This dataset provides the components of change involved in the calculation of the population projections for local authorities in Wales. Data cover the change between each successive projection year and relate to the change from the middle of each year to the middle of the following year. The first year's data represent the change from the base year of mid-2018 to mid-2019, through the projection period to show the change for mid-2042 to mid-2043. This is the fifth set of population projections published for the 22 local authorities in Wales. Note that the projections become increasingly uncertain the further we try to look into the future.

Data contains 2022 world population data publised by the UN DESA for six most populous countries of the world. File also contains the analysis of decomposition of demographic indicators on population growth.

1Historical estimates for 1–1950 CE are from refs. [72]–[78]. The UN global census data for 1955–2012 is from ref. [24], which provides an open-access web-based summary of these data. The historical world population estimates are also summarized by Cohen [23] in his Appendix 2. Note that the average population values—which are used to calculate (the distance for a given model population trajectory from the average population estimate/census value for the 1750–2012 data)—exclude duplicate estimates, in which a later study relies on an earlier study's result (e.g., Kremer's extensive use of the earlier estimates from McEvedy & Jones [74], [77]).

Monthly Intercensal Estimates of the Civilian Population by Single Year of Age and Sex: April 1, 1990 to April 1, 2000 // Source: U.S. Census Bureau, Population Division // For detailed information about the methods used to create the intercensal population estimates, see https://www.census.gov/popest/methodology/intercensal_nat_meth.pdf. // The Census Bureau's Population Estimates Program produces intercensal estimates each decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next. They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade. Meanwhile, the postcensal estimates incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census. The Population Estimates Program provides additional information including historical and postcensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/popest/index.html.

A Population technical workgroup was formed in Sana’a (IOM, UNFPA, OCHA, NAMCHA and CSO ) and another one in Aden (IOM, OCHA, CSO, MOPIC including Executive Unit). IDP data flow figures (from district to district) were collected, cross checked between different sources (IOM DTM, NAMCHA, Executive unit) in cases multiple datasets for the same location were available. This resulted in a country-wide IDP movement database.

The TWGs agreed to use this database to calculate the estimated population using the following formula : Estimated Population = CSO projected population( 2019) – IDPS who left the district + idps who came to the district

All members in both workgroups agreed on the final results on 3 Dec 2018. The attached dataset represents the results of the work of both groups and will be used for 2019 HNO and HRP.

A Population technical workgroup was formed in Sana’a (IOM, UNFPA, OCHA, NAMCHA and CSO ) and another one in Aden (IOM, OCHA, CSO, MOPIC including Executive Unit). IDP data flow figures (from district to district) were collected, cross checked between different sources (IOM DTM, NAMCHA, Executive unit) in cases multiple datasets for the same location were available. This resulted in a country-wide IDP movement database.

The TWGs agreed to use this database to calculate the estimated population using the following formula : Estimated Population = CSO projected population( 2019) – IDPS who left the district + idps who came to the district

All members in both workgroups agreed on the final results on 3 Dec 2018. The attached dataset represents the results of the work of both groups and will be used for 2019 HNO and HRP.

Maintaining up to date population figures going forward Both TWGs are discussing the frequency of population figures updates for 2019. The intent is to agree on quarterly updates, using the same methodological approach

Original dataset title: Yemen Population Estimates 2019

Abstract

The Estimating the Size of Populations through a Household Survey (EPSHS), sought to assess the feasibility of the network scale-up and proxy respondent methods for estimating the sizes of key populations at higher risk of HIV infection and to compare the results to other estimates of the population sizes. The study was undertaken based on the assumption that if these methods proved to be feasible with a reasonable amount of data collection for making adjustments, countries would be able to add this module to their standard household survey to produce size estimates for their key populations at higher risk of HIV infection. This would facilitate better programmatic responses for prevention and caring for people living with HIV and would improve the understanding of how HIV is being transmitted in the country.

