Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health. The primary objective of the Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, fertility preferences, as well as maternal and child health and nutrition that can be used by program managers and policy makers to evaluate and improve existing programs. In addition, the JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional or crossnational studies.

The content of the 2002 JPFHS was significantly expanded from the 1997 survey to include additional questions on women’s status, reproductive health, and family planning. In addition, all women age 15-49 and children less than five years of age were tested for anemia.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The estimates from a sample survey are affected by two types of errors: 1) nonsampling errors and 2) sampling errors. Nonsampling errors are the result 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 2002 JPFHS 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 2002 JPFHS 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 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used 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.

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

Mode of data collection

Face-to-face

Research instrument

The 2002 JPFHS used two questionnaires – namely, the Household Questionnaire and the Individual Questionnaire. Both questionnaires were developed in English and translated into Arabic. The Household Questionnaire was used to list all usual members of the sampled households and to obtain information on each member’s age, sex, educational attainment, relationship to the head of household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. The Household Questionnaire was also used to identify women who are eligible for the individual interview: ever-married women age 15-49. In addition, all women age 15-49 and children under five years living in the household were measured to determine nutritional status and tested for anemia.

The household and women’s questionnaires were based on the DHS Model “A” Questionnaire, which is designed for use in countries with high contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Jordan, using experience gained from the 1990 and 1997 Jordan Population and Family Health Surveys. For each evermarried woman age 15 to 49, information on the following topics was collected:

1. Respondent’s background
2. Birth history
3. Knowledge and practice of family planning
4. Maternal care, breastfeeding, immunization, and health of children under five years of age
5. Marriage
6. Fertility preferences
7. Husband’s background and respondent’s employment
8. Knowledge of AIDS and STIs

In addition, information on births and pregnancies, contraceptive use and discontinuation, and marriage during the five years prior to the survey was collected using a monthly calendar.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding of the open-ended questions.

Data entry and verification started after one week of office data processing. The process of data entry, including one hundred percent re-entry, editing and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by the end of October 2002. A data processing specialist from ORC Macro made a trip to Jordan in October and November 2002 to follow up data editing and cleaning and to work on the tabulation of results for the survey preliminary report. The tabulations for the present final report were completed in December 2002.

Response rate

A total of 7,968 households were selected for the survey from the sampling frame; among those selected households, 7,907 households were found. Of those households, 7,825 (99 percent) were successfully interviewed. In those households, 6,151 eligible women were identified, and complete interviews were obtained with 6,006 of them (98 percent of all eligible women). The overall response rate was 97 percent.

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 result 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 2002 JPFHS 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 2002 JPFHS 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 2002 JPFHS sample is the result of a multistage stratified design and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2002 JPFHS is the ISSA Sampling Error Module (ISSAS). This module used 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.

Note: See detailed

The Woods & Poole Economics, Inc. 2023 Complete Economic and Demographic Data Source contains some of the Woods & Poole Economics, Inc. regional data and projections for the U.S. and all regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), Designated Market Areas (DMAs), and counties for 1969 or 1970 or 1990 through 2060. The remainder of this introduction contains the technical description of the and Download. Chapter 1 is an overview of the 2023 projections. Please read "Technical Description of the 2023 Regional Projections and Database" (Chapter 2) for an explanation of data sources, data definitions, and forecast methods. Appendices to Chapter 2 define the geographic areas used by Woods & Poole.

The mid-year estimates refer to the population on 30 June of the reference year and are produced in line with the standard United Nations (UN) definition for population estimates. They are the official set of population estimates for the UK and its constituent countries, the regions and counties of England, and local authorities and their equivalents.

Greece ELSTAT Projection: Population: Mean Age data was reported at 48.610 Year in 2050. This records an increase from the previous number of 48.530 Year for 2049. Greece ELSTAT Projection: Population: Mean Age data is updated yearly, averaging 45.725 Year from Dec 2007 (Median) to 2050, with 44 observations. The data reached an all-time high of 48.610 Year in 2050 and a record low of 41.630 Year in 2007. Greece ELSTAT Projection: Population: Mean Age data remains active status in CEIC and is reported by Hellenic Statistical Authority. The data is categorized under Global Database’s Greece – Table GR.G002: Population: Projection: Hellenic Statistical Authority.

