5 concentration per unit CO2 emission per capita (PM2.5/CO2, written as PC in abbreviation), was developed to assess country-specific air pollution control efficacy. We estimated and mapped the global average distribution of PC and PC changes during 2000–2016 across 196 countries. Pearson correlation coefficients and Generalized Additive Mixed Model (GAMM) were used to evaluate the relationship between PC and health metrics.ResultsPC varied by country with an inverse association with the economic development. PC showed an almost stable trend globally from 2000 to 2016 with the low income groups increased. The Pearson correlation coefficients between PC and life expectancy at birth (LE), Infant-mortality rate (IMR), Under-five mortality rate (U5MR) and logarithm of GDP per capita (LPGDP) were –0.566, 0.646, 0.659, –0.585 respectively (all P-values <0.001). Compared with PM2.5 or CO2, PC could explain more variation of LE, IMR and U5MR. The association between PC and health metrics was independent of GDP per capita.ConclusionsPC might be a good indicator for air pollution control efficacy and was related to important health indicators. Our findings provide a new way to interpret health inequity across the globe from the point of air pollution control efficacy.

scholarly journals COVID-19 Mortality Rate and Its Incidence in Latin America: Dependence on Demographic and Economic Variables

2021 ◽  
Vol 18 (13) ◽  
pp. 6900
Author(s):  
Javier Cifuentes-Faura
Keyword(s):  
Latin America ◽  
Mortality Rate ◽  
Population Density ◽  
Life Expectancy ◽  
Latin American ◽  
Positive Influence ◽  
Death Rates ◽  
Per Capita ◽  
Latin American Countries ◽  
Quality Infrastructure

The pandemic caused by COVID-19 has left millions infected and dead around the world, with Latin America being one of the most affected areas. In this work, we have sought to determine, by means of a multiple regression analysis and a study of correlations, the influence of population density, life expectancy, and proportion of the population in vulnerable employment, together with GDP per capita, on the mortality rate due to COVID-19 in Latin American countries. The results indicated that countries with higher population density had lower numbers of deaths. Population in vulnerable employment and GDP showed a positive influence, while life expectancy did not appear to significantly affect the number of COVID-19 deaths. In addition, the influence of these variables on the number of confirmed cases of COVID-19 was analyzed. It can be concluded that the lack of resources can be a major burden for the vulnerable population in combating COVID-19 and that population density can ensure better designed institutions and quality infrastructure to achieve social distancing and, together with effective measures, lower death rates.

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