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.

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.

The graph shows the population growth in China from 2000 to 2024. In 2024, the Chinese population decreased by about 0.1 percent or 1.39 million to around 1.408 billion people. Declining population growth in China Due to strict birth control measures by the Chinese government as well as changing family and work situations of the Chinese people, population growth has subsided over the past decades. Although the gradual abolition of the one-child policy from 2014 on led to temporarily higher birth figures, growth rates further decreased in recent years. As of 2024, leading countries in population growth could almost exclusively be found on the African continent and the Arabian Peninsula. Nevertheless, as of mid 2024, Asia ranked first by a wide margin among the continents in terms of absolute population. Future development of Chinese population The Chinese population reached a maximum of 1,412.6 million people in 2021 but decreased by 850,000 in 2022 and another 2.08 million in 2023. Until 2022, China had still ranked the world’s most populous country, but it was overtaken by India in 2023. Apart from the population decrease, a clear growth trend in Chinese cities is visible. By 2024, around 67 percent of Chinese people lived in urban areas, compared to merely 36 percent in 2000.

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.

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.

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.

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.

Multiple myeloma (MM) is an incurable malignancy of mature B-lymphoid cells, and its pathogenesis is only partially understood. Previous studies have demonstrated that a number of Non-Hodgkin Lymphoma (NHL) associated genes also show susceptibility to MM, suggesting malignancies originating from B cells may share similar genetic susceptibility. Several recent large-scale genome-wide association studies (GWAS) have identified HLA-I, HLA-II, CXCR5, ETS1, LPP and NCOA1 genes as genetic risk factors associated with NHL, and this study aimed to investigate whether these genes polymorphisms confer susceptibility with MM in the Chinese Han population. In 827 MM cases and 709 healthy controls of Chinese Han, seven single nucleotide polymorphisms (SNPs) in the HLA–I region (rs6457327), the HLA–II region (rs2647012 and rs7755224), the CXCR5 gene (rs4938573), the ETS1 gene (rs4937362), the LPP gene (rs6444305), and the NCOA1 region (rs79480871) were genotyped using the Sequenom platform. Our study indicated that genotype and allele frequencies of rs79480871 showed strong associations with MM patients (pa = 3.5×10−4 and pa = 1.5×10−4), and the rs6457327 genotype was more readily associated with MM patients than with controls (pa = 4.9×10−3). This study was the first to reveal the correlation between NCOA1 gene polymorphisms and MM patients, indicating that NCOA1 might be a novel susceptibility gene for MM patients in the Chinese Han population.

aMann-Whitney test.Demographic and clinical characteristics of the PE cohort and normal pregnant women.

By 2035, over 34 million people are projected to call Shanghai home. To reduce this number, the Chinese Government implemented population controls for the city in 2017 which aimed to limit the population living in the administrative area of Shanghai municipality to just around 25 million people in 2035.

Megacity – Shanghai

As China’s cities become increasingly urbanized, the demographic of this megacity has also changed considerably over the years, with more and more Chinese locals and foreigners opting to dwell in Shanghai for work and cultural opportunities. A huge proportion of residents in the city originate from other regions in China. Over 39 percent of the city’s residents are long-term migrants and Shanghai host’s many foreigners and expats.

A global financial hub as well as the largest city by population, Shanghai is located on China’s central coast, making it an ideal location to accommodate the world’s busiest container port. The economic contribution of the city to China is significant - Shanghai’s gross domestic product contribution amounted to almost 4.7 trillion yuan in 2023. Despite recent restrictions to land made available for construction, the value of investment in real estate development in Shanghai has continued to increase. To mitigate the effects of its high population, the city has stated it will intensify environmental protection measures.

Objectives The SLCO6A1 gene belongs to a superfamily of genes which is known to be a solute carrier family of OATPs (SLCO). The SLCO6A1 gene encodes OATP6A1 protein in humans. A previous genome-wide association study (GWAS) of schizophrenia conducted in the Swedish population demonstrated a significant association of rs6878284, which is located in the SLCO6A1 gene, with schizophrenia. To further investigate whether this gene is also a risk locus for schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD) in the Han Chinese population, a case–control study was designed. Methods In total 1,248 unrelated SCZ cases, 1,344 BPD cases, 1,056 unrelated MDD cases and 1,248 normal controls were analysed in this study. We genotyped five SNPs using the Sequenom MassARRAY platform. Results We found no association of rs6878284 with SCZ [Corrected Pallele = 0.969, Corrected Pgenotype = 0.997]. Furthermore, we found a statistically significant association of the rs7734060 genotype with MDD after correction [rs7734060: Corrected Pallele = 0.114, Corrected Pgenotype = 0.036] in the Han Chinese population. Conclusions This is the first study which reveals no association of rs6878284 with SCZ and also predicts that rs7734060 could be a risk locus for MDD in the Han Chinese population.

