Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

Management of invasive populations is typically investigated case-by-case. Comparative approaches have been applied to single aspects of management, such as demography, with cost or efficacy rarely incorporated. We present an analysis of the ranks of management actions for 14 species in five countries that extends beyond the use of demography alone to include multiple metrics for ranking management actions, which integrate cost, efficacy and demography (cost-effectiveness) and managers’ expert opinion of ranks. We use content analysis of manager surveys to assess the multiple criteria managers use to rank management strategies. Analysis of the matrix models for managed populations showed that all management actions led to reductions in population growth rate (λ), with a median 48% reduction in λ across all management units; however, only 66% of the actions led to declining populations (λ < 1). Each management action ranked by cost-effectiveness and cost had a unique rank; however, elasticity ranks were often tied, providing less discrimination among management actions. Ranking management actions by cost alone aligned well with cost-effectiveness ranks and demographic elasticity ranks were also well aligned with cost-effectiveness. In contrast, efficacy ranks were aligned with managers’ ranks and managers identified efficacy and demography as important. 80% of managers identified off-target effects of management as important, which was not captured using any of the other metrics. Synthesis and applications. A multidimensional view of the benefits and costs of management options provides a range of single and integrated metrics. These rankings, and the relationships between them, can be used to assess management actions for invasive plants. The integrated cost-effectiveness approach goes well ‘beyond demography’ and provides additional information for managers; however, cost-effectiveness needs to be augmented with information on off-target effects and social impacts of management in order to provide greater benefits for on-the-ground management.

Orkney and Shetland, the population isolates which make up the Northern Isles of Scotland, are of particular interest to multiple sclerosis (MS) research. While MS prevalence is high in Scotland, Orkney has the highest global prevalence, higher than more northerly Shetland. Many hypotheses for the excess of MS cases in Orkney have been investigated, including vitamin D deficiency and homozygosity: neither was found to cause the high prevalence of MS. It is possible that this excess prevalence may be explained through unique genetics. We used polygenic risk scores (PRS) to look at the contribution of common risk variants to MS. Analyses were conducted using ORCADES (97/2118 cases/controls), VIKING (15/2000 cases/controls) and Generation Scotland (30/8708 cases/controls) datasets. However, no evidence of a difference in MS associated common variant frequencies was found between the three control populations, aside from HLA-DRB1*1501 tag SNP rs9271069. This SNP had a significantly higher risk allele frequency in Orkney (0.23, p-value = 8 x 10-13) and Shetland (0.21, p-value = 2.3 x 10-6) than mainland Scotland (0.17). This difference in frequency is estimated to account for 6 (95% CI 3, 8) out of 150 observed excess cases per 100,000 individuals in Shetland and 9 (95% CI 8, 11) of the observed 257 excess cases per 100,000 individuals in Orkney, compared with mainland Scotland. Common variants therefore appear to account for little of the excess burden of MS in the Northern Isles of Scotland.

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..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical 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..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations 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 delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, 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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This dataset covers anonymised student-level population data on students enrolled in grade 8 during the school year 2020-2021, including historical data for this student cohort. The NO-GAP research team was provided with student-level primary population data by the National Agency for Education (NAE) from the Education Information Management System (EMIS) database. This database contains the data that is needed by education stakeholders to analyse and assess the state of education in various aspects, forecast educational change, make data-driven decisions, and manage education for quality. The primary data provided by the NAE was cleaned, additionally coded to prevent reidentification, and merged into a single data file by the NO-GAP research team. In addition, the team prepared the codebook "NO-GAP Codebook. School and Student Level Variables: 2020-2021 Cohort of 8th-Grade Students". Dataset "NO-GAP: Student-Level Data: Cohort of 8th Graders of the School Year 2020-2021" metadata and data were prepared implementing project "Disparities in School Achievement from a Person and Variable-Oriented Perspective: A Prototype of a Learning Analytics Tool NO-GAP" from 2020 to 2023. Project leader is chief research fellow Rasa Erentaitė. Project is funded by the European Regional Development Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments, under measure’s No. 01.2.2-LMT-K-718 activity “Research Projects Implemented by World-class Researcher Groups to develop R&D activities relevant to economic sectors, which could later be commercialized” under a grant agreement with the Lithuanian Research Council (LMTLT). These data are not open for external use based on the agreement with NAE.

