The economy was seen by 51 percent of people in the UK as one of the top three issues facing the country in March 2025. The ongoing cost of living crisis afflicting the UK, driven by high inflation, is still one of the main concerns of Britons. Health has generally been the second most important issue since early 2022, possibly due to NHS staffing problems, and increasing demand for health services, which have plunged the National Health Service into a deep crisis. From late 2022 onwards, immigration emerged as the third main concern for British people, just ahead of the environment for much of 2023 and as of the most recent month, the second most important issue for voters. Labour's popularity continues to sink in 2025 Despite winning the 2024 general election with a strong majority, the new Labour government has had its share of struggles since coming to power. Shortly after taking office, the approval rating for Labour stood at -2 percent, but this fell throughout the second half of 2024, and by January 2025 had sunk to a new low of -47 percent. Although this was still higher than the previous government's last approval rating of -56 percent, it is nevertheless a severe review from the electorate. Among several decisions from the government, arguably the least popular was the government withdrawing winter fuel payments. This state benefit, previously paid to all pensioners, is now only paid to those on low incomes, with millions of pensioners not receiving this payment in winter 2024. Sunak's pledges fail to prevent defeat in 2024 With an election on the horizon, and the Labour Party consistently ahead in the polls, addressing voter concerns directly was one of the best chances the Conservatives had of staying in power in 2023. At the start of that year, Rishi Sunak attempted to do this by setting out his five pledges for the next twelve months; halve inflation, grow the economy, reduce national debt, cut NHS waiting times, and stop small boats. A year later, Sunak had at best only partial success in these aims. Although the inflation rate fell, economic growth was weak and even declined in the last two quarters of 2023, although it did return to growth in early 2024. National debt was only expected to fall in the mid to late 2020s, while the trend of increasing NHS waiting times did not reverse. Small boat crossings were down from 2022, but still higher than in 2021 or 2020. .

Indicators from the Opinions and Lifestyle Survey (OPN) of what people report are the most important issues facing the UK. Uses longer data collection periods to allow estimates from various personal characteristics.

As of January 2025, the economy was seen as the most important issue facing the UK according to young voters (aged between 18 and 24). Compared with the overall population, housing and the environment are seen as more important issues than immigration, which was the joint-second most important issue for the general population.

For the year ending June 2024, approximately 1.2 million people migrated to the United Kingdom, while 479,000 people migrated from the UK, resulting in a net migration figure of 728,000. There have consistently been more people migrating to the United Kingdom than leaving it since 1993 when the net migration figure was negative 1,000. Although migration from the European Union has declined since the Brexit vote of 2016, migration from non-EU countries accelerated rapidly from 2021 onwards. In the year to June 2023, 968,000 people from non-EU countries migrated to the UK, compared with 129,000 from EU member states. Immigration and the next UK election Throughout 2023, immigration, along with the economy and healthcare, was consistently seen by UK voters as one of the top issues facing the country. Despite a pledge to deter irregular migration via small boats, and controversial plans to send asylum applicants to Rwanda while their claims are being processed, the current government is losing the trust of the public on this issue. As of February 2024, 20 percent of Britons thought the Labour Party would be the best party to handle immigration, compared with 16 percent who thought the Conservatives would handle it better. With the next UK election expected at some point in 2024, the Conservatives are battling to improve their public image on this and many other issues. Historical context of migration The first humans who arrived in the British Isles, were followed by acts of conquest and settlement from Romans, Anglo-Saxons, Danes, and Normans. In the early modern period, there were also significant waves of migration from people fleeing religious or political persecution, such as the French Huguenots. More recently, large numbers of people also left Britain. Between 1820 and 1957, for example, around 4.5 million people migrated from Britain to America. After World War Two, immigration from Britain's colonies and former colonies was encouraged to meet labor demands. A key group that migrated from the Caribbean between the late 1940s and early 1970s became known as the Windrush generation, named after one of the ships that brought the arrivals to Britain.

Background
The British Social Attitudes (BSA) survey series began in 1983. The series is designed to produce annual measures of attitudinal movements to complement large-scale government surveys that deal largely with facts and behaviour patterns, and the data on party political attitudes produced by opinion polls. One of the BSA's main purposes is to allow the monitoring of patterns of continuity and change, and the examination of the relative rates at which attitudes, in respect of a range of social issues, change over time. Some questions are asked regularly, others less often. Funding for BSA comes from a number of sources (including government departments, the Economic and Social Research Council and other research foundations), but the final responsibility for the coverage and wording of the annual questionnaires rests with NatCen Social Research (formerly Social and Community Planning Research). The BSA has been conducted every year since 1983, except in 1988 and 1992 when core funding was devoted to the British Election Study (BES).

