China Disposable Income per Capita: Urban: Upper Middle Income data was reported at 68,151.000 RMB in 2024. This records an increase from the previous number of 65,430.000 RMB for 2023. China Disposable Income per Capita: Urban: Upper Middle Income data is updated yearly, averaging 11,827.130 RMB from Dec 1985 (Median) to 2024, with 40 observations. The data reached an all-time high of 68,151.000 RMB in 2024 and a record low of 861.960 RMB in 1985. China Disposable Income per Capita: Urban: Upper Middle Income data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Income by Income Level. Since 2013, All households in the sample are grouped, by per capita disposable income of the household, into groups of low income, lower middle income, middle income, upper middle income, and high income, each group consisting of 20%, 20%, 20%, 20%, and 20% of all households respectively.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

IntroductionMobile genetic elements (MGEs) play a crucial role in the dissemination of antibiotic resistance genes (ARGs), posing significant public health concerns. Despite their importance, the impact of socioeconomic factors on MGEs within the human gut microbiome remains poorly understood.MethodsWe reanalyzed 1,382 publicly available human gut metagenomic datasets from Chinese populations, including 415 individuals from high-income eastern regions and 967 individuals from low- and middle-income western regions. MGEs were identified and categorized into functional groups, and statistical analyses were conducted to assess regional differences and correlations with economic indicators.ResultsA total of 638,097 nonredundant MGEs were identified. Among these, MGEs related to integration/excision had the highest mean abundance, while those involved in stability/transfer/defense had the lowest. The abundance of MGEs was significantly higher in the eastern population compared to the western population. Moreover, MGE abundance was positively correlated with regional GDP per capita and with ARG abundance within individuals.DiscussionOur findings suggest that socioeconomic development and industrialization are associated with increased MGE abundance in the human gut microbiome, which may in turn facilitate the spread of ARGs. These results highlight a potential unintended consequence of economic advancement on public health through microbiome-mediated antibiotic resistance.

BackgroundThe urbanization process may affect the lifestyle of rural residents in China. Limited information exists on the extent of sedentarism and physical activity (PA) level of rural residents in middle-income countries. This is the first survey on sedentary time (ST) and PA among rural residents in eastern China.MethodsThis cross-sectional observational study randomly samples rural adults from Zhejiang Province in eastern China (n = 1,320). Participants' ST and PA levels were determined from the International Physical Activity Questionnaire Short Form through face-to-face interviews, and the influencing factors of PA levels were assessed through multi-class logistic regression analysis.ResultsThe findings showed that the daily ST of the participants ranged from 30 to 660 min, with a median of 240 min (P25, P75:120, 240 min), and 54.6% of participants were sedentary for 240 min or above. The daily ST in men, people aged 18 to 44 years, people with bachelors' degree and above, people working for government agencies or institutions, people with unmarried status, and people with an average income of < 2,000 Yuan was longer than that of other respective groups (p < 0.01). In contrast, the daily ST of people with hypertension or with patients with osteoporosis or osteopenia was less than that of normal people (p < 0.01). Additionally, 69.4% of participants generally had a low level of PA (LPA). Compared with those living in northern Zhejiang, people living in southern Zhejiang who were aged 18–44 years, had bachelor's degree or above, were farmers, and had household incomes below 10,000 Yuan per month were more likely to engage in LPA compared to people > 60 years, with high school or technical education levels or with junior college degrees, working in government agencies and institutions, and with household income above 10,000 Yuan per month (p < 0.05). Furthermore, there was no correlation between ST and PA levels.ConclusionMost rural residents in the Zhejiang Province of eastern China had longer daily ST and a LPA. This was predominant in men, young people, highly educated people, unmarried people, and middle to high-income people. Health education programs should be targeted toward specific population groups to decrease the ST and increase PA.

Macau MO: Imports: % of Total Goods Imports: Residual data was reported at 0.000 % in 2016. This records a decrease from the previous number of 0.133 % for 2015. Macau MO: Imports: % of Total Goods Imports: Residual data is updated yearly, averaging 0.028 % from Dec 1963 (Median) to 2016, with 53 observations. The data reached an all-time high of 0.876 % in 1988 and a record low of 0.000 % in 2011. Macau MO: Imports: % of Total Goods Imports: Residual data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau SAR – Table MO.World Bank.WDI: Imports. Merchandise imports by the reporting economy residuals are the total merchandise imports by the reporting economy from the rest of the world as reported in the IMF's Direction of trade database, less the sum of imports by the reporting economy from high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise imports by the economy.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;