The life expectancy for men aged 65 years in the U.S. has gradually increased since the 1960s. Now men in the United States aged 65 can expect to live 17 more years on average. Women aged 65 years can expect to live around 19.7 more years on average.

Life expectancy in the U.S.

As of 2021, the average life expectancy at birth in the United States was 76.33 years. Life expectancy in the U.S. had steadily increased for many years but has recently dropped slightly. Women consistently have a higher life expectancy than men but have also seen a slight decrease. As of 2019, a woman in the U.S. could be expected to live up to 79.3 years.

Leading causes of death

The leading causes of death in the United States include heart disease, cancer, unintentional injuries, chronic lower respiratory diseases and cerebrovascular diseases. However, heart disease and cancer account for around 38 percent of all deaths. Although heart disease and cancer are the leading causes of death for both men and women, there are slight variations in the leading causes of death. For example, unintentional injury and suicide account for a larger portion of deaths among men than they do among women.

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

This table contains 2394 series, with data for years 1991 -1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

Over the past 160 years, life expectancy (from birth) in the United States has risen from 39.4 years in 1860, to 78.9 years in 2020. One of the major reasons for the overall increase of life expectancy in the last two centuries is the fact that the infant and child mortality rates have decreased by so much during this time. Medical advancements, fewer wars and improved living standards also mean that people are living longer than they did in previous centuries.

Despite this overall increase, the life expectancy dropped three times since 1860; from 1865 to 1870 during the American Civil War, from 1915 to 1920 during the First World War and following Spanish Flu epidemic, and it has dropped again between 2015 and now. The reason for the most recent drop in life expectancy is not a result of any specific event, but has been attributed to negative societal trends, such as unbalanced diets and sedentary lifestyles, high medical costs, and increasing rates of suicide and drug use.

About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 30 to 34 years years with a population of 22.71 million (6.86%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in United States was the 80 to 84 years years with a population of 6.25 million (1.89%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

Slovakia - At Risk of Poverty rate of older people: Less than 60 years was 16.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Slovakia - At Risk of Poverty rate of older people: Less than 60 years - last updated from the EUROSTAT on June of 2025. Historically, Slovakia - At Risk of Poverty rate of older people: Less than 60 years reached a record high of 16.10% in December of 2024 and a record low of 12.00% in December of 2020.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Finland - At Risk of Poverty rate of older people: 60 years or over was 12.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - At Risk of Poverty rate of older people: 60 years or over - last updated from the EUROSTAT on July of 2025. Historically, Finland - At Risk of Poverty rate of older people: 60 years or over reached a record high of 16.60% in December of 2012 and a record low of 11.40% in December of 2017.

In 2020, about 17.9 percent of the population in China had been 60 years and older. This share is growing rapidly and was estimated to reach 40 percent by 2050. China's aging population With China’s boomer generation growing old and life expectancy increasing at the same time, the number of people at an age of 60 or above nearly doubled between 2000 and 2020 and reached around 255 million. This development is even more pronounced for the age group of 80 and above, which nearly tripled and is expected to reach a size of roughly 132 million in 2050, up from only 32 million in 2020. At the same time, the share of the working-age population is forecasted to decrease gradually from 64 percent of the total population in 2020 to around 50 percent in 2050, which could pose a heavy economic strain on the social security system. The old-age dependency ratio, which denotes the relation of the old-age to the working-age population, is estimated to grow from 18.2 percent in 2020 to more than 50 percent in 2050, implying that by then, statistically, two working-age adults would have to support one elderly. Strain on the social security net During the last 15 years, China's government has successfully increased the coverage of the pension insurance and health insurance. Today, most of the people are covered by some kind of social insurance. Conditions in the pension system are generous, with a regular retirement age for males at 60 years and women at 50 or 55. With the number of retirees increasing quickly, the social insurance system is now under pressure. From an economic point of view, improving the productivity of China's economy would be the primary choice for mitigating alleged inconsistencies of the system. However, without increasing the burden on the working people while tightening payment conditions, balancing the social security net could prove to be challenging.

