An increase in prices concerned over 60 percent of Russians in February 2022, recorded as the most worrying problem in the society. An issue of unemployment growth was named as one of the most critical by nearly three out of ten survey participants. Besides political and economic matters, the deterioration of the environmental situation and a morality crisis were named among the most worrying topics.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

What is Social Vulnerability?Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.What is the CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.Below, text that describes “tract” methods also refers to county methods.How can the SVI help communities be better prepared for hazardous events?SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.SVI can be used to:Assess community need during emergency preparedness planning.Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.Decide the number of emergency personnel required to assist people.Identify areas in need of emergency shelters.Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not speak English well.Identify communities that will need continued support to recover following an emergency or natural disaster.For more detailed methodology and attribute details, please review this document.

The Levada Center has been conducting surveys of the Russian population on a regular basis (random-sample questionnaire-based nationally representative polls conducted as omnibus surveys). This data collection includes five questions (plus standard demographic data) from all surveys conducted from January 1995 to February 2021. The questions concern the perception of most urgent problems of the country, the own material situation and social inequality in the country as well as the inclination to protest against worsening living standards.

Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges.Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues.Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively).Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities.

When I was searching for COVID-19 datasets online, I soon realized that there were no comprehensive datasets of the United States on a county level basis which included social, economic, and demographic factors in addition to the general case information that was already available on several sites. To quench my thirst for clean and relevant data, I proceeded to gather information from several various sources to compile the dataset I was looking for.

I started by looking for a reliable dataset that has general information such as confirmed cases, deaths, etc. I found John Hopkin's COVID-19 dataset to be the best one for this purpose as it is well organized and updated daily. Then, I set out looking for economic factors and population data for each county in the United States. I found a collection of such files compiled by the Economic Research Service branch of the USDA on their website. Finally, I had to find a dataset which had racial and demographic information for each county, which I found on the US Census Bureau's website under a page which was dedicated to county population data by several characteristics. Now that I had all the data I was looking for, I proceeded to find which counties were common in all datasets. After several hours of cleaning each dataset and extracting relevant information, I combined all the information into one CSV file with 2959 counties of clean information - exactly what I was looking for.

I hope that the Kaggle community will use this dataset to answer important questions regarding COVID-19 in the United States and the role that external economic, social, and demographic factors play in the shaping of the pandemic. I know that there are several patterns to be discovered and I sincerely hope that this helps our community understand just a little more about the pandemic than we do right now.

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Snapshot img

US Population Health Management (PHM) Market Size 2025-2029

The us population health management (phm) market size is forecast to increase by USD 6.04 billion at a CAGR of 7.4% between 2024 and 2029.

The Population Health Management (PHM) market in the US is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and analytics. These technologies enable healthcare providers to collect, analyze, and act on patient data to improve health outcomes and reduce costs. However, the high perceived costs associated with PHM solutions pose a challenge for some organizations, limiting their ability to fully implement and optimize these technologies. Despite this obstacle, the potential benefits of PHM, including improved patient care and population health, make it a strategic priority for many healthcare organizations. To capitalize on this opportunity, companies must focus on cost-effective solutions and innovative approaches to addressing the challenges of PHM implementation and optimization. By leveraging advanced analytics, cloud technologies, and strategic partnerships, organizations can overcome cost barriers and deliver better care to their patient populations.

What will be the size of the US Population Health Management (PHM) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Population Health Management (PHM) market in the US is experiencing significant advancements, integrating various elements to improve patient outcomes and reduce healthcare costs. Public health surveillance and data governance ensure accurate population health data, enabling healthcare leaders to identify health disparities and target interventions. Quality measures and health literacy initiatives promote transparency and patient activation, while data visualization and business intelligence facilitate data-driven decision-making. Behavioral health integration, substance abuse treatment, and mental health services address the growing need for holistic care, and outcome-based contracts incentivize providers to focus on patient outcomes. Health communication, community health workers, and patient portals enhance patient engagement, while wearable devices and mHealth technologies provide real-time data for personalized care plans. Precision medicine and predictive modeling leverage advanced analytics to tailor treatment approaches, and social service integration addresses the social determinants of health. Health data management, data storytelling, and healthcare innovation continue to drive market growth, transforming the industry and improving overall population health.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareServicesDeploymentCloudOn-premisesEnd-userHealthcare providersHealthcare payersEmployers and government bodiesGeographyNorth AmericaUS