Snapshot img

Population Health Management Market Size 2025-2029

The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.

The Population Health Management Market is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and the rising focus on personalized medicine. The implementation of electronic health records (EHRs) and other digital health technologies has enabled healthcare providers to collect and analyze large amounts of patient data, facilitating proactive care and population health management. Moreover, the trend towards personalized medicine, which aims to tailor healthcare treatments to individual patients based on their unique genetic makeup and health history, is further fueling the demand for PHM solutions. However, the high cost of installing and implementing these platforms poses a significant challenge for market growth.
Despite this, the potential benefits of PHM, including improved patient outcomes, reduced healthcare costs, and enhanced population health, make it an attractive area for investment and innovation. Companies seeking to capitalize on these opportunities must navigate the challenges of data privacy and security, interoperability, and integration with existing healthcare systems. By addressing these challenges and focusing on delivering actionable insights from patient data, PHM solution providers can help healthcare organizations optimize their resources, improve patient care, and ultimately, improve population health.

What will be the Size of the Population Health Management Market during the forecast period?

Request Free Sample

The market is experiencing significant growth, driven by the increasing focus on accountable care organizations (ACOs) and payer organizations to improve health outcomes and reduce costs. Healthcare professionals are leveraging big data, data analytics services, and clinical data integration to develop personalized care plans and implement intervention strategies for various populations. Telehealth services have become essential in population health management, enabling care coordination, health promotion, and health navigation for patients. Health equity is a critical factor in population health management, with a growing emphasis on addressing disparities and ensuring equal access to care.
Data security and interoperability standards are essential in population health management, as healthcare providers exchange sensitive patient data for risk adjustment, care pathways, and quality reporting. Data mining and data visualization tools are used to identify health behavior changes and lifestyle modifications, leading to better health outcomes. Consumer health technology, such as patient engagement tools and wearable technology, are playing an increasingly important role in population health management. Health coaching and evidence-based medicine are intervention strategies used to prevent diseases and improve health outcomes. In summary, the market in the US is characterized by the adoption of precision medicine, health literacy, clinical guidelines, and personalized care plans.
The market is driven by the need for care coordination, data analytics, and patient engagement to improve health outcomes and reduce costs. The use of data security, data mining, and interoperability standards ensures the effective exchange and utilization of health data.

How is this Population Health Management Industry segmented?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based
  On-Premise
  Cloud-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies
  Providers
  Payers
  Employer Groups


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market's software segment is experiencing significant growth and innovation. Healthcare organizations are utilizing these solutions to effectively manage and enhance the health outcomes of diverse populations. The software component incorporates various tools that collect, analyze, and utilize health data for informed decision-making. Population health management platforms gather data from multiple sources, such as electronic health records, claims data, and patient-generated data. These platforms employ advanced analytics to generate valuable insi

Context

Human height is an inheritable, polygenic trait under complex and multi-locus genetic regulation. Familial short stature (FSS; also called genetic short stature) is the most common type of short stature and is insufficiently known.

Objective

To investigate the FSS genetic profile and develop a polygenic risk predisposition score for FSS risk prediction.

Design and Setting

The FSS case group of Han Chinese ancestry was diagnosed by pediatric endocrinologists in Taiwan.

Patients and Interventions

The genetic profile of 1,163 FSS cases was identified by using a bootstrapping sub-sampling and genome-wide association studies (GWAS) method.

Main Outcome Measures

Genetic profile, polygenic risk predisposition score for risk prediction.

Results

Ten novel genetic SNPs and 9 reported GWAS human height-related SNPs were identified for FSS risk. These 10 novel SNPs served as a polygenic risk predisposition score for FSS risk prediction (area under curve (AUC): 0.940 in the testing group). This FSS polygenic risk predisposition score was also associated with the height reduction regression tendency in the general population.

Conclusion

A polygenic risk predisposition score composed of 10 genetic SNPs is useful for FSS risk prediction and the height reduction tendency. Thus, it might contribute to FSS risk in the Han Chinese population from Taiwan.

