In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

Monaco is the country with the highest median age in the world. The population has a median age of around 57 years, which is around six years more than in Japan and Saint Pierre and Miquelon – the other countries that make up the top three. Southern European countries make up a large part of the top 20, with Italy, Slovenia, Greece, San Marino, Andorra, and Croatia all making the list. Low infant mortality means higher life expectancy Monaco and Japan also have the lowest infant mortality rates in the world, which contributes to the calculation of a higher life expectancy because fewer people are dying in the first years of life. Indeed, many of the nations with a high median age also feature on the list of countries with the highest average life expectancy, such as San Marino, Japan, Italy, and Lichtenstein. Demographics of islands and small countries Many smaller countries and island nations have populations with a high median age, such as Guernsey and the Isle of Man, which are both island territories within the British Isles. An explanation for this could be that younger people leave to seek work or education opportunities, while others choose to relocate there for retirement.

World life expectancy for 2025 is 73.49, a 0.23% increase from 2024.

<ul style='margin-top:20px;'>

<li>World life expectancy for 2024 was <strong>73.33</strong>, a <strong>0% increase</strong> from 2023.</li>
<li>World life expectancy for 2023 was <strong>73.33</strong>, a <strong>0.49% increase</strong> from 2022.</li>
<li>World life expectancy for 2022 was <strong>72.97</strong>, a <strong>2.46% increase</strong> from 2021.</li>
</ul>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.

This statistic shows the median age of the world population from 1950 to 2100. By 2100, the global median age is projected to be 41.9 years of age.

U.S. life expectancy for 2025 is 79.40, a 0.18% increase from 2024.

<ul style='margin-top:20px;'>

<li>U.S. life expectancy for 2024 was <strong>79.25</strong>, a <strong>1.11% increase</strong> from 2023.</li>
<li>U.S. life expectancy for 2023 was <strong>78.39</strong>, a <strong>1.23% increase</strong> from 2022.</li>
<li>U.S. life expectancy for 2022 was <strong>77.43</strong>, a <strong>1.45% increase</strong> from 2021.</li>
</ul>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.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Man. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Man. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Man, the median household income stands at $86,875 for householders within the 25 to 44 years age group, followed by $51,875 for the 65 years and over age group. Notably, householders within the under 25 years age group, had the lowest median household income at $40,417.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Man median household income by age. You can refer the same here

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

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

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.

About this dataset

Context

The dataset tabulates the population of Man by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Man. The dataset can be utilized to understand the population distribution of Man by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Man. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Man.

Key observations

Largest age group (population): Male # 60-64 years (52) | Female # 5-9 years (94). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Man population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Man is shown in the following column.
• Population (Female): The female population in the Man is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Man for each age group.

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 Man Population by Gender. You can refer the same here

In 2021, a woman in the United States aged 65 years could expect to live another 19.7 years on average. This number decreased in the years 2020 and 2021, after reaching a high of 20.8 years in 2019. Nevertheless, the life expectancy of a woman aged 65 years in the United States is still higher than that of a man of that age. In 2021, a man aged 65 years could be expected to live another 17 years on average.

Why has the life expectancy in the U.S. declined? Overall, life expectancy in the United States has declined in recent years. In 2019, the life expectancy for U.S. women was 81.4 years, but by 2021 it had decreased to 79.3 years. Likewise, the life expectancy for men decreased from 76.3 years to 73.5 years in the same period. The biggest contributors to this decline in life expectancy are the COVID-19 pandemic and the opioid epidemic. Although deaths from the COVID-19 pandemic have decreased significantly since 2022, deaths from opioid overdose continue to increase, reaching all-time highs in 2021.

The leading causes of death among U.S. women The leading causes of death among women in the United States in 2020 were heart disease, cancer, and COVID-19. That year heart disease and cancer accounted for a combined 37 percent of all deaths among women, while around 10 percent of deaths were due to COVID-19. The overall leading causes of death in the United States generally reflect the leading causes among women with some slight variations. For example, Alzheimer’s disease is the fourth leading cause of death among women, but the seventh leading cause of death overall in the United States.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

The statistical data on personnel and age at the end of 2016 of the Taichung City Fire Bureau

About this dataset

Context

The dataset tabulates the Man household income by age. The dataset can be utilized to understand the age-based income distribution of Man income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Man, WV annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Man, WV household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Man income distribution by age. You can refer the same here

Russia Employment: Age 15 and Above: Period Avg: OKVED2: Human Health & Social Work Activities data was reported at 5,854.064 Person th in Sep 2018. This records an increase from the previous number of 5,565.240 Person th for Aug 2018. Russia Employment: Age 15 and Above: Period Avg: OKVED2: Human Health & Social Work Activities data is updated monthly, averaging 5,765.628 Person th from Jan 2017 (Median) to Sep 2018, with 21 observations. The data reached an all-time high of 6,105.354 Person th in Feb 2018 and a record low of 5,497.441 Person th in May 2017. Russia Employment: Age 15 and Above: Period Avg: OKVED2: Human Health & Social Work Activities data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GB020: Employment: by Activity.

