Life Expectancy In Countries Research Articles

Income-Based Economic Determinants of Life Expectancy in Four Groups of Countries Classified by their Levels of Income

This study seeks to determine the role of per capita value added in the main sectors of; agriculture, industry, services, plus per capita GDP, on life expectancy at birth in the four groups of countries classified internationally by; high income, upper middle income, lower middle income, and low-income countries, during the period 1990-2022. Panel data analysis adopted for each group countries, where CS-ARDL approach is used to measure the effect of the independent variables (sector’s value-added per capita income) on the dependent variable (life expectancy at birth). The findings indicate that the per capita values provided by the sectors of industry and services are important factors in increasing life expectancy in high-income countries. Whereas, increases in per capita GDP would significantly extend life expectancy in countries ranging from upper-middle to lower-middle and low-incomes.

Logistic and CSG Growth Models for Predicting Life Expectancy

The tables that allow calculating the probability of death at a certain age by recording the number of births/deaths in a population are called life tables. The concept of life expectancy, which is a measure that determines how long a creature will live, is also determined by mortality rates obtained from life tables. It is also possible to model the expected lifetime with some nonlinear mathematical functions. One of the functions that is often used in modeling mortality rates is the logistic growth function. The aim of this study is to propose a model that can be used as an alternative to the logistic growth model and to interpret the mortality rates of countries. In this study, the life expectancy of males and females in Turkey, Singapore, Norway, and China was modeled using the logistic and the CSG growth model, which was newly introduced to the literature. When modeling the life expectancy of countries, adjusted graph was drawn in accordance with the data of each growth model. Then, the performances of the logistic growth model and the CSG growth model were compared with R^2, RMSE, and MAPE statistical criteria. As a result of the comparison, it was revealed that the CSG growth model is more suitable than the logistic model for estimating life expectancy for overall data and for each gender. The originality of this study is the CSG model which is very new nonlinear model that predicts life expectancy effectively for related datasets.

From Recession to Pandemic

Abstract Embarking on an exploration of socio-economic disparities within OECD countries over two decades, this research investigates the effects of the Great Recession and the COVID-19 pandemic on income distribution, labor market dynamics, and health outcomes. Using Exploratory Data Analysis of time-series and mixed-effects models, the study reveals a context characterized by intensifying inequalities, with variations reflecting the unique socio-economic structures of individual countries. It uncovers a pronounced increase in income inequality in countries including Lithuania, Luxembourg, Italy, and the United States, alongside shifts in labor market conditions, with the United States being particularly notable. Furthermore, the analysis highlights a widening of health disparities, evidenced by a decline in life expectancy in countries like the United States, Poland, and Slovakia, and a slowdown in reducing infant mortality rates. The use of mixed-effects models elucidates the role of country-specific factors in shaping these inequalities, revealing a complex interplay of influences that highlight the varied impacts of the pandemic across the OECD. This study not only charts the evolving contours of socio-economic inequalities but also enhances the understanding of global crises’ effects on developed economies.

Factors Influencing Life Expectancy in Low-Income Countries: A Panel Data Analysis

Understanding the factors influencing life expectancy in low-income countries is crucial for formulating effective health and economic policies. This study aims to explore the determinants of life expectancy in these countries by analyzing panel data from 2004 to 2021. The hypothesis posits that economic indicators, healthcare expenditure, and agricultural land significantly affect life expectancy, while high HIV prevalence negatively impacts it. The research procedure involved using Ordinary Least Squares (OLS) regression analysis with life expectancy at birth as the dependent variable and GDP per capita (PPP, constant 2021 international $), government health expenditure per capita (current international $), agricultural land (% of land area), HIV prevalence (% of population ages 15–49), and current health expenditure per capita (current international $) as independent variables. The main results indicate that GDP per capita, government health expenditure, and agricultural land positively influence life expectancy, highlighting the importance of economic growth, healthcare investment, and food security. In contrast, HIV prevalence has a significant negative effect on life expectancy, underscoring the health burden of the epidemic. Statistical tests for heteroskedasticity and normality reveal some deviations, but the robustness of the analysis is maintained through appropriate econometric techniques. Theoretically, this study contributes to the literature by providing a nuanced understanding of how economic and health-related factors interact to affect life expectancy in low-income countries. Practically, the findings suggest that policies aimed at boosting economic growth, increasing healthcare spending, improving agricultural practices, and addressing HIV/AIDS are vital for enhancing life expectancy. This research offers valuable insights for policymakers in low-income countries striving to improve population health outcomes.

