Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

The strength of agreement of students’ academic performances as a counseling guide for the university prospective admission seekers

Abstract This research examines the strength of agreement of students’ academic performances for their first and graduating year in the University using Cohen’s kappa. Academic records of 710 students which consist of students Grade Point Average (GPA) and Cumulative Grade Point Average (CGPA) for their first and graduating year. This paper is to examine the final academic performances of students in the University based on specific information regarding their academic performances during their first year at the University. This study reveals that a strong agreement exists between the students’ first and graduating year academic performance in their result. This work will serve as a useful counseling guide to prospective admission seekers and all stakeholders at enhancing students’ academic performances in the University system. This study is divided into five sections: introduction, literature, methodology, discussion while the study limitation and future study forms the part of the conclusion

Trend of social media news:a viewpoint of COVID-19 tweets using natural language processing

Abstract The meteoric rise of social media news during the ongoing COVID-19 is worthy of advanced research. Freedom of speech in many parts of the world, especially the developed countries and liberty of socialization, calls for noteworthy information sharing during the panic pandemic. However, as a communication intervention during crises in the past, social media use is remarkable; the Tweets generated via Twitter during the ongoing COVID-19 is incomparable with the former records. This study examines social media news trends and compares the Tweets on COVID-19 as a corpus from Twitter. By deploying Natural Language Processing (NLP) methods on tweets, we were able to extract and quantify the similarities between some tweets over time, which means that some people say the same thing about the pandemic while other Twitter users view it differently. The tools we used are Spacy, Networkx, WordCloud, and Re. This study contributes to the social media literature by understanding the similarity and divergence of COVID-19 tweets of the public and health agencies such as the World Health Organization (WHO). The study also sheds more light on the COVID-19 sparse and densely text network and their implications for the policymakers. The study explained the limitations and proposed future studies

University student’s academic performance:an approach of Tau statistic

Abstract The poor performance of tertiary graduates in Nigeria has been the subject of speculation for stakeholders in the education sector. In pursuance of Academic excellence, Nigeria’s target is to become one of the top 20 economies. Performance is the ability of a student to complete a task. The task completion results could be positive or negative. Academic performance in private universities is undulating between first and third classes. These results are in public universities. If the result is positive, it indicates that the student performs brilliantly or excellently, but on the other hand, if it is negative, it indicates woeful performance. Student performance is an outcome of a rigorous evaluation through examination or other assessment methods. Performance criteria start from day one on campus, and it extends and accumulates to the end of the student’s study. The study uses 1841 students’ academic records from seven Engineering departments from the School of Engineering, Covenant University, Nigeria. This study examines the relationship between the first year and final year results and the reliability between first year results and final year results. The methodology adopted in this study is a quantitative technique. The analysis for the study carried out with IBM SPSS version 27 using Pearson correlation and Tau statistic. The Pearson correlation coefficient shows a strong positive correlation between the students of the first year and final year results, and it shows a significant linear relationship between students’ first and final year results from the seven departments. This work will serve as a valuable source of advice to stakeholders in the education sector, inside and outside the university system, to enhance students’ academic performance in the University system

Oxidative Stress in Extrahepatic Tissues of Rats Co-Exposed to Aflatoxin B1 and Low Protein Diet

Early life exposure to aflatoxin B1 (AFB1) and low protein diet through complementary foods during weaning is common in parts of Africa and Asia. This study evaluated the effect of co-exposure to AFB1 and low protein diet on the extrahepatic tissues of rats. Twenty-four three-week old weanling male albino rats were used for this study and were randomly assigned into four groups: group 1 served as control and was fed normal protein diet (20% protein), group 2 was fed low protein diet (5% protein), group 3 was fed normal protein diet + 40 ppb AFB1 while group 4 received low protein diet + 40 ppb AFB1, all for eight weeks. Afterward, biomarkers of anemia (packed cell volume (PCV), hemoglobin) and kidney function (urea, uric acid, and creatinine) were determined in the blood while biomarkers of oxidative stress were determined in the tissues spectrophotometrically. Co-exposure to AFB1 and low protein diet significantly (p < 0.05) decreased body weight gain and PCV, increased biomarkers of kidney functions and induced oxidative stress in the tissues studied. There was significant (p < 0.05) reduction in glutathione concentration while TBARS was significantly increased in the tissues. Co-exposure to AFB1 and low protein diet had additive effects on decreasing the weight gain and potentiation effect of kidney dysfunction in the rats. The co-exposure also decreased antioxidant enzymes and increased oxidant status in the tissues. Our results demonstrate that this co-exposure has deleterious health effects on extrahepatic tissues and should be a public health concern especially in developing countries where AFB1 contamination is common

Do teamwork experience and self-regulated learning determine the performance of students in an online educational technology course?

