Historical dataset showing China life expectancy by year from 1950 to 2025.

This dataset is about countries per year in China. It has 64 rows. It features 4 columns: country, death rate, and life expectancy at birth.

China Life Expectancy data was reported at 78.200 Year Old in 2021. This records an increase from the previous number of 77.930 Year Old for 2020. China Life Expectancy data is updated yearly, averaging 76.340 Year Old from Dec 1981 (Median) to 2021, with 13 observations. The data reached an all-time high of 78.200 Year Old in 2021 and a record low of 67.770 Year Old in 1981. China Life Expectancy data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region. According to the National Health Commission, from 2016 to 2017, the average life expectancy of residents per capita has increased from 76.5 to 76.7 years. For reference only. 根据国家卫生健康委员会，从2016年到2017年，居民人均预期寿命由76.5岁提高到76.7岁。以供參考。

CN: Life Expectancy: Fujian data was reported at 78.490 Year Old in 12-01-2020. This records an increase from the previous number of 75.760 Year Old for 12-01-2010. CN: Life Expectancy: Fujian data is updated decadal, averaging 74.155 Year Old from Dec 1990 (Median) to 12-01-2020, with 4 observations. The data reached an all-time high of 78.490 Year Old in 12-01-2020 and a record low of 68.570 Year Old in 12-01-1990. CN: Life Expectancy: Fujian data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

This dataset is about countries per year in China. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, land area, and life expectancy at birth.

Chinese Longitudinal Healthy Longevity Survey (CLHLS) Chinese Longitudinal Healthy Longevity Study (CLHLS) collected longitudinal data coordinated by the Center for Healthy Aging and Development Studies of National School of Development at Peking University. The baseline survey was conducted in 1998 and the follow-up surveys were conducted in 2000, 2002, 2005, 2008-2009, 2011-2012, 2014 and 2017-2018 in randomly selected about half of the counties and city districts in 23 Chinese provinces. In the 8 waves of the CLHLS conducted in 1998-2018, we have conducted face-to-face home-based 113 thousands interviews, including 19.5 thousand centenarians, 26.8 thousands nonagenarians, 29.7 thousands octogenarians, 25.5 thousands younger elders aged 65-79, and 11.3 thousands middle-age adults aged 35-64. In the latest follow-up survey (2017-2018), 15,874 elder people aged 65 and above were visited, and information about 2,226 elder people deceased during 2014-2018 were collected. The questionnaire data collected provides information on family structure, living arrangements and proximity to children, activities of daily living (ADL), the capacity of physical performance, self-rated health, self-evaluation of life satisfaction, cognitive functioning, chronic disease prevalence, care needs and costs, social activities, diet, smoking and drinking behaviors, psychological characteristics, economic resources, and care giving and family support among elderly respondents and their relatives. Information about the health status of the CLHLS participants who were interviewed in the previous wave but died before the current survey was collected by interviewing a close family member. Information provided consists of cause of death, chronic diseases, ADL before dying, frequency of hospitalization or instances of being bedridden from the last interview until death, whether bedridden before death, length of disability and suffering before death, etc. As of March 10, 2020 according to incomplete statistics, there are 8019 registered CLHLS data users (excluding their students and other group members), and they produced following publications using the CLHLS datasets: 356 papers written in English, published in U.S. or European peer-reviewed journals, 455 papers written and published in peer-reviewed Chinese journals, 17 books (in English or in Chinese), 35 Ph.D. dissertations and 104 M.A. theses successfully defended at Universities inside and outside of China.

CN: Life Expectancy: Liaoning data was reported at 78.680 Year Old in 12-01-2020. This records an increase from the previous number of 76.380 Year Old for 12-01-2010. CN: Life Expectancy: Liaoning data is updated decadal, averaging 74.860 Year Old from Dec 1990 (Median) to 12-01-2020, with 4 observations. The data reached an all-time high of 78.680 Year Old in 12-01-2020 and a record low of 70.220 Year Old in 12-01-1990. CN: Life Expectancy: Liaoning data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