Assessment of Factors Associated with Life Expectancy Gap Based on the Preston Curve: A Cross-Sectional Study

Background: Some countries experience lower or higher life expectancy than what is predicted based on their income. This study examines why life expectancy deviation is experienced with the aim of exploring which factors and conditions contribute to better health outcome (life expectancy) at low cost.
 Methods: In this study at the first stage, the well-known Preston curve is reproduced and updated using the cross-sectional data of variables of life expectancy at birth (years) and per capita gross domestic product (GDP) by purchasing power parity (PPP) of 182 countries around the World in 2018 based on the latest available data. After estimating the deviation of each countries life expectancy from the curve, the characteristics of countries with more than four years of positive (group 1) and negative (group 2) gaps from the curve were compared by applying the mean comparison test of two independent groups (t-test).
 Results: The identified drivers of gains or losses in longevity relative to income included using at least basic sanitation (P = 0.012) and drinking water services (P = 0.045), Universal Health Coverage (UHC) (P = 0.012), access to electricity (P = 0.004), CO2 emissions (P = 0.037), inequality in income (P = 0.003), health expenditure per capita (P = 0.000), non-communicable (P = 0.000) and communicable diseases and maternal, prenatal, and nutrition conditions (P = 0.000), literacy rate (P = 0.057), and road injuries (P = 0.001).
 Conclusion: Better health outcome in countries and regions with relatively low income or few resources can be achieved that would be critical for global improvement in population health. However, it needs to take effective measures and is of great importance for policy-making.

An assessment of the Impact of Good Governance on Health Outcome among Selected African Countries

Good governance is an essential component that drives better health outcome and education output required as a catalyst that will stimulate economic growth, and this constitutes a vital social objective as a result of good level of human capital development. The study evaluated the impact of good governance on health outcome among selected African countries using panel data from 2000 to 2020. The Panel unit root tests indicated that real gross domestic product, health outcome, and indicators of good governance are stationary at level while health expenditure and foreign aid are stationary at first difference. The Generalized Method of Moment (GMM) results show that indicators of good governance have positive and statistically significant effects on health outcome in the selected African Countries. Real gross domestic product has a positive and a statistically significant effect on health outcome in the selected African Countries Life expectancy has a positive and statistically significant effect on health outcome in the selected African Countries. Health expenditure has a positive and statistically significant effect on health outcome in the selected countries. Foreign aid has a positive and statistically significant effect on health outcome in the selected African Countries. The study recommends that African Countries should ensure that indicators of good governance such as political stability, accountability and voice, and control of corruption are moving smoothly without any interruption, by so doing economic growth of African countries will increases.

A Survey of Convolutional Neural Network in Breast Cancer.

For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

Dynamics of life expectancy change during the transition to democracy in the ex-Yugoslav countries

The break-up of Yugoslavia led to political turmoil, accompanied by socio-economic changes, wars and civil unrest. Health as a social phenomenon is directly and indirectly affected by such changes. To give an overview of the changes in life expectancy, as a measure of population health, during times of crisis and large societal changes. The dynamics of change of life expectancy in the different Balkan countries are analysed by calculating the contributions of age and cause specific mortality to the observed changes of life expectancy.An original dataset of life-tables is used to calculate life expectancy, created based on mortality data from national civil registers from the 6 ex-Yugoslav republics, assessed for accuracy and completeness. Arriaga’s method is used to decompose the contributions of age groups and causes of death to the observed changes in life expectancy during three time periods: Socialist Federation (1975-1985), Transition (1988-2000), Independence (2007-2017). Large improvements in life expectancy at birth are noted in all countries, although there is heterogeneity in the dynamics of change. During the Socialist Federation period, life expectancy increased consistently across countries mainly due to reductions in infant and child mortality, as well as reduction in infectious diseases mortality. During the Transition there was a divergence of trends and some countries noted continuous increase in life expectancy, contrasting the decrease in life expectancy in Serbia and Montenegro. In all countries life expectancy increased during the Independence period, due to a reduction in mortality from non-communicable diseases among the older population.The findings of this study are important not only because they create an understanding of changes in population health during a period of crisis, but also because they provide accurate estimates of life-expectancy and dynamics of mortality change, allowing for further analysis on the social determinants of health.

Is life expectancy higher in countries and territories with publicly funded health care? Global analysis of health care access and the social determinants of health.

