About this dataset

Context

The dataset tabulates the data for the Social Circle, GA population pyramid, which represents the Social Circle population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Social Circle, GA, is 26.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Social Circle, GA, is 18.1.
• Total dependency ratio for Social Circle, GA is 44.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Social Circle, GA is 5.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Social Circle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Social Circle for the selected age group is shown in the following column.
• Population (Female): The female population in the Social Circle for the selected age group is shown in the following column.
• Total Population: The total population of the Social Circle for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Social Circle Population by Age. You can refer the same here

Note: Taken from the Agincourt HDSS 2007. +A household is defined as “a group of people who reside and eat together, plus the linked temporary migrants who would eat with them on return” [62]. Location refers to the physical dwelling place at which an individual is located.

Data and R code supporting NMDS findings from the article: Are you adopting Artificial Intelligence products? Social-demographic factors to explain customer acceptance. The following code and data enable the creation of the article's Non-Metric Multidimensional Scale (NMDS) map using the R Vegan package. The NMDS maps created show the attitudes towards robots of men and women as a function of their age or socioeconomic status and mediated by their opinion of the innovation.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

Participants socio-demographics.

The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales.

The Cardiff Travel Survey 2022 (Wave 2) is an opportunity sample that was collected in 2022 (n=968) by the Centre for Climate Change and Social Transformations (CAST), and is the second of a longitudinal series of surveys to be held annually for the duration of the centre. Data for the Cardiff Travel Survey 2022 were collected between 03 May 2022 and 20 July 2022. Participants of the Cardiff Travel Survey 2021 who consented (n=512) were recontacted via email to invite them to take part in the 2022 survey. Furthermore, participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts. The posts on Twitter and Facebook were promoted to make them more visible in the area. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 1,034 respondents and the Welsh version by 28 respondents. Incomplete responses (n=95), defined as those without any answers beyond socio-demographic, were removed from the dataset. A further 4 respondents did not complete the first section on current travel behaviours and were also removed. This left a final sample of n=968 adults. Two hundred and ten (210) of these had also responded to the Cardiff Travel Survey 2021. Participants were asked to create a unique code that can be used match this survey to the previous and next surveys without knowing their identity.

Main topic areas of the questionnaire are: Demographics, Travel behaviours, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Travel-related social identity, Attitudes to active travel, Social norms, Support for transport policies, and Unique ID.

The Centre for Climate Change Transformations (C3T) will be a global hub for understanding the profound changes required to address climate change. At its core, is a fundamental question of enormous social significance: how can we as a society live differently - and better - in ways that meet the urgent need for rapid and far-reaching emission reductions?

While there is now strong international momentum on action to tackle climate change, it is clear that critical targets (such as keeping global temperature rise to well within 2 degrees Celsius relative to pre-industrial levels) will be missed without fundamental transformations across all parts of society. C3T's aim is to advance society's understanding of how to transform lifestyles, organisations and social structures in order to achieve a low-carbon future, which is genuinely sustainable over the long-term.

Our Centre will focus on people as agents of transformation in four challenging areas of everyday life that impact directly on climate change but have proven stubbornly resistant to change: consumption of goods and physical products, food and diet, travel, and heating/cooling. We will work across multiple scales (individual, community, organisational, national and global) to identify and experiment with various routes to achieving lasting change in these challenging areas. In particular, we will test how far focussing on 'co-benefits' will accelerate the pace of change. Co-benefits are outcomes of value to individuals and society, over and above the benefits from reducing greenhouse gas emissions. These may include improved health and wellbeing, reduced waste, better air quality, greater social equality, security, and affordability, as well as increased ability to adapt and respond to future climate change. For example, low-carbon travel choices (such as cycling and car sharing) may bring health, social and financial benefits that are important for motivating behaviour and policy change. Likewise, aligning environmental and social with economic objectives is vital for behaviour and organisational change within businesses.