Abstract This study uses the quantitative research approach to examine the connection between students’ teamwork experience, self-regulated learning, technology self-efficacy, and performance in an online educational technology course. Sixty-three (63) students participated in this study. The study data were collected through an online questionnaire that included background information, course satisfaction, motivation strategies for learning, and online technology self-efficacy, to study the variables’ interactions using quantitative research. To realize this study’s aims, multivariate regression and correlation approaches were employed to analyze the online students’ data. The multivariate regression analysis results show a relationship between self-regulated learning, the online course level, and the number of online courses that the students have completed. Right self-regulated learning strategies in online courses motivate students to strive for a good teamwork experience, leading to increased interest in online learning. In addition, the results also show that there is a relationship between satisfaction and the level of the online course. Achieving good grades makes the student more satisfied and improves the level of technology use. Finally, this study established a relationship between the students’ motivation and the online course level. Therefore, teachers and course designers should implement learning objects that promote students’ engagement and motivation in online learning environments

Investigating machine learning methods for tuberculosis risk factors prediction:a comparative analysis and evaluation

Abstract Tuberculosis (TB) is a killer disease, and its root can be traced to Mycobacterium tuberculosis. As the world population increases, the burden of tuberculosis is growing along. Low-and-middle-income nations are not exempted from the tuberculosis crisis. Due to a shortage of medical supplies, tuberculosis bacteria have become a huge public health concern. This study reviewed recent literature from 2015 to 2020 to critically examine what earlier researchers have done about TB burden and treatment. The data used were based on the hospital’s medical department’s record and used a machine-learning algorithm to predict and determine the risk factors associated with the disease. Furthermore, it developed five predictive models to offer the medical managers a valid alternative to the manual estimation of TB patients’ status as cured or not cured. The overall classification showed that all the classification methods performed well for classifying the TB treatment outcome (ranging between 67.5% and 73.4%). Our findings showed that MLP (testing) is the best model to predict TB patients’ treatment outcomes. Age and length of stay were identified as significant risk factors for TB patients in this study. This study explains the study’s limitation, contributions, managerial implications, and suggest future work

Concomitant with Nigerian road traffic accidents:an application of a generalized linear model

Abstract This study aims to apply a generalized linear model for investigating the relationship between road traffic accidents and the resulting fatalities in Nigeria. The main objectives are to determine the most suitable model fits, compare the models used, and examine the relationship between the total cases and log deaths by modelling the number of road traffic accidents in Nigeria. The study adopts Poisson regression and negative binomial regression model for data analysis to achieve the set goals. The data used for this research are secondary data collected from annual reports on road traffic accidents of the Federal Road Safety Commission of Nigeria between 1960 and 2017. The study establishes that the number of traffic accidents on roads in Nigeria is continually increasing, and efforts by the government and relevant agencies have been mostly unsuccessful in addressing this danger. Moreover, the highly dangerous conditions on Nigerian roads result in a daily loss of innocent lives that otherwise would have significantly contributed to economic growth

Data analytics:an exploration of quality control to determine students’ academic performance

Abstract Quality control and improvement is a crucial process development of any institution that craves growth. One part of the SPC approach is to aid the constant improvement of performance by further reducing unexplained variability. Another aspect of Statistical Process Control (SPC) is that planned and unplanned changes signaled as fast as considering the natural process variability. This paper aimed to determine whether students’ performance is significantly distributed according to academic patterns using the quality control procedure. This study found that one of the notable Nigerian Private University student academic performances drawn from three engineering departments based on the mean chart is in control and out of control, indicating excellent, intermediate, and lower results. The study also shows upper, average, and lower results with a close margin. This insight is an interdepartmental issue. The school managers need to formulate a holistic policy that will improve the existing academic performance to move the outlier students from worst to better and from better to best

The mediation of financial return between innovation and sustainability of small social entrepreneurship:a case of developing country

Abstract The purpose of this study is to establish the mediation of financial return between innovation and sustainability of small social entrepreneurship. Earlier research is limited in this perspective, and therefore this study focuses on filling the gap as far as the relationship between innovation and sustainability of social entrepreneurship is concerned. The research data for this study comes from Nigeria by adopting a Likert Scale questionnaire through a convenience sampling approach. In the data analysis, this research utilized SPSS ver. 25 and SmartPLS ver. 3.2.8 for descriptive statistics, structural equation modeling, and mediation analysis. At the end of the study, the findings show that there is a direct relationship between entrepreneurial innovation and its sustainability, which is mediated by financial return in the developing countries’ settings. While this study has successfully established the clarity concerning the relationship between the chosen variables including, innovation, financial returns, and sustainability of social entrepreneurship in the developing countries, there is the need for further research due to the global significance of social entrepreneurship. The research discusses the limitation of the study and recommends future research to focus on comparative study between countries, gender divergence study, culture, and technological effects in social entrepreneurship