The specific objectives of the ESPHS were: 1. To assess the feasibility of the network scale-up method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 2. To assess the feasibility of the proxy respondent method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 3. To estimate the population size of MSM, FSW, IDU, and clients of sex workers in Rwanda at a national level; 4. To compare the estimates of the sizes of key populations at higher risk for HIV produced by the network scale-up and proxy respondent methods with estimates produced using other methods; and 5. To collect data to be used in scientific publications comparing the use of the network scale-up method in different national and cultural environments.

Geographic coverage

National

Analysis unit

• Household
• Individual

Sampling procedure

The Estimating the Size of Populations through a Household Survey (ESPHS) used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC), which was conducted in 2012; it was provided by the National Institute of Statistics of Rwanda (NISR).

The sampling frame was a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means "to know" someone.

For further details on sample design and implementation, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Estimating the Size of Populations through a Household Survey (ESPHS) used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

A total of 2,125 households were selected in the sample, of which 2,120 were actually occupied at the time of the interview. The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99 percent.

From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98 percent. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98 percent. The response rates do not significantly vary by type of questionnaire or residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling 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 to minimize this type of error during the implementation of the Rwanda ESPHS 2011, non-sampling 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 ESPHS 2011 is only one of many samples that could have been selected from the same population, using the same design and identical 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 ESPHS 2011 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the ESPHS 2011 is a SAS program. This program uses the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

Intercensal estimates are produced once a decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade Meanwhile, the nbsp postcensal estimates nbsp incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census Note Intercensal Estimates as of July 1 1 The April 1, 2000 Population Estimates base reflects changes to the Census 2000 population from the Count Question Resolution program, legal boundary updates, and other geographic program revisions 2 The data source for April 1, 2010 is the 2010 Census count 3 The values for 2010 were produced by applying estimates of change in the population between April 1 and July 1 of 2010 to the 2010 Census counts Further details on this methodology are available at http www census gov popest methodology intercensal nat meth pdf

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

BackgroundEstimating the burden of healthcare-associated infections (HAIs) compared to other communicable diseases is an ongoing challenge given the need for good quality data on the incidence of these infections and the involved comorbidities. Based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) project and 2011–2012 data from the European Centre for Disease Prevention and Control (ECDC) point prevalence survey (PPS) of HAIs and antimicrobial use in European acute care hospitals, we estimated the burden of six common HAIs.Methods and FindingsThe included HAIs were healthcare-associated pneumonia (HAP), healthcare-associated urinary tract infection (HA UTI), surgical site infection (SSI), healthcare-associated Clostridium difficile infection (HA CDI), healthcare-associated neonatal sepsis, and healthcare-associated primary bloodstream infection (HA primary BSI). The burden of these HAIs was measured in disability-adjusted life years (DALYs). Evidence relating to the disease progression pathway of each type of HAI was collected through systematic literature reviews, in order to estimate the risks attributable to HAIs. For each of the six HAIs, gender and age group prevalence from the ECDC PPS was converted into incidence rates by applying the Rhame and Sudderth formula. We adjusted for reduced life expectancy within the hospital population using three severity groups based on McCabe score data from the ECDC PPS. We estimated that 2,609,911 new cases of HAI occur every year in the European Union and European Economic Area (EU/EEA). The cumulative burden of the six HAIs was estimated at 501 DALYs per 100,000 general population each year in EU/EEA. HAP and HA primary BSI were associated with the highest burden and represented more than 60% of the total burden, with 169 and 145 DALYs per 100,000 total population, respectively. HA UTI, SSI, HA CDI, and HA primary BSI ranked as the third to sixth syndromes in terms of burden of disease. HAP and HA primary BSI were associated with the highest burden because of their high severity. The cumulative burden of the six HAIs was higher than the total burden of all other 32 communicable diseases included in the BCoDE 2009–2013 study. The main limitations of the study are the variability in the parameter estimates, in particular the disease models’ case fatalities, and the use of the Rhame and Sudderth formula for estimating incident number of cases from prevalence data.ConclusionsWe estimated the EU/EEA burden of HAIs in DALYs in 2011–2012 using a transparent and evidence-based approach that allows for combining estimates of morbidity and of mortality in order to compare with other diseases and to inform a comprehensive ranking suitable for prioritization. Our results highlight the high burden of HAIs and the need for increased efforts for their prevention and control. Furthermore, our model should allow for estimations of the potential benefit of preventive measures on the burden of HAIs in the EU/EEA.

