Socio-demographic data of study participants.

BackgroundBetter medication adherence among people with diabetes mellitus was found to be associated with improved glycaemic control. However, medication non-adherence is a significant concern in older people with uncontrolled type 2 diabetes mellitus.PurposeTo explore the perspectives of older people with uncontrolled type 2 diabetes mellitus towards medication adherence.DesignA qualitative descriptive exploratory study.MethodologyA purposive sample of older people with uncontrolled type 2 diabetes mellitus living in the community was recruited. Snowball sampling was applied in community recruitment. In‐depth telephone interviews were conducted using a semi‐structured interview guide. Interviews were transcribed verbatim. Thematic analysis was used in data analysis. The consolidated criteria for reporting qualitative research (COREQ) guidelines were followed.ResultsThe emerged six themes were: (a) impact of knowledge, attitudes and practices on medication adherence, (b) treatment-related barriers to medication adherence, (c) impact of age-related changes on medication adherence, (d) person-related barriers to medication adherence, (e) impact of COVID-19 on medication adherence and, (f) role of support systems in medication adherence. Knowledge of the disease process and medications, attitudes towards medication adherence, the practice of different treatment approaches, self-medication and dosing, negative experiences related to medications, polypharmacy, changes in lifestyle and roles, the influence of work-life, motivation, negligence, family support, support received from health workers, facilities available and financial capability are the main factors influence medication adherence. Age-related memory impairment, visual disturbances and physical weaknesses affect medication adherence in older people. Additionally, COVID-19-related guidelines imposed by the government and healthcare system-related issues during the COVID-19 pandemic also affected medication adherence.ConclusionAdherence to medications among older people is hampered by a variety of factors, including their knowledge, attitudes and practices, person and treatment-related factors and age-related changes. The COVID-19 pandemic has brought additional challenges. Individualised patient care for older people with uncontrolled type 2 diabetes mellitus to improve medication adherence is timely. Strengthening support mechanisms for the above population is essential.

Our consumer 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.

Consumer Graph 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).

Consumer Graph 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 Consumer Data graph 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 Consumer 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_desc...

This project aimed to understand the public acceptability of a Low Emission Zone in the city of Bath, UK (formally known as the 'Clean Air Zone'). The dataset consists of socio-demographic, travel-related, and psychological variables, and a measure of Low Emission Zone acceptability.

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.

Abstract

The 2022 Socio-Demographic and Economic Survey is a nationally representative household survey designed to provide information on population, migration, education, labour and employment, fertility, disability, household, and housing characteristics. The key objectives of the survey are:

-to generate essential key indicators as inputs in the preparation of national plans and programs for the well-being of the population -to monitor the progress of development programs as stipulated in the Sustainable Development Goals (SDGs), Medium Term Development Plans, Vision 2050 and other national policies/plans and priorities.

Geographic coverage

National coverage. 43 strata and 22 provinces were covered.

Analysis unit

Household and Individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

-Used a stratified, two-stage cluster sampling method, with a third stage in very large sample census units (CU, enumeration areas selected within the sample CUs).

-Produced 43 strata, 22 provinces by urban/rural (National Capital District has only urban areas).

-Allocation was done proportionately according to size (in terms of the number of households).

-Thus, 335 CUs / clusters were selected in the first- stage while a fixed number of 15 households per cluster were selected at the second stage resulting to a total sample size of 5,025 households.

Sampling deviation

Coverage: 95.8% (14 out of 335 clusters not accessed) due to security issues (tribal fights/lawlessness), and election related misconceptions.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was generated using the World Bank's software Survey Solutions. It contains a set of 47 questions covering several modules such as Employment, Fertility, Housing, Disability, Education. The questionnaire is provided in English in the External Resources section in this documentation.

Cleaning operations

-Checking of data submitted from field, identifying unique / valid households and removing invalid or duplicate households, coding of responses, consistency checks -Tabulations - generating tables for data analysis and generation of key indicators

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.

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.

This excel file contains the unemployment expectations for 16 assessed socio-demographic consumer groups, as well as the assessed macroeconomic variables.

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.

The dataset presented provides the compilation of extensive socio-demographic profile variables as age, gender, family income measurements and so on. The purpose of this data is to analyze if the panelists of Ortiz, et al. participatory Delphi methodology correctly represents the diverse community of USFQ. This data supports the variables taken into consideration in the research design, process, and analysis.

Socio-demographic profile of study participants (n = 603).

This dataset consists of a summary measure that identifies where countries or other geographic areas sit on the spectrum of development. Expressed on a scale of 0 to 1, SDI (Socio-Demographic Index) is a composite average of the rankings of the incomes per capita, average educational attainment, and fertility rates of all areas in the GBD (Global Burden of Disease) study.

SOCIO-DEMOGRAPHIC CHARACTERISTICS OF RESPONDENTS (STUDY ON AWARENESS,PERCEPTION & BEHAVIOUR TOWARDS E-CIGARETTE IN MALAYSIA) 2014

This research delves into the socio-demographic tapestry of Himalayan hill destination selection, unravelling the complex interplay of demographic characteristics, social influences, and individual motivations that shape tourists’ choices. This research aims to answer why different tourist have different travel choices and what factors are the drivers behind such choice.

+Mean and standard deviation (SD) are presented, unless otherwise stated.++n/a  =  non-applicable *p

Socio-demographic indicators of the study regions.

Background: Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

Methods: A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pretested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles' households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Results: Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x2 = 20.392, df=3), educational status(x2 = 19.358, df=4), source of income (x2 = 21.777, df=3), monthly income (x2 = 13.322, df=3), availability of additional facilities (x2 = 98.144, df=7), cleanness status (x2 =42.979, df=4), scarcity of water (x2 = 5.1388, df=1) and family size (x2 = 9.934, df=2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

Conclusion: The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore, ; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

Selected demographic, social, economic, and housing estimates data by community district/PUMA (Public Use Micro Data Sample Area). Three year estimates of population data from the Census Bureau's American Community Survey

The dataset includes data from the survey on the Gdańsk University of Technology foreign graduates socio-demographic characteristics. The research was conducted over a four-month period, from December 2019 to March 2020, using the Computer-Assisted Web Interview (CAWI). The research sample included 142 respondents. The study concerned such variables such as i.a. nationality, gender, and the faculty graduated. Summarizing, the most of the graduates came from India, Eastern Europe (Ukraine and Belarus) and China.