To better understand factors influencing life expectancy, this paper examines how the availability of publicly funded health care in a country and multiple social determinants of health impact longevity of life. In this descriptive statistical analysis, data regarding publicly funded health care, life expectancy, and social determinants of health were obtained for 196 countries and 4 territories. Social determinants included 10 indicators detailing country-level information to represent 5 key categories: economic stability, education, health & health care, neighbourhood & built environment, and social & community context. Analyses consisted of: 1) comparison of mean life expectancy among countries and territories with- and without- publicly funded health care; 2) correlations in life expectancy across social determinants by health care access and level of burden; and 3) correlations in life expectancy within social determinants for health care access by level of burden. Overall, life expectancy in countries and territories with- publicly funded health care (Mean (m) = 76.7 years) was significantly longer compared to countries and territories without- publicly funded health care (m = 66.8 years, P < 0.0001). For each social determinant, we observed longer life expectancy continued to be associated with publicly funded health care access across stratum (P < 0.0001), but difference in years of life expectancy existed both by burden of social determinant, as well as access to health care within quartiles of burden (Publicly funded care (yes): 68.12-80.88 years, (no): 62.39-77.33 years, all P < 0.05). Both social determinants as well as the availability of publicly funded health care were individually and simultaneously associated with mean longevity of life between countries and territories worldwide. These findings demonstrate how, if made widely available, publicly funded health care could extend longevity of life. If combined with programs to reduce the burden of social determinants, a substantial impact can be made to promote more equitable distribution of life expectancies across the world. Ultimately, both access to publicly funded care and reducing inequalities in social determinants are needed in order to promote longer and healthier aging in populations worldwide.

Increasing life expectancy in China by achieving its 2025 air quality target

China is striving to build a “Beautiful China” characterized by clean air. The country has committed to further reducing its national mean fine particle (PM2.5) concentration by 10% from 2020 to 2025, following the substantial improvements in its air quality during the past decade. Meanwhile, the “Healthy China” mission has pledged to increase the national mean life expectancy by one year during the same period. Yet, to what extent will the “Beautiful China” mission contribute to the “Healthy China” vision by reducing the levels of the detrimental PM2.5 is still unclear. Here, by coupling the life table approach and an epidemiological concentration-response model, this study quantifies the potential benefits of achieving China's 2025 air quality target on the national life expectancy. The analysis reveals that the Chinese citizen could expect to extend the average life expectancy by 42.5 days by 2025 due to improved air quality. In addition, if the Chinese government outperforms the planned air quality target, as it usually does, the gains would increase to 65.4 days, ∼18% of the “Healthy China” life expectancy increment task. Further reductions in PM2.5 concentration would lead to accelerated gains in life expectancy both nationally and at the city level, providing strong incentives for the authorities to keep improving air quality. This study reveals the notable benefits on individual life that could be expected from air quality improvement in China and suggests that longer life expectancy is achievable by implementing a health-prioritized air quality management mechanism.

The impact of economic, social, and political globalization and democracy on life expectancy in low-income countries: are sustainable development goals contradictory?

The 17 Sustainable Development Goals announced by the United Nations are important guides for the development processes of developing countries. However, achieving all of these goals is only possible if the goals are consistent with each other. It has been observed in the literature that possible contradictions between these goals are ignored. Therefore, the main purpose of this study is to investigate whether two sustainable development goals (SDGs) of the UN are contradictory or supporting each other in low-income countries. These SDGs are “Good Health and Well-Being” (SDG3) and “Partnerships for the Goals” (SDG17). For this purpose, the role of globalization and democracy in life expectancy is empirically investigated in 16 low-income countries over the period 1970–2017. While globalization has been used as an indicator of the partnership between countries, democracy has been used as an indicator of accountability and cooperation between governments and societies. According to estimations of the continuous-updated fully modified (CUP-FM) and bias-adjusted ordinary least squares (BA-OLS), globalization and its subcomponents such as economic, social, and political globalization affect life expectancy positively. Democracy also increases life expectancy in those countries. The GDP per capita is also used as a control variable. Our results show that a higher level of per capita income is positively associated with higher levels of life expectancy. In conclusion, no contradiction was found between SDG3 and SDG17 in those countries. Achieving a healthier society requires economic, social, and political integration between governments and societies.