In 1971, John Sved derived an approximate relationship between linkage disequilibrium and effective population size for an ideal finite population. This seminal work was extended by Sved and Feldman (1973) and Weir and Hill (1980) who derived additional equations with the same purpose. These equations yield useful estimates of effective population size, as they require a single sample in time. As these estimates of effective population size are now commonly used on a variety of genomic data, from arrays of single nucleotide polymorphisms to whole genome data, some authors have investigated their bias through simulation studies and proposed corrections for different mating systems. However, the cause of the bias remains elusive. Here we show the problems of using linkage disequilibrium as a statistical measure and, analogously, the problems in estimating effective population size from such measure. For that purpose, we compare three commonly used approaches with a transition probability ...

Italy IT: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -2.130 % in 2015. Italy IT: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -2.130 % from Dec 2015 (Median) to 2015, with 1 observations. Italy IT: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Monthly Intercensal Estimates of the Resident plus Armed Forces Overseas Population by Single Year of Age and Sex: April 1, 1990 to April 1, 2000 // Source: U.S. Census Bureau, Population Division // For detailed information about the methods used to create the intercensal population estimates, see https://www.census.gov/popest/methodology/intercensal_nat_meth.pdf. // The Census Bureau's Population Estimates Program produces intercensal estimates each decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next. They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade. Meanwhile, the postcensal estimates incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census. The Population Estimates Program provides additional information including historical and postcensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/popest/index.html.

Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -0.610 % in 2015. Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -0.610 % from Dec 2015 (Median) to 2015, with 1 observations. Slovakia SK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Population-Averaged Generalized Estimating Equations (PA-GEEs) results examining the effects of time, distance and site on Evechinus density. Log-link and Poisson errors. **P

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health.

The 1990 Jordan Population and Family Health Survey (JPFHS) was carried out as part of the Demographic and Health Survey (DHS) program. The Demographic and Health Surveys is assisting governments and private agencies in the implementation of household surveys in developing countries.

The JPFIS was designed to provide information on levels and trends of fertility, infant and child mortality, and family planning. The survey also gathered information on breastfeeding, matemal and child health cam, the nutritional status of children under five, as well as the characteristics of households and household members.

The main objectives of the project include: a) Providing decision makers with a data base and analyses useful for informed policy choices, b) Expanding the international population and health data base, c) Advancing survey methodology, and d) Developing skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The sample for the JPFHS survey was selected to be representative of the major geographical regions, as well as the nation as a whole. The survey adopted a stratified, multi-stage sampling design. In each governorate, localities were classified into 9 strata according to the estimated population size in 1989. The sampling design also allowed for the survey results to be presented according to major cities (Amman, Irbid and Zarqa), other urban localities, and the rural areas. Localities with fewer than 5,000 people were considered rural.

For this survey, 349 sample units were drawn, containing 10,708 housing units for the individual interview. Since the survey used a separate household questionnaire, the Department of Statistics doubled the household sample size and added a few questions on labor force, while keeping the original individual sample intact. This yielded 21,172 housing units. During fieldwork for the household interview, it was found that 4,359 household units were ineligible either because the dwelling was vacant or destroyed, the household was absent during the team visit, or some other reason. There were 16,296 completed household interviews out of 16,813 eligible households, producing a response rate of 96.9 percent.

The completed household interviews yielded 7,246 women eligible for the individual interview, of which 6,461 were successfully interviewed, producing a response rate of 89.2 percent.

