According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

This study explores the behavioral impact of the One Child Policy in China. Using experimental data it examines whether the One Child Policy affected altruism, trust, trust-worthiness, risk-taking, and competitiveness in individuals. Survey data on personality traits and demographics of the sample are included.

In 2024, about 60.9 percent of the Chinese population was between 16 and 59 years old. Apart from the information given on broad age groups in this statistic, some more information is provided by a timeline for the age distribution and a population breakdown by smaller age groups. Demographic development in China China ranked as the second most populous country in the world with a population of nearly 1.41 billion as of mid 2024, surpassed only by India. As the world population reached more than eight billion in mid 2024, China represented almost one fifth of the global population. China's population increased exponentially between the 1950s and the early 1980s due to Mao Zedong's population policy. To tackle the problem of overpopulation, a one-child policy was implemented in 1979. Since then, China's population growth has slowed from more than two percent per annum in the 1970s to around 0.5 percent per annum in the 2000s, and finally turned negative in 2022. China's aging population One outcome of the strict population policy is the acceleration of demographic aging trends. According to the United Nations, China's population median age has more than doubled over the last five decades, from 18 years in 1970 to 37.5 years in 2020. Few countries have aged faster than China. The dramatic aging of the population is matched by slower growth. The total fertility rate, measuring the number of children a woman can expect to have in her life, stood at just around 1.2 children. This incremental decline in labor force could lead to future challenges for the Chinese government, causing instability in current health care and social insurance mechanisms. To learn more about demographic development of the rural and urban population in China, please take a look at our reports on population in China and aging population in China.

We consider age-structured models with an imposed refractory period between births. These models can be used to formulate alternative population control strategies to China's one-child policy. By allowing any number of births, but with an imposed delay between births, we show how the total population can be decreased and how a relatively older age distribution can be generated. This delay represents a more "continuous" form of population management for which the strict one-child policy is a limiting case. Such a policy approach could be more easily accepted by society. Our analyses provide an initial framework for studying demographics and how social constraints influence population structure. This dataset includes the raw population data for 1981 China and 2000 Japan, and some Matlab code files used to process such raw data and produce predictions.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

In 2024, there were around 719 million male inhabitants and 689 million female inhabitants living in China, amounting to around 1.41 billion people in total. China's total population decreased for the first time in decades in 2022, and population decline is expected to accelerate in the upcoming years. Birth control in China From the beginning of the 1970s on, having many children was no longer encouraged in mainland China. The one-child policy was then introduced in 1979 to control the total size of the Chinese population. According to the one-child policy, a married couple was only allowed to have one child. With the time, modifications were added to the policy, for example parents living in rural areas were allowed to have a second child if the first was a daughter, and most ethnic minorities were excepted from the policy. Population ageing The birth control led to a decreasing birth rate in China and a more skewed gender ratio of new births due to boy preference. Since the negative economic and social effects of an aging population were more and more felt in China, the one-child policy was considered an obstacle for the country’s further economic development. Since 2014, the one-child policy has been gradually relaxed and fully eliminated at the end of 2015. However, many young Chinese people are not willing to have more children due to high costs of raising a child, especially in urban areas.

As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

BackgroundThe birth rate is an important indicator of the health of the population. However, persistently low birth rate has become a pressing demographic challenge for many countries, including China. This has significant implications for sustainable population planning.MethodsThis study applied hot spot analysis and the spatiotemporal geographically weighted regression (GTWR) modeling, used panel data of 286 cities in China from 2012 to 2021 to explore the spatiotemporal heterogeneity of the relationship between the socioeconomic development and birth rate.ResultsThe research has found that 2017 was an important turning point in China’s demographic transition. The hot spot analysis reveals that the birth rate hot spots are characterized by a multipolar kernel distribution, shifting from spatial diffusion to convergence, with the cold spots mainly located in the northeast. And the GTWR modeling found that the relationship between socioeconomic development and birth rate varies and change dynamically over space and time. Key findings include: (1) the negative impact of GDP per capita on birth rates has intensified; (2) housing prices exhibit both wealth and crowding-out effects on birth rates, and there are obvious regional differences between the north and the south; (3) fiscal education expenditure on birth rates has the most pronounced income effect in the eastern region.ConclusionThis study adopts spatiotemporal perspective to reveal the spatiotemporal heterogeneity of the association between socioeconomic development and birth rate. It provides new evidence on the influence of macro factors on fertility in China. And emphasizes the importance of incorporating regional variations into population policy design.