The model “EP24 – Projections of dependent elderly people” is the result of joint work between DREES and INSEE. It was mobilised for the projections made public in 2019 (Larbi and Roy, 2019). The data available here are projections of the number of people aged over 60 between 2015 and 2050 with distributions by sex, age group and level of loss of autonomy, all at the departmental grid.The definition of dependency is based on the GIR estimated from the CARE surveys. This leads to a higher loss of autonomy than the current APA beneficiaries. Description of columns of datasets: Column name | description —|— DEP | department. “ENS” means the whole of France. GENDER | sex. 1: men, 2: women minus_75 | age group: if TRUE then 60-74 years, if FALSE then 75 years and over Population| Total number of individuals volume_pop_dependent | Number of dependents (in GIR estimated 1 to 4) volume_pop_dependent_heavy | Number of severely dependent people (in GIR estimated 1 to 2) EV60a| Life expectancy at 60 years old EVSI60a| Life expectancy without disability at age 60 The projections are available under several scenarios relating to the evolution of life expectancy, and several hypotheses on the evolution of dependence. There is a file by crossover scenario x hypothesis. Life expectancy scenarios correspond to those of INSEE’s Omphale projections. “edv_bass” corresponds to the low life expectancy scenario, “edv_high” to the high and “central” life expectancy scenario to a central scenario (https://www.insee.fr/fr/information/3683517). The assumptions for the evolution of dependency are defined as follows: ** Hypothesis** | definition —|— “optimistic”| Life expectancy gains at age 60 correspond in total to gains from years of independent living. “intermediate”| The proportion of moderately dependent years in life expectancy at age 60 remains constant; the gains in life expectancy thus correspond for one part to fully independent life and for another to years of moderate dependence, in proportion to the share observed today. “pessimistic”| Life expectancy gains at 60 years without moderate dependence are zero; in other words, all life expectancy gains of seniors have passed into a state of moderate loss of autonomy. These data are used as input to the Livia model to calculate the distribution of seniors in different types of living places (household, intermediate habitat and Ehpad), the code of which is available here: (future link)

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

In the world's most populous country, life expectancy has been continuously rising over the last decades, benefitting greatly from China's economic ascendance. In 2022, average life expectancy at birth in China reached about 78.6 years. Life expectancy at birth Life expectancy at birth refers to the average number of years a group of people born in the same year would live, assuming constant mortality rates. San Marino and Monaco had the highest life expectancy at birth, while China had reached a life expectancy above global average. People who were born in San Marino or Monaco in 2023 had a life expectancy of approximately 87 years or 86 years on average respectively. Demographic development in China Whereas average life expectancy at birth has been growing steadily, birth rates in China have been experiencing a slowdown. In 2024, about 6.77 babies had been born per 1,000 women in China, the second lowest point in the recent decade. As a result of low fertility rates and the extended life expectancy in China, the share of elderly people had been rising rapidly. The number of Chinese population aged 60 and older had more than doubled over the past three decades and is projected to reach its peak at 504 million in 2050. People aged 60 and older have been estimated to account for approximately one fourth of China’s total population by 2030, indicating a sharp climb from just around 13 percent in 2010. In order to pinpoint this massive shift in the age pyramid of China, an important indicator for measuring the pressure of aging population on productive population may be consulted. The old-age dependency ratio in China was expected to reach 52.3 percent in 2050.

Latvia - At Risk of Poverty rate of older people: 60 years or over was 37.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Latvia - At Risk of Poverty rate of older people: 60 years or over - last updated from the EUROSTAT on June of 2025. Historically, Latvia - At Risk of Poverty rate of older people: 60 years or over reached a record high of 42.90% in December of 2019 and a record low of 10.90% in December of 2011.

About this dataset

Context

The dataset tabulates the Live Oak population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Live Oak. The dataset can be utilized to understand the population distribution of Live Oak by age. For example, using this dataset, we can identify the largest age group in Live Oak.

