The Global Demographic Data collection holds global gridded data products describing demographic information and water demand in relation to population data. Currently, water demand data are being distributed; population data will be added in the near future.

Country-level urban, rural and total population estimate data from World Resources Institute (WRI) for the years 1985, 1995, and 2025 were gridded by the University of New Hampshire's Water Systems Analysis Groupusing methods outlined in Vorosmarty et al. (2000) for use in estimating global water resources based on climate and population changes.

Currently available are five relative water demand (RWD) fraction data sets/ maps, produced by Vorosmarty et al. in their analysis of future water resources. The relative water demand is defined to be the total volume of water used either domestically, industrially or agriculturally (DIA) divided by the water discharge (Q). "Values of .2 to .4 indicate medium to high stress." (see Vorosmarty et al., 2000) This analysis deals only with sustainable water sources, and does not look at nonsustainable water sources, such a ground water mining. The RWD is computed on a .5 by .5 degree grid for two sentinel years: 1985 and 2025, which are two of the data sets. The ratio of the RWD for these two years provides a measure of change under scenarios of climate change only, population change only and the combination of climate change and population to produce the other three datasets. The ratio RWD values is relative to the RWD in the base year, 1985.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men was the failure of interviewers to find them at home despite repeated callbacks.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1998 GDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 GDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Togo TG: Population: Growth data was reported at 2.484 % in 2017. This records a decrease from the previous number of 2.524 % for 2016. Togo TG: Population: Growth data is updated yearly, averaging 2.690 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4.754 % in 1968 and a record low of 0.949 % in 1962. Togo TG: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Togo – Table TG.World Bank: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.25 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020). More detailed can be found on the page of the original version (https://doi.org/10.5281/zenodo.3768003).

This release increases the resolution to 0.25˚ and is explicitly designed to match with the grid definition of the ERA5 climate reanalysis dataset. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution.

The total fertility rate of the world has dropped from around five children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have five or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

United States US: Population: Total data was reported at 325,719,178.000 Person in 2017. This records an increase from the previous number of 323,405,935.000 Person for 2016. United States US: Population: Total data is updated yearly, averaging 245,659,000.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 325,719,178.000 Person in 2017 and a record low of 180,671,000.000 Person in 1960. United States US: Population: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

This dataset provides a curated and comprehensive overview of global health, demographic, economic, and environmental metrics for 188 recognized countries over a period of 10 years (2012-2021). It was created by combining reliable data from the World Bank and the World Health Organization (WHO). Due to the absence of a single source containing all necessary indicators, over 60 datasets were analyzed, cleaned, and merged, prioritizing completeness and significance.

The dataset includes 29 key indicators, ranging from life expectancy, population metrics, and economic factors to environmental conditions and health-related behaviors. Missing values were carefully handled, and only the most relevant data with substantial coverage were retained.

This dataset is ideal for researchers, analysts, and policymakers interested in exploring relationships between economic development, health outcomes, and environmental factors at a global scale.

Abstract

The 1996 Uzbekistan Demographic and Health Survey (UDHS) is a nationally representative survey of 4,415 women age 15-49. Fieldwork was conducted from June to October 1996. The UDHS was sponsored by the Ministry of Health (MOH), and was funded by the United States Agency for International Development. The Institute of Obstetrics and Gynecology implemented the survey with technical assistance from the Demographic and Health Surveys (DHS) program.

The 1996 UDHS was the first national-level population and health survey in Uzbekistan. It was implemented by the Research Institute of Obstetrics and Gynecology of the Ministry of Health of Uzbekistan. The 1996 UDHS was funded by the United States Agency for International development (USAID) and technical assistance was provided by Macro International Inc. (Calverton, Maryland USA) through its contract with USAID.

OBJECTIVES AND ORGANIZATION OF THE SURVEY

The purpose of the 1996 Uzbekistan Demographic and Health Survey (UDHS) was to provide an information base to the Ministry of Health for the planning of policies and programs regarding the health of women and their children. The UDHS collected data on women's reproductive histories, knowledge and use of contraception, breastfeeding practices, and the nutrition, vaccination coverage, and episodes of illness among children under the age of three. The survey also included, for all women of reproductive age and for children under the age of three, the measurement of the hemoglobin level in the blood to assess the prevalence of anemia and measurements of height and weight to assess nutritional status.