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools werer sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey

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..Tell us what you think. Provide feedback to help make American Community Survey data more useful for you..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 2011-2015 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..When information is missing or inconsistent, the Census Bureau uses a method called imputation to assign values. Responses assigned using the Census Bureau's imputation method are called imputed values. The "Percent Imputed" section is the percent of respondents who received an imputed value for a particular subject. ..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation. Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area. School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers. Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.” Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).” In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12. Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire. Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

IDEA Section 618 Data Products: Static Tables Part B Assessment Number and percent of students grades 3 through 8 and high school, served under IDEA, Part B, who participated in reading and math assessments, by assessment type and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for math, by assessment type, grade level, and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for reading, by assessment type, grade level, and state. Part B Child Count and Educational Environments Number of children and students served under IDEA, Part B, by age group and state. Number of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by disability and state. Number of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by disability and state. Number and percent of children ages 3 through 5 (not in kindergarten) and students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by EL status and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by race/ethnicity and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by race/ethnicity and state. Children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, as a percentage of population, by disability category and state. Students ages 5 (in kindergarten) through 21 served under IDEA, Part B, as a percentage of population, by disability category and state. Children and students ages 3 through 21 served under IDEA, Part B, as a percentage of population, by age and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of female/male children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of female/male students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by educational environment and state. Number and percent of students in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by educational environment and state. Number of children and students served under IDEA, Part B, in the US, Outlying Areas, and Freely Associated States by age and disability category. Part B Discipline Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, suspended/expelled by total number of days removed and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state by type of disability. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, and state by race/ethnicity. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of removed and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number of children and students, ages 3 through 21, subject to expulsion, by disability status, receipt of educational services and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, race/ ethnicity, and state. Part B Dispute Resolution Number and percent of written, signed complaints initiated through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of mediations held through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of hearings (fully adjudicated) through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number of expedited hearing requests (related to disciplinary decision) filed through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Part B Exiting Number of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number of students ages 14 through 21 with disabilities served under IDEA, Part B, in the U.S., Outlying Areas, and Freely Associated States who exited special education, by exit reason and age. Number and percent of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason, race/ethnicity, and state. Number and percent of female and male students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Part B Maintenance of Effort Reduction and Coordinated Early Intervening Services Number and percent of LEAs reported under each determination level that controls whether the LEA may be able to reduce MOE Amount reduced under the IDEA MOE provision in IDEA §613(a)(2)(C) Number and percent of LEAs that met requirements and had an increase in 611 allocations and took the MOE reduction Number and percent of LEAs required to use 15% of funds for CEIS due to significant disproportionality or voluntarily reserved funds for CEIS Number of children who received CEIS anytime in the past two years and who received special education and related services Number and percent of LEAs/ESAs that were determined to meet the MOE compliance standard in SY 2016-17 Part B Personnel Teachers employed (FTE) to work with children, ages 3 through 5, who are receiving special education under IDEA, Part B, by qualification status and state. Teachers employed

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical 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 populations, 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 2013-2017 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..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates

Background/Aims: Insulin-like growth factor-1 (IGF-1) plays important roles in cellular proliferation, differentiation, and growth. Previous studies showed that single-nucleotide polymorphisms (SNPs) of IGF-1 are associated with various diseases. This case-control study aimed to examine the relationship between IGF-1 polymorphisms and IgA nephropathy (IgAN) risk in a Chinese Han population. Methods: We recruited 351 IgAN patients and 310 healthy controls from Northwestern China. Sequenom MassARRAY was utilized to examine the genotypes of two common IGF-1 SNPs (rs1520220 and rs2195239). Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated by the Chi square test to evaluate the associations between IGF-1 and IgAN. Results: Our study demonstrated that IGF-1 gene rs1520220 and rs2195239 polymorphisms did not confer susceptibility to IgAN. We found no correlation between gender, blood pressure, proteinuria, eGFR, and IgAN in both SNPs. However, the rs1520220 and rs2195239 variants were correlated with M1 and E1 in patients with IgAN (M0/M1: CC vs. CG+GG: OR = 1.62, P = 0.04; E0/E1: CC vs. CG+GG: OR = 1.95, P = 0.004; GG vs. GC+CC: OR = 1.90, P = 0.004, respectively). Conclusion: These results indicate that IGF-1 gene polymorphisms play crucial roles in the histopathological progression of IgAN in the Chinese Han population.