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

Mode of data collection

Face-to-face

Research instrument

The 1990 JPFIS utilized two questionnaires, one for the household interview and the other for individual women. Both questionnaires were developed first in English and then translated into Arabic. The household questionnaire was used to list all members of the sample households, including usual residents as well as visitors. For each member of the household, basic demographic and socioeconomic characteristics were recorded and women eligible for the individual interview were identified. To be eligible for individual interview, a woman had to be a usual member of the household (part of the de jure population), ever-married, and between 15 and 49 years of age. The household questionnaire was expanded from the standard DHS-II model questionnaire to facilitate the estimation of adult mortality using the orphanhood and widowhood techniques. In addition, the questionnaire obtained information on polygamy, economic activity of persons 15 years of age and over, family type, type of insurance covering the household members, country of work in the summer of 1990 which coincided with the Gulf crisis, and basic data for the calculation of the crude birth rate and the crude death rate. Additional questions were asked about deceased women if they were ever-married and age 15-49, in order to obtain information for the calculation of materoal mortality indices.

The individual questionnaire is a modified version of the standard DHS-II model "A" questionnaire. Experience gained from previous surveys, in particular the 1983 Jordan Fertility and Family Health Survey, and the questionnaire developed by the Pan Arab Project for Child Development (PAPCHILD), were useful in the discussions on the content of the JPFHS questionnaire. A major change from the DHS-II model questionnaire was the rearrangement of the sections so that the marriage section came before reproduction; this allowed the interview to flow more smoothly. Questions on children's cause of death based on verbal autopsy were added to the section on health, which, due to its size, was split into two parts. The first part focused on antenatal care and breastfeeding; the second part examined measures for prevention of childhood diseases and information on the morbidity and mortality of children loom since January 1985. As questions on sexual relations were considered too sensitive, they were replaced by questions about the husband's presence in the household during the specified time period; this served as a proxy for recent sexual activity.

The JPFHS individual questionnaire consists of nine sections: - Respondent's background and household characteristics - Marriage - Reproduction - Contraception - Breastfeeding and health - Immunization, morbidity, and child mortality - Fertility preferences - Husband's background, residence, and woman's work - Height and weight of children

Response rate

For the individual interview, the number of eligible women found in the selected households and the number of women successfully interviewed are presented. The data indicate a high response rate for the household interview (96.9 percent), and a lower rate for the individual interview (89.2 percent). Women in large cities have a slightly lower response rate (88.6 percent) than those in other areas. Most of the non-response for the individual interview was due to the absence of respondents and the postponement of interviews which were incomplete.

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

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of standard error of 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 one can reasonably assured that, apart from nonsampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the JPFI-IS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

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

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

This dataset contains polygons that represent the boundaries of statistical neighborhoods as defined by the DC Department of Health (DC Health). DC Health delineates statistical neighborhoods to facilitate small-area analyses and visualization of health, economic, social, and other indicators to display and uncover disparate outcomes among populations across the city. The neighborhoods are also used to determine eligibility for some health services programs and support research by various entities within and outside of government. DC Health Planning Neighborhood boundaries follow census tract 2010 lines defined by the US Census Bureau. Each neighborhood is a group of between one and seven different, contiguous census tracts. This allows for easier comparison to Census data and calculation of rates per population (including estimates from the American Community Survey and Annual Population Estimates). These do not reflect precise neighborhood locations and do not necessarily include all commonly-used neighborhood designations. There is no formal set of standards that describes which neighborhoods are included in this dataset. Note that the District of Columbia does not have official neighborhood boundaries. Origin of boundaries: each neighborhood is a group of between one and seven different, contiguous census tracts. They were originally determined in 2015 as part of an analytical research project with technical assistance from the Centers for Disease Control and Prevention (CDC) and the Council for State and Territorial Epidemiologists (CSTE) to define small area estimates of life expectancy. Census tracts were grouped roughly following the Office of Planning Neighborhood Cluster boundaries, where possible, and were made just large enough to achieve standard errors of less than 2 for each neighborhood's calculation of life expectancy. The resulting neighborhoods were used in the DC Health Equity Report (2018) with updated names. HPNs were modified slightly in 2019, incorporating one census tract that was consistently suppressed due to low numbers into a neighboring HPN (Lincoln Park incorporated into Capitol Hill). Demographic information were analyzed to identify the bordering group with the most similarities to the single census tract. A second change split a neighborhood (GWU/National Mall) into two to facilitate separate analysis.

