As of 2023, the countries with the highest life expectancy included Switzerland, Japan, and Spain. As of that time, a new-born child in Switzerland could expect to live an average of **** years. Around the world, females consistently have a higher average life expectancy than males, with females in Europe expected to live an average of *** years longer than males on this continent. Increases in life expectancy The overall average life expectancy in OECD countries increased by **** years from 1970 to 2019. The countries that saw the largest increases included Turkey, India, and South Korea. The life expectancy at birth in Turkey increased an astonishing 24.4 years over this period. The countries with the lowest life expectancy worldwide as of 2022 were Chad, Lesotho, and Nigeria, where a newborn could be expected to live an average of ** years. Life expectancy in the U.S. The life expectancy in the United States was ***** years as of 2023. Shockingly, the life expectancy in the United States has decreased in recent years, while it continues to increase in other similarly developed countries. The COVID-19 pandemic and increasing rates of suicide and drug overdose deaths from the opioid epidemic have been cited as reasons for this decrease.

Life Expectancy by Country in 2013. This is a filtered layer based on the "Life Expectancy by country, 1960-2010 time series" layer.Life expectancy values are included for males, females, and total population. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data Sources: United Nations Population Division. World Population Prospects, United Nations Statistical Division. Population and Vital Statistics Report, Census reports and other statistical publications from national statistical offices, Eurostat: Demographic Statistics, Secretariat of the Pacific Community: Statistics and Demography Programme, U.S. Census Bureau: International Database via World Bank DataBank; Natural Earth 50M scale data.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

The Atlas of the Biosphere is a product of the Center for Sustainability and the Global Environment (SAGE), part of the Gaylord Nelson Institute for Environmental Studies at the University of Wisconsin - Madison. The goal is to provide more information about the environment, and human interactions with the environment, than any other source.

The Atlas provides maps of an ever-growing number of environmental variables, under the following categories:

Human Impacts (Humans and the environment from a socio-economic perspective; i.e., Population, Life Expectancy, Literacy Rates);

Land Use (How humans are using the land; i.e., Croplands, Pastures, Urban Lands);

Ecosystems (The natural ecosystems of the world; i.e., Potential Vegetation, Temperature, Soil Texture); and

Water Resources (Water in the biosphere; i.e., Runoff, Precipitation, Lakes and Wetlands).

Map coverages are global and regional in spatial extent. Users can download map images (jpg) and data (a GIS grid of the data in ESRI ArcView Format), and can view metadata online.

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

Algeria had the highest life expectancy at birth in Africa as of 2023. A newborn infant was expected to live over 77 years in the country. Cabo Verde, Tunisia, and Mauritius followed, with a life expectancy between 77 and 75 years. On the other hand, Chad registered the lowest average, at nearly 54 years. Overall, the life expectancy in Africa was almost 63 years in the same year.

The World Development Indicators from the World Bank contain over a thousand annual indicators of economic development from hundreds of countries around the world.

Here's a list of the available indicators along with a list of the available countries.

For example, this data includes the life expectancy at birth from many countries around the world:

The dataset hosted here is a slightly transformed verion of the raw files available here to facilitate analytics.

GAMI is an updated dataset providing global forest age distributions for 2010 and 2020 with 100-meter resolution, improving upon the MPI-BGC forest age product. Utilizing machine learning, specifically XGBoost, the estimates are based on over 40,000 forest inventory plots, biomass/height data, remote sensing, and climate data. The dataset incorporates Landsat-based disturbance history and uses multiple XGBoost models with varied hyperparameters to address aleatoric and epistemic uncertainties. Twenty realizations of biomass data with controlled perturbations simulate natural variability, offering robust statistical measures and confidence intervals. Additionally, GAMI provides age class fraction products at different spatial resolutions for custom analyses.

The full description of the data and the updates to version 1.0 (Besnard, 2021, https://doi.org/10.17871/ForestAgeBGI.2021) is provided in the associated data description file.

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

Belgian National Burden of Disease Study

Estimates of the burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

Prevalence

Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.

Years Lived with Disability

In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

Disability-Adjusted Life Years

Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.

Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division(2014), World Bank (2014) and IMF (2014). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

ABSTRACT CONTEXT AND OBJECTIVE: Traffic accidents have gained prominence as one of the modern epidemics that plague the world. The objective of this study was to identify the spatial distribution of potential years of life lost (PYLL) due to accidents involving motorcycles in the state of São Paulo, Brazil. DESIGN AND SETTING: Ecological and exploratory study conducted in São Paulo. METHODS: Data on deaths among individuals aged 20-39 years due to motorcycle accidents (V20-V29 in the International Classification of Diseases, 10th revision) in the state of São Paulo in the years 2007-2011 were obtained from DATASUS. These data were stratified into a database for the 63 microregions of this state, according to where the motorcyclist lived. PYLL rates per 100,000 inhabitants were calculated. Spatial autocorrelations were estimated using the Global Moran index (IM). Thematic, Moran and Kernel maps were constructed using PYLL rates for the age groups of 20-29 and 30-39 years. The Terraview 4.2.2 software was used for the analysis. RESULTS: The PYLL rates were 486.9 for the ages of 20-29 years and 199.5 for 30-39 years. Seventeen microregions with high PYLL rates for the age group of 20-29 years were identified. There was higher density of these rates on the Kernel map of the southeastern region (covering the metropolitan region of São Paulo). There were no spatial autocorrelations between rates. CONCLUSIONS: The data presented in this study identified microregions with high accident rates involving motorcycles and microregions that deserve special attention from regional managers and traffic experts.

Belgian National Burden of Disease Study

Estimates of the burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

Prevalence

Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.

Years Lived with Disability

In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

Disability-Adjusted Life Years

Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.

Belgian National Burden of Disease Study

Estimates of the fatal burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

More information

For additional background on BeBOD, please visit https://www.sciensano.be/en/projects/belgian-national-burden-disease-study.

Explore the estimates via https://burden.sciensano.be/shiny/mortality.

地図(マップ)上に20歳男性の平均余命の統計データを都道府県別で色分け表示しています。過去から現在までの20歳男性の平均余命の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が長いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

Countries from Natural Earth 50M scale data with a Human Development Index attribute, repeated for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, & 2013, to enable time-series display using the YEAR attribute. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division (2014), World Bank (2014) and IMF (2014).

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

地図(マップ)上に40歳女性の平均余命の統計データを都道府県別で色分け表示しています。過去から現在までの40歳女性の平均余命の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が長いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に65歳女性の平均余命の統計データを都道府県別で色分け表示しています。過去から現在までの65歳女性の平均余命の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が長いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。