A secondary objective of the survey was to enhance the capabilities of institutions in Uzbekistan to collect, process and analyze population and health data so as to facilitate the implementation of future surveys of this type.

MAIN RESULTS

• Fertility Rates. Survey results indicate a total fertility rate (TFR) for all of Uzbekistan of 3.3 children per woman. Fertility levels differ for different population groups. The TFR for women living in urbml areas (2.7 children per woman) is substantially lower than for women living in rural areas (3.7). The TFR for Uzbeki women (3.5 children per woman) is higher than for women of other ethnicities (2.5). Among the regions of Uzbekistan, the TFR is lowest in Tashkent City (2.3 children per woman).
• Family Planning. Knowledge. Knowledge of contraceptive methods is high among women in Uzbekistan. Knowledge of at least one method is 89 percent. High levels of knowledge are the norm for women of all ages, all regions of the country, all educational levels, and all ethnicities. However, knowledge of sterilization was low; only 27 percent of women reported knowing of this method.
• Fertility Preferences. A majority of women in Uzbekistan (51 percent) indicated that they desire no more children. Among women age 30 and above, the proportion that want no more children increases to 75 percent. Thus, many women come to the preference to stop childbearing at relatively young ages when they have 20 or more potential years of childbearing ahead of them. For some of these women, the most appropriate method of contraception may be a long-acting method such as female sterilization, However, there is a deficiency of both knowledge and use of this method in Uzbekistan. In the interest of providing couples with a broad choice of safe and effective methods, information about this method and access to it should be made available so that informed choices about its suitability can be made by individual women and couples.
• Induced Aboration : Abortion Rates. From the UDHS data, the total abortion rate (TAR)--the number of abortions a woman will have in her lifetime based on the currently prevailing abortion rates--was calculated. For Uzbekistan, the TAR for the period from mid-1993 to mid-1996 is 0.7 abortions per woman. As expected, the TAR for Uzbekistan is substantially lower than recent estimates of the TAR for other areas of the former Soviet Union such as Kazakstan (1.8), Romania (3.4 abortions per woman), and Yekaterinburg and Perm in Russia (2.3 and 2.8, respectively).
• Infant mortality : In the UDHS, infant mortality data were collected based on the international definition of a live birth which, irrespective of the duration of pregnancy, is a birth that breathes or shows any sign of life (United Nations, 1992).
• Mortality Rates. For the five-year period before the survey (i.e., approximately mid- 1992 to mid- 1996), infant mortality in Uzbekistan is estimated at 49 infant deaths per 1,000 births. The estimates of neonatal and postneonatal mortality are 23 and 26 per 1,000.
• Maternal and child health : Uzbekistan has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. This system includes special delivery hospitals, the obstetrics and gynecology departments of general hospitals, women's consulting centers, and doctor's assistant/midwife posts (FAPs). There is an extensive network of FAPs throughout rural areas.
• Nutrition : Breastfeeding. Breastfeeding is almost universal in Uzbekistan; 96 percent of children born in the three years preceding the survey are breastfed. Overall, 19 percent of children are breastfed within an hour of delivery and 40 percent within 24 hours of delivery. The median duration of breastfeeding is lengthy (17 months). However, durations of exclusive breastfeeding, as recommended by WHO, are short (0.4 months).
• Prevalence of anemia : Testing of women and children for anemia was one of the major efforts of the 1996 UDHS. Anemia has been considered a major public health problem in Uzbekistan for decades. Nevertheless, this was the first anemia study in Uzbekistan done on a national basis. The study involved hemoglobin (Hb) testing for anemia using the Hemocue system. Women. Sixty percent of the women in Uzbekistan suffer from some degree of anemia. The great majority of these women have either mild (45 percent) or moderate anemia (14 percent). One percent have severe anemia.

Geographic coverage

National Seven raions were excluded from the survey because they were considered too remote and sparsely inhabited.