In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

The American Community Survey Education Tabulation (ACS-ED) is a custom tabulation of the ACS produced for the National Center of Education Statistics (NCES) by the U.S. Census Bureau. The ACS-ED provides a rich collection of social, economic, demographic, and housing characteristics for school systems, school-age children, and the parents of school-age children. In addition to focusing on school-age children, the ACS-ED provides enrollment iterations for children enrolled in public school. The data profiles include percentages (along with associated margins of error) that allow for comparison of school district-level conditions across the U.S. For more information about the NCES ACS-ED collection, visit the NCES Education Demographic and Geographic Estimates (EDGE) program at: https://nces.ed.gov/programs/edge/Demographic/ACSAnnotation values are negative value representations of estimates and have values when non-integer information needs to be represented. See the table below for a list of common Estimate/Margin of Error (E/M) values and their corresponding Annotation (EA/MA) values.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. -9 An '-9' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small. -8 An '-8' means that the estimate is not applicable or not available. -6 A '-6' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. -5 A '-5' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. -3 A '-3' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate. -2 A '-2' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..The category "with a broadband Internet subscription" refers to those who said "Yes" to a DSL, cable, fiberoptic, mobile broadband, satellite, or fixed wireless subscription..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see http://www.census.gov/library/working-papers/2010/demo/coverage_edits_final.html for more details. The 2008 data table in American FactFinder does not incorporate these edits. Therefore, the estimates that appear in these tables are not comparable to the estimates in the 2009 and later tables. Select geographies of 2008 data comparable to the 2009 and later tables are available at http://www.census.gov/data/tables/time-series/acs/1-year-re-run-health-insurance.html. The health insurance coverage category names were modified in 2010. See http://www.census.gov/topics/health/health-insurance/about/glossary.html#par_textimage_18 for a list of the insurance type definitions..Occupation codes are 4-digit codes and are based on Standard Occupational Classification 2010..Industry codes are 4-digit codes and are based on the North American Industry Classification System 2012. The Industry categories adhere to the guidelines issued in Clarification Memorandum No. 2, "NAICS Alternate Aggregation Structure for Use By U.S. Statistical Agencies," issued by the Office of Management and Budget..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Due to methodological changes to data collection that began in data year 2013, comparisons of language estimates from that point to estimates from 2013 forw...

In 2024, the share of the population in Taiwan aged 65 and older accounted for approximately 19.2 percent of the total population. While the share of old people on the island increased gradually over recent years, the percentage of the working-age population and the children have both declined. Taiwan’s aging population With one of the lowest fertility rates in the world and a steadily growing life expectancy, the average age of Taiwan’s population is increasing quickly, and the share of people aged 65 and above is expected to reach around 38.4 percent of the total population in 2050. This development is also reflected in Taiwan’s population pyramid, which shows that the size of the youngest age group is only half of the size of age groups between 40 and 60 years. The rapid aging of the populations puts a heavy burden on the social insurance system. Old-age dependency is expected to reach more than 70 percent by 2050, meaning that by then three people of working age will have to support two elders, compared to only one elder supported by four working people today. Aging societies in East Asia Today, many countries in East Asia have very low fertility rates and face the challenges of aging societies. This is especially true among those countries that experienced high economic growth in the past, which often resulted in quickly receding birth rates. Japan was one of the first East Asian countries witnessing this demographic change, as is reflected in its high median age. South Korea had the lowest fertility rate of all Asian countries in recent years, and with China, one of the largest populations on earth joined the ranks of quickly aging societies.

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records. Methods Description of methods used for collection/generation of data: We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny. First paper filter To be useful, the papers must have met the following criteria: Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites); Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic); Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms); Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/); Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions; Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses; The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering. Second paper filter In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species. Methods for processing the data: We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses. References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12. Tummers, B. (2006). DataThief III. https://datathief.org/

United States US: Population: Growth data was reported at 0.713 % in 2017. This records a decrease from the previous number of 0.734 % for 2016. United States US: Population: Growth data is updated yearly, averaging 0.979 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1.702 % in 1960 and a record low of 0.711 % in 2013. United States US: Population: Growth 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. 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;

In 2024, 40-59-year-olds made up the largest age group in Germany, at around 22.3 million people. The most recent figures confirm that the next-largest age group was 65 years and older, at roughly 19 million. Aging population With the number of people belonging to older age groups visibly outstripping younger ones, in recent years it has become clear that Germany’s population is aging. In fact, figures on age structure in Germany depict a constant trend of a slowly increasing population share aged over 65 since 2012. Meanwhile, the share of population members aged 0 to 14 years has been falling, which was also reflected in the fluctuating national birth rate in recent years. A look at the future Germany’s current total population is around 83.6 million. While this number is predicted to increase, the same goes for the age group of 65 years and older. This means that the national population will continue to age.

