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 2024, just under 41 percent of Sub-Saharan Africa's population was below the age of 15; in contrast, this figure was just 17 percent in Europe & Central Asia and in North America. Across these regions, the share of the population aged 65 and over inversely correlated with the younger population, in that the regions with the largest share aged under 15 had the smallest share aged over 64, and vice versa. For most regions, the share of the population aged between 15 and 64 years ranged between 64 and 65 percent, except for Sub-Saharan Africa where it was below 56 percent. These trends can largely be explained by looking at global demographic development.

About this dataset

Context

The dataset tabulates the data for the Continental, OH population pyramid, which represents the Continental population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Continental, OH, is 29.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Continental, OH, is 33.4.
• Total dependency ratio for Continental, OH is 63.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Continental, OH is 3.0.

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 for the Continental population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Continental for the selected age group is shown in the following column.
• Population (Female): The female population in the Continental for the selected age group is shown in the following column.
• Total Population: The total population of the Continental for the selected age group is shown in the following column.

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 Continental Population by Age. You can refer the same here

As of September 2024, the northern region of Thailand had an elderly population of around ** percent. This figure exceeded the national average.

About this dataset

Context

The dataset tabulates the Continental population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Continental. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 784 (56.52% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Continental population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Continental is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Continental is shown in the following column.

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 Continental Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Continental, OH population pyramid, which represents the Continental population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Continental, OH, is 34.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Continental, OH, is 29.7.
• Total dependency ratio for Continental, OH is 63.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Continental, OH is 3.4.

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 for the Continental population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Continental for the selected age group is shown in the following column.
• Population (Female): The female population in the Continental for the selected age group is shown in the following column.
• Total Population: The total population of the Continental for the selected age group is shown in the following column.

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 Continental Population by Age. You can refer the same here

In 2025, in the north Italian region of Liguria, there were around 283 elderly people to every 100 young individuals. Liguria ranked as the oldest region of the country, whereas the southern region of Campania was the youngest. The Aging Index refers to the number of elderly population (aged 65 years and over) per 100 individuals younger than 14 years old in a specific population.

China Elderly Dependency Ratio data was reported at 21.800 % in 2022. This records an increase from the previous number of 20.800 % for 2021. China Elderly Dependency Ratio data is updated yearly, averaging 10.700 % from Dec 1982 (Median) to 2022, with 35 observations. The data reached an all-time high of 21.800 % in 2022 and a record low of 8.000 % in 1982. China Elderly Dependency Ratio data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Hubei data was reported at 24.850 % in 2023. This records an increase from the previous number of 23.850 % for 2022. Elderly Dependency Ratio(Sample Survey): Hubei data is updated yearly, averaging 13.700 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 24.850 % in 2023 and a record low of 11.100 % in 2003. Elderly Dependency Ratio(Sample Survey): Hubei data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China’s eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions’ impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.

The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China’s eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions’ impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.

Elderly Dependency Ratio(Sample Survey): Tianjin data was reported at 25.750 % in 2023. This records an increase from the previous number of 24.280 % for 2022. Elderly Dependency Ratio(Sample Survey): Tianjin data is updated yearly, averaging 14.500 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 25.750 % in 2023 and a record low of 10.430 % in 2010. Elderly Dependency Ratio(Sample Survey): Tianjin data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

In 2024, the population of Africa was projected to grow by 2.27 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.5 percent from 2000 onwards, and it peaked at 2.63 percent in 2013. Despite a slowdown in the growth rate after that, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2023, the total population of Africa amounted to almost 1.5 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 831 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.8 billion people, compared to 4.6 billion in Asia. The world's youngest continent The median age in Africa corresponded to 19.2 years in 2024. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of ten percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

Elderly Dependency Ratio(Sample Survey): Yunnan data was reported at 17.140 % in 2023. This records an increase from the previous number of 16.770 % for 2022. Elderly Dependency Ratio(Sample Survey): Yunnan data is updated yearly, averaging 11.450 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 17.140 % in 2023 and a record low of 10.300 % in 2003. Elderly Dependency Ratio(Sample Survey): Yunnan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Shanghai data was reported at 23.990 % in 2021. This records an increase from the previous number of 22.020 % for 2020. Elderly Dependency Ratio(Sample Survey): Shanghai data is updated yearly, averaging 17.850 % from Dec 2002 (Median) to 2021, with 20 observations. The data reached an all-time high of 23.990 % in 2021 and a record low of 9.400 % in 2011. Elderly Dependency Ratio(Sample Survey): Shanghai data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Hainan data was reported at 16.700 % in 2023. This records an increase from the previous number of 16.190 % for 2022. Elderly Dependency Ratio(Sample Survey): Hainan data is updated yearly, averaging 11.650 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 16.700 % in 2023 and a record low of 9.400 % in 2011. Elderly Dependency Ratio(Sample Survey): Hainan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Beijing data was reported at 21.980 % in 2023. This records an increase from the previous number of 20.760 % for 2022. Elderly Dependency Ratio(Sample Survey): Beijing data is updated yearly, averaging 14.000 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 21.980 % in 2023 and a record low of 10.500 % in 2014. Elderly Dependency Ratio(Sample Survey): Beijing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Jilin data was reported at 26.260 % in 2023. This records an increase from the previous number of 24.840 % for 2022. Elderly Dependency Ratio(Sample Survey): Jilin data is updated yearly, averaging 11.950 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 26.260 % in 2023 and a record low of 8.700 % in 2002. Elderly Dependency Ratio(Sample Survey): Jilin data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

Elderly Dependency Ratio(Sample Survey): Chongqing data was reported at 28.170 % in 2023. This records an increase from the previous number of 27.260 % for 2022. Elderly Dependency Ratio(Sample Survey): Chongqing data is updated yearly, averaging 18.450 % from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 28.170 % in 2023 and a record low of 12.800 % in 2003. Elderly Dependency Ratio(Sample Survey): Chongqing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Sample Survey: Elderly Dependency Ratio: By Region.

ObjectiveTo explore the burden and trend of osteoarthritis (OA) at different sites in middle-aged and elderly people (45 years and older) from 1990 to 2021.MethodsAge-standardized incidence rates, prevalence rates, disability-adjusted life years (Daly) rates and average annual percent change were used to quantify the disease burden and trend of OA at different sites. Decomposition analysis was conducted to explore the impact of three population-level determinants on the burden of OA and the distribution of OA burden inequality in the Socio-Demographic Index (SDI) across countries.ResultsThe age-standardized prevalence rate had increased by 8.9%, and the OA cases had increased by 2.41 times compared to 1990. The incidence and prevalence of knee, hip and hand OA decreased sequentially, while high SDI regions tended to have higher age-standardized incidence rates, prevalence rates, and Daly rates. Decomposition analysis revealed that 85.9% of the increase in OA age-standardized Daly rates was attributable to population growth. This increase was most pronounced in high SDI populations for hip OA and middle SDI populations for knee and hand OA. From 1990 to 2021, the inequality in overall OA burden between countries had decreased. The absolute inequality gap for hand OA had narrowed the most significantly (45.3%), which followed by knee OA (11.9%), while the inequality gap for hip OA has slightly increased.ConclusionIn summary, all parts of the OA burden in middle-aged and elderly people had steadily increased from 1990 to 2021, which calls to implement personalized prevention targeting different parts of OA.