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software in the US gathers patient data from healthcare systems and utilizes advanced analytics tools, including data visualization and business intelligence, to predict health conditions and improve patient care. PHM software aims to enhance healthcare efficiency, reduce costs, and ensure quality patient care. By analyzing accurate patient data, PHM software enables the identification of community health risks, leading to proactive interventions and better health outcomes. The adoption of PHM software is on the rise in the US due to the growing emphasis on value-based care and the increasing prevalence of chronic diseases. Machine learning, artificial intelligence, and predictive analytics are integral components of PHM software, enabling healthcare payers to develop personalized care plans and improve care coordination. Data integration and interoperability facilitate seamless data sharing among various healthcare stakeholders, while data visualization tools help in making informed decisions. Public health agencies and healthcare providers leverage PHM software for population health research, disease management programs, and quality improvement initiatives. Cloud computing and data warehousing provide the necessary infrastructure for storing and managing large volumes of population health data. Healthcare regulations mandate the adoption of PHM software to ensure compliance with data privacy and security standards. PHM software also supports care management services, patient engagement platforms, and remote patient monitoring, empowering patients

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

The social environment represents the external conditions under which people engage in social activity within their community. It includes aspects of social opportunity, leisure and recreation, education, access to health services, health status and participation in democratic processes. Fourteen indicators have been used to assess aspects of quality of the social environment.

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.

The study of social phenomena requires an integrated system of social indicators. The Survey of Living Conditions (ECV) provides comprehensive information about social habits, relationships and the physical and social environment in which the daily life of the population living in families takes place within the Basque Country. The five-year operation allows the development of internationally comparable indicators in each of the areas that shape people’s quality of life.

According to a survey carried out in 2022 in Mexico, the majority of the population considers that there is a strong gender inequality in the political sphere. The male population sharing this idea reached **** percent, **** percent in the case of women.

Social capital is understood as a resource that is accessed when extensive personal networks are available that actively participate in the different economic and social fields, in an environment of trust and that can facilitate personal and social development, as well as the economic development of a society. Specifically, in the Social Capital Survey, conducted by Eustat, social capital is conceived as a set of dimensions of relationship and participation, among which are: social networks of family and friends, trust in people and institutions, social participation and cooperation, information and communication, cohesion and social inclusion and happiness and health. Social capital is understood as a resource that is accessed when extensive personal networks are available that actively participate in the different economic and social fields, in an environment of trust and that can facilitate personal and social development, as well as the economic development of a society. Specifically, in the Social Capital Survey, conducted by Eustat, social capital is conceived as a set of dimensions of relationship and participation, among which are: social networks of family and friends, trust in people and institutions, social participation and cooperation, information and communication, cohesion and social inclusion and happiness and health.

This dataset covers ballots 333-38, spanning January, March, May, July, September and October 1969. The dataset contains the data resulting from these polls in ASCII. The ballots are as follows: 333 - January This Gallup poll seeks the opinions of Canadians on various political and social issues. Subjects include discipline in schools, preferred political parties and leaders, and the overall development of the country. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: Canadian development; changes in savings; feelings towards the future; putting limits on debates in Parliament; the outcome of giving women more say; political preferences; the preferred size of the population; the proposed reconstruction of the Provinces; the sale of beer in grocery stores; satisfaction with the government; and the idea of going back to a two-party system in Canada. Basic demographic variables are also included. 334 - March This Gallup poll seeks the opinions of Canadians on a variety of political and social issues of importance to the country and government. Some of the subjects include political leaders, parties and issues, abortion, international development and foreign aid, and lotteries. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: abortions for physical and mental reasons; approval of the language rights bill; the court's treatment of criminals; the effectiveness of the Federal government; foreign aid; interest in international development; the legalization of sweepstakes and lotteries; militant students causing damage; political preference; a politician's right to privacy; recognizing Red China; the issue of public workers striking; the use of Medicare money; whether or not regional differences will break confederation; and if Canada will be better off if it was governed federally. Basic demographic variables are also included. 335 - May This Gallup poll seeks the opinions of Canadians on political and social issues of interest to the country and government. Topics of interest include: involvement in politics, opinions on Trudeau as prime minister, the nature of the U.S. vs Canada, livable income, how the government should raise money, U.S.-Canada relations, integrating neighbourhoods, whether Quebec will gain its independence, opinions on Nixon as president, Rene Levesque, and voting behavior. Basic demographic variables are also included. 336 - July This Gallup poll seeks the opinions of Canadians on political and social issues of interest to the country and government. There are questions about elections, world conflicts, money matters and prices. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: the cutback of NATO forces in Europe; the dispute between Arabs and Jews; the amount of government money spent on Expo '67; opinions on who gets the most profit with the increased prices of vegetables; the amount of objectionable material in the media; the opinions about John Robarts; the opinions about topless waitresses; political preferences; provinces with power; the ratings of Stanfield as leader of the opposition; whether or not some proportion of income is saved; sex education in schools, the use of alcohol; which household member decides on money matters; which family member gets a fixed amount of pocket money; and who gets profit from the increased price of meat. Basic demographic variables are also included 337 - September This Gallup poll seeks the opinions of Canadians on current issues of importance to the country and government. Some of the questions are politically-based, collecting opinions about political parties, leaders, and policies. There are also other questions of importance to the country, such as problems facing the government, and attitudes towards inflation. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: Allowing the police to go on strike; baby bonus cuts to the rich; the biggest worry for the future; the greatest problem facing the Federal government; inflation problems; will the NDP gain support; the opinion of Trudeau; the performance of the police; political preferences; the ratings of Federal MPs; the ratings of Provincial MPs; reducing the work week from 40 to 35 hours; and the Trudeau plan of efficiency. Basic demographic variables are also included. 338 - October This Gallup poll seeks the opinions of Canadians on important current events topics of the day. Many of the questions in this survey deal with predictions of social, political and economic conditions for the future. The respondents were also asked questions so that they could be grouped according to geographical and social variables. Topics of interest include: American power in 1970; the amount of student demonstrations; chance of atomic war by 1990; changing the voting age; Chinese power in 1970; the collapse of capitalism; the collapse of civilization; continuation of space programmes; the country with the strongest claim to the South Pole; a cure for cancer; the disappearance of Communism; economic prosperity in 1970; the amount of excitement in life; heart transplant operations; International discord in 1970; the length of life span in the future; man living on the moon; the manufacturing of H-bombs; opinions of 1969; political preferences; predictions for 1990; predictions for the future; predictions of peace in 1990; Russian power in 1970; opinions of a three day work week; and travel involving passports. Basic demographic variables are also included.The codebook for this dataset is available through the UBC Library catalogue, with call number HN110.Z9 P84.

