These data contain lifetables derived from the ONS Longitudinal study dataset, and according to age, sex and individual socio-economic status measured with education, occupation or wage in England and Wales in 2011. Life table according to age, sex and individual’s education, or occupation or wage for the England & Wales population in 2011 The data contained in these files are aggregated data from the ONS Longitudinal Study (ONS LS). The ONS LS is a long-term census-based multi-cohort study. It uses four annual birthdates as random selection criteria, giving a 1% sample of the England and Wales population (10.1093/ije/dyy243). The initial sample was drawn from the 1971 Census, and study members’ census records have been linked every 10 years up to the 2011 Census. New members enter the study through birth or immigration, and existing members leave through death or emigration. Vital life events information (births, deaths and cancer registrations) are also linked to sample members’ records. File lifetab_2011_educ.csv Life table according to age, sex and education level for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m) and female (f) educ: 6 categories of highest educational attainment: A: no qualifications; B: 1-4 GCSEs/O levels; C: 5+ GCSEs/O levels, D: Apprenticeships/Vocational qualifications, E: A/AS levels, F: Degree/Higher Degree mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_inc.csv Life table from age 20 onwards and according to age, sex and income level for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) inc: 5 categories of income: Least deprived; 4; 3; 2; Most deprived mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_occ.csv Life table from age 20 onwards and according to age, sex and occupation for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) occ: 3 categories of occupation: C: Technical/Routine; B: Intermediate; A: Managerial/Administrative/Professional mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) File lifetab_2011_overall.csv Life table from age 20 onwards and according to age and sex for the England & Wales population in 2011 age x: attained age (years) from 20 to 100 sex: 2 categories: male (m); female (f) mx: mortality rate for 1 person-year qx: annual probability of death ( = 1 - exp(-mx) ) ex: life-expectancy (years) More details can be found in the following paper: Ingleby F, Woods L, Atherton I, Baker M, Elliss-Brookes L, Belot A. (2021). Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study. SSM - Population Health, doi: 10.1016/j.ssmph.2021.100815

In the UK, people who reside within more income-deprived areas live a shorter period of time after a diagnosis of cancer compared to people living in less income-deprived areas. At least part of these inequalities in cancer survival are due to inequalities in cancer care, even considering differential patient and tumour factors such as stage at diagnosis. The specific mechanisms by which area-based deprivation levels lead to poorer individual health outcomes within the context of a universal healthcare system, free at the point of use, are not well understood. These analyses will enable, for the first time, the examination of how an individual patient's socio-economic status is associated with poorer cancer survival in England, and will demonstrate how these associations might be modified by the level of deprivation in the small area within which the patient resides. Our aim is to perform an in-depth study of the association between the individual patient's deprivation and cancer survival, considering in particular how this is influenced by their socio-economic context, whether it varies over time since diagnosis and whether it has changed over calendar time. We will focus on three indicators of deprivation: income, education and occupation. We will first examine the correlation between individual and area deprivation, by each of these indicators, and then secondly describe the association between individual deprivation and survival. Third, we will assess whether the association between individual deprivation and patients' survival is modified by area deprivation; that is, whether equally deprived individuals in different areas fare better, or worse, according to the socio-economic context of the area within which they live. Finally we will gain the insights of patients, carers, and healthcare professionals on these data, and communicate these to cancer policy...

This is a series of semi-structured interview transcripts collected at two health and social care sector sites in the UK. This qualitative study analyses if and how organisations in three countries (Germany, Finland, and the UK) report similar challenges and how they support longer working careers in the HCS. Therefore, we conducted multiple case studies in care organisations. Altogether 54 semi-structured interviews with employees and representatives of management were carried out and analysed thematically. Only the UK data are uploaded here.

Europe is currently undergoing significant demographic change, with an ageing population, shrinking workforce, and increasing life expectancy. In this context, it is necessary to raise the activity rate of older workers in a way that is healthy and productive for workers, employers and countries as a whole. A major issue in extending working lives is that those in different circumstances will be affected differentially by any proposed changes. EXTEND is a cross-national collaborative project which therefore aims to examine inequalities in relation to extending working lives. It addresses inequalities in relation to a number of pertinent issues, including changes to retirement and pension policies, the health and well-being of older workers and retirees, workplace factors, employee skills and training, and regulative and legislative frameworks. The project will take the social services sector as a particularly important example due to the barriers faced by health and care professionals. The evidence base will be generated by drawing on the varied expertise of its partners across five EU countries, employing a range of quantitative and qualitative methods, including policy analysis, panel data methods, natural experiments, a field trial, case studies, interviews and focus groups. We will engage numerous stakeholders with our findings, including policy makers, the business community, workers and their representatives, older people, the general public, and practitioners in the social services sector. The EXTEND project is strongly solution-driven, and has the overall aim of reducing inequalities in retirement structures.

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

29 percent of respondents in the UK used fitness apps in the course of 2017 while fitness trackers, such as fitness wristbands, were used by 20 percent of the consumers during the same period.

Which is the healthiest country in the world?

Spain was the healthiest country in the world in 2019. The Southern European country had a health grade of 92.75 out of 100, followed by Italy, Iceland and Japan. United Kingdom ranked number 19th.

Life expectancy around the world

Life expectancy can be termed as one of the key metrics for assessing general public’s health. Different factors come into play here, but one is, that people who are born in developed countries generally tend to have a higher life expectancy than their counterparts from the lesser developed nations. This may be due to various reasons, including better health facilities, proper diet and environment.

In 2019, both males and females born in China were expected to live the longest compared to any other country around the globe, with a life expectancy of 88 for women and 82 for men. San Marino, Japan and Liechtenstein were other countries with high life expectancies.

