COVID-19-related fear negatively affects the public’s psychological well-being and health behaviours. Although psychological distress including depression and anxiety under COVID-19 is well-established in literature, research scarcely evaluated the fear of COVID-19 with a large sample using validated scale. This study aimed to validate a Korean version of fear scale(K-FS-8) using an existing fear scale(Breast Cancer Fear Scale; 8 items) and to measure the fear of COVID-19 in South Korea. A cross-sectional online survey was conducted with 2235 Korean adults from August to September 2020. The Breast Cancer Fear Scale was translated from English into Korean using forward-backward translation, and then face validity was assessed. Patient Health Questionnaire-4 and Primary Care Post-Traumatic Stress Disorder Screen for DSM-5 were used for assessing convergent validity of K-FS-8, and item response theory analysis was also conducted to further validate the K-FS-8. This study confirmed the validity and reliability of the K-FS-8. The validity of the scale was confirmed by convergent validity, known-group validity and item response theory analysis, and internal consistency was also examined(Cronbach’s α coefficient = 0.92). This study also identified that 84.6% participants had high COVID-19 fear; whilst 26.3%, 23.2% and 13.4% participants had high risk of post-traumatic stress disorder, depressive and anxiety symptoms, respectively. The K-FS-8 showed the acceptability measuring the fear of COVID-19 in the Korean population. The K-FS-8 can be applied to screen for fear of COVID-19 and related major public health crises identifying individuals with high levels of fear in primary care settings who will benefit from psychological support.

About the Socio-Demographic Data Dashboards The U.S. Department of Housing and Urban Development (HUD) has created interactive dashboards that display maps, tables, and charts of local housing, economic, and demographic conditions for each FY23 HUD Jurisdiction. These dashboards can help local governments, Public Housing Agencies (PHAs), states, and community organizations assess housing needs, prepare HUD reports, and advance local housing, and community development goals. The dashboards use data from the 2017-2021 5-Year American Community Survey, the Department of Transportation ETC Explorer, the Environmental Protection Agency Facility Registry Service, internal HUD administrative data, and other sources. For this dashboard, a jurisdiction is defined as any PHA or unit of local government (city, township, county, municipality, etc.) receiving HUD funding through the Community Development Block Grant (CDBG), Emergency Solutions Grant (ESG), Housing Opportunities for Persons with AIDS (HOPWA), or the HOME Investment Partnerships Program. HUD specific jurisdiction boundaries will likely differ from official U.S. Census geographic boundaries, so users should prioritize using HUD-defined jurisdictions in reports.

Socio-demographic profile of study participants

Women’s socio-demographic profile and delivery site characteristics (n = 269).

The current appendix supports a manuscript entitled: "Student Learning Ecologies in Online Higher Education: A Model to Support Connected Learning Across Contexts"

Sample socio-demographic profile.

Socio-demographic profile of the study respondents at baseline by group.

Census data, as a traditional data source of resident socio-demographics, provides valuable information for decision-makers, researchers, and the public. While numerous efforts have been made to develop more comprehensive data products based on published census datasets, most approaches treat census units as static and independent entities, overlooking their interactions. In this paper, we introduce the visitor census dataset, a semantically enriched census that incorporates human visitations extracted from large-scale mobile positioning data. We identified and validated the potential home locations of 3.58 million anonymous mobile phone users across seven U.S. metropolitan statistical areas in July 2021 and utilized home detection results to enrich the socio-demographic profile of the places users visited. The proposed data generation framework is adaptive, allowing future integration of diverse socio-demographic features at varying spatial and temporal scales. Overall, this visitor-based census represents an effort to enrich resident-based census knowledge by incorporating mobilities and spatial interactions in human digital traces, bridging the gap between aggregated and individual analysis, as well as between conventional census and mobile phone data.Seven MSAs include Angeles–Long Beach–Anaheim (LA), Houston–Pasadena–The Woodlands (Houston), Atlanta–Sandy Springs–Roswell (Atlanta), Miami–Fort Lauderdale–West Palm Beach (Miami), Seattle–Tacoma–Bellevue (Seattle), Denver–Aurora–Centennial (Denver), and Minneapolis-Saint. Paul (Twin Cities). There are ten files for each MSA:Visitor-based aggregation census table (e.g., Atlanta_visitor_July2021.csv): one fileVisit-based aggregation census table (e.g., Atlanta_visit_July2021.csv): one fileOne-week (July 19-25, 2021) intermediate home-visit table (e.g., Atlanta_homevisit_July21.csv.gz): seven filesGeographic boundary file at the CBG level (e.g., Atlanta_cbg.geojson): one file