Methods Ethics and consent

This study is a cross-sectional study on the clinical, biochemical, and genetic findings collected from cases of familial short stature (FSS) sequentially identified from the Children’s Hospital, China Medical University, Taichung, Taiwan, from August 1999 to September 2018. This study was approved by the institutional review board and the ethics committee of Human Studies Committee of China Medical University Hospital. Written informed consent was obtained from the participants, their parents, or legal guardians according to institutional requirements and Declaration of Helsinki principles (https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/).

Participants

The case group used in this study was diagnosed by pediatric endocrinologists in Taiwan (Fig. 1 and Supplementary Fig. S1). The case group included FSS cohort (N = 1,163). All recruited cases were of Han Chinese ancestry and diagnosed with FSS (1,20,22). The selection criteria for FSS was (1) height less than the 3rd percentile (Supplementary Fig. 7), (2) Their fathers’ and/or mothers’ height less than the 3rd percentile, (3) bone age appropriate for chronologic age, (4) normal onset of puberty, (5) normal annual growth rate, and (6) normal results of clinical biochemistry examination. Excluded individuals were those with all other abnormal morphology and karyotyping results, abnormal bone age or puberty stage, or who had abnormal serum or plasma levels of clinical biochemistry examinations for GH-IGF1 axis, thyroid function or precocious puberty (Supplementary Fig. S1). Finally, 1,163 FSS subjects were served as the cases in the study.

In this study, the controls consisted of 4,168 individuals from the Taiwan Biobank (TW-Biobank; http://www.twbiobank.org.tw/new_web/index.php) and our type 2 diabetes cohorts (Fig. 1 and Supplementary Fig. 6). Furthermore, the selection criteria for controls with their top 75th percentile of human height in our study (N = 1,071) was (1) no history of FSS diagnosis, (2) height exceeding that of the top 75th percentile of the general population in Taiwan, and (3) age <61 years. All case and control groups in this study were of Han Chinese origin based on principal component analysis of genome-wide data (Supplementary Fig. S2).

Genotyping and quality control

Genomic DNA was extracted from the blood samples of participants according to standard protocols using the Qiagen genomic DNA isolation kit (Qiagen, Taichung, Taiwan). Each FSS case (N = 1,163) was genotyped at the National Genotyping Centre at Academia Sinica (Taipei, Taiwan) using the Axiom genome-wide CHB array plate, according to the manufacturer’s procedure. For the control group from Taiwan Biobank, the GWAS data of each sample was genotyped using the Axiom genome-wide TWB array plate. For the control group from our type 2 diabetes cohorts, the GWAS data of each type 2 diabetes patient was genotyped using the Axiom genome-wide TWB array plate, the Affymetrix genome-wide human SNP array 6.0, and the Illumina HumanHap550-Duo BeadChip according to the manufacturer’s procedure.

Because GWAS data were from different genotyping platforms, genotype imputations were performed in both FSS cases and controls according to a two-step genotype imputation approach. We used SHAPEIT2 to pre phase the study genotypes into full haplotypes(25). We then performed imputation using IMPUTE2 and Phase I 1000 Genomes Project reference panel (June 2011 interim release) consisting of 1,094 phased individuals from multiple ancestry groups The 1000 Genomes Project Consortium, 2010. Finally, we used the GTOOL software (http://www.well.ox.ac.uk/~cfreeman/software/gwas/ gtool.html) to homogenize strand annotation by merging the imputed results obtained from each set of genotyped data.

Genotype and imputed genotype data were quality controlled and genetic variants were excluded for further analysis if (1) only one allele appeared in cases and/or controls; (2) the total call rate was less than 95% for both cases and controls; (3) the minor allele frequency was less than 0.5% in the controls in the Han Chinese population; (4) genetic variants significantly departed from Hardy-Weinberg equilibrium proportions (p < 0.01).

Genetic predisposition score calculation

The genetic predisposition score (also known as polygenic risk score or genetic risk score) is calculated by multiplying each beta-coefficient (log OR) value by the number of the corresponding risk allele under the additive model for each individual and then summing the products for the risk alleles identified from the multiple susceptible genetic variants (27). In this study, the genetic predisposition score was calculated based on the 10 genetic variants (SNPs). Each genetic variant was given a weightage based on the average effect size (beta-coefficient) for the FSS obtained from our study (Table 1). The genetic predisposition score was calculated by multiplying each beta-coefficient by the number of corresponding risk alleles (risk allele homozygote (the risk genotype is coded as “2”), risk allele heterozygote (the risk genotype is coded as “1”), and non-risk allele homozygote (the non-risk genotype is coded as “0”) according to the additive inheritance model) and then summing the products from these 10 genetic variants weighted by their estimated effect sizes (log OR).