No change in life expectancy: the devil is in the detail

Abstract Background The rate of improvement in life expectancy in high income countries has slowed down over the past few years, and instances where life expectancy is lower than a year before are increasingly common. This paper aims to analyse changes in life expectancy over the last decade to better understand what causes and age groups contribute to the slowdown. Methods We use WHO mortality data by age and cause to construct life tables, and we use Arriaga decomposition method to analyse the contribution of specific causes and age groups to changes in life expectancy in Australia, Canada, France, Germany, Netherlands, United Kingdom and the United States of America. We look at the change between 2007-2012 and 2012-2017 (or latest available). Results All countries experienced a slowdown in life expectancy in the past 5 years (2012-2017), in comparison to the preceding period. Slowdown in under 65s was particularly pronounced, with younger age groups only contributing minimally (between 0.4 years for males in Germany and -0.4 years for males in the United States) to changes in life expectancy. Among people aged 65 and over, gains ranged between 0.05 years for females in France and 0.6 years for males in the Netherlands. Certain causes of death contributed negatively to change in life expectancy between 2012 and 2017, with notable increases in deaths from accidental poisonings in males (up to -0.09 year in the UK and Canada, and -0.34 in the US) and suicides (up to -0.08 year in Australia and -0.07 in the US). Conclusions While recent slowdown in life expectancy gains in high income countries is often attributed to lack of improvement in people of older ages, we show that, beyond this, there are increases in mortality in younger age groups from external causes, that contribute negatively to change in life expectancy in some countries. This pattern is of a particular concern, as deterioration in preventable mortality points to broader worsening of socio-economic climate. Key messages Improvements in life expectancy in high income countries slowed down markedly over the past few years, but contributing mortality patterns differ for age groups and causes of death across countries. Persistent increases in preventable mortality from certain external causes in younger age groups in Australia, Canada, US and UK point to broader deterioration of socio-economic climate.

Life Expectancy of Population of the Country: The Role of Health Services Effectiveness

The impact of health care on life expectancy of population in different countries has been studied. The subject of the study was analysis of efficiency of public health expenditures and their percentage of Gross Domestic Product. The authors employed a relatively new method of comparative analysis, Data Envelopment Analysis (DEA), which involves to measure technical efficiency of activity of an economic entity. DEA method allowed identifying the effectiveness of economic measures in healthcare system, as well as provision of the necessary volume of medical services in different countries. A study was conducted to compare the actual effectiveness of the country's medical services to the maximum possible effectiveness. As factor indicators, the summary of health expenditure and its percentage of GDP were considered. The average life expectancy of countries was taken as an average resulting indicator. According to the results, all the surveyed indicators proved to be the most effective measures in providing population with medical services in such countries as Andorra and San Marino, Monaco. It was determined that in order to increase the average life expectancy for one year, an average annual increase in the expenditure on the health care system by 0.48% of GDP is required.

Response time in the emergency services. Systematic review.

The growth of the urban population raises concern about municipal public managers in the sense of providing emergency medical services (EMS) that are aligned with the needs of prehospital emergency medical care demanded by the population. The literature review aims at presenting the response time of emergency medical services in several parts of the world and discussing some factors that interfere in the result of this indicator such as GDP (Gross Domestic Product) percentage spent on health and life expectancy of countries. The study will also show that in some of the consulted articles, authors suggest to EMS recommendations for decreasing the response time using simulations, heuristics and metaheuristics. Response time is a basic indicator of emergency medical services, in such a way that researchers use the descriptive statistics to evaluate this parameter. Europe and the USA outstand in the publication of studies that present this information. Some articles use stochastic and mathematical methods to suggest models that simulate scenarios of response time reduction and suggest such proposals to the local EMS. Countries in which the response time was identified have a high index of human development and life expectancy between 74.7 and 83.7 years.

Your fathers’ mistakes: Critiques of GDP and the search for an alternative

Your fathers’ mistakes: Critiques of GDP and the search for an alternative

Global health inequalities and the need for solidarity: a view from the Global South.

Although the world has experienced remarkable progress in health care since the last half of the 20th century, global health inequalities still persist. In some poor countries life expectancy is between 37-40 years lower than in rich countries; furthermore, maternal and infant mortality is high and there is lack of access to basic preventive and life-saving medicines, as well a high prevalence of neglected diseases, HIV/AIDS, tuberculosis, and malaria. Moreover, globalization has made the world more connected than before such that health challenges today are no longer limited within national or regional boundaries, making all persons equally vulnerable. Because of this, diseases in the most affluent countries are closely connected with diseases in the poorest countries. In this paper, we argue that, because of global health inequalities, in a situation of equal vulnerability, there is need for global solidarity not only as a means of reducing health inequalities, but also as a way of putting up a united force against global health challenges. We argue for an African approach to solidarity in which the humanity of a person is not determined by his/her being human or rational capacity, but by his/her capacity to live a virtuous life. According to this view of solidarity, because no one is self-sufficient, no individual can survive alone. If we are to collectively flourish in a world where no individual, nation or region has all the health resources or protection needed for survival, we must engage in solidarity where we remain compassionate and available to one another at all times.