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1996 UDHS is defined as the universe of all women age 15-49 in Uzbekistan

Kind of data

Sample survey data

Sampling procedure

The UDHS employed a probability sample of women age 15 to 49, representative of 98.7 percent of the country. Seven raions were excluded from the survey because they were considered too remote and sparsely inhabited. These raions are: Kungradskiyi, Muyinakskiyi, and Takhtakupyrskiyi in Karakalpakstan; Uchkudukskiyi, Tamdynskiyi, and Kanimekhskiyi in Navoiiskaya; and Romitanskiyi in Bukharskaya. The remainder of the country was divided into five survey regions. Tashkent City constituted a survey region by itself, while the remaining four survey regions consisted of groups of contiguous oblasts. The five survey regions were defined as follows: Region 1: Karakalpakstan and Khoresmskaya. Region 2: Navoiyiskaya, Bukharskaya, Kashkadarinskaya, and Surkhandarinskaya. Region 3: Samarkandskaya, Dzhizakskaya, Syrdarinskaya, and Tashkentskaya. Region 4: Namanganskaya, Ferganskaya, and Andizhanskaya. Region 5: Tashkent City.

CHARACTERISTICS OF THE UDHS SAMPLE

The sample for the UDHS was selected in three stages. In the rural areas, the primary sampling units (PSUs) corresponded to the raions which were selected with probabilities proportional to size, the size being the 1994 population. At the second stage, one village was selected in each selected raion. A complete listing of the households residing in each selected village was carried out. The lists of households obtained were used as the frame for third-stage sampling, which is the selection of the households to be visited by the UDHS interviewing teams during the main survey fieldwork. In each selected household, women between the ages of 15 and 49 were identified and interviewed.

In the urban areas, the PSUs were the cities and towns themselves. In the second stage, one health block was selected from each town except in self-representing cities (large cities that were selected with certainty), where more than one health block was selected. The selected health blocks were segmented prior to the household listing operation which provided the household lists for the third-stage selection of households.

SAMPLE ALLOCATION

The regions, stratified by urban and rural areas, were the sampling strata. There were thus nine strata with Tashkent City constituting an entire stratum. A proportional allocation of the target number of 4,000 women to the 9 strata would yield the sample distribution.

The proportional allocation would result in a completely self-weighting sample but would not allow for reliable estimates for at least two of the five survey regions, namely Region 1 and Tashkent City. Results of other demographic and health surveys show that a minimum sample of 1,000 women is required in order to obtain estimates of fertility and childhood mortality rates at an acceptable level of sampling errors. Given that the total sample size for the UDHS could not he increased so as to achieve the required level of sampling errors, it was decided that the sample would be divided equally among the five regions, and within each region, it would be distributed proportionally to the urban and the rural areas. With this type of allocation, demographic rates (fertility and mortality) could not be produced for regions separately.

The number of sample points (or clusters) to be selected for each stratum was calculated by dividing the

Abstract

The 2001 Nepal Demographic and Health Survey (NDHS) is a nationally representative survey of 8,726 women age 15-49 and 2,261 men age 15-59. This Survey is the sixth in a series of national-level population and health surveys conducted in Nepal. It is the second nationally representative comprehensive survey conducted as part of the global Demographic and Health Survey (DHS) program, the first being the 1996 Nepal Family Health Survey (NFHS). The 2001 NDHS is the first in the history of demographic and health surveys conducted in Nepal that included a male sample. The 2001 NDHS was carried out under the aegis of the Family Health Division of the Department of Health Services, Ministry of Health, and was implemented by New ERA, a local research organization, which also conducted the 1996 NFHS. ORC Macro provided technical support through its MEASURE DHS+ project. The survey was funded by the United States Agency for International Development (USAID) through its mission in Nepal.

The principal objective of the 2001 NDHS is to provide current and reliable data on fertility and family planning, infant and child mortality, children's and women's nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS. This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both the national and regional levels.

A long-term objective of the survey is to strengthen the technical capacity of the Family Health Division of the Ministry of Health to plan, conduct, process, and analyze data from complex national population and health surveys. The 2001 NDHS data is comparable to data collected in the 1996 NFHS and similar to survey data conducted in other developing countries. This allows for temporal and spatial comparisons of demographic health information. The 2001 NDHS also adds to the vast and growing international database on demographic and health variables. The inclusion of data on men adds to the richness of this data.

Geographic coverage

The 2001 NDHS collected demographic and health information from a nationally representative sample of ever-married women and men in the reproductive age groups of 15-49 and 15-59, respectively. The primary focus of the 2001 NDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas separately.

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2008 DHS is defined as the universe of all women ever-married women and men in the reproductive age groups of 15-49 and 15-59

Kind of data

Sample survey data

Sampling procedure

The survey was designed to obtain completed interviews of 8,400 ever-married women age 15-49. In addition, all ever-married males age 15-59 in every third household were interviewed. To take nonresponse into account, a total of 8,700 households nationwide were selected. The sample size was allocated to each district by urban and rural areas and the numbers of PSUs were calculated based on an average sample "take" (the number of ultimate sampled units in a cluster) of 34 completed interviews per PSU.

SAMPLE DESIGN

The 2001 NDHS collected demographic and health information from a nationally representative sample of ever-married women and men in the reproductive age groups of 15-49 and 15-59, respectively. The primary focus of the 2001 NDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of most key variables for the 13 domains obtained by cross-classifying the three ecological zones (mountains, hills, and terai) with the five development regions (Eastern, Central, Western, Mid-western, and Far-western). Due to their small size, the mountain areas of the Western, Mid-western, and Far-western regions were combined.

SAMPLING FRAME

The 2001 NDHS used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 1991 Population Census. Administratively, Nepal is divided into 75 districts. Each district is subdivided into village development committees (VDCs), and each VDC is divided into wards. The primary sampling unit (PSU) for the 2001 NDHS is a ward or group of wards in rural areas and subwards in urban areas. In rural areas, the ward is small enough for a complete household listing, but in urban areas, the ward size is large. It was therefore necessary to subdivide each urban ward into subwards. Information on the subdivision of the urban wards was obtained from the Living Standards Measurement Survey, a project funded by the World Bank.

SAMPLE SELECTION

The sample for the survey is based on a two-stage, stratified, nationally representative sample of households. At the first stage of sampling, 257 PSUs - 42 in urban areas and 215 in rural areas were selected using systematic sampling with probability proportional to size. During fieldwork, six PSUs in the Mid-western region were dropped from the sample due to security issues, reducing the total number of PSUs covered to 251 and reducing the number of rural PSUs to 209. This also reduced the expected number of completed interviews to 8,170 from 8,400.

A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second-stage selection of households. Sketch maps were constructed to identify the relative position of housing units in an EA to help interviewers locate selected households during fieldwork. Table A.1 shows the sample distribution of PSUs.

Global positioning system (GPS) units were used to calculate latitude and longitude coordinates for each selected ward (or subward) during the household listing stage. One latitude/longitude coordinate was taken for the center of each settlement or community within the ward. The altitude reading was also taken with the GPS units. The positional accuracy of the GPS readings is approximately 5 to 10 meters for latitude/longitude and approximately 30 meters for altitude. This geographic information allows the 2001 NDHS data to be integrated into a geographic information system (GIS) along with other spatial data collected in the same localities and adds to the depth of information available from the 2001 NDHS.

At the second stage of sampling, systematic samples of 34 households per PSU on average were selected in all the regions in order to provide statistically reliable estimates of key demographic and health variables. However, since Nepal is predominantly rural, in order to obtain statistically reliable estimates for urban areas, it was necessary to oversample the urban areas. As such, the total sample is weighted and a final weighting procedure was applied to provide estimates for the different domains and for the urban and rural areas of the country as a whole.

Mode of data collection

Face-to-face

Research instrument

The 2001 NDHS used three questionnaires: the Household Questionnaire, the Women's Questionnaire, and the Men's Questionnaire. The content and design of the questionnaires were based on the MEASURE DHS+ Model 'B' Questionnaire. The questionnaires were specifically geared toward obtaining the kind of information needed by health and family planning program managers and policymakers. The model questionnaires were then adapted to local conditions and a number of additional questions specific to ongoing health and family planning programs in Nepal were added. These questionnaires were developed in English and translated into the three principal languages in use in the country: Nepali (the national language), Bhojpuri, and Maithili. They were then independently translated back to English and appropriate changes were made in the translation of questions in which the back-translated version did not compare well with the original English version. A pretest of all three questionnaires was conducted in the three local languages in September 2000.

a) All usual members in a selected household and visitors who stayed there the previous night were enumerated using the Household Questionnaire. Specifically, the Household Questionnaire obtained information on the relationship to the head of the household, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify eligible women and men for the individual interview. Ever-married women age 15-49 in all selected households and ever-married men age 15-59 in every third selected household, whether usual residents or visitors, were deemed eligible and were interviewed. The Household Questionnaire also obtained information on some basic socioeconomic indicators such as the source of drinking water, the type of toilet facilities, the ownership of a variety of consumer durable items, and the flooring material. All eligible women and all children born since Baisakh 2052 in the Nepali calendar (which roughly corresponds to April 1995 in the Gregorian calendar) were weighed and measured.

b) The Women's Questionnaire collected information on female respondent's background characteristics; reproductive history; contraceptive knowledge and use; antenatal, delivery, and postnatal care; infant feeding practices; child immunization and health; marriage; fertility preferences; attitudes about family planning;

Prior research documents strong associations between health limitations and well-being outcomes. However, less is known about how levels of health limitations differ across cultures and across demographic groups within those different cultures. This study presents an in-depth, cross-national exploration of self-rated mental health across cultures, and its variations across key demographic groups. Using a diverse and international dataset of approximately 200,000 individuals from 22 countries, we will examine relationships between health limitations and key demographics, including: age, gender, marital status, employment status, religious service attendance, education, and immigration status. Our descriptive results will also present the ordered means of health limitations in life across countries. We will be mindful of potential interpretation challenges due to varying cultural contexts and response scales used. Our work will illuminate the distributions and descriptive statistics of health limitations across demographic features, offer insight into country-specific variations in health limitations, and lay a valuable foundation for future investigations into sociocultural influences that might shape health limitations.

Equatorial Guinea GQ: Population: Total data was reported at 1,267,689.000 Person in 2017. This records an increase from the previous number of 1,221,490.000 Person for 2016. Equatorial Guinea GQ: Population: Total data is updated yearly, averaging 408,232.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,267,689.000 Person in 2017 and a record low of 244,485.000 Person in 1978. Equatorial Guinea GQ: Population: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Equatorial Guinea – Table GQ.World Bank: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

Explore population projections for China on this dataset webpage. Get valuable insights into the future demographic trends of one of the world's most populous countries.

Population, China, projections ChinaFollow data.kapsarc.org for timely data to advance energy economics research..Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimatesSource: (1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.

Demographic fluctuations are crucial for assessing species’ threat levels, yet their global spatiotemporal patterns and historical drivers remain unknown. Here, we used single whole-genome sequence data for 527 extant and widespread terrestrial vertebrates to investigate their demographic fluctuations during the Late Pleistocene. Effective population size (Ne) simulations indicated that all taxa experienced a population decline from the Last Interglacial to the Last Glacial Maximum (LGM). After the LGM, birds, and amphibians underwent population expansion, whereas mammals and reptiles’ populations declined. Regions with high Ne shifted from Neotropical to Afrotropical and to Palearctic, some overlapping with recognized glacial refugia and biodiversity hotspots. In addition, climate-related factors exerted long-term effects on Ne, while human disturbances might confine to specific regions around the Pleistocene-Holocene boundary. This study underscores the significance of quantifying ver..., , , # Data from: Global spatiotemporal patterns of demographic fluctuations for terrestrial vertebrates during the Late Pleistocene

https://doi.org/10.5061/dryad.31zcrjdwq

Files and variables

The_genomic_information.csv

This file contains the names of each species and their Genbank accession IDs used in this study:

• Latin name: The scientific name of each species
• Class: The class of each species
• Order: The order to which each species belongs
• Family: The family classification for each species
• Accession ID: The GenBank ID of the genome used for each species
• Project ID: The project ID associated with each genome
• Sample ID: The sample ID for each genome

01.Ne_cluster_analysis

This folder contains the effective population size (Ne) data, IUCN conservation status information, and taxonomic information for each species in this study.

• species_raw_Ne.txt: This file contains the effect...,

Ireland IE: Population: Growth data was reported at 1.218 % in 2017. This records an increase from the previous number of 1.129 % for 2016. Ireland IE: Population: Growth data is updated yearly, averaging 0.813 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.891 % in 2007 and a record low of -0.428 % in 1988. Ireland IE: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ireland – Table IE.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This dataset consists of a summary measure that identifies where countries or other geographic areas sit on the spectrum of development. Expressed on a scale of 0 to 1, SDI (Socio-Demographic Index) is a composite average of the rankings of the incomes per capita, average educational attainment, and fertility rates of all areas in the GBD (Global Burden of Disease) study.