Description: Individual teacher and classroom contextual background information questionnaire are contained in the file. The data set contains 176 cases and 145 variables. Abstract: The evaluation follows an experimental- and control-group design. At the outset, it determined the contextual conditions at schools, in classrooms and at home, as well as learner achievement levels. All learners in Grades 1, 4 and 7 in 11 project schools in 2013 comprised the first of three cohorts of the intervention population. Their anticipated achievement gains are compared with those of all learners from five matched control schools. The evaluation data were collected by means of self-report background questionnaires completed by school principals, teachers and parents/caregivers, as well as a range of language assessment instruments administered among/to the learners. Difference-in-difference analysis was conducted to establish relative achievement gains among learners from project and control schools. Since the study was population based for all grade 1, 4 and 7 learners from the selected schools within a predetermined regional area no additional sampling within the school was done. Self-completion The study is a community based study limited to villages within a demarcated part of a local tribal authority in the Limpopo study. Villages considered for the study had unemployment rates as high as 85% and parents and caregivers were generally unable to support their children's education optimally. The study is community based and focused on tribal villages in the Limpopo province. A local tribal authority within the province was sampled and had to meet certain criteria of deprivation: High unemployment rate; High inability of parents and caregivers to support their children's education; Parents lack of mastery of the English language which would eventually be the school language Once these village areas had been selected, a complete population of schools, teachers and learners, as it were, would participate in the intervention implementation and evaluation. A total of 11 experimental and 5 control schools formed part of the study. Within each of the schools all grade 1, 4 and 7 learners were included together with their teachers. There was approximately one school in each village with the exception being where schools were split into junior and senior primary schools. The number of learners per school ranged from approximately 40 to 200 per grade, averaging at just more than 100.

Winter Storm 2021Census tracts with dependent age groups under 18 years old and over 65 years old. This service was created to support emergency response efforts from the 2021 winter storm . This layer shows total population count by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population that are considered dependent (ages 65+ and <18). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B01001Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

MMSE scores are significantly different in AD patients compared with control subjects (*denotes significant differences between groups).

Distribution of study participants by background characteristics and place of residence.

Association between individual-level factors and practices towards TB infection control.

Introduction: The educational background and size of the elderly population are undergoing significant changes in Finland during the 2020s. A similar process is likely to occur also in several European countries. For cognitive screening of early Alzheimer’s disease (AD), using outdated norms and cutoff scores may negatively affect clinical accuracy. The aim of the present study was to examine the effects of education, age, and gender on the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery (CERAD-nb) in a large register-based, clinical sample of patients with mild AD and nondemented at-risk persons from the general population (controls) and to examine whether corrected cutoff scores would increase the accuracy of differentiation between the 2 groups. Methods: CERAD-nb scores were obtained from AD patients (n = 389, 58% women, mean age 74.0 years) and from controls (n = 1,980, 52% women, mean age 68.5 years). The differences in CERAD-nb performance were evaluated by univariate GLM. Differentiation between the 2 groups was evaluated using a receiver operating characteristic (ROC) curve, where a larger area under the ROC curve represents better discrimination. Youden’s J was calculated for the overall performance and accuracy of each of the measures. Results: Of the demographic factors, education was the strongest predictor of CERAD-nb performance, explaining more variation than age or gender in both the AD patients and the controls. Education corrected cutoff scores had better diagnostic accuracy in discriminating between the AD patients and controls than existing uncorrected scores. The highest level of discrimination between the 2 groups overall was found for two CERAD-nb total scores. Conclusions: Education-corrected cutoff scores were superior to uncorrected scores in differentiating between controls and AD patients especially for the highest level of education and should therefore be used in clinical cognitive screening, also as the proportion of the educated elderly is increasing substantially during the 2020s. Our results also indicate that total scores of the CERAD-nb are better at discriminating AD patients from controls than any single subtest score. A digital tool for calculating the total scores and comparing education-based cutoffs would increase the efficiency and usability of the test.

Model A: 5 features selected by backwards elimination (FeretY_ave, MaxDiameter_ave, Elongation_ave, Slope_ave and ODKurtosis_ave).*Model B: 5 features (SumOD_sd, MaxDiam_sd, TSD_sd, TEntropy_sd, No.MedDensityObjects_sd), selected after competition among all models with ≤5 covariates based on leave-one-out AUC.

Association between residence and knowledge, attitudes and practices regarding tuberculosis.

Clinical characteristics of the patient populations according to histological classification and the control population enrolled in the study.