Our Research Themes recognise that transformative change requires: inspiring yet workable visions of the future (Theme 1); learning lessons from past and current societal shifts (Theme 2); experimenting with different models of social change (Theme 3); together with deep and sustained engagement with communities, business and governments, and a research culture that reflects our aims and promotes action (Theme 4).

Our Centre integrates academic knowledge from...

An Environmental Quality Index (EQI) for all counties in the United States for the time period 2000-2005 was developed which incorporated data from five environmental domains: air, water, land, built, and socio-demographic. The EQI was developed in four parts: domain identification; data source identification and review; variable construction; and data reduction using principal components analysis (PCA). The methods applied provide a reproducible approach that capitalizes almost exclusively on publically-available data sources. The primary goal in creating the EQI is to use it as a composite environmental indicator for research on human health. A series of peer reviewed manuscripts utilized the EQI in examining health outcomes. This dataset is not publicly accessible because: This series of papers are considered Human health research - not to be loaded onto ScienceHub. It can be accessed through the following means: The EQI data can be accessed at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: EQI data, metadata, formats, and data dictionary all available at website. This dataset is associated with the following publications: Gray, C., L. Messer, K. Rappazzo, J. Jagai, S. Grabich, and D. Lobdell. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(8): e0203301, (2018). Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018). Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

Street-children (STch) and controls (Con) socio-demographic characteristics. Numbers may not add to total due to missing data or rounding.a–Health care coverage was defined as children’s access to preventive healthcare (i.e., vaccination, disease screening, malaria protection) and basic disease treatments (i.e., treatment of malaria, fever and diarrhea).b–Access of basic needs was defined as children’s possibility to obtain adequate food, clean water, clothes and shelter.c–Full-time salaried jobs comprehend physician, nurse, educator, employee, social worker.Socio-demographic description of the samples.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

Data set of a community based cross-sectional survey done to find the prevalence , its correlates and patterns in a population of a district in southern Kerala, IndiaBackground: Multi-morbidity is the coexistence of multiple chronic conditions in the same individual. With advancing epidemiological and demographic transitions, the burden of multi-morbidity is expected to increase India. The state of Kerala in India is also in an advanced phase of epidemiological transition. However, very limited data on prevalence of multi-morbidity are available in the Kerala population.

Methods: A cross sectional survey was conducted among 410 participants in the age group of 30-69 years. A multi-stage cluster sampling method was employed to identify the study participants. Every eligible participant in the household were interviewed to assess the household prevalence. A structured interview schedule was used to assess socio-demographic variables, behavioral risk factors and prevailing clinical conditions, PHQ-9 questionnaire for screening of depression and active measurement of blood sugar and blood pressure. Co-existence of two or more conditions out of 11 was used as multi-morbidity case definition. Bivariate analyses were done to understand the association between socio-demographic factors and multi-morbidity. Logistic regression analyses were performed to estimate the effect size of these variables on multi-morbidity.

Results: Overall, the prevalence of multi-morbidity was 45.4% (95% CI: 40.5-50.3%). Nearly a quarter of study participants (25.4%) reported only one chronic condition (21.3-29.9%). Further, 30.7% (26.3-35.5), 10.7% (7.9-14.2), 3.7% (2.1-6.0) and 0.2% reported two, three, four and five chronic conditions, respectively. Nearly seven out of ten households (72%, 95%CI: 65-78%) had at least one person in the household with multi-morbidity and one in five households (22%, 95%CI: 16.7-28.9%) had more than one person with multi-morbidity. With every year increase in age, the propensity for multi-morbidity increased by 10 percent (OR=1.1; 95% CI: 1.1-1.2). Males and participants with low levels of education were less likely to suffer from multi-morbidity while unemployed and who do recommended level of physical activity were significantly more likely to suffer from multi-morbidity. Diabetes and hypertension was the most frequent dyad.