This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are:

• Family Expenditure Survey (FES) from 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 onwards

Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including:

• United Kingdom National Accounts (ONS Blue Book)
• HM Revenue and Customs (HMRC)
• Department for Transport (DfT)
• Department of Health (DH)
• Department for Education and Employment (DfEE)
• Department for Communities and Local Government (DCLG)

The Effects of Taxes and Benefits on Household Income (ETB) has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK. The estimates in this analysis are based mainly on data derived from the LCF Survey, which replaced the Family Expenditure Survey (FES) from 2001/02, and was known as the EFS until 2008. The LCF is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people’s homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design.

The LCF data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First the data is weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to Labour Force Survey (LFS) employment totals was also applied.

There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children).

In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB Quality and methodology information webpage and the Effects of taxes and benefits on household income technical report.

The Living Costs and Food Survey (LCF) is the source of the microdata on households from 2008-09 onwards. Previously, the Expenditure and Food Survey (EFS) was the data source. Derived variables are created using information from LCF and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government.

For further information, see the ONS Effects of taxes and benefits on household income webpage.

Variables available in the Secure Access version
The Secure Access version of the ETB datasets include additional variables not included in the standard End User Licence (EUL) versions (available under GN 33299). Extra variables include:

• CASENO (case number): all years
• CESAGE (age of chief economic supporter): 1991-2015
• CESEMPST (economic position of chief economic supporter): 1991-2015
• GGOR (Government Office Region): 2000-2015
• CES (chief economic supporter flag): 2001-2015

Prospective users of a Secure Access version of the ETB will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

The second edition (June 2021) includes data files for 2016/17, 2017/18 and 2018/19. The documentation has been updated accordingly.

Abstract copyright UK Data Service and data collection copyright owner. Carried out every four years, the European Quality of Life Survey (EQLS) examines both the objective circumstances of European citizens' lives and how they feel about those circumstances and their lives in general. It collects data on a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, life satisfaction, and perceived quality of society. By running the survey regularly, it has also become possible to track key trends in the quality of people's lives over time. Previous surveys have shown, for instance, that people are having greater difficulty making ends meet since the economic crisis began. In many countries, they also feel that there is now more tension between people from different ethnic groups. And across Europe, people now trust their governments less than they did before. However, people still continue to get the greatest satisfaction from their family life and personal relationships. Over the years, the EQLS has developed into a valuable set of indicators which complements traditional indicators of economic growth and living standard such as GDP or income. The EQLS indicators are more inclusive of environmental and social aspects of progress and therefore are easily integrated into the decision-making process and taken up by public debate at EU and national levels in the European Union. In each wave a sample of adult population has been selected randomly for a face to face interview. In view of the prospective European enlargements the geographical coverage of the survey has expanded over time from 28 countries in 2003 to 34 countries in 2011-12. Further information about the survey can be found on the European Foundation for the Improvement of Living and Working Conditions (Eurofound) EQLS web pages. Main Topics: The survey examines a range of issues, such as employment, income, education, housing, family, health, work-life balance, life satisfaction and perceived quality of society. Multi-stage stratified random sample See documentation for details Face-to-face interview 2007 AGE ATTITUDES Austria BASIC NEEDS Belgium Bulgaria CARE OF DEPENDANTS CHARITABLE ORGANIZA... CHIEF INCOME EARNERS CHILD CARE CHILDREN CHRONIC ILLNESS Croatia Cyprus Czech Republic DEBILITATIVE ILLNESS DEBTS DISADVANTAGED GROUPS DOMESTIC RESPONSIBI... Denmark ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL LEVELS EMOTIONAL STATES EMPLOYMENT ENGLISH LANGUAGE ETHNIC GROUPS EVERYDAY LIFE EXPECTATION EXPOSURE TO NOISE Estonia European Union Coun... FAMILY LIFE FATHER S PLACE OF B... FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FURNITURE Finland France GENDER GENERAL PRACTITIONERS GROUPS Germany October 1990 Greece HAPPINESS HEALTH HEALTH CONSULTATIONS HEALTH SERVICES HOBBIES HOME OWNERSHIP HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSEWORK HOUSING CONDITIONS HOUSING TENURE Hungary INCOME INTERGROUP CONFLICT INTERNET USE Ireland Italy JOB SATISFACTION JOB SECURITY LEISURE GOODS LIFE EXPECTANCY LIFE SATISFACTION LIFE STYLES LIVING CONDITIONS LOCAL COMMUNITY FAC... Latvia Lithuania Luxembourg MARITAL STATUS MENTAL HEALTH MIGRANTS MOTHER S PLACE OF B... MOTOR VEHICLES Macedonia Malta NEIGHBOURHOODS Netherlands Norway OCCUPATIONAL SAFETY OCCUPATIONAL STATUS OCCUPATIONS PARENTS PERSONAL CONTACT PLACE OF BIRTH POLITICAL PARTICIPA... POLLUTION POVERTY PUBLIC SERVICES Poland Portugal QUALITY OF LIFE RECREATIONAL FACILI... RELIGIOUS ATTENDANCE RELIGIOUS GROUPS ROOMS RURAL AREAS Romania SATISFACTION SOCIAL ATTITUDES SOCIAL CAPITAL SOCIAL DISADVANTAGE SOCIAL EXCLUSION SOCIAL INDICATORS SOCIAL LIFE SOCIAL SECURITY BEN... SOCIAL SUPPORT STANDARD OF LIVING STATE RETIREMENT PE... STATUS IN EMPLOYMENT STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS Slovakia Slovenia Social behaviour an... Social conditions a... Spain Sweden TIME TRUST TRUST IN GOVERNMENT Turkey URBAN AREAS United Kingdom VOLUNTARY WORK WAGES WATER PROPERTIES WORK ATTITUDE WORKING CONDITIONS