Chinese Longitudinal Healthy Longevity Survey (CLHLS) WELCOME! The Chinese Longitudinal Healthy Longevity Survey (CLHLS) has been supported by NIA/NIH grants R01 AG023627-01 (PI: Zeng Yi) (Grant name: Demographic Analysis of Healthy Longevity in China) and P01 AG 008761 (PI: Zeng Yi; Program Project Director: James W. Vaupel), awarded to Duke University, with Chinese matching support for personnel costs and some local expenses. UNFPA and the China Social Sciences Foundation provided additional support for expanding the 2002 CLHLS survey. The Max Planck Institute for Demographic Research has provided support for international training since the CLHLS 1998 baseline survey. Finally, in December 2004 the China Natural Sciences Foundation and the Hong Kong Research Grants Council (RGC) partnered with NIA/NIH, providing grants to partially support the CLHLS project. Until present, the CLHLS conducted face-to-face interviews with 8,959, 11,161, 20,421, 18,524 and 19,863 individuals in 1998, 2000, 20002, 2005, and 2008-09, respectively, using internationally compatible questionnaires. Among the approximately 80,000 interviews conducted in the five waves, 14,290 were with centenarians, 18,910 with nonagenarians, 20,743 with octogenarians, 14,416 with younger elders aged 65-79, and 10,569 with middle-age adults aged 35-64. At each wave, survivors were re-interviewed, and deceased interviewees were replaced with new participants. Data on mortality and health status before dying for the 17,721 elders aged 65-110 who died between waves were collected in interviews with a close family member of the deceased. The CLHLS has the largest sample of centenarians in the world according to a report in Science (see the report). Our general goal is to shed new light on a better understanding of the determinants of healthy longevity of human beings. We are compiling extensive data on a much larger population of the oldest-old aged 80-112 than has previously been studied, with a comparison group of younger elders aged 65-79. We propose to use innovative demographic and statistical methods to analyze longitudinal survey data. Our goal is to determine which factors, out of a large set of social, behavioral, biological, and environmental risk factors, play an important role in healthy longevity. The large population size, the focus on healthy longevity (rather than on a specific disease or disorder), the simultaneous consideration of various risk factors, and the use of analytical strategies based on demographic concepts make this an innovative demographic data collection and research project. Our specific objectives are as follows: Collect intensive individual interview data including health, disability, demographic, family, socioeconomic, and behavioral risk factors for mortality and healthy longevity. Follow up the oldest-old and the comparison group of the younger elders, as well as some of the elders’ adult children to ascertain changes in their health status, care needs and costs, and associated factors. We will also ascertain mortality and causes of death, as well as care needs, costs, and health/disability status before death. Analyze the collected data to estimate the impacts of social, behavioral, environmental, and biological risk factors that are determinants of healthy longevity and mortality in the oldest-old. Compare the findings with results from other studies of large populations at advanced age.

The CLHLS biomarkers datasets were collected by the Center for Healthy Aging and Development Studies (CHADS) of National School of Development at Peking University and Chinese Center for Disease Control and Prevention (CDC) from the in-depth studies in the 8 longevity areas in the CLHLS 5th, 6th and 7th waves in 2009, 2012 and 2014. During the in-depth studies, China CDC local network medical doctors conducted physical examinations for the participants and collected 7,334 samples from the centenarians, the oldest-old aged 90-99 and other younger age groups. The biomarkers datasets, which contain about 30 indicators on routine blood/urine tests and blood biochemical tests, are very valuable research resources for interdisciplinary studies on healthy aging.

BackgroundChina's rapid economic and social development since the early 2000s has caused significant shifts in its epidemiological transition, potentially leading to health disparities across regions.ObjectivesThis study employs Life Expectancy (LE) to assess health disparities and trends among China's eastern, central, and western regions. It also examines the pace of LE gains relative to empirical trends and investigates age and causes of death mortality improvement contributing to regional LE gaps.Data and methodsUsing a log-quadratic model, the study estimates LE in China and its regions from 2004 to 2020, using census and death cause surveillance data. It also utilizes the Human Mortality Database (HMD) and the LE gains by LE level approach to analyze China and its regions' LE gains in comparison to empirical trend of developed countries. The study investigates changes in LE gaps due to age and causes of death mortality improvements during two periods, 2004–2012 and 2012–2020, through the LE factor decomposition method.ResultsFrom 2000 to 2020, China's LE exhibited faster pace of gains compared to developed countries. While men's LE growth gradually aligns with empirical trends, women experience slightly higher growth rates. Regional LE disparities significantly reduced from 2004 to 2012, with a marginal reduction from 2012 to 2020. In the latter period, the changing LE gap aligns with expected trends in developed countries, with all Chinese regions surpassing empirical estimates. Cardiovascular diseases and malignant neoplasms emerged as the primary contributors to expanding regional LE gaps, with neurological disorders and diabetes playing an increasingly negative role.ConclusionLE disparities in China have consistently decreased, although at a slower pace in recent years, mirroring empirical trends. To further reduce regional LE disparities, targeted efforts should focus on improving mortality rates related to cardiovascular diseases, neoplasms, neurological disorders and diabetes, especially in the western region. Effective health interventions should prioritize equalizing basic public health services nationwide.