As of January 2025, ** percent of social media users in the United States aged 40 to 49 years were users of Facebook, as were ** percent of ** to ** year olds in the country. Overall, ** percent of those aged 18 to 29 years were using Instagram in the U.S. The social media market in the United States The number of social media users in the United States has shown continuous growth in the past years, and it is forecast to continue increasing to reach *** million users in 2029. As of 2023, the social network user penetration in the United States amounted to an impressive ***** percent, meaning that more than nine in ten people in the country engaged with online platforms. Furthermore, Facebook was by far the most popular social media platform in the United States, accounting for ** percent of all social media visits in 2023, followed by Pinterest with **** percent of visits. The global social media landscape As of April 2024, **** billion people were social media users, accounting for **** percent of the world’s population. Northern Europe was the region with the highest social media penetration rate with a reach of **** percent, followed by Western Europe with **** percent and Eastern Asia **** percent. In contrast, less than one in ten people in Middle Africa used social networks. Facebook’s popularity is not limited to the United States: this network leads the market on a global scale, and it accumulated more than three billion monthly active users (MAU) as of 2024, which is far more any other social media platform. YouTube, Instagram, and WhatsApp followed, all with *** billion or more MAU.

The costs of climate change are often estimated in monetary terms but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’ – defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 °C global warming could leave one third (22-39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (MAT ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose 1 future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

Abstract

The 2013 Turkey Demographic and Health Survey (TDHS-2013) is a nationally representative sample survey. The primary objective of the TDHS-2013 is to provide data on socioeconomic characteristics of households and women between ages 15-49, fertility, childhood mortality, marriage patterns, family planning, maternal and child health, nutritional status of women and children, and reproductive health. The survey obtained detailed information on these issues from a sample of women of reproductive age (15-49). The TDHS-2013 was designed to produce information in the field of demography and health that to a large extent cannot be obtained from other sources.

Specifically, the objectives of the TDHS-2013 included: - Collecting data at the national level that allows the calculation of some demographic and health indicators, particularly fertility rates and childhood mortality rates, - Obtaining information on direct and indirect factors that determine levels and trends in fertility and childhood mortality, - Measuring the level of contraceptive knowledge and practice by contraceptive method and some background characteristics, i.e., region and urban-rural residence, - Collecting data relative to maternal and child health, including immunizations, antenatal care, and postnatal care, assistance at delivery, and breastfeeding, - Measuring the nutritional status of children under five and women in the reproductive ages, - Collecting data on reproductive-age women about marriage, employment status, and social status

The TDHS-2013 information is intended to provide data to assist policy makers and administrators to evaluate existing programs and to design new strategies for improving demographic, social and health policies in Turkey. Another important purpose of the TDHS-2013 is to sustain the flow of information for the interested organizations in Turkey and abroad on the Turkish population structure in the absence of a reliable and sufficient vital registration system. Additionally, like the TDHS-2008, TDHS-2013 is accepted as a part of the Official Statistic Program.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Children under age of five

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years and women age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design and sample size for the TDHS-2013 makes it possible to perform analyses for Turkey as a whole, for urban and rural areas, and for the five demographic regions of the country (West, South, Central, North, and East). The TDHS-2013 sample is of sufficient size to allow for analysis on some of the survey topics at the level of the 12 geographical regions (NUTS 1) which were adopted at the second half of the year 2002 within the context of Turkey’s move to join the European Union.

In the selection of the TDHS-2013 sample, a weighted, multi-stage, stratified cluster sampling approach was used. Sample selection for the TDHS-2013 was undertaken in two stages. The first stage of selection included the selection of blocks as primary sampling units from each strata and this task was requested from the TURKSTAT. The frame for the block selection was prepared using information on the population sizes of settlements obtained from the 2012 Address Based Population Registration System. Settlements with a population of 10,000 and more were defined as “urban”, while settlements with populations less than 10,000 were considered “rural” for purposes of the TDHS-2013 sample design. Systematic selection was used for selecting the blocks; thus settlements were given selection probabilities proportional to their sizes. Therefore more blocks were sampled from larger settlements.