Raw data Trend estimate 2019: population by age. The Trend Estimate (TR) is a scenario calculation of a demographic model, in which, among other things, the domestic migration trend per municipality is extended over the next ten years. The TR is calculated in order to form a picture of future population development, household formation and the associated desired housing stock if the domestic migration trend continues in the future. The scenario calculation is used to see how housing plans relate to the domestic migration trend. For more information: https://staatvan.zuid-holland.nl/portfolio_page/trendraming/ Note: this estimate is no longer valid.

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables:

Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology

Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables:

Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology

Fore more information see the related READ ME file.

Abstract

The basic goal of this survey is to provide a necessary database for formulating national policies at various levels. This survey providing the contribution of the household sector to the Gross National Product (GNP), This survey determining the incidence of poverty, Providing weighted data which reflects the relative importance of the consumption items to be employed to determine the benchmark for rates and prices of items and services. The survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) .

Analysis unit

Household, individual

Universe

The survey covered all the Palestinian households who are a usual residence in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population in this sample survey comprises all households living in the West Bank and Gaza Strip, excluding nomads and students.

The sample design is a stratified two-stage design for households selected to be interviewed. At the first stage a sample of cells (PSUs) was selected from the PCBS master sample frame. At the second stage, a sample of households was selected after a complete household listing of the sampled cells.

Sample Design

Stratification Four levels of stratification have been made: Stratification by District. Stratification by place of residence which comprises: (a) Municipalities (b) Villages (c) Refugee Camps
Stratification by locality size. Stratification by cell identification in that order.

Sample Size The sample size is about 3,591 households allowing for non-response and related losses .

Target cluster size

The next important issue in the sample design is the target cluster size or “sample-take” which is the average number of households to be selected per PSU. In this survey, the sample take is around 10 households.

Self-weighting design:

At the first stage, clusters or “cells” have been selected with PPS probability proportional to estimated measure of size (Mi) for unit (I):

Where the summation covers all clusters in the population; a-300 is the total number of selected clusters. It is highly desirable for the PECS to have a constant overall sampling rate (f), i.e. to have a self- weighting sample. This requires the second stage probability for the selection of households and persons within any sample cluster i to be as follows:

Where b is a constant (independent of i) to be determined to obtain the required sample size, n =3,591 households. Since the measure of size are likely to differ from the actual number of households listed in any cluster i, the actual number of households selected with the above shall vary from one cluster to another and are presented as:

Adding all clusters in the sample results in the required constant b, to achieve the target sample size n as:

Hence to control the overall sample size, b is determined after completing the listing in all sample areas.

The above procedure allows for variation in sample sizes bi at the level of individual clusters, in order to provide a self-weighting sample. Households within each sample cluster shall be selected systematically from the lists prepared for that purpose, using the sampling interval,

Where:

a  Number of cells in the sample (equals 360) 
Number of housing units in cell I    
Number of listed of households in cell I    
n  Proposed sample size (n= 3,591 HHs) 
b  Average sample take 
Sample take in cell I    
f  Sample rate 
First-stage sampling rate    
Second-stage sampling rate   

Which is fixed for each cluster but varies between clusters depending on the measure of size () with which the area was selected at the first stage.

The sample-take must be allowed to vary depending on the actual number of households found after listing. However, provision must be made to avoid extreme variation in cluster sample size. This could be done by using the above procedure to compute the ratio for each cluster in the sample. If this ratio lies outside the range say 0.5 - 4.0, adjust , i.e. the interval to be applied for the selection of households in the cluster, so as to keep the ratio within the above range.