Further information about the series and links to publications may be found on the NatCen Social Research British Social Attitudes webpage.

Emergency Care Module
The British Social Attitudes Survey: Emergency Care Module, 2018 was collected as part of a grant-funded project called Drivers of Demand for Emergency and Urgent Care (DEUCE). The project was funded by the National Institute for Health Research (NIHR) and the lead institution was the University of Sheffield. The project as a whole aimed to understand people’s help-seeking behaviour from their perspectives rather than health professionals’ perspectives, and from the perspective of an emergency and urgent care system. The BSA module was designed to identify factors affecting population tendency to use emergency services for minor or non-urgent problems, partly through the use of vignettes asking what actions people would take in relation to minor or non-urgent health problems.

This research aims to stimulate the nascent research agenda on the environmental sustainability of the ongoing mushrooming of international student mobility (ISM). The higher education (HE) system in the UK and elsewhere is increasingly predicated upon the hosting of international students. Whilst this drive towards internationalisation undoubtably has multiple benefits, little attention thus far has been paid to its potentially very considerable environmental impact. The drive for internationalisation within HE thus potentially sits at odds with ambitions and strategies to promote sustainability within the sector and beyond. Design/methodology/approach – In-depth interviews with 21 students and representatives of university international offices offer insights into how the environment features in the decisions that young people and HE institutions make with regards to partaking in and promoting education-related mobility and online survey. Findings – The results find that students take environmental considerations into account when undertaking education-related mobility, but these aspirations are often secondary to logistical issues concerning the financial cost and longer travel times associated with greener travel options. At the institutional scale, vociferously championed university sustainability agendas have yet to be reconciled with the financial imperative to recruit evermore international students.

Building on the achievements and key findings from the past eight years of CPC, the scientific programme during the transition funding period consists of a set of projects that consolidate and extend that research, providing an opportunity to follow-up on new avenues of enquiry suggested by our prior work and to response to advances in the field generated by CPC and elsewhere. The scientific agenda also lays the foundation for an anticipated bid for full Centre funding i.e. for CPC-III, retaining key research staff and, importantly the Administrative and KE Hub. The innovative research within CPC-II has, and will continue to, generate exciting and novel findings. Maximising the impact of these, both within the scientific community and wider economic and societal impact will therefore be a core activity during transition.

Our research will continue to be organised around the five thematic areas of: 1. Fertility and family change 2. Increasing longevity and the changing life course 3. New mobilities and migration 4. Understanding intergenerational relations & exchange 5. Integrated demographic estimation and forecasting

These thematic areas explicitly recognise the dynamic interaction of the individual components of population change both with each other and with economic and social processes. The first three themes reflect the three main components of population change: fertility, mortality and migration. Understanding how trends such as the ageing of the population, changes in family formation and dissolution and increased mobility (spatial, economic and social) are both shaped by and in turn shape international relations and flows of support is essential for assessing the role of the family beyond the household and for debates around intergenerational solidarity and justice. Finally, one of the most notable successes of CPC has been in the area of innovative methods and modelling, and we will continue to work at the cutting edge of developments in demographic modelling, collaborating closely with ONS and other national statistical agencies.

CPC will continue its contribution to three areas identified by the ESRC as of key importance: the design of academic research with a consideration for its policy implications and a high impact on the wellbeing of persons in society; the incorporation of a significant capacity-building element in the research programme with the training of emerging social scientists in the multi-disciplinary area of population change; and the exploitation of existing and newly-available sources of quantitative data, some of which are core ESRC investments. Continued engagement with our partners ONS and NRS and other users will ensure our research remains timely and relevant.