The second stage of sample selection involved the systematic selection of a fixed number of households from each block, after block lists were obtained from TURKSTAT and were updated through a field operation; namely the listing and mapping fieldwork. Twentyfive households were selected as a cluster from urban blocks, and 18 were selected as a cluster from rural blocks. The total number of households selected in TDHS-2013 is 14,490.

The total number of clusters in the TDHS-2013 was set at 642. Block level household lists, each including approximately 100 households, were provided by TURKSTAT, using the National Address Database prepared for municipalities. The block lists provided by TURKSTAT were updated during the listing and mapping activities.

All women at ages 15-49 who usually live in the selected households and/or were present in the household the night before the interview were regarded as eligible for the Women’s Questionnaire and were interviewed. All analysis in this report is based on de facto women.

Note: A more technical and detailed description of the TDHS-2013 sample design, selection and implementation is presented in Appendix B of the final report of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two main types of questionnaires were used to collect the TDHS-2013 data: the Household Questionnaire and the Individual Questionnaire for all women of reproductive age. The contents of these questionnaires were based on the DHS core questionnaire. Additions, deletions and modifications were made to the DHS model questionnaire in order to collect information particularly relevant to Turkey. Attention also was paid to ensuring the comparability of the TDHS-2013 findings with previous demographic surveys carried out by the Hacettepe Institute of Population Studies. In the process of designing the TDHS-2013 questionnaires, national and international population and health agencies were consulted for their comments.

The questionnaires were developed in Turkish and translated into English.

Cleaning operations

TDHS-2013 questionnaires were returned to the Hacettepe University Institute of Population Studies by the fieldwork teams for data processing as soon as interviews were completed in a province. The office editing staff checked that the questionnaires for all selected households and eligible respondents were returned from the field. A total of 29 data entry staff were trained for data entry activities of the TDHS-2013. The data entry of the TDHS-2013 began in late September 2013 and was completed at the end of January 2014.

The data were entered and edited on microcomputers using the Census and Survey Processing System (CSPro) software. CSPro is designed to fulfill the census and survey data processing needs of data-producing organizations worldwide. CSPro is developed by MEASURE partners, the U.S. Bureau of the Census, ICF International’s DHS Program, and SerPro S.A. CSPro allows range, skip, and consistency errors to be detected and corrected at the data entry stage. During the data entry process, 100% verification was performed by entering each questionnaire twice using different data entry operators and comparing the entered data.

Response rate

In all, 14,490 households were selected for the TDHS-2013. At the time of the listing phase of the survey, 12,640 households were considered occupied and, thus, eligible for interview. Of the eligible households, 93 percent (11,794) households were successfully interviewed. The main reasons the field teams were unable to interview some households were because some dwelling units that had been listed were found to be vacant at the time of the interview or the household was away for an extended period.

In the interviewed 11,794 households, 10,840 women were identified as eligible for the individual interview, aged 15-49 and were present in the household on the night before the interview. Interviews were successfully completed with 9,746 of these women (90 percent). Among the eligible women not interviewed in the survey, the principal reason for nonresponse was the failure to find the women at home after repeated visits to the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TDHS-2013 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TDHS-2013 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall

Abstract Work underwent transformations that changed the values and determinants of their meanings, putting its centrality in check. This research investigates the meanings of work among Brazilians, as well as the influence of demographic and structural elements on this attribution. The meanings of work refer to individual interpretation, influenced by the social context, about work and what it represents. World Values Survey Brazilian’s sample was used. The influence of socioeconomic and structural characteristics was analyzed via structural equation modeling. The model was well adjusted, having a coefficient of determination of .951. Descriptive results indicated high valuation of work and strong perception of it as a social obligation. The SEM results indicated that men attribute higher meaning to work compared to women and that increasing age influences the attribution of meaning to work. Activities with creativity, intellectuality and independence have indirect (via NSE) and negative influence on the perception of work meanings. Analyzes prioritized the articulation between social and economic aspects with the process of meaning of work, a perspective little explored in the Brazilian’s scientific production, but fundamental for a broader understanding of the phenomenon, especially in stratified societies such as Brazil.