CN: Life Expectancy: Yunnan data was reported at 74.020 Year Old in 12-01-2020. This records an increase from the previous number of 69.540 Year Old for 12-01-2010. CN: Life Expectancy: Yunnan data is updated decadal, averaging 67.515 Year Old from Dec 1990 (Median) to 12-01-2020, with 4 observations. The data reached an all-time high of 74.020 Year Old in 12-01-2020 and a record low of 63.490 Year Old in 12-01-1990. CN: Life Expectancy: Yunnan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

Objectives: To assess the effect of health check-ups on health among the elderly Chinese.Methods: The first dataset was panel data extracted from the 2011, 2014, and 2018 waves of the Chinese Longitudinal Health Longevity Survey (CLHLS). The second dataset was cross-sectional data come from CLHLS 2018 linked with the lagged term of health check-ups in CLHLS 2011. Health check-ups were measured by a binary variable annual health check-up (AHC). Health was assessed by a binary variable self-rated health (SRH). A coarsened exact matching method and individual fixed-effects models, as well as logistic regressions were employed.Results: AHC attendance among the elderly increased from 2011 to 2018, with higher utilization of AHC also detected in the rural group. AHC had positive effects on SRH among rural respondents (short-term effect: OR = 1.567, p < 0.05; long-term effect: OR = 3.385, p < 0.001).Conclusion: This study highlights a higher utilization of AHC in rural area, and the effectiveness of AHC in SRH improvement among rural participants. It indicates enhanced access to public healthcare services in rural area and underlying implications of health check-ups for reducing urban–rural health inequalities.

Objectives: We examined the magnitude and determinants of socioeconomic disparities in disability-free life expectancy and life expectancy at age 65 (DFLE65 and LE65) in China.Methods: Data from Chinese Longitudinal Healthy Longevity Survey collected during 2011–2018 (8,184 participants aged ≥65) were used. Socioeconomic status (SES) was measured by economic status (ES), and education, respectively. Multistate Markov models and microsimulations were fitted to estimate DFLE65 and LE65.Results: LE65 between high- and low-ES groups differed by 2.20 years for males and 2.04 years for females. The DFLE65 disparity in ES was 1.51 and 1.29 years for males and females, respectively. Not undergoing physical examinations, inadequate fruit/vegetable intake, and stress contributed to 35.10% and 57.36% of DFLE65 disparity in ES, as well as 26.36% and 42.65% of LE65 disparity for males and females, respectively. These disparities in education and ES were of a similar magnitude, while the above factors contributed little to education disparity.Conclusion: Socioeconomic disparities in DFLE65 and LE65 existed in China. Physical examination, fruit/vegetable intake and stress partly explained these disparities.

CN: Life Expectancy: Female: Gansu data was reported at 77.850 Year Old in 12-01-2020. This records an increase from the previous number of 74.060 Year Old for 12-01-2010. CN: Life Expectancy: Female: Gansu data is updated decadal, averaging 71.160 Year Old from Dec 1990 (Median) to 12-01-2020, with 4 observations. The data reached an all-time high of 77.850 Year Old in 12-01-2020 and a record low of 68.250 Year Old in 12-01-1990. CN: Life Expectancy: Female: Gansu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

Additional file 2: Table S1. Top ten frequent unique combination clusterswith multimorbidity, stratified by age. Table S2. Top ten frequentunique combination clusters with multimorbidity, stratified by sex. Table S3. Top ten frequent unique combination clusters with multimorbidity, stratified by residence. Table S4. Top ten frequent unique combination clusters with multimorbidity, stratified by region.

China Life Expectancy: Male data was reported at 75.370 Year Old in 2020. This records an increase from the previous number of 73.640 Year Old for 2015. China Life Expectancy: Male data is updated yearly, averaging 70.830 Year Old from Dec 1981 (Median) to 2020, with 7 observations. The data reached an all-time high of 75.370 Year Old in 2020 and a record low of 66.280 Year Old in 1981. China Life Expectancy: Male data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

Objectives: To assess the effect of health check-ups on health among the elderly Chinese.Methods: The first dataset was panel data extracted from the 2011, 2014, and 2018 waves of the Chinese Longitudinal Health Longevity Survey (CLHLS). The second dataset was cross-sectional data come from CLHLS 2018 linked with the lagged term of health check-ups in CLHLS 2011. Health check-ups were measured by a binary variable annual health check-up (AHC). Health was assessed by a binary variable self-rated health (SRH). A coarsened exact matching method and individual fixed-effects models, as well as logistic regressions were employed.Results: AHC attendance among the elderly increased from 2011 to 2018, with higher utilization of AHC also detected in the rural group. AHC had positive effects on SRH among rural respondents (short-term effect: OR = 1.567, p < 0.05; long-term effect: OR = 3.385, p < 0.001).Conclusion: This study highlights a higher utilization of AHC in rural area, and the effectiveness of AHC in SRH improvement among rural participants. It indicates enhanced access to public healthcare services in rural area and underlying implications of health check-ups for reducing urban–rural health inequalities.

CN: Life Expectancy: Female: Chongqing data was reported at 81.640 Year Old in 12-01-2020. This records an increase from the previous number of 78.600 Year Old for 12-01-2010. CN: Life Expectancy: Female: Chongqing data is updated decadal, averaging 78.600 Year Old from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 81.640 Year Old in 12-01-2020 and a record low of 73.890 Year Old in 12-01-2000. CN: Life Expectancy: Female: Chongqing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

CN: Life Expectancy: Male: Tianjin data was reported at 79.320 Year Old in 12-01-2020. This records an increase from the previous number of 77.420 Year Old for 12-01-2010. CN: Life Expectancy: Male: Tianjin data is updated decadal, averaging 75.365 Year Old from Dec 1990 (Median) to 12-01-2020, with 4 observations. The data reached an all-time high of 79.320 Year Old in 12-01-2020 and a record low of 71.030 Year Old in 12-01-1990. CN: Life Expectancy: Male: Tianjin data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

IntroductionRecent evidence supports a role for the gut microbe-metabolites in longevity. However, the phenomenon of hypertension is more common in the longevity area and whether hypertension is associated with longevity remains unclear. Here, we hypothesize that the levels of gut microbiota, SCFAs, and urine metabolites were different between hypertension elderly and hypertension longevity.MethodsWe recruited 46 elderly volunteers from Donglan County, Guangxi, and 32 were selected and included in the experiment. The subjects with hypertension were divided into two groups according to age, Hypertension Elderly (HTE, aged 70.5 ± 8.59, n = 19) and Hypertension Longevity (HTL, aged 100 ± 5.72, n = 13). The gut microbiota, SCFAs, and urine metabolites were determined by three-generation 16S rRNA full-length sequencing, GC-MS, and 1H-NMR, respectively.ResultsCompared with the HTL group, the HTE group had higher levels of hypertension-related genera Klebsiella and Streptococcus, while having lower levels of the SCFA-producing genera Bacteroides, Faecalibacterium, and Alistipes. Based on LEFse analysis, Klebsiella pneumoniae, Lactobacillus gasseri, Streptococcus salivarius, Ruminococcus, Actinomyces, Rikenellaceae, f_Saccharimonadaceae, Clostridium perfringens, and Bacteroids, Faecalibacterium prausnitzii, Parabacteroides, Alistipes were biomarkers that showed significant differences between the groups. In addition, the microbial pathways associated with K. pneumoniae and E. coli may promote hypertension, while A. muciniphila may play a role in reversing the development of hypertension in long-lived elderly. Metabolomics revealed that HTL contained a lower concentration of fecal acetate and propionate than HTE, while it contained a higher concentration of serum acetate and urine acetate. Furthermore, their immune cells exhibited no significant changes in SCFAs receptors.ConclusionAlthough long-lived elderly have extremely high systolic blood pressure, their unique gut microbiota composition and efficient acetate absorption in the colon may offset the damages caused by hypertension and maintain healthy homeostasis.