About this dataset

Context

The dataset tabulates the China 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 China. 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 459 (51.46% 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 China population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in China is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the China 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 China Population by Age. You can refer the same here

Purpose: Rupture of an intracranial aneurysm is a life-threatening acute cerebrovascular event. The purpose of this study was to investigate whether aneurysmal subarachnoid haemorrhage (SAH) incidence rate is higher or lower in elderly population than in middle aged population. Materials and methods: Aneurysmal SAH cases were collected retrospectively from the archives of 21 hospitals in Mainland China. All the cases were collected from September 2016 and backward consecutively for a period of time up to 8 years. SAH was initially diagnosed by brain computed tomography (CT). CT angiography (CTA) or digital subtraction angiography (DSA) was followed and SAH was confirmed to be due to cerebral aneurysm rupture. For cases when multiple bleeding occurred, the age of the first SAH was used in this study. The total incidence from all hospitals at each age group were summed together for females and males respectively; then adjusted by the total population number at each age group for females and males which was from the 2010 population census of the People’s Republic of China. Results: In total there were 8,144 cases of intracranial aneurysmal SAH, with 4,861 females and 3,283 males. For females the relative aneurysmal SAH incidence rate started to decrease after around 65 years old, while for males the relative aneurysmal SAH incidence rate started to decrease after around 53 years old. Conclusion: Our data tentatively suggest elderly patients may be at a reduced risk of rupture compared with patients who are younger while have similar other risk factors.

The table Basic Information about Senior Management is part of the dataset Executive & Supervisory Information A-share Listed Companies China A股上市公司董监高信息数据, available at https://stanford.redivis.com/datasets/966f-eqkppp4b8. It contains 391431 rows across 14 variables.

The table Changes in Shareholdings of Senior Management is part of the dataset Executive & Supervisory Information A-share Listed Companies China A股上市公司董监高信息数据, available at https://stanford.redivis.com/datasets/966f-eqkppp4b8. It contains 2896381 rows across 13 variables.

The table Changes in Positions of Senior Management is part of the dataset Executive & Supervisory Information A-share Listed Companies China A股上市公司董监高信息数据, available at https://stanford.redivis.com/datasets/966f-eqkppp4b8. It contains 1580772 rows across 13 variables.

About this dataset

Context

The dataset tabulates the China town 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 China town. 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 2,829 (63.22% 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 China town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in China town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the China town 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 China town Population by Age. You can refer the same here

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

Dividend-Per-Share Time Series for Shenzhen Senior Technology Material Co Ltd. Shenzhen Senior Technology Material Co., Ltd. engages in the research and development, manufacture, and sale of lithium-ion battery separators in China and internationally. The company offers dry, wet, and coated process separator products; and RO membrane and other functional products. Its products are used for power and 3C battery applications. The company was founded in 2003 and is headquartered in Shenzhen, China.

Density of population aging in Yangtze River delta urban agglomeration.

BackgroundInsulin resistance (IR) is recognized as a potential modifiable risk factor for cognitive decline, but findings within Asian populations have been inconsistent. Given the high prevalence of dementia and its substantial economic burden in China, large-scale longitudinal studies are essential to elucidate the complex relationship between IR and cognitive function.MethodsThis longitudinal cohort study included 8,734 middle-aged and older adults (median age: 58 years; 53.6% females) from the China Health and Retirement Longitudinal Study (CHARLS), followed from 2011 to 2018. Estimated glucose disposal rate (eGDR) was used to assess IR and was calculated using waist circumference, hypertension status, and HbA1c levels. Participants were categorized into tertiles based on eGDR levels (Tertile 1: lowest; Tertile 3: highest). Cognitive function was calculated as the sum of episodic memory and executive function scores, which was then standardized to a Z-score. Linear mixed-effects models and dose-response analyses were performed to evaluate the association between baseline eGDR and cognitive changes in the total population and stratified by sex.ResultsHigher eGDR levels were significantly associated with slower global cognitive decline (Tertile 3 vs. Tertile 1: β = 0.007; 95% CI: 0.000–0.014; P = 0.047). This association was stronger in females (Tertile 3 vs. Tertile 1: β = 0.011; 95% CI: 0.002–0.021; P = 0.021), while no significant association was observed in males. Dose-response analyses indicated a linear positive relationship between baseline eGDR and global cognitive function in the total population and in females, but not in males. Similar patterns were found for episodic memory and executive function, with significant associations predominantly in females.ConclusionHigher eGDR was significantly associated with slower cognitive decline, particularly among women. These findings underscore the potential of eGDR as a marker for identifying and mitigating cognitive decline and highlight the importance of sex-specific strategies to address insulin resistance and promote cognitive health.