Background The aim of the present work was to investigate the relative importance of socio-demographic and physical health status factors for subjective functioning, as well as to examine the role of social support. Methods A cross-sectional health survey was carried out in a Greek municipality. 1356 adults of the general population were included in the study. Personal interviews were conducted with house-to-house visits. The response rate was 91.2%. Functioning has been measured by five indexes: 'The Social Roles and Mobility' scale (SORM), 'The Self-Care Restrictions' scale (SCR), 'The Serious Limitations' scale (SL), 'The Minor Self-care Limitations' scale (MSCR) and 'The Minor Limitations in Social Roles and Mobility' scale (MSORM). Results Among the two sets of independent variables, the socio-demographic ones had significant influence on the functional status, except for MSORM. Allowing for these variables, the physical health status indicators had also significant effects on all functioning scales. Living arrangements and marital status had significant effects on four out of five indexes, while arthritis, Parkinson's disease, past stroke and kidney stones had significant effects on the SCR and SL scales. Conclusions These results suggest that socio-demographic factors are as important as physical health variables in affecting a person's ability to function normally in their everyday life. Social support appears to play a significant role in explaining differences in subjective functioning: people living alone or only with the spouse, particularly the elderly, seem to be in greater risk for disability problems and should be targeted by preventive programs in the community.

Participant socio-demographic information.

The Stata data file "CAP_Demographics_Jumla_Kavre_recoded.dta” and equivalent excel file of the same name comprises data collected by adolescent secondary school students during a "Citizen Science" project in the district of Kavre in the central hills of Nepal during April 2022 and in the district of Jumla in the remote mountains of West Nepal during June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project.

The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. Researchers from KLL and UCL trained the adolescents to record basic socio-demographic information about themselves and their households including caste/ethnicity, religion, education, water sources, assets, household characteristics, and income sources. The form also asked about their access to mobile phones or other devices and internet and their concerns with respect to climate change. The resulting data describe the participants in the citizen science project, but their names and addresses have been removed. The app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Tables 2 and 3 of this paper.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Our People data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

People Data Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

People Data Use Cases:

360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori People Data you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of People Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic...

Income of Immigrant taxfilers, by sex, immigrant admission category, socio-demographic profile, admission year and tax year, for Canada and provinces, 2022 constant dollars.

Single status was defined as being unmarried. Data on income were from the 1990 census and were not available for 4,676 individuals with epilepsy and 53,916 matched population controls, and for 5,048 individuals with traumatic brain injury and 19,278 corresponding controls. Data on single status were not available for 4,157 individuals with epilepsy and 21,052 matched population controls, and for 3,986 individuals with traumatic brain injury and 19,278 corresponding controls. No data were missing on the other variables.n/a, not applicable; SD, standard deviation; SEK, Swedish Kronor.

This county geography dataset includes selected indicators (2011-2015 5-Year Averages) pertaining to population, age, race/ethnicity, language, housing, poverty/income, education, disability, health insurance, employment, and age*race*gender groups. This dataset is assembled annually from the U.S. Census American Community Survey American Factfinder website and is maintained by the Colorado Department of Public Health and Environment.

Socio-demographic characteristics.

BackgroundOur aim was to assess the level and socio-demographic correlates of knowledge about rights to healthcare services among children in post-communist Albania in order to inform targeted interventions and policies to promote equitable healthcare access for all children.MethodsAn online survey conducted in Albania in September 2022 included a nationwide representative sample of 7,831 schoolchildren (≈54% girls) aged 12–15 years. A structured and anonymous questionnaire was administered inquiring about children’s knowledge on their rights to healthcare services. Binary logistic regression was used to assess the association of children’s knowledge about their rights to healthcare services with socio-demographic characteristics.ResultsOverall, about 78% of the children had knowledge about their rights to healthcare services. In multivariable adjusted logistic regression models, independent “predictors” of lack of knowledge about rights to healthcare services included male gender (OR = 1.2, 95% CI = 1.1–1.3), younger age (OR = 1.3, 95% CI = 1.1–1.4), pertinence to Roma/Egyptian community (OR = 1.6, 95% CI = 1.1–2.2), and a poor/very poor economic situation (OR = 1.3, 95% CI = 1.0–1.6).ConclusionOur findings indicate a significantly lower level of knowledge about rights to healthcare services among children from low socioeconomic families and especially those pertinent to ethnic minorities such as Roma/Egyptian communities, which can result in limited access to essential health services, increased vulnerability to health disparities, and barriers to receiving appropriate care and advocacy for their health and well-being. Seemingly, gender, ethnicity, and economic status are crucial for children’s knowledge of their healthcare rights because these factors shape their access to information, influence their experiences with healthcare systems, and can drive policy and practice to address disparities and ensure equitable access to health services. Health professionals and policymakers in Albania and elsewhere should be aware of the unmet needs for healthcare services due to lack of awareness to navigate the system particularly among disadvantaged population groups.

Characteristics from the 2016 Census of Population related to marital status, living arrangements, education, place of birth, housing, and health limitations among people who overdosed in Simcoe Muskoka between 2018 and 2019.

Income of Immigrant taxfilers, by sex, immigrant admission category, socio-demographic profile, admission year and tax year, for Canada and provinces, 2017 constant dollars.