Key observations

The largest age group in Live Oak, FL was for the group of age 20 to 24 years years with a population of 782 (11.33%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Live Oak, FL was the 80 to 84 years years with a population of 89 (1.29%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Live Oak is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Live Oak total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Live Oak Population by Age. You can refer the same here

IntroductionWhile tremendous progress has been made in recent years to improve the health of people living in low- and middle-income countries (LMIC), significant challenges remain. Chief among these are poor health systems, which are often ill-equipped to respond to current challenges. It remains unclear whether intensive intervention at the health system level will result in improved outcomes, as there have been few rigorously designed comparative studies. We present results of a complex health system intervention that was implemented in Zambia using a cluster randomized design.MethodsBHOMA was a complex health system intervention comprising intensive clinical training and quality improvement measures, support for commodities procurement, improved community outreach, and district level management support. The intervention was introduced as a stepped wedge cluster-randomized trial in 42 predominately rural health centers and their surrounding communities in Lusaka Province, Zambia. Baseline survey was conducted between January–May 2011, mid-line survey was conducted February–November, 2013 and Endline survey, February–November 2015.The primary outcome was all-cause mortality among those between 28 days and 60 years of age and assessed through community-based mortality surveys. Secondary outcomes included post-neonatal under-five mortality and service coverage scores. Service coverage scores were calculated across five domains (child preventative services; child treatment services; family planning; maternal health services, and adult health services). We fit Cox proportional hazards model with shared frailty at the cluster level for the primary analysis. Mortality rates were age-standardized using the WHO World Standard Population.ResultsMortality declined substantially from 3.9 per 1,000 person-years in the pre-intervention period, to 1.5 per 1,000 person-years in the post intervention period. When we compared intervention and control periods, there were 174 deaths in 49,230 person years (age-standardized rate = 4.4 per 1,000 person-years) in the control phase and 277 deaths in 74,519 person years (age-standardized rate = 4.6 per 1,000 person-years) in the intervention phase. Overall, there was no evidence for an effect of the intervention in minimally-adjusted [hazard ratio (HR) = 1.18; 95% confidence interval (CI): 0.88, 1.56; value of p = 0.265], or adjusted (HR = 1.12; 95% CI: 0.84, 1.49; value of p = 0.443) analyses.Coverage scores that showed some evidence of changing with time since the cluster joined the intervention were: an increasing proportion of children sleeping under insecticide treated bed-net (value of p < 0.001); an increasing proportion of febrile children who received appropriate anti-malarial drugs (value of p = 0.039); and an increasing proportion of ever hypertensive adults with currently controlled hypertension (value of p = 0.047). No adjustments were made for multiple-testing and the overall coverage score showed no statistical evidence for a change over time (value of p = 0.308).ConclusionWe noted an overall reduction in post-neonatal under 60 mortality in the study communities during the period of our study, but this could not be attributed to the BHOMA intervention. Some improvements in service coverage scores were observed.Clinical Trial Registrationclinicaltrials.gov, Identifier NCT01942278.

About this dataset

Context

The dataset tabulates the Milton town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Milton town. The dataset can be utilized to understand the population distribution of Milton town by age. For example, using this dataset, we can identify the largest age group in Milton town.

Key observations

The largest age group in Milton, Massachusetts was for the group of age 15 to 19 years years with a population of 2,784 (9.77%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Milton, Massachusetts was the 85 years and over years with a population of 515 (1.81%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Milton town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Milton town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Milton town Population by Age. You can refer the same here

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

In 2019, ** percent of female Christians worldwide aged 60 and older lived alone. In comparison, *** percent of male Hindus worldwide aged 60 and older lived alone. Overall, women across all religions were more likely to live alone in their old age than men.

United Kingdom UK: Survival To Age 65: Male: % of Cohort data was reported at 87.661 % in 2016. This records an increase from the previous number of 87.418 % for 2015. United Kingdom UK: Survival To Age 65: Male: % of Cohort data is updated yearly, averaging 77.353 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 87.661 % in 2016 and a record low of 67.931 % in 1960. United Kingdom UK: Survival To Age 65: Male: % of Cohort data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Health Statistics. Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Weighted average;

Life expectancy at birth and at age 65, by sex, on a three-year average basis.