BackgroundSocial isolation and loneliness (SIL) are complex issues that impact mental and physical wellbeing and are significant public health concerns. People from minority ethnic backgrounds living in Organisation for Economic Cooperation and Development (OECD) member states may be particularly vulnerable to experiencing SIL. This is due to various challenges associated with life in foreign countries, including cultural differences, settlement issues, low incomes, and discrimination. While many interventions have been developed to address SIL in the general population, there is little information about interventions designed for minority ethnic populations in OECD countries. Our study aimed to 1) Investigate existing interventions for minority ethnic communities in OECD countries; 2) Assess how these interventions are conceptualised to increase awareness of SIL risks on health factors.3) Explore culturally sensitive approaches in these interventions, and 4) Identify the most effective interventions in reducing SIL in minority ethnic populations.Methods and findingsWe searched Medline, APA PsycINFO, Psychology and Behavioural Sciences Collection, CINAHL, Web of Science, and Scopus from their inception to September 19th, 2023, and registered the scoping review at https://osf.io/fnrvc. Our search yielded 10,479 results, of which 12 studies were included: five RCTs, six non-randomized quasi-experimental studies, and one qualitative study. Interventions were grouped into four main categories: social facilitation, befriending, leisure and skills development, and health education programmes. While only a few interventions targeted minority ethnic populations specifically, our findings highlight the potential of culturally sensitive interventions in reducing social isolation and loneliness among minority ethnic communities in OECD countries. However, given the type and extent of evidence, it is still unclear which interventions are superior in reducing SIL in minority ethnic populations in OECD countries. Further research is necessary to understand which activities may be most effective for which communities. Such interventions should be designed and tailored to account for the broader risk implications of SIL to raise awareness of the population’s peculiar health risk profile.DiscussionInterventions designed to address SIL among minority ethnic groups in OECD member states are scarce and have not been designed to account for the health risks profile of the population. Integrated research designs involving groups linked with minority ethnic populations are needed to link individual, community, and societal factors alongside population risk profiles for increased recognition of SIL as an important health factor.

Link Function: information

This table provides comparative data from 2015 and 2021 on the estimated population under 16 years of age in the Canary Islands due to health problems suffered in the last 12 months and social classes based on the occupation of the reference person.

This Gallup poll seeks the opinions of Canadians on various topics. Included in the discussion are questions about problems facing the country and their solutions, sweepstakes, marriage, divorce, and issues involving the employment of women. Respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: belief in Hell; birth control information; canned laughter on television; credit cards and purchases; divorce; employment equity; federal elections; goods produced by different countries; marriage issues; the population explosion; quarrels in marriage; the United Nations; voting behaviour; women's fashion; and working women. Basic demographics variables are also included.