Statistical analysis

All the genotyped and imputed GWAS results of FSS cases and their controls were used for association studies using a regression framework implemented in PLINK under the additive inherited genetic model (28). The difference in allelic frequency in the additive model between the cases and controls were measured by odds ratios (ORs) with 95% confidence intervals (CIs) using logistical regression models (Tables 1-2 and Supplementary Tables 1-6 and 8-9). All data management and statistical analyses were performed using PLINK and SAS software (version 9.4; SAS Institute, Cary, NC, USA).

For haplotype block analysis, the Lewontin D′ and R2 values were used to evaluate the intermarker coefficient of linkage disequilibrium (LD) in both FSS cases and controls (29). The confidence interval for LD was estimated using a resampling procedure and was used to construct the haplotype blocks (Supplementary Fig. S3) (30,31).

The risk prediction model predicts the health outcome by using several predictor variables based on the observed patient’s characteristics (32). Risk prediction was evaluated by the area under the receiver operating characteristic (ROC) curves (AUCs). The AUC ranged from 0.5 (total lack of discrimination) to 1.0 (perfect discrimination). AUCs were calculated for the predicted risks of 10 novel, 9 reported, and combined SNPs, respectively (Figs. 2a and 2b).

For the linear human height curve model, participants with the genetic predisposition score calculated from the 10 novel SNPs (Fig. 3a) and 9 human height-related SNPs (Fig. 3b) were used as continuous variables in a linear regression with human height (cm) as the dependent variable, respectively.

A total of 889 subjects presenting at the physical examination center of Third Hospital of Hebei Medical University, Shijiazhuang, China was enrolled in the study. Demographic characteristics including age, gender, height, body weight and biochemical characteristics were collected. Six SNPs (rs2336384, rs873458, rs873457, rs4846085, rs2878677 and rs2236057) were selected based on the previous reports of variants associated with metabolic dysfunction. Rs2336384, rs873458, rs873457 and rs4846085 are located within intron 2; rs2878677 within intron 3 and rs2236057 within intron 11. Genomic DNAs were extracted from peripheral blood leukocytes using a genomic DNA kit (GENEray Biotechnology, Shanghai, China). Mfn2 gene polymorphisms were detected by the ligase detection reaction (LDR). Primer Premier 5.0 software (Premier Biosoft Intl., CA USA) was used to design primers for amplification based on Genbank reference sequence, NG_007945.1. Probes were designed based on GENEray Biotechnology. Genotypes were evaluated using DNA sequencing on an ABI 3730 genetic analyzer (Applied Biosystems, USA), in accordance with the manufacturer’s instructions, and sequenced with GeneMarker V2.2.0 (PA, USA).

This graph shows the average size of households in China from 1990 to 2023. That year, statistically about 2.8 people were living in an average Chinese household. Average household size in China A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations. The average number of people living in one household in China dropped from 3.96 in 1990 to 2.87 in 2011. Since 2010, the figure was relatively stable and ranged between 2.87 and 3.17 people per household. The average Chinese household still counts as rather large in comparison to other industrial countries. In 2023, an average American household consisted of only 2.51 people. Comparable figures have already been reached in the bigger cities and coastal areas of China, but in the rural provinces the household size is still much larger. According to the National Bureau of Statistics of China, the household size in China was diametrically correlated to its income. Birth rates and household sizes The receding size of Chinese households may be linked to the controversial one-child policy introduced in 1979. The main aim of the policy was to control population growth. While the fertility rate in China had been very high until the 1970s, it fell considerably in the following decades and resided at only 1.7 children per woman in 2018, nearly the same as in the United States or in the United Kingdom. A partial ease in the one-child policy was introduced in 2013, due to which couples where at least one parent was an only child were allowed to have a second child. In October 2015, the law was changed into a two-child policy becoming effective in January 2016.