AimTo examine the prevalence and potential risk factors of multimorbidity among older adult in China. In addition, we investigated the pattern of multimorbidity.MethodsThis study is based on data from the fourth Sample Survey of the Aged Population in Urban and Rural China (SSAPUR) in 2015, a comprehensive survey of individuals aged 60 years or older in China. We calculated baseline data and prevalence rates for comorbidities, stratified by household registration, age, sex, education, exercise, and health insurance. Univariate and multivariate logistic regression analyses were conducted to identify potential risk factors for comorbidities. Furthermore, we determined the prevalence rates for the three most frequent disease combinations.ResultsA total of 215,040 participants were included in our analysis. The prevalence of multimorbidity was 50.5% among the older adult in China. The prevalence rate was slightly higher in rural areas than in urban areas, with rates of 51.5 and 49.6%, respectively (p < 0.001). Moreover, the prevalence rate was higher in females than in males, with rates of 55.2 and 45.3%, respectively (p < 0.001). Multivariate logistic regression analysis revealed that individuals aged 70–79 years (OR:1.40, 95% CI: 1.38–1.43, p < 0.001) and over 80 years (OR:1.41, 95% CI: 1.38–1.45, p < 0.001) had a higher prevalence of multimorbidity than those aged 60–69 years. The most prevalent pair of comorbidities was hypertension and osteoarthropathy, with 19.6% of the participants having these two conditions, accounting for 5.4% of the total participants.ConclusionOur findings indicate a high prevalence of multimorbidity among the older adult in China. Increased expenditure on preventive health care, popularization of general medicine and popular medical education may be adopted by the Government to cope with the high prevalence of multimorbidity.

Unemployment Rate in China decreased to 5.10 percent in October from 5.20 percent in September of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

BackgroundThe burden of type 2 diabetic kidney disease (DKD) continues to rise in China. We analyzed time trends in DKD mortality and associations with age, period, and birth cohort from 1990 to 2019, made projections up to 2030, and examined the drivers of deaths from DKD.Methods and FindingsThe number of DKD deaths in China from 1990 to 2019 was obtained from the GBD 2019. We used age-period-cohort modeling to estimate age, period, and cohort effects in DKD mortality between 1990 and 2019. We calculated net drift (overall annual percentage change), local drift (annual percentage change in each age group), longitudinal age curves (expected longitudinal age-specific rates), period, and cohort relative risks. We used Bayesian age-period-cohort analysis with integrated nested Laplace approximations to project future age-specific DKD death cases from 2020 to 2030. We used a validated decomposition algorithm to attribute changes in DKD deaths to population growth, population aging, and epidemiologic changes from 1990 to 2030. From 1990 to 2019, the age-standardized mortality rate of DKD in China was relatively stable, but the absolute number of DKD deaths showed a noticeable increasing trend. The overall annual percentage change (net drift) was -0.75% (95% confidence interval, CI: -0.93 to -0.57) for males and -1.90% (95% CI, -2.19 to -1.62) for females. The age-specific annual percentage changes (local drifts) were below zero in all age groups from 1990 to 2019 except for males aged above 65 to 69 years, and for females aged above 70 to 74 years. The risk of DKD deaths increased exponentially with age for both sexes after controlling for period deviations. The Bayesian age-period-cohort analysis projects that there would be 88,803 deaths from DKD in 2030, increased by 224.2% from 1990. Despite a decrease in age-specific DKD death rates, the reduction would be entirely offset by population aging.ConclusionsAlthough China has made progress in reducing DKD deaths, demographic changes have entirely offset the progress. The burden of DKD deaths is likely to continue increasing. Our findings suggest that large-scale screening is imperative for DKD control and prevention, particularly for high-risk groups.