Legacy unique identifier: P00031

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

UPDATE February 2021: Two issues affecting the contextual information for indicator 1.5.i have been identified. Neither of these issues affected the indicator values and both have been corrected in the excel and CSV files for this indicator: Issue 1: The confidence intervals for the mental health mortality rate were originally calculated using Dobson’s method for counts where less than 389 deaths were observed. Although this is a valid method, the assured methodology for this indicator does not include this adjustment. The indicator specification has also been updated to remove reference to Dobson's method. Issue 2: There were some minor errors in the England level mental health population due to the inclusion of some duplicates. --------------------------------------------------------------------------------------------------------- This indicator is a measure of the extent to which adults with a serious mental illness (SMI) die younger than adults without a serious mental illness (nSMI). To measure premature mortality in adults diagnosed with serious mental illness (SMI). This indicator was put on hold in November 2016. The introduction of the new mental health services data set (MHSDS) meant that a new indicator methodology needed to be developed. The indicator was republished with new data in December 2020. The republished data uses a different methodology to the data published in 2016 and prior to this. As such, comparisons should not be made between the two. Legacy unique identifier: P01740

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

For the second edition of the study, the depositor supplied a re-weighted version of the data file. The re-weighting has been done to bring LFS data in line with the population estimates from the 2001 Census.
Main Topics:
The five-quarter longitudinal datasets include a subset of the most commonly used variables from the Quarterly Labour Force Survey (QLFS),...

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of diabetes mellitus in persons (aged 17+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to diabetes mellitus in persons (aged 17+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 17+) with diabetes mellitus was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with diabetes mellitus was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have diabetes mellitusB) the NUMBER of people within that MSOA who are estimated to have diabetes mellitusAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have diabetes mellitus, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from diabetes mellitus, and where those people make up a large percentage of the population, indicating there is a real issue with diabetes mellitus within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of diabetes mellitus, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of diabetes mellitus.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

A 2024 survey found that over half of individuals in Great Britain indicated that access to treatment and long waiting times were the biggest problem facing the national healthcare system. Access to treatment and/or long waiting times were also considered to be pressing issues. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in Great Britain in 2024.

The overall aim was to conduct a wide-ranging survey of Catholic adults living in Britain, which asked about many aspects of their lives, including their socio-demographic circumstances, the nature and extent of their religious engagement (belonging, behaviour and beliefs), their views of the Catholic Church’s leadership, institutions and teachings, and their social and political attitudes. The survey was conducted online by Savanta ComRes, in October-November 2019. This is a cross-sectional dataset, based on interviews with 1,823 self-identifying Catholics adults in Britain (aged 18 and over).

In recent decades, the religious profile of British society has changed significantly, with a marked increase in 'religious nones', declining proportions identifying as Anglican or with a particular Non-Conformist tradition, an increase in non-denominational Christians, and the spread of non-Christian faiths. Within this wider context, Roman Catholics have remained broadly stable as a proportion of the adult population and represent the second largest Christian denomination in Britain, after Anglicans. However, there have been significant internal and external developments which have affected the institutional church and wider Roman Catholic community in Britain, and which could have shaped how Catholics' think about and engage with their faith and how it impacts their daily lives. Recent years have seen demographic change through significant inflows of Catholic migrants coming from Eastern Europe, the papal visit of Pope Benedict XVI to Britain in autumn 2010 (the first since 1982), Pope Francis's pontificate from 2013 onwards, Catholic leaders' political interventions against 'aggressive secularism' and in other policy debates, and internal crises and debates impacting on the perceived authority of the Catholic Church. The last major academic investigation of the Catholic community (and only in England and Wales) was undertaken in the late 1970s (Hornsby-Smith and Lee 1979; Hornsby-Smith 1987, 1991). It found that the 'distinctive subculture' of the Catholic community in the post-war period was evolving and dissolving in complex ways due to processes of social change and developments within the wider faith, such as the Second Vatican Council (Hornsby-Smith 1987, 1991). It also demonstrated growing internal heterogeneity in Catholics' religious beliefs, practices and social attitudes (Hornsby-Smith 1987, 1991). However, while there has been some recent scholarship on particular topics relating to Catholics and Catholicism in Britain, using both general social surveys and limited one-off denomination-specific opinion polls (Clements 2014a, 2014b; 2016; Bullivant 2016a, 2016b), there has been no systematic academic inquiry into the Roman Catholic population in Britain. In comparison, an academic-led survey series has profiled the Catholic population in the United States on five occasions between 1987 and 2011, with other large-scale surveys carried out in recent years by organisations such as the Pew Research Center. Most existing research into the waning of religious belief, practice, and affiliation in Britain has focused either on the very large, macro level or on the very small, micro level. While both are important and necessary, largely missing has been sustained sociological attention on how secularising trends have affected - and are being mediated within - individual religious communities. This project would undertake such a task for Roman Catholics in Britain, by conducting a large-scale, thematically wide-ranging and nationally representative survey. It would provide a detailed study of personal faith, social attitudes and political engagement within a significant religious minority with distinctive historical roots and in which 'tribal' feelings of belonging have been strong. The core topics would consist of personal faith, religiosity and associational involvement in parish life; attitudes towards leadership and governance within the institutional church; attitudes on social and moral issues; and political attitudes and engagement. It would be thematically wide-ranging and analytically rich, providing a detailed portrait of contemporary social, religious and attitudinal heterogeneity amongst Catholics. By undertaking this large-scale and wide-ranging survey, an important and distinctive contribution would be made to the sociology of religion in Britain in general and to the study of its Catholic population in particular.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of asthma (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to asthma (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with asthma was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with asthma was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with asthma, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have asthmaB) the NUMBER of people within that MSOA who are estimated to have asthmaAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have asthma, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from asthma, and where those people make up a large percentage of the population, indicating there is a real issue with asthma within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of asthma, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of asthma.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Abstract copyright UK Data Service and data collection copyright owner.