BackgroundStroke is a common cerebrovascular disease. The purpose of this study was to explore the association between LIPC single nucleotide polymorphisms (SNPs) and the risk of stroke in the Chinese population.MethodsThis study recruited 710 stroke patients and 701 healthy controls. The four SNPs (rs690, rs6083, rs3829461, and rs6074) in LIPC were genotyped by the Agena MassARRAY. The correlation between LIPC polymorphisms and stroke risk was measured by odds ratio (OR) and 95% confidence interval (CI). In addition, multifactor dimensionality reduction (MDR) analysis was used to evaluate the impact of SNP–SNP interaction on stroke risk.ResultsOverall analysis showed that rs690 was associated with an increased risk of stroke (T vs. G: OR = 1.19, 95% CI: 1.01–1.40, p = 0.041; additive: OR = 1.20, 95% CI: 1.01–1.42, p = 0.036). The stratified analysis revealed that rs690 was associated with an increased risk of stroke in subjects aged ≤ 64 years, male patients, and smokers, and rs6074 was associated with an increased risk of stroke in subjects aged > 64 years, male patients, drinkers, and non-smokers (p < 0.05). The results of the MDR analysis suggested the four-locus model as the most favorable model for assessing the risk of stroke. The analysis of clinical parameters of stroke patients showed that rs690 was correlated with platelet distribution width (PDW) (p = 0.014) and hematocrit levels (p = 0.004), and rs6074 was correlated with low-density lipoprotein cholesterol (LDL-C) level (p = 0.033). Furthermore, bioinformatics analysis results demonstrated that the expression levels of LIPC and its related genes (APOB, CETP, PNPLA2, and LMF1) were significantly different between the control and stroke groups (p < 0.05), and LIPC-related proteins were mainly related to lipid metabolism.ConclusionThis study indicated that rs690 and rs6074 in LIPC were significantly associated with increased risk of stroke in the Chinese population, possibly by regulating the levels of PDW, HCT, and LDL-C.

Additional file 4. The original data of daily mortality.

Between 2017 and 2021, the prevalence of wasting among young children under five years in China remained below *** percent. In the past two decades, nutrition among the Chinese population continued to improve, with childhood wasting and stunting under control. Wasting refers to a child being too thin for his or her height and is an important indicator reflecting rapid weight loss and acute undernutrition.

In 2023, the birth rate across different regions in China varied from around 13.7 births per 1,000 inhabitants (per mille) in Tibet to 2.9 per mille in Heilongjiang province. The average national birth rate ranged at 6.4 per mille that year. High disparity of birth rates across China Regional birth rates in China reach their highest values in western and southwestern provinces and autonomous regions. In this part of the country, the economy is less developed than in the coastal provinces and traditional values are more prevalent. At the same time, many people from minority communities live in these areas, who were less affected by strict birth control measures in the past and traditionally have more children. In contrast, the lowest birth rates in recent years were registered in the northwestern provinces Jilin, Liaoning, and Heilongjiang, which is the rust belt of China. This region offers few economic opportunities, and many young people leave for a better life in the eastern provinces. They often leave old people behind, which is one reason why these provinces also have some of the highest mortality rates in China. Future developments As most Chinese regions with a higher fertility rate have only few inhabitants, they cannot compensate for the increasing number of provinces with a declining populace. In the future, only economically successful cites will be able to escape this trend, while many provinces and rural areas will slowly lose a significant share of their population.

BackgroundRecent genome-wide association (GWA) studies in Caucasians identified multiple single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD). The associations of those SNPs with myocardial infarction (MI) have not been replicated in Asian populations. Among those previously identified SNPs, we selected nine (rs10953541, rs1122608, rs12190287, rs12413409, rs1412444, rs1746048, rs3798220, rs4977574, rs579459, in or near genes 7q22, LDLR, TCF21, CYP17A1, LIPA, CXCL12, LPA, CDKN2A, ABO, respectively) because of the relatively high minor allele frequencies in Chinese individuals and tested the associations of the SNPs with MI and MI related risk factors in Chinese population.Methods and ResultsWe conducted a case–control association study on a cohort of 2365 MI patients and 2678 unrelated controls from the Chinese population. Genotyping of 9 SNPs were performed by the TaqMan Real Time PCR method. After age, sex, and BMI adjustment, we observed the SNPs rs12190287, rs12413409, rs1412444, rs1746048 and rs4977574, were significantly associated with MI in additive models and rs12190287, rs12413409, rs4977574 were significantly associated with phenotypes of MI at the same time. We also found three SNPs rs1122608, rs3798220 and rs579459 were significantly associated with risk factors of MI, although they had no association with MI in Chinese population.ConclusionResults of this study indicate that 5 SNPs were associated with MI and 3 SNPs were associated with associated with lipoprotein levels but not with MI in a Chinese population. The present study supports some CAD-related genes in Caucasian as important genes for MI in a Chinese population.