Healthy life expectancy (HLE) is an estimate of expected years of life spent in self-reported good health. The figure used is for males aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

It is used as a high-level outcome to contrast and monitor the health status of different populations at specific points in time, giving context to the impacts of policy changes and interventions at both national and local levels.

Healthy life expectancy has value across state, private, and voluntary sectors, in the assessment of healthy ageing, fitness for work, health improvement monitoring, extensions to the state pension age, pension provision, and health and social care need. This indicator is an extremely important summary measure of mortality and morbidity. It complements the supporting indicators such as mortality by cause by showing the overall trends and setting the context in which local authorities can assess the other indicators and identify the drivers of healthy life expectancy.

The health prevalence data used in calculating HLE estimates for the various geographies in England were derived from the Annual Population Survey (APS).

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Healthy life expectancy (HLE) is an estimate of expected years of life spent in self-reported good health. The figure used is for females aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

It is used as a high-level outcome to contrast and monitor the health status of different populations at specific points in time, giving context to the impacts of policy changes and interventions at both national and local levels.

Healthy life expectancy has value across state, private, and voluntary sectors, in the assessment of healthy ageing, fitness for work, health improvement monitoring, extensions to the state pension age, pension provision, and health and social care need. This indicator is an extremely important summary measure of mortality and morbidity. It complements the supporting indicators such as mortality by cause by showing the overall trends and setting the context in which local authorities can assess the other indicators and identify the drivers of healthy life expectancy.

The health prevalence data used in calculating HLE estimates for the various geographies in England were derived from the Annual Population Survey (APS).

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

File Description: "Life Expectancy Data.csv" This dataset contains 2,938 entries and 22 columns, covering life expectancy and related health indicators for multiple nations from 2000 to 2015. It includes country-wise data and other economic, social, and health metrics. Column Description: 1. Country – Name of the country. 2. Year – Data year (ranging from 2000 to 2015). 3. Status – Economic classification (Developing/Developed). 4. Life expectancy – Average lifespan in years. 5. Adult Mortality – Probability of death between ages 15-60 per 1,000 individuals. 6. Infant Deaths – Number of infant deaths per 1,000 live births. 7. Alcohol – Per capita alcohol consumption. 8. Percentage Expenditure – Government health expenditure as a percentage of GDP. 9. Hepatitis B – Immunization coverage percentage. 10. Measles – Number of reported measles cases. 11. BMI – Average Body Mass Index. 12. Under-Five Deaths – Mortality rate for children under five. 13. Polio & Diphtheria – Immunization rates. 14. HIV/AIDS – Deaths due to HIV/AIDS per 1,000 individuals. 15. GDP – Gross Domestic Product per capita. 16. Population – Total population of the country. 17. Thinness (1-19 years, 5-9 years) – Percentage of underweight children. 18. Income Composition of Resources– Human development index proxy. 19. Schooling– Average number of years of schooling. Missing Data: Some columns (like Hepatitis B, GDP, Population, Total Expenditure) contain missing values. Further File Information: Total Countries: 193 Years Covered: 2000–2015 Total Entries: 2,938 Missing Data Overview: Some columns have missing values, notably: Hepatitis B (553 missing) GDP (448 missing) Population (652 missing) Total expenditure (226 missing) Income Composition of Resources (167 missing) Schooling (163 missing) Summary Statistics: Life Expectancy:

Range: 36.3 to 89 years Mean: 69.2 years Adult Mortality:

Mean: 165 per 1,000 Max: 723 per 1,000 GDP per Capita:

Mean: $7,483 Max: $119,172 Population:

Mean: ~12.75 million Max: 1.29 billion Education:

Schooling Average: 12 years Max: 20.7 years

Futuristic Scope of this data: For comparative analysis of the 2000–2015 life expectancy dataset with new datasets on the same parametres , you can perform several statistical tests and analytical methods based on different research questions. Below are some key tests and approaches:

1. Trend Analysis (Time-Series) Objective: Identify trends in life expectancy and related indicators over time. Methods: Moving Averages: Smooth fluctuations to detect trends. Linear/Polynomial Regression: Check whether life expectancy follows an increasing or decreasing trend. Time-Series Decomposition: Separate data into trend, seasonality, and residuals.
2. Descriptive Statistics & Comparative Summary Objective: Compare summary statistics between years or groups. Tests/Methods: Mean, Median, Standard Deviation: Compare distributions of life expectancy, GDP, or schooling. Boxplots & Histograms: Show variations over different years or between developing vs. developed countries. Coefficient of Variation (CV): Compare variability in life expectancy across regions.
3. Correlation & Regression Analysis Objective: Examine relationships between variables. Methods: Pearson/Spearman Correlation: Check relationships between life expectancy and GDP, health expenditure, etc. Multiple Linear Regression: Predict life expectancy based on GDP, immunization, and schooling. Multicollinearity (VIF Test): Ensure independent variables are not highly correlated.
4. Hypothesis Testing (Comparative Analysis) Test Objective When to Use? t-Test (Independent Samples) Compare life expectancy between developed & developing nations Two groups (e.g., 2000 vs. 2015, or developed vs. developing) Paired t-Test Compare life expectancy in the same country over two time periods Before/after comparison (e.g., 2000 vs. 2015 for the same country) ANOVA (One-Way) Compare life expectancy across multiple groups More than two groups (e.g., continents or income groups) Chi-Square Test Test if categorical distributions (e.g., immunization coverage) differ over time Categorical variables (e.g., immunization rates vs. year)
5. Clustering & Classification (Machine Learning) Objective: Group countries based on life expectancy patterns. Methods: K-Means Clustering: Identify groups with similar life expectancy trends. Hierarchical Clustering: Create a country similarity tree. Decision Trees/Random Forest: Classify countries based on development status using life expectancy factors.
6. Forecasting Future Trends Objective: Predict life expectancy in future years using historical data. Methods: ARIMA (AutoRegressive Integrated Moving Average): Time-series forecasting. Exponential Smoothing: Forecast gradual trends. Machine Learning (LSTM, XGBoost): Predict based on multiple indicators.
7. Comparative Regional Analysis O...

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

CZ-level estimates of trends in life expectancy for men and women, by income quartile.

Female healthy life expectancy of Georgia dipped by 3.08% from 67.4 years in 2020 to 65.3 years in 2021. Since the 0.10% upward trend in 2019, female healthy life expectancy decreased by 3.90% in 2021.

This table represents five variants of health expectancies: -life expectancy in perceived good health. -life expectancy without physical limitations. -life expectancy without chronic morbidity. -without psychological complaints (until 2023) -life expectancy without GALI-limitations In addition, figures of 'normal' life expectancy are included, so the figures of health expectancy can be related to them. In the table, the data on health expectancy can be split into the following characteristics: -sex (starting from the data of 2018, the category ‘total, men + women’ is added). -age.

Using this table one can see the developments over time of health expectancies. For example it can be seen that morbidity free life expectancy of women shortened during the eighties and nineties. In the same period the life expectancy free of moderate and severe limitations of men increased.

Data available from: 1981

Status of the figures: The figures in this table are definitive.

Changes as of June 19, 2025: The 2024 figures have been added.

When will new figures be published? The figures for 2025 will be published in the third quarter of 2026.

Introduction

Life Expectancy Statistics: Life expectancy is the average number of years a person is expected to live based on current mortality rates in a specific population.

It is influenced by healthcare quality, lifestyle choices, economic conditions, genetics, environmental factors, and social determinants like education and public health policies.

Typically measured as life expectancy at birth, it reflects the average lifespan of a newborn. However, it can also be assessed for older ages, such as 65, to predict additional years of life.

Life expectancy worldwide has seen significant improvements over the past three decades, with notable variations across regions. In 2021, a child born in the Americas could expect to live an average of **** years, compared to ** years in 1990. However, the COVID-19 pandemic caused a universal decline in life expectancy from 2019 to 2021, affecting all World Health Organization regions. Regional disparities and global trends While global life expectancy has generally increased over time, stark regional differences persist. ****** consistently reports the lowest life expectancy, with **** years in 2021. In fact, the twenty countries with the lowest life expectancy in the world are all found in ******, with **** and ******* reporting the lowest life expectancies at just ** years. In contrast, the *************** now has the highest life expectancy, reaching **** years in 2021. These disparities reflect broader socioeconomic factors, with low-income countries facing challenges such as limited healthcare access and higher rates of infectious diseases. Impact of health issues on life expectancy Various health issues contribute to differences in life expectancy across countries and regions. Mental health has emerged as a significant concern, with a survey of 31 countries identifying it as the biggest health problem facing people in these countries in 2024. The COVID-19 pandemic not only directly impacted life expectancy but also exacerbated mental health issues worldwide. Additionally, non-communicable diseases play a crucial role in determining life expectancy. In 2021, ********************** was the leading cause of death globally, highlighting the importance of addressing chronic health conditions to improve overall life expectancy.

Female healthy life expectancy of Azerbaijan dipped by 0.75% from 65.5 years in 2020 to 65.0 years in 2021. Since the 0.58% upward trend in 2019, female healthy life expectancy decreased by 3.90% in 2021.

Female healthy life expectancy of Bulgaria dipped by 3.35% from 66.8 years in 2020 to 64.6 years in 2021. Since the 0.27% upward trend in 2019, female healthy life expectancy decreased by 4.99% in 2021.

Published as part of Health in Ireland: Key Trends 2016 (Department of Health)

Female healthy life expectancy of Tunisia dipped by 3.80% from 66.9 years in 2020 to 64.4 years in 2021. Since the 0.24% upward trend in 2019, female healthy life expectancy decreased by 4.46% in 2021.

We systematically reviewed the evidence on secular trends in main chronic conditions, disability and self-assessed general health among adults in the United Kingdom, as reported in primary/secondary care databases and population-based surveys. Searches were conducted separately for: (1) trends in age-standardised or age-specific prevalence of major non-communicable diseases, disability, and self-reported general health; (2) trends in health expectancy. The databases searched were MEDLINE, EMBASE/EMBASE Classic and Web of Science (all from 1946/7). The evidence was synthesised narratively. There were 39 studies reporting trends in prevalence of health conditions and 15 studies in health expectancy. We did not find evidence for improvement in the age-standardised or age-specific prevalence of any of the studied major chronic conditions over the last few decades, apart from Alzheimer's disease and other dementias. Both increasing or stable prevalence rates with simultaneous rising life expectancy support the expansion of morbidity theory, meaning that people are expected to spend a greater number of years with chronic condition(s). The evidence on disability—expressed as prevalence or health expectancy—was mixed, but also appeared to support the expansion of morbidity among those aged 65 or over. The evidence on trends in disability for younger age is lacking. Across the studied period (1946–2017), the UK population endured more years with chronic morbidity and disability, which may place a serious strain on the health care system, the economy and the society.

Female healthy life expectancy of Gambia dipped by 1.15% from 56.8 years in 2020 to 56.1 years in 2021. Since the 0.46% upward trend in 2019, female healthy life expectancy decreased by 1.46% in 2021.

This statistic shows the average life expectancy in North America for those born in 2022, by gender and region. In Canada, the average life expectancy was 80 years for males and 84 years for females.

Life expectancy in North America

Of those considered in this statistic, the life expectancy of female Canadian infants born in 2021 was the longest, at 84 years. Female infants born in America that year had a similarly high life expectancy of 81 years. Male infants, meanwhile, had lower life expectancies of 80 years (Canada) and 76 years (USA).

Compare this to the worldwide life expectancy for babies born in 2021: 75 years for women and 71 years for men. Of continents worldwide, North America ranks equal first in terms of life expectancy of (77 years for men and 81 years for women). Life expectancy is lowest in Africa at just 63 years and 66 years for males and females respectively. Japan is the country with the highest life expectancy worldwide for babies born in 2020.