The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

This dataset is the definitive set of annually released statistical area 1 (SA1) boundaries for 2022 as defined by Stats NZ (the custodian). This version contains 29,913 SA1 features.

SA1s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA18) which replaced the New Zealand Standard Areas Classification (NZSAC92). SA1 is an output geography that allows the release of more detailed information about population characteristics than is available at the meshblock level.

Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables. SA1s either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils. Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities have more than 500 residents.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

The SA1 classification can also be downloaded from the Stats NZ classification and concordance tool Ariā.

Abstract

The survey on financial literacy among the citizens of Bosnia and Herzegovina was conducted within a larger project that aims at creating the Action Plan for Consumer Protection in Financial Services.

The conclusion about the need for an Action Plan was reached by the representatives of the World Bank, the Federal Ministry of Finance, the Central Bank of Bosnia and Herzegovina, supervisory authorities for entity financial institutions and non-governmental organizations for the protection of consumer rights, based on the Diagnostic Review on Consumer Protection and Financial Literacy in Bosnia and Herzegovina conducted by the World Bank in 2009-2010. This diagnostic review was conducted at the request of the Federal Ministry of Finance, as part of a larger World Bank pilot program to assess consumer protection and financial literacy in developing countries and middle-income countries. The diagnostic review in Bosnia and Herzegovina was the eighth within this project.

The financial literacy survey, whose results are presented in this report, aims at establishing the basic situation with respect to financial literacy, serving on the one hand as a preparation for the educational activities plan, and on the other as a basis for measuring the efficiency of activities undertaken.

Geographic coverage

Data collection was based on a random, nation-wide sample of citizens of Bosnia and Herzegovina aged 18 or older (N = 1036).

Analysis unit

Household, individual

Universe

Population aged 18 or older

Kind of data

Sample survey data [ssd]

Sampling procedure

SUMMARY

In Bosnia and Herzegovina, as is well known, there is no completely reliable sample frame or information about universe. The main reasons for such a situation are migrations caused by war and lack of recent census data. The last census dates back to 1991, but since then the size and distribution of population has significantly changed. In such a situation, researchers have to combine all available sources of population data to estimate the present size and structure of the population: estimates by official statistical offices and international organizations, voters? lists, list of polling stations, registries of passport and ID holders, data from large random surveys etc.

The sample was three-stage stratified: in the first stage by entity, in the second by county/region and in the third by type of settlement (urban/rural). This means that, in the first stage, the total sample size was divided in two parts proportionally to number of inhabitants by entity, while in the second stage the subsample size for each entity was further divided by regions/counties. In the third stage, the subsample for each region/county was divided in two categories according to settlement type (rural/urban).

Taking into the account the lack of a reliable and complete list of citizens to be used as a sample frame, a multistage sampling method was applied. The list of polling stations was used as a frame for the selection of primary sampling units (PSU). Polling station territories are a good choice for such a procedure since they have been recently updated, for the general elections held in October 2010. The list of polling station territories contains a list of addresses of housing units that are certainly occupied.

In the second stage, households were used as a secondary sampling unit. Households were selected randomly by a random route technique. In total, 104 PSU were selected with an average of 10 respondents per PSU. The respondent from the selected household was selected randomly using the Trohdal-Bryant scheme.

In total, 1036 citizens were interviewed with a satisfactory response rate of around 60% (table 1). A higher refusal rate is recorded among middle-age groups (table 2). The theoretical margin of error for a random sample of this size is +/-3.0%.

Due to refusals, the sample structure deviated from the estimated population structure by gender, age and education level. Deviations were corrected by RIM weighting procedure.

MORE DETAILED INFORMATION

IPSOS designed a representative sample of approximately 1.000 residents age 18 and over, proportional to the adult populations of each region, based on age, sex, region and town (settlement) type.

For this research we designed three-stage stratified representative sample. First we stratify sample at entity level, regional level and then at settlement type level for each region.