BackgroundMalnutrition is very common in elderly patients admitted to the hospital. The aim of our study is to assess the nutritional status of elderly patients and the use of nutritional support in a tertiary care hospital in China and to analyze the impacts of nutritional status and nutritional support on clinical outcomes.MethodsStatistical analysis was performed on a sample of 745 elderly patients in the geriatric medicine department of Qilu Hospital of Shandong University from March 2012 to March 2015. The Nutrition Risk Screening 2002 (NRS 2002) and Mini Nutritional Assessment-short forms (MNA-SF) were utilized for the nutritional risk screening at admission. Personal information, anthropometric measurements, laboratory tests, nutritional support and clinical outcomes were recorded. Comparisons were carried out to analyze impacts on clinical outcomes and prognosis based on incidence rate of nutritional risk, nutritional support rate, and different methods of support.ResultsNRS 2002 and MNA-SF were utilized to screen for nutritional risk at admission. The results of this screening were 39.81% and 44.10%, respectively. Based on the results of the MNA-SF, 33.38% of elderly patients were at risk of malnutrition and 5.5% were malnourished. The incidence of nutritional risk in the departments of Gastroenterology, Hematology, and Respiratory were 51.72%, 46.88%, 43.33%, respectively, higher than in other departments. Patients with nutritional risk were more likely to have a longer hospital stay compared to those without (P < 0.05). The nutritional support rate of patients overall was 16.49%, and the ratio of Parenteral nutrition (PN):Enteral nutrition (EN) was 5.13:1. Patients at nutritional risk had an in-hospital support rate of 29.63% and 28.57%, respectively, identified via screening by NRS 2002 and MNA-SF. Nutritional support rate of patients without nutritional risk was 7.8%(35/449) and 6.96%(29/417), respectively. Patients in the departments of Gastroenterology and Hematology had higher rates of nutritional support than patients in other departments. In addition, results showed that in patients with nutritional risk and malnutrition, nutritional support decreased the length of hospital stay (P<0.05). The patients that received nutritional support also had a lower incidence of infectious complications than the patients without nutritional support (NRS 2002 was 6.82%:18.18% and MNA-SF was 9.57%:20.23%)(P<0.05).ConclusionsUndernourishment and nutritional risk in elderly patients at hospital admission is a common occurrence. In the current study, the nutritional risk rate in the Gastroenterology department was higher than in other departments. Patients with normal nutritional status were still receiving nutritional support. Overall, there is a need to better apply nutritional support in the clinical treatment of elderly patients. In elderly patients with nutritional risk and malnutrition, nutritional support reduced the length of hospital stay and the incidence of infectious complications.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Guangzhou Baiyunshan Pharmaceutical Holdings Co Ltd. Guangzhou Baiyunshan Pharmaceutical Holdings Company Limited researches, develops, manufactures, and sells Chinese patent and Western medicines, chemical raw materials, natural and biological medicines, and intermediates of chemical raw materials. The company operates through Great Southern TCM, Great Commerce, Great Health, and Other segments. It is also involved in the wholesale, retail, sale, import, and export of Western and Chinese medicines, medical instruments, pharmaceutical products, medical apparatus, and healthcare products; research and development, production, and sale of beverages, food, healthcare products, and other products; and production and sale of pre-packaged food, dairy products, etc., as well as offers herbal tea, tablets, capsules, lozenges, tortoise herb jelly, etc. In addition, the company invests in health industry, such as medical services, health management, health preservation and elderly care, etc.; and provides leasing and commercial, health, advertising, loading offloading, and warehouse services. It operates through retail chain pharmacy outlets and retail stores. The company operates in the People's Republic of China and internationally. The company was formerly known as Guangzhou Pharmaceutical Company Limited and changed its name to Guangzhou Baiyunshan Pharmaceutical Holdings Company Limited in August 2013. Guangzhou Baiyunshan Pharmaceutical Holdings Company Limited was incorporated in 1997 and is based in Guangzhou, China.

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

ObjectivesNational data on the admission rate, distribution, in-hospital mortality, and economic burden of traumatic fractures in China is unclear. We aimed to conduct a cross-sectional population-based study to determine such above data at the national level in China.MethodsA national administrative database was used to review all traumatic fracture hospitalizations in China during 2020, from which a total of 2,025,169 inpatients with traumatic fractures was retrieved. Admission rates and in-hospital mortality rates stratified by age, sex, and region were calculated. The causes of traumatic fracture and economic burden were described.ResultsThe admission rate of traumatic fractures of all China population in 2020 was 1.437‰. The admission rate increased with age and varied with genders and causes of injuries. Falls are the leading cause of traumatic fracture hospitalization, followed by road traffic injuries. The most common diagnoses were femoral neck fractures, with a number of 138,377. The in-hospital mortality was 1.209‰. Road traffic injuries led to the highest in-hospital mortality. The median length of stay was 10 days, with the median hospitalization cost of ¥20,900 (about $3,056).ConclusionTraumatic fractures are concerning conditions with a high admission rate and in-hospital mortality in China, which are mainly caused by falls and road traffic injuries. The government should implement more public health policies to enhance the health of the elderly and improve transportation safety to prevent traumatic fractures.

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.

ObjectiveTo examine the COVID-19 vaccination rate among a representative sample of adults from 31 provinces on the Chinese mainland and identify its influencing factors.MethodsWe gathered sociodemographic information, data on people's awareness and behavior regarding COVID-19 and the COVID-19 vaccine, the accessibility of COVID-19 vaccination services, community environmental factors influencing people's awareness and behavior regarding the vaccination, information about people's skepticism on COVID-19 vaccine, and information about people's trust in doctors as well as vaccine developers through an online nationwide cross-sectional survey among Chinese adults (18 years and older). The odds ratios (OR) and 95% confidence intervals (CI) for the statistical associations were estimated using logistic regression models.ResultsA total of 29,925 participants (51.4% females and 48.6% males) responded. 89.4% of the participants had already received a COVID-19 vaccination. After adjusting for demographic characteristics, awareness of COVID-19 pandemic/ COVID-19 vaccine, community environmental factors, awareness and behavior of general vaccinations, we discovered that having no religious affiliation, having the same occupational status as a result of coronavirus epidemic, being a non-smoker, always engaging in physical activity, having a lower social status, perceiving COVID-19 to be easily curable, and having easier access to vaccination are all associated with high vaccination rate (all P

About this dataset

Context

The dataset tabulates the China Grove 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 China Grove. 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 782 (56.06% 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 China Grove population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in China Grove is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the China Grove 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 China Grove Population by Age. You can refer the same here