Conclusion: One of two participants in the productive age group of 30-69 years report multi-morbidity. Further, seven of ten households have at least one person with multi-morbidity. Preventive and management guidelines for chronic non-communicable conditions should focus on multi-morbidity especially in the older age group. Health-care systems that function within the limits of vertical disease management and episodic care (e.g., maternal health, tuberculosis, malaria, cardiovascular disease, mental health etc.) require optimal re-organization and horizontal integration of care across disease domains in managing people with multiple chronic conditions.

Key words: Multi-morbidity, cross-sectional, household, active measurement, rural, India, pattern

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

Socio-demographic and clinical characteristics.

Unweighted demographics of survey respondents and full sample demographics.

Abstract

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

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in all nine provinces in South Africa. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older were selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

South Africa used a stratified multistage cluster sample design. Strata were defined by the nine provinces:(Eastern Cape, Free State, Gauteng, Kwa-Zulu Natal, Limpopo, Mpumalanga, North West, Northern Cape and Western Cape), locality (urban or rural), and predominant race group (African/Black, White, Coloured and Indian/Asian), as not all combinations of stratification variables were possible, there were 50 strata in total. The Human Science Research Council's master sample was used as the sampling frame which comprised 1000EAs. A sample of 600 EAs was selected as the primary sampling units(PSU). The number of EAs to be selected from each strata was based on proportional allocation (determined by the number of EAs in each strataspecified on the Master Sample). EAs were then selected from each strata with probability proportional to size; the measure of size being the number of individuals aged 50 years or more in the EA. In each selected EA 30 households were randomly selected from the Master Sample. A listing of the 30 selected households was conducted to classify each household into one of two mutually exclusive categories: (1) households with one or more members aged 50 years or more (defined as '50 plus households'); (2) households which did not include any members aged 50 years or more, but included residents aged 18-49 (defined as '18-49 households'). All 50 plus households were eligible for the household interview, and all 50 plus members of the household were eligible for the individual interview. Two of the remaining 18-49 households were randomly selected for the household interview. In each of these household one person aged 18-49 was eligible for the individual interview, and the individual to be included was selected using a Kish Grid.

Stages of selection Strata: Province, Predominant Race Group, Locality=50 PSU: EAs=408 surveyed SSU: Households=4020 surveyed TSU: Individual=4227 surveyed

Sampling deviation

Originally 600 EAs were drawn into the sample. However due to time and financial contraints only 396 EAs were visited.

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionnaire was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into six of the major languages in South Africa: Afrikaans, IsiZulu, IsiXhosa, Sepedi, Setswana and Xitsonga. All SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=67% Cooperation rate=99%

Individual: Response rate=77% Cooperation rate=99%

The data refer to a study of teachers’ preferences regarding working conditions that affect job satisfaction using the Discrete Choice Experiment (DCE) methodology, according to which respondents make choices in hypothetical situations. The data contain the results of a survey conducted among teachers in the city of Kutno, in addition to variables defining choices, also variables regarding socio-demographic characteristics are included. Variables and their type are defined in the Codebook file. This is a unique study for Poland using DCE to study teachers’ preferences.The study shows significant heterogeneity in teachers’ tastes regarding working conditions. In addition to income, teachers value other elements that can compensate for their relatively low salaries, potentially reducing the burden on state budgets compared to wage increases.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The HIECS 2010/2011 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2010/2011, among a long series of similar surveys that started back in 1955. The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (26.5 thousand households) is divided into two sections:

a- A new sample of 16.5 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.

b- A panel sample with 2008/2009 survey data of around 10 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- The number of enumeration area segments is reduced from 2526 in the previous survey to 1000 segments for the new sample, with decreasing the number of households selected from each segment to be (16/18) households instead of (19/20) in the previous survey.