Sample universe:

Population of B&H -18+; 1991 Census figures and estimated population dynamics, census figures of refugees and IDPs, 1996. Central Election Commision - 2008; CIPS - 2008;

Sampling frame:

Polling stations territory (approximate size of census units) within strata defined by regions and type of settlements (urban and rural) Polling stations territories are chosen to be used as primary units because it enables the most reliable sample selection, due to the fact that for these units the most complete data are available (dwelling register - addresses)

Type of sample:

Three stage random representative stratified sample

Definition and number of PSU, SSU, TSU, and sampling points

• PSU - Polling station territory Definition: Polling stations territories are defined by street(s) name(s) and dwelling numbers; each polling station territory comprises approximately 300 households, with exception of the settlements with less than 300 HH which are defined as one unite. Number of PSUs in sample universe: 4710
• SSU - Household Definition: One household comprises people living in the same apartment and sharing the expenditure for food
• TSU - Respondent Definition: Member of the HH , 18+ Number of TSUs in sample universe: = 2.966.766
• Sampling points Approximately 10 respondents per one PSU, total 104

Stratification, purpose and method

• First level strata: Federation of B&H Republika Srpska Brc ko District
• Second level strata: 10 cantons 2 regions -
• Third level strata: urban and rural settlements
• Purpose: Optimisation of the sample plan, and reducing the sampling error

• Method: The strata are defined by criteria of optimal geographical and cultural uniformity

• Selection procedure of PSU, SSU, and respondent Stratification, purpose and method

• PSU Type of sampling of the PSU: Polling station territory chosen with probability proportional to size (PPS) Method of selection: Cumulative (Lachirie method)

• SSU Type of sampling of the SSU: Sample random sampling without replacement Method of selection: Random walk - Random choice of the starting point

• TSU - Respondent Type of sampling of respondent: Sample random sampling without replacement Method of selection: TCB (Trohdal-Bryant scheme)

• Sample size N=1036 respondents

• Sampling error Marginal error +/-3.0%

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was modelled after the identical survey conducted in Romania. The questionnaire used in the Financial Literacy Survey in Romania was localized for Bosnia and Herzegovina, including adaptations to match the Bosnian context and methodological improvements in wording of questions.

Cleaning operations

Before data entry, 100% logic and consistency controls are performed first by local supervisors and once later by staff in central office.

Verification of correct data entry is assured by using BLAISE system for data entry (commercial product of Netherlands statistics), where criteria for logical and consistency control are defined in advance.

Response rate

• Nobody at home: 2,8%
• Eligible person is not home: 2,8%
• Refusal : 32,79%
• Given up after a minimum of two visits: 0,82%
• Other (excluded after control): 0,29%
• Finished: 60,5%

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: Yes - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: Not defined

UNIT DESCRIPTIONS: - Dwellings: Separated space with independent access that serves as a human lodging - Households: Individuals living in the same dwelling. For indigenous population definition of household requires sharing at least one meal. - Group quarters: Group of persons who share a common roof and food because of work, health, religion, etc.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: DANE

SAMPLE DESIGN: Systematic sample of dwellings pre-selected before fieldwork based on pre-census enumeration. In rural areas selection was determined in the field by the enumerator.

SAMPLE UNIT: Dwelling

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 2,643,125

Mode of data collection

Face-to-face [f2f]

Research instrument

5 enumeration forms applied to 5 different target populations: (f1) short form for private dwellings (90%) of the population, requested information on age, sex, and relationship to householder; (f2) long form for private dwellings (10%); (f3) group quarters, 0.17% of dwellings; (f4) indigenous private dwellings (100%), representing 0.95% of dwellings; and (f5) indigenous group-quarters, 0.01% of dwellings.

Response rate

COVERAGE: 91.2%

This dataset contains information on:

· Estimated resident population (ERP) at 30 June 1996, 2001, 2006, 2013, and 2018 for total population

· ERP at 30 June 2018 by ethnic groups (European or Other (including New Zealander), Māori, Pacific, Asian, and Middle Eastern/Latin American/African) – estimates and percentage

· Sex ratio – number of males per 100 females

· ERP at 30 June 2018 by broad age groups and median age

· Geographies available are regional council areas, territorial authority and Auckland local board areas, Statistical Area 2, and urban rural.

Note: The geography corresponds to 2020 boundaries

Note: -999 indicates data are not available.

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About the estimated resident population

The estimated resident population at 30 June in the census year is based on the census usually resident population count, with updates for:

· net census undercount (as measured by a post-enumeration survey)

· residents temporarily overseas on census night

· births, deaths and net migration between census night and 30 June

· reconciliation with demographic estimates at the youngest ages.

The estimated resident population is not directly comparable with the census usually resident population count because of these adjustments.

For more detailed information about the methods used to calculate each base population, see DataInfo+ Demographic estimates.

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Ethnic groups

It is important to note that these ethnic groups are not mutually exclusive because people can and do identify with more than one ethnicity. People who identify with more than one ethnicity have been included in each ethnic group.