In 2024, the mortality rate in China ranged at approximately 7.76 deaths per 1,000 inhabitants. The mortality rate in China displayed an uneven development over the last two decades. This is mainly related to the very uneven sizes of Chinese age groups, improvements in health care, and the occurrence of epidemics. However, an overall growing trend is undisputable and related to China's aging population. As the share of the population aged 60 and above will be growing significantly over the upcoming two decades, the mortality rate will further increase in the years ahead. Population in China China was the second most populous country in the world in 2024. However, due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades and finally turned negative in 2022. The major factor for this development was a set of policies introduced by the Chinese government in 1979, including the so-called one-child policy, which was intended to improve people’s living standards by limiting the population growth. However, with the decreasing birth rate and slower population growth, China nowadays is facing the problems of a rapidly aging population. Birth control in China According to the one-child policy, a married couple was only allowed to have one child. Only under certain circumstances were parents allowed to have a second child. As the performance of family control had long been related to the assessment of local government’s achievements, violations of the rule were severely punished. The birth control in China led to a decreasing birth rate and a more skewed gender ratio of new births due to a widely preference for male children in the Chinese society. Nowadays, since China’s population is aging rapidly, the one-child policy has been re-considered as an obstacle for the country’s further economic development. Since 2014, the one-child policy has been gradually relaxed and fully eliminated at the end of 2015. In May 2021, a new three-child policy has been introduced. However, many young Chinese people today are not willing to have more children due to high costs of raising a child, especially in urban areas.

This study is part of a program of in-depth surveys on the population fertility and related factors in various provinces and municipalities of China.. The study aimed to improve understanding of the levels and trends in fertility of the Chinese population and to provide the government with reliable data useful in the formation of population policy. Data were collected in the provinces of Beijing, Liaoning, Gansu, Guangdong, Guizhou, and Shandong. For each province, data were collected on complete pregnancy and marriage history, fertility preferences and contraception, and socio-economic background.

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

In 2024, around **** million babies were born in China. The number of births has increased slightly from **** million in the previous year, but is much lower than the ***** million births recorded in 2016. Demographic development in China In 2022, the Chinese population decreased for the first time in decades, and population decline is expected to accelerate in the upcoming years. To curb the negative effects of an aging population, the Chinese government decided in 2013 to gradually relax the so called one-child-policy, which had been in effect since 1979. From 2016 onwards, parents in China were allowed to have two children in general. However, as the recent figures of births per year reveal, this policy change had only short-term effects on the general birth rate: the number of births slightly increased from 2014 onwards, but then started to fell again in 2018. In 2024, China was the second most populous country in the world, overtaken by India that year. China’s aging population The Chinese society is aging rapidly and facing a serious demographic shift towards older age groups. The median age of China’s population has increased massively from about ** years in 1970 to **** years in 2020 and is projected to rise continuously until 2080. In 2020, approximately **** percent of the Chinese were 60 years and older, a figure that is forecast to rise as high as ** percent by 2060. This shift in demographic development will increase social and elderly support expenditure of the society as a whole. One measure for this social imbalance is the old-age dependency ratio, measuring the relationship between economic dependent older age groups and the working-age population. The old-age dependency ratio in China is expected to soar to ** percent in 2060, implying that by then three working-age persons will have to support two elderly persons.

In 2024, the average number of children born per 1,000 people in China ranged at ****. The birth rate has dropped considerably since 2016, and the number of births fell below the number of deaths in 2022 for the first time in decades, leading to a negative population growth rate. Recent development of the birth rate Similar to most East-Asian countries and territories, demographics in China today are characterized by a very low fertility rate. As low fertility in the long-term limits economic growth and leads to heavy strains on the pension and health systems, the Chinese government decided to support childbirth by gradually relaxing strict birth control measures, that had been in place for three decades. However, the effect of this policy change was considerably smaller than expected. The birth rate increased from **** births per 1,000 inhabitants in 2010 to ***** births in 2012 and remained on a higher level for a couple of years, but then dropped again to a new low in 2018. This illustrates that other factors constrain the number of births today. These factors are most probably similar to those experienced in other developed countries as well: women preferring career opportunities over maternity, high costs for bringing up children, and changed social norms, to name only the most important ones. Future demographic prospects Between 2020 and 2023, the birth rate in China dropped to formerly unknown lows, most probably influenced by the coronavirus pandemic. As all COVID-19 restrictions were lifted by the end of 2022, births figures showed a catch-up effect in 2024. However, the scope of the rebound might be limited. A population breakdown by five-year age groups indicates that the drop in the number of births is also related to a shrinking number of people with child-bearing age. The age groups between 15 and 29 years today are considerably smaller than those between 30 and 44, leaving less space for the birth rate to increase. This effect is exacerbated by a considerable gender gap within younger age groups in China, with the number of females being much lower than that of males.