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.

There is no scientific consensus on the fundamental question whether the probability distribution of the human life span has a finite endpoint or not and, if so, whether this upper limit changes over time. Our study uses a unique dataset of the ages at death—in days—of all (about 285,000) Dutch residents, born in the Netherlands, who died in the years 1986–2015 at a minimum age of 92 years and is based on extreme value theory, the coherent approach to research problems of this type. Unlike some other studies, we base our analysis on the configuration of thousands of mortality data of old people, not just the few oldest old. We find compelling statistical evidence that there is indeed an upper limit to the life span of men and to that of women for all the 30 years we consider and, moreover, that there are no indications of trends in these upper limits over the last 30 years, despite the fact that the number of people reaching high age (say 95 years) was almost tripling. We also present estimates for the endpoints, for the force of mortality at very high age, and for the so-called perseverance parameter. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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 ...).

BackgroundGenomic research advances the understanding of human health and disease. It also drives both the discovery of salient genetic association(s) as well as targeted screening, diagnostic and therapeutic strategies. Human subject participation is crucial for the success of genomic research.MethodsThis is a cross sectional analytical study conducted at two tertiary centers in Enugu Southeast Nigeria. Semi structured questionnaires were administered to eligible consenting participants. Data on their demographics, willingness to participate in genomic research and motivation for participation were obtained. Data was analyzed using Stata version 17 and summarized using median, frequencies and interquartile range(IQR). Associations between covariates were evaluated with Chi square test and multivariable logistic regression.ResultsAmong 228 glaucoma subjects who participated in our study,119(52.2%) were female and 109(47.8%) were male. The median age was 64 years(IQR = 50–76). Although 219 (96.0%) participants expressed willingness to participate in a glaucoma genetic study, only 27(11.9%) of them will be willing to participate if there will not be feedback of results to participants (χ2 = 18.59, P

Human Trafficking Statistics: Human trafficking remains a pervasive global issue, with millions of individuals subjected to exploitation and abuse each year. According to recent statistics, an estimated 25 million people worldwide are victims of human trafficking, with the majority being women and children. This lucrative criminal industry generates profits of over $150 billion annually, making it one of the most profitable illegal trades globally. As market research analysts, it's imperative to understand the scale and impact of human trafficking to develop effective strategies for prevention and intervention. Efforts to combat human trafficking have intensified in recent years, driven by increased awareness and advocacy. However, despite these efforts, the problem persists, with trafficking networks adapting to evade law enforcement and exploit vulnerabilities in communities. Through comprehensive data analysis and research, we can uncover trends, identify high-risk areas, and develop targeted interventions to disrupt trafficking networks and support survivors. In this context, understanding human trafficking statistics is crucial for informing policy decisions, resource allocation, and collaborative efforts to combat this grave violation of human rights. Editor’s Choice Every year, approximately 4.5 billion people become victims of forced sex trafficking. Two out of three immigrants become victims of human trafficking, regardless of their international travel method. There are 5.4 victims of modern slavery for every 1000 people worldwide. An estimated 40.3 million individuals are trapped in modern-day slavery, with 24.9 million in forced labor and 15.4 million in forced marriage. Around 16.55 million reported human trafficking cases have occurred in the Asia Pacific region. Out of 40 million human trafficking victims worldwide, 25% are children. The highest proportion of forced labor trafficking cases occurs in domestic work, accounting for 30%. The illicit earnings from human trafficking amount to approximately USD 150 billion annually. The sex trafficking industry globally exceeds the size of the worldwide cocaine market. Only 0.4% of survivors of human trafficking cases are detected. Currently, there are 49.6 million people in modern slavery worldwide, with 35% being children. Sex trafficking is the most common type of trafficking in the U.S. In 2022, there were 88 million child sexual abuse material (CSAM) files reported to the National Center for Missing and Exploited Children (NCMEC) tip line. Child sex trafficking has been reported in all 50 U.S. states. Human trafficking is a USD 150 billion industry globally. It ranks as the second most profitable illegal industry in the United States. 25 million people worldwide are denied their fundamental right to freedom. 30% of global human trafficking victims are children. Women constitute 49% of all victims of global trafficking. In 2019, 62% of victims in the US were identified as sex trafficking victims. In the same year, US Department of Health and Human Services (HHS) grantees reported that 68% of clients served were victims of labor trafficking. Human traffickers in the US face a maximum statutory penalty of 20 years in prison. In France, 74% of exploited victims in 2018 were victims of sex trafficking. You May Also Like To Read Domestic Violence Statistics Sexual Assault Statistics Crime Statistics FBI Crime Statistics Referral Marketing Statistics Prison Statistics GDPR Statistics Piracy Statistics Notable Ransomware Statistics DDoS Statistics Divorce Statistics