The 'Māori', 'Pacific', 'Asian' and 'Middle Eastern/Latin American/African' ethnic groups are defined in level 1 of the Ethnicity New Zealand Standard Classification 2005. The estimates for the 'European or Other (including New Zealander)' group include people who belong to the 'European' or 'Other ethnicity' groups defined in level 1 of the standard classification. If a person belongs to both the 'European' and 'Other ethnicity' groups they have only been counted once. Almost all people in the 'Other ethnicity' group belong to the 'New Zealander' sub-group.

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Time series

This time series is irregular. Because the 2011 Census was cancelled after the Canterbury earthquake on 22 February 2011, the gap between the 2006-base and 2013-base estimated resident population is seven years. The change in data between 2006 and 2013 may be greater than in the usual five-year gap between censuses. Be careful when comparing trends.

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Rounding

Individual figures may not sum to stated totals due to rounding.

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More information

See Estimated resident population (2018-base): At 30 June 2018 for commentary about the 2018 ERP.

Subnational population estimates concepts – DataInfo+ provides definitions of terms used in the map.

Access more population estimates data in NZ.Stat:

Theme: Population estimates.

TH: Population: Male: Aged 0-14 data was reported at 6,144,671.000 Person in 2017. This records a decrease from the previous number of 6,246,677.000 Person for 2016. TH: Population: Male: Aged 0-14 data is updated yearly, averaging 7,959,803.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9,501,422.000 Person in 1980 and a record low of 5,913,974.000 Person in 1960. TH: Population: Male: Aged 0-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Thailand – Table TH.World Bank: Population and Urbanization Statistics. Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision.; Sum;

This dataset is the definitive version of the annually released statistical area 1 (SA1) boundaries as at 1 January 2025, as defined by Stats NZ. This version contains 33,164 SA1s (33,148 digitised and 16 with empty or null geometries (non-digitised)).

SA1 is an output geography that allows the release of more low-level data than is available at the meshblock level. Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables.

The SA1 should:

form a contiguous cluster of one or more meshblocks,

be either urban, rural, or water in character,

be small enough to:

• allow flexibility for aggregation to other statistical geographies,

• allow users to aggregate areas into their own defined communities of interest,

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

• be built from meshblocks,

• either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils.

SA1s generally have a population of 100–200 residents, with some exceptions:

• SA1s with nil or nominal resident populations are created to represent remote mainland areas, unpopulated islands, inland water, inlets, or oceanic areas.

• Some SA1s in remote rural areas and urban industrial or business areas have fewer than 100 residents.

• Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities (prisons, boarding schools, etc.) have more than 500 residents.

SA1 numbering

SA1s are not named. SA1 codes have seven digits starting with a 7 and are numbered approximately north to south. Non-digitised codes start with 79.

As new SA1s are created, they are given the next available numeric code. If the composition of an SA1 changes through splitting or amalgamating different meshblocks, the SA1 is given a new code. The previous code no longer exists within that version and future versions of the SA1 classification.

Digitised and non-digitised SA1s

The digital geographic boundaries are defined and maintained by Stats NZ.

Aggregated from meshblocks, SA1s cover the land area of New Zealand, the water area to the 12-mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, off-shore oil rigs, and Ross Dependency. The following 16 SA1s are held in non-digitised form.

7999901; New Zealand Economic Zone, 7999902; Oceanic Kermadec Islands,7999903; Kermadec Islands, 7999904; Oceanic Oil Rig Taranaki,7999905; Oceanic Campbell Island, 7999906; Campbell Island, 7999907; Oceanic Oil Rig Southland, 7999908; Oceanic Auckland Islands, 7999909; Auckland Islands, 7999910; Oceanic Bounty Islands, 7999911; Bounty Islands, 7999912; Oceanic Snares Islands, 7999913; Snares Islands, 7999914; Oceanic Antipodes Islands, 7999915; Antipodes Islands, 7999916; Ross Dependency.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

This data set represents greater sage-grouse populations to be used in work for the USFWS 2015 Status Review for the greater sage-grouse. Boundaries taken from BLM/WAFWA revised population boundaries (‘COT_SG_Populations_2014_WAFWA_UT’ data layer). The original data layer was slightly modified for the USFWS 2015 Status Review. Modifications include dissolving populations across State boundaries and merging several polygons together. Five additional polygons were added to the data set including four polygons in Utah and one polygon in Canada. These additional polygons were added from the original WAFWA Sage-Grouse Populations layer and covered important areas for GRSG missed in the original data set. Other modifications made to incorporate updated information and data. This data set provides population base data needed for USFWS analysis while remaining consistent with work being done by BLM and other agencies/groups. More detailed information on modifications can be found in the Supplemental Information.