Life expectancy is calculated according to current mortality rates of the population in question. Global variations in life expectancy are caused by differences in medical care, public health and diet, and reflect global inequalities in economic circumstances. Africa’s low life expectancy, for example, can be attributed in part to the AIDS epidemic. In 2019, around 72,000 people died of AIDS in South Africa, the largest amount worldwide. Nigeria, Tanzania and India were also high on the list of countries ranked by AIDS deaths that year. Likewise, Africa has by far the highest rate of mortality by communicable disease (i.e. AIDS, neglected tropics diseases, malaria and tuberculosis).

In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.

BackgroundSocioeconomic disparities in life expectancy are well-documented in various contexts, including Chile. However, there is a lack of research examining trends in life expectancy inequalities and lifespan variation over time. Addressing these gaps can provide crucial insights into the dynamics of health inequalities.MethodsThis study utilizes data from census records, population surveys, and death certificates to compare the life expectancy and the lifespan variation at age 26 of individuals according to their rank in the distribution of years of education within their own birth cohort. The analysis spans three periods (1991, 2002, and 2017) and focuses on two educational groups: individuals in the first (lowest) quintile and tenth (highest) decile of educational attainment. Changes in life expectancy are disaggregated by major causes of death to elucidate their contributions to overall trends.ResultsConsistent with existing literature, our findings confirm that individuals with lower education levels experience lower life expectancy and higher lifespan variation compared to their more educated counterparts. Notably, by 2017, life expectancy for individuals in the lowest quintile of education has caught up with that of the top decile in 1991, albeit with contrasting trends between genders. Among women, the gap has reduced, while it has increased for males. Moreover, lifespan variation decreased (increased) over time for individuals in the tenth decile (first quintile). The leading causes of death that explain the increase in life expectancy in women and men in the tenth decile as well as women in the first quintile are cardiovascular, cancer, respiratory and digestive diseases. In the case of males in the first quintile, few gains have been made in life expectancy resulting from cancer and a negative contribution is associated with digestive conditions.ConclusionsThis study underscores persistent socioeconomic disparities in life expectancy in Chile, emphasizing the importance of ongoing monitoring of health inequalities across different demographic segments. The gender-specific and educational gradient trends highlight areas for targeted interventions aimed at reducing health disparities and improving overall population health outcomes. Further research is warranted to delve into specific causes of death driving life expectancy differentials and to inform evidence-based policy interventions.

Life expectancy by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sourcesYear (Numeric): Year of dataCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); United StatesAge, in years (Numeric): Life expectancy

Asian and Pacific Islander populations are only available in 5-year estimates due to low numbers.

Data Source: DC Vital Records, CDC WONDER single-race single-year population estimates and American Community Survey (ACS) 1-year estimates

Why This Matters

Life expectancy reflects a community’s mortality levels and overall health. In the U.S. life expectancy has been stagnant since 2010 and declined during the COVID-19 Pandemic, primarily due to heart disease, cancer, COVID-19, and fatal drug overdoses.

Changes and disparities in life expectancy at birth reflect trends and inequities in living standards, access to quality health care, and other social and economic factors.

Nationally, life expectancy at birth is lower among Black and Native Americans compared to other racial and ethnic groups. These racial disparities are rooted in a long history of racial segregation, economic and employment discrimination, and environmental racism, among other racist practices, as noted by the National Health Atlas.

The District Response

Ensuring District residents access to various healthcare programs, such as Medicaid, DC Healthcare Alliance Program, and DC Healthy Families. For more information on these programs, click here.

Initiatives and programs to reduce disparities in housing, employment, and food insecurity through programs and services, such as Supplemental Nutrition Assistance Program (SNAP), DC Child Care Subsidy Program, and DC Infrastructure Academy.

Promoting health through free DPR fitness centers, wellness classes, the MoveDC Plan for active transportation, and health and PE classes in public schools to encourage lifelong exercise habits.