Sample Rotation

The total number of (480) cells have been divided into (24) groups (subsided sample), each one constituted of (20) cells. A sub-sample of (360) cells is used year round by a monthly sample constituted of two minor samples (30 cells). The survey includes independent cells and not cross section ones, each of these is formed from (300) households for each month (round).

(Replication) L K J I H G F E D C B A Month
× 1
× 2
× 3
× 4
× 5
× 6
× 7
× 8
× 9
× 10
× 11
× 12

Estimations Procedure

The sample is self-weighting by design. To estimate a given total Y for a given sub-population A, we introduce the following formula:

But since W is constant for all j within i, then: the estimating formula becomes:

Where, U YA = Estimated total for variable Y in sub-population A h = The sub-stratum within the estimation domain i = The sample PSU (cell) j = The unit of analysis or element A = Subset of elements possessing a given attribute, that is, belonging to a given sub- population A = Observed value of variable “y” for j-the element of i-the sample PSU in stratum h = Final (adjusted) sampling weight for the element is the unweighted PSU total within h for sub-population A

The estimator for a given ratio for sub-population A is the following: (2)

Where: U RA =Estimate for the ratio of two variables, Y/X, in sub-population A U XA = Estimated total for variable X in sub-population A, given by formula (1)B U YA = Estimated total for variable Y in sub-population A, also given by formula (1)B

Means and proportions are special types of ratios. In the case of the mean, the variable X, in the denominator of the ratio, is defined to equal 1 for each element, so that denominator is the sum of weights in the sub-population. In the case of the proportion, the variable X in the denominator is also defined to equal 1 for all elements. In addition, the variable Y in the numerator is binomial and is defined to equal either 0 or 1, depending on the absence or presence of a specified attribute in the element observed.

Calculation of Variance

It is very important to calculate standard errors for the main survey estimates so that the user can have an idea of their reliability or precision.

The variance calculation will use the method of ultimate clusters. Within any domain of estimation, for a sub-population A, and for a characteristic Y, the formulas are: (a) The variance of an estimator of a total is estimated by:

(3)
Where:

(4)

and: (5)

The expression in (3) is an unbiased estimator of the variance. (b) The variance of an estimator of a ratio is estimated by:

(6)

Where:

U           U

V (YA) and V(XA) are calculated according to formula (3); U XA is calculated according to formula (1); and U RA according to formula (2).

Mode of data collection

Face-to-face [f2f]

Research instrument

The PECS questionnaire consists of two main sections:

First section: Certain articles / provisions of the form filled at the beginning of the month, and the remainder filled out at the end of the month. The questionnaire includes the following provisions:

Cover sheet: It contains detailed and particulars of the family, date of visit, particular of the field/office work team, number/sex of the family members.

Statement of the family members: Contains social, economic and demographic particulars of the selected family.

Statement of the long-lasting commodities and income generation activities: Includes a number of basic and indispensable items (i.e., Livestock, or agricultural lands).

Housing Characteristics: Includes information and data pertaining to the housing conditions, including type of house, number of rooms, ownership, rent, water, electricity supply, connection to the sewer system, source of cooking and heating fuel, and remoteness/proximity of the house to education and health facilities.

Monthly and Annual Income: Data pertaining to the income of the family is collected from different sources at the end of the registration / recording period.

Assistance and poverty: includes questions about household conditions and assistances that got through the the past month.

Second section: The second section of the questionnaire includes a list of 55 consumption and expenditure groups itemized and serially numbered according to its importance to the family. Each of these groups contains important commodities. The number of commodities items in each for all groups stood at 667 commodities and services items. Groups 1-21 include food, drink, and cigarettes. Group 22 includes homemade commodities. Groups 23-45 include all items except for food, drink and cigarettes. Groups 50-55 include all of the long-lasting commodities. Data on each of these groups was collected over different intervals of time so as to reflect expenditure over a period of one full year, except the cars group the data of which was collected for three previous years. These data was abotained from the recording book which is covered a period of month for each household.

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