Frailty: Prevalence in the Resident Population of Germany 70 - 79 Years of Age - a Population-Based Approach

In all countries life expectancy is rising, and many older persons spend a longer period in good health. Nevertheless, frailty is present in older people and can be considered as a risk factor for limitations. The "German Health Interview and Examination Survey for Adults" (DEGS1) 2008 - 2011 comprised interviews, examinations and tests. Data on 1110 community-dwelling people aged 70 to 79 years participating in DEGS1 with full records on frailty were analysed. Physical frailty was defined as exhaustion (SF-36 item), low grip strength, slowness (Timed Up-and-Go test > 15 seconds or unable to perform) and low physical activity (no sports or exertion). In men 36,6% (CI 32,2 - 41,2) were classified as pre-frail, 2,5% (CI 1,4 - 4,4) as frail; in women 41,8% (CI 37,3 - 46,5) were classified as pre-frail, and 2,9% (CI 1,9 - 4,5) as frail. Frail persons significantly show more polypharmacy, recurrent falls, receive more frequently nursing care financial benefits, are officially recognized disabled and have poor social support. This population-based information may help to identify frail people with high risk of adverse health outcomes. Prediction models in operative anaesthesiology for specific health care settings should be developed.

Contribution of demography to economic growth

From 1850 to 2000, in Western European countries life expectancy rose from 30–40 to 80 years and the average number of children per woman fell from 4 to 5 children to slightly more than one. To gauge the economic consequences of these demographic trends, we implement an overlapping generations model with heterogeneity by level of education in which individuals optimally decide their consumption of market- and home-produced goods as well as the time spent on paid and unpaid work. We find that around 17% of the observed increase in per-capita income growth from 1850 to 2000 was due to the demographic transition. Around 50% of the demographic contribution is explained by the increase in the average productivity per worker (productivity component), which arises from the change in the population’s age structure and the rise in households’ saving rate. The remaining 50% is explained by the higher growth rate of workers relative to the total population (translation component).

Bayesian projection of life expectancy accounting for the HIV/AIDS epidemic.

BackgroundWhile probabilistic projection methods for projecting life expectancy exist, few account for covariates related to life expectancy. Generalized HIV/AIDS epidemics have a large, immediate negative impact on the life expectancy in a country, but this impact can be mitigated by widespread use of antiretroviral therapy (ART). Thus, projection methods for countries with generalized HIV/AIDS epidemics could be improved by accounting for HIV prevalence, the future course of the epidemic, and ART coverage.MethodsWe extend the current Bayesian probabilistic life expectancy projection methods of Raftery et al. (2013) to account for HIV prevalence and adult ART coverage in countries with generalized HIV/AIDS epidemics.ResultsWe evaluate our method using out-of-sample validation. We find that the proposed method performs better than the method that does not account for HIV prevalence or ART coverage for projections of life expectancy in countries with a generalized epidemic, while projections for countries without an epidemic remain essentially unchanged.ConclusionsIn general, our projections show rapid recovery to pre-epidemic life expectancy levels in the presence of widespread ART coverage. After the initial life expectancy recovery, we project a steady rise in life expectancy until the end of the century.ContributionWe develop a simple Bayesian hierarchical model for long-term projections of life expectancy while accounting for HIV/AIDS prevalence and coverage of ART. The method produces well-calibrated projections for countries with generalized HIV/AIDS epidemics up to 2100 while having limited data demands.

The Importance of Human Inequalities and Health Care Quality in Life Expectancy of Countries

In Human Development Reports published by The United Nations Development Programme (UNDP), the development in the health dimension is measured by life expectancy at birth. In the context of this study, factors that affect life expectancy in 130 countries were analyzed with the help of established multiple regression model. The independent variables of the model, except the proportion of health expenditure in GDP, human inequality, gross national income per capita and satisfaction with health care quality are found to be statistically significant. This situation indicates that the quality of health services in the country in terms of life expectancy is more important than the amount of health care expenditures. The most influential variables on life expectancy at birth were found in order as; human inequality, gross national income per capita and satisfaction with health care quality.