1. Climate zone China’s climate zone data is compiled by China meteorological administration. The data takes the average daily temperature that more than 10 ℃ and the coldest temperature or multi-year extreme minimum temperature as heat index. Taking dryness as water index. Combining these two indices with topography and geomorphology characteristics, the country is divided into 8 primary climate regions and 32 secondary climate regions. Data Format: Shapefile. Projected Coordinate System: Krassovsky_1940_Albers. URL: https://www.resdc.cn/data.aspx?DATAID=243
2. Ecological services Ecological services are the many and varied benefits to humans provided by the natural environment and from healthy ecosystems. To assess the value of ecological services is an important basis for ecological environment protection, ecological function zoning, environmental economic accounting and ecological compensation decision-making. Based on the assessment of global ecological services by Costanza et al. (1997) and Xie et al. (2003), the equivalent weight factor table of ecological services of terrestrial ecosystems was formulated in China. This method has been widely adopted and quoted. In the estimation process, considering the basic equivalent of each ecosystem type, the method also considered the influence of spatiotemporal dynamic factors of NPP, precipitation and soil conservation regulation. The data was revised by biomass parameters to reflect regional differences in ecological services value. Units:￥·hm-1. Spatial resolution: 1 km. Projected Coordinate System: GCS_Krassovsky_1940. Data format: ArcGis Grid. URL: https://www.resdc.cn/DOI/DOI.aspx?DOIID=48
3. Population density The kilometre grid dataset of China's population spatial distribution is based on the demographic data of the national population statistics. Taking into account land use type, night light intensity, residential density and other factors that closely related to population. Using the multi-factor weight allocation method to upscale the population data which based on administrative regions as the basic statistical units. Firstly, calculate the population distribution weight of land use type, night light intensity, and residential density. Secondly, the total weight of each country-level administrative unit is calculated on the basic of standardizing the influence weights of above three aspects. Finally, using grid space calculation method to combine the unit weight of population with the total weight to spatialize population. Units: person·km-2. Spatial resolution: 1 km. Projected Coordinate System: GCS_Krassovsky_1940. Data format: ArcGis Grid. URL: https://www.resdc.cn/DOI/DOI.aspx?DOIid=32

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

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Objectives: To simulate the growth trend of diabetes mellitus in Chinese population.Methods: The system dynamic modeling methodology was used to establish a population prediction model of diabetes with or without cardiovascular diseases. Lifestyle therapy and the use of metformin, acarbose, and voglibose were assumed to be intervention strategy. The outcomes will be examined at 5, 15, and 30 years after 2020.Results: The projected number of diabetic population in China would increase rapidly from 141.65 million in 2020 to 202.84 million in 2050. Diabetic patients with cardiovascular disease would rapidly increase from 65.58 million in 2020 to 122.88 million by 2050. The annual cost for the entire population with diabetes mellitus in China would reach 182.55 billion by 2050. When the treatment of cardiovascular disease was considered, expenditure was 1.5–2.5-fold higher. Lifestyle therapy and the use of metformin, acarbose and voglibose could effectively slow the growth of the diabetic population.Conclusion: The diabetic population in China is expected to increase rapidly, and diabetic patients with cardiovascular disease will increase greatly. Interventions could delay it.

The fertility rate of a country is the average number of children that women from that country will have throughout their reproductive years. In 1930, China's fertility rate was 5.5 children per woman, and this number then dropped to just under five over the next fifteen years, as China experienced a civil war and the Second World War. The fertility rate rose rather quickly after this to over 6.1 in 1955, before dropping again in the late 1950s, as Chairman Mao's 'Great Leap Forward' failed to industrialize the nation, and resulted in widespread famine that killed an estimated 45 million people. In the decade following this, China's fertility rate reached it's highest level in 1970, before the implementation of the two-child policy in the 1970s, and the one-child policy** in the 1980s, which radically changed the population structure. The fertility rate fell to an all time low in the early 2000s, where it was just 1.6 children per woman. However this number has increased to 1.7 today, and the two-child policy was reintroduced in 2016, replacing the one-child policy that had been effective for over 36 years.

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

As China continues to implement its progressive fertility promotion policy, there has been a drastic decline in the fertility rate. Given that the migrant population constitutes more than a quarter of China’s total population, enhancing the willingness of this demographic to have additional children through policy-guided urban public services is pivotal for optimizing China’s population development strategy. This study analyzes the influence of urban public services on the reproductive intentions of the migrant population, utilizing data from 110,667 migrant families with one child, drawn from China’s Migrant Population Dynamic Monitoring data in 2016 and 2018. The data analysis reveals several key findings: (1) Urban public services, overall, exhibit a notable positive effect on the willingness of the migrant population to have more children, albeit with limitations and a declining trend. (2) Among urban public services, primary basic education significantly impacts the willingness of the migrant population to expand their families. (3) Large cities have created a ’reverse screening’ effect on the migrant population, leading to differential access to public services. This scenario caters effectively to the high human capital migrant individuals while reducing accessibility to livelihood public services for the low human capital migrant population. This paper critically evaluates China’s progressively adjusted fertility policy from the perspective of the migrant population. It underscores the necessity of establishing a comprehensive fertility support policy system across China.