This study is part of the Growing Older: Extending Quality of Life research programme funded by the ESRC. The research for this study has sought to generate new insights into the experience of inequality in later life. It has examined aspects relating to the quality of life of older people living in areas of intense social deprivation, and has developed understanding of factors contributing to social inclusion and exclusion in later life. The aims and objectives, which have not changed since the original proposal, have been to:
Contribute to knowledge about the circumstances of older people living in areas of concentrated poverty, examining the specific characteristics of social exclusion as it affects different groups within the older population;
Explore ways in which older people manage their daily lives in deprived urban environments. The research has addressed how individuals handle the multiple risks associated with living in such localities, along with the survival strategies and support networks that develop;
Develop research methods relevant to studying quality of life issues for older people living in areas of concentrated poverty. The research seeks to contribute to the development of research techniques designed to measure poverty, social deprivation and social exclusion;
Identify forms of deprivation which have yet to be addressed fully in gerontological and social policy research;
Examine the dynamics of social exclusion at a neighbourhood-level, and the implications of this for social policy.
Main Topics:

The dataset arises from a survey of 600 people aged 60 and over living in deprived electoral wards in Liverpool, Manchester and the London Borough of Newham. The survey collected, first, socio-demographic data about the circumstances of older people living in deprived areas and second, information relating to themes of social exclusion and quality of life, including: characteristics of poverty and its impact on daily life; networks (including support networks) of older people; patterns of support within the older population and with other social groups; quality of life of older people (using standardised measures of psychological well-being); characteristics of social participation within deprived localities; experiences of the urban environment in relation to services, crime, transportation and related issues.
Standard Measures
Quality of life: Philadelphia Geriatric Center Morale Scale (Lawton)
Satisfaction with Life Scale (Diener)
Loneliness: De Jong Gierveld Loneliness Scale

Abstract copyright UK Data Service and data collection copyright owner.

This project arose out of research conducted during the 1990s (by O'Reilly and others) which explored the trends, motivations, mobility patterns, identity and way of life of British migrant communities in the Costa del Sol. Reviewing the data collected during that period of fieldwork raised key issues of interest for theories of globalisation, migration and tourism, and for social policy related to integration, which are becoming increasingly relevant in the current context. Spain's tourist towns have recently experienced huge population growth (especially at certain times of the year), increasing European immigration, and the presence of a fluid, multinational migrant population, especially since free movement of individuals was introduced by the Maastricht Treaty in 1992. The effects at the local level have been augmented by the passing of time and continued property development. Some towns are growing at dramatic rates and now inland areas are being affected as people look for cheaper property and land away from the built-up coastal zones. The constant presence of what are locally termed 'residential tourists' is becoming a feature of everyday life for people in towns like Fuengirola, Mijas and Alhaurin El Grande, in Andalusia. The growing and important body of research exploring the phenomenon of International Retirement Migration (IRM) in Europe has failed to specifically explore the extent and nature of social integration of these and younger European migrants in Spanish society.

The objectives of this study were to: systematically explore, using surveys, the extent of social integration of European (especially British) migrants in Spain; explore in depth, using qualitative interviews, the nature of social integration; and gain an understanding, using case studies, of the role of town councils in aiding or impeding integration.

The data collection includes:

• self-completion survey. Questionnaires were distributed via newsagents, town hall foreign residents departments, social clubs, and using snowball techniques from personal contacts
• 53 face to face in-depth interviews (individuals and couples)
• 6 focus group interviews
• 48 short student essays from 11-13 year old school students and transcribed into Word documents

Main Topics:

Main topics include: social and economic integration; political integration; reasons for migration; language difficulties; schooling; work situation; health and fitness; official registration.