3- Some additional questions that showed to be important based on previous surveys results, were added, such as:

a- Collect the expenditure data on education and health on the person level and not on the household level to enable assessing the real level of average expenditure on those services based on the number of beneficiaries.

b- The extent of health services provided to monitor the level of services available in the Egyptian society.

c- Smoking patterns and behaviors (tobacco types- consumption level- quantities purchased and their values).

d- Counting the number of household members younger than 18 years of age registered in ration cards.

e- Add more details to social security pensions data (for adults, children, scholarships, families of civilian martyrs due to military actions) to match new systems of social security.

f- Duration of usage and current value of durable goods aiming at estimating the service cost of personal consumption, as in the case of imputed rents.

4- Quality control procedures especially for fieldwork, are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

National

Analysis unit

1- Household/family

2- Individual/Person

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS, 2010-2011 is a self-weighted two-stage stratified cluster sample, of around 26500 households. The main elements of the sampling design are described in the following:

1- Sample Size It has been deemed important to collect a smaller sample size (around 26.5 thousand households) compared to previous rounds due to the convergence in the time period over which the survey is conducted to be every two years instead of five years because of its importance. The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 26500 households has been considered, and was distributed between urban and rural with the percentages of 47.1 % and 52.9, respectively. This sample is divided into two parts: a- A new sample of 16.5 thousand households selected from main enumeration areas. b- A panel sample with 2008/2009 survey data of around 10 thousand households.

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 16 households (that was increased to 18 households in urban governorates and Giza, in addition to urban areas in Helwan and 6th of October, to account for anticipated non-response in those governorates: in view of past experience indicating that non-response may almost be nil in rural governorates). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area

Biological, genetic, and socio-demographic factors are all important in explaining reproductive behavior, yet these factors are typically studied in isolation. In this study, we explore an innovative sociogenomic approach, which entails including key socio-demographic (marriage, education, occupation, religion, cohort) and genetic factors related to both behavioral [age at first birth (AFB), number of children ever born (NEB)] and biological fecundity-related outcomes (endometriosis, age at menopause and menarche, polycystic ovary syndrome, azoospermia, testicular dysgenesis syndrome) to explain childlessness. We examine the association of all sets of factors with childlessness as well as the interplay between them. We derive polygenic scores (PGS) from recent genome-wide association studies (GWAS) and apply these in the Health and Retirement Study (N = 10,686) and Wisconsin Longitudinal Study (N = 8,284). Both socio-demographic and genetic factors were associated with childlessness. Whilst socio-demographic factors explain 19–46% in childlessness, the current PGS explains

BackgroundThe Self-Administered Food Security Survey Module for Children was developed to assess food insecurity of individual children and has not been used in Portugal. We aimed to apply the mixture item response theory model to the Self-Administered Food Security Survey Module for Children, to assess its reliability and validity, and to estimate the cut-offs of the food security status for Portuguese children.MethodsThe scale was self-administered to 2132 children of the Generation XXI birth cohort. The internal consistency was assessed using Cronbach’s alpha. We evaluated dimensionality and/or clustering, and Latent Class Analysis, Latent Trait Analysis and Mixture Latent Trait Analysis were tested. The number of classes and/or traits were defined according to the Akaike Information Criterion, Bayesian Information Criterion, Adjusted Bayesian Information Criterion, Vuong-Lo-Mendell-Rubin Likelihood Ratio Test, Bootstrapped Likelihood Ratio Test and Entropy. Construct validity was explored using socio-demographic characteristics. The classification tree was used to define cut-offs to predict cluster membership.ResultsThe best model was a Mixture Latent Trait Analysis with 1 factor and 2 classes (food security and food insecurity), assuming class variant item parameters (for items 1 and 3). Based on the estimated posterior probabilities, the food insecurity prevalence was 17.6%. Cronbach’s alpha was 0.617. A higher proportion of less-educated mothers and low-income households was observed in the food insecurity class. The classification tree showed an accuracy of 100.0% by identifying the food security and food insecurity groups.ConclusionOur results supported that the Self-Administered Food Security Survey Module for Children provides a valid and reliable measure, which allows the identification of food insecurity among Portuguese children.

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries