This dataset contains information about China’s portfolio of collateralized loans to borrowing institutions in low-income and middle-income countries that qualify as public and publicly-guaranteed (PPG) debt. It captures 620 collateralized PPG loan commitments worth $418 billion from 31 Chinese state-owned creditors to 158 borrowers in 57 countries between 2000 and 2021.

The dataset captures 20,985 projects across 165 low- and middle-income countries supported by loans and grants from official sector institutions in China worth $1.34 trillion. It tracks projects over 22 commitment years (2000-2021) and provides details on the timing of project implementation over a 24-year period (2000-2023).

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...

The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al.. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2018. Taiwan 1km Births. Version 1.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). DOI: 10.5258/SOTON/WP00581

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey. Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Social health insurance (SHI) is a form of health finance mechanism that had been implemented in many countries to achieve universal health care (UHC). To emulate the successes of SHI in many developed countries, many developing and middle-income countries (MICs) have attempted to follow suit. However, the SHI implementation has problems and obstacles. Many more obstacles were observed despite some successes. This scoping review aimed to study the various developments of SHI globally in its uses, implementation, successes, and obstacles within the last 5 years from 2017 to 2021. Using three databases (i.e., PubMed, EBSCO, and Google Scholar), we reviewed all forms of articles on SHI, including gray literature. The PRISMA-ScR protocol was adapted as the guideline. We used the following search terms: social health insurance, national health insurance, and community health insurance. A total of 57,686 articles were screened, and subsequently, 46 articles were included in the final review. Results showed that the majority of SHI studies were in China and African countries, both of which were actively pursuing SHI programs to achieve UHC. China was still regarded as a developing country. There were also recent experiences from other Asian countries, but only a few from South America. Implementing SHI to achieve UHC was desirable but will need to consider several factors and issues. This was especially the case in developing and MICs. Eventually, full UHC would only be possible with a combination of general taxation and SHI.

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: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data was reported at 0.309 % in 2016. This records a decrease from the previous number of 0.461 % for 2015. Macau MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data is updated yearly, averaging 0.159 % from Dec 1978 (Median) to 2016, with 32 observations. The data reached an all-time high of 0.538 % in 2003 and a record low of 0.000 % in 1987. Macau MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau – Table MO.World Bank: Imports. Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Macau MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data was reported at 0.221 % in 2016. This records a decrease from the previous number of 0.322 % for 2015. Macau MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data is updated yearly, averaging 0.265 % from Dec 1965 (Median) to 2016, with 49 observations. The data reached an all-time high of 1.401 % in 1985 and a record low of 0.001 % in 1976. Macau MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau – Table MO.World Bank: Imports. Merchandise imports from low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Sub-Saharan Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data was reported at 0.889 % in 2016. This records an increase from the previous number of 0.806 % for 2015. Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data is updated yearly, averaging 0.109 % from Dec 1960 (Median) to 2016, with 55 observations. The data reached an all-time high of 0.962 % in 2014 and a record low of 0.009 % in 1972. Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong – Table HK.World Bank: Exports. Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Middle East & North Africa data was reported at 0.361 % in 2016. This records an increase from the previous number of 0.323 % for 2015. Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Middle East & North Africa data is updated yearly, averaging 0.375 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 1.615 % in 1981 and a record low of 0.189 % in 2005. Hong Kong HK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Middle East & North Africa data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong – Table HK.World Bank: Exports. Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data was reported at 0.002 % in 2016. This records an increase from the previous number of 0.000 % for 2015. Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data is updated yearly, averaging 0.030 % from Dec 1966 (Median) to 2016, with 41 observations. The data reached an all-time high of 0.420 % in 1966 and a record low of 0.000 % in 2015. Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau – Table MO.World Bank: Exports. Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Middle East & North Africa data was reported at 0.054 % in 2016. This records a decrease from the previous number of 0.058 % for 2015. MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Middle East & North Africa data is updated yearly, averaging 0.020 % from Dec 1975 (Median) to 2016, with 35 observations. The data reached an all-time high of 0.407 % in 1975 and a record low of 0.000 % in 1992. MO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Middle East & North Africa 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 from low- and middle-income economies in Middle East and North Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Middle East and North Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific data was reported at 21.155 % in 2016. This records an increase from the previous number of 19.354 % for 2015. Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific data is updated yearly, averaging 6.693 % from Dec 1963 (Median) to 2016, with 53 observations. The data reached an all-time high of 21.155 % in 2016 and a record low of 0.078 % in 1979. Macau MO: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: East Asia & Pacific data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macau – Table MO.World Bank: Exports. Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;