In 2023, almost nine million people lived in Greater London, making it the most populated ceremonial county in England. The West Midlands Metropolitan County, which contains the large city of Birmingham, was the second-largest county at 2.98 million inhabitants, followed by Greater Manchester and then West Yorkshire with populations of 2.95 million and 2.4 million, respectively. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with around 1.89 million people. A patchwork of regions England is just one of the four countries that compose the United Kingdom of Great Britain and Northern Ireland, with England, Scotland and Wales making up Great Britain. England is therefore not to be confused with Great Britain or the United Kingdom as a whole. Within England, the next subdivisions are the nine regions of England, containing various smaller units such as unitary authorities, metropolitan counties and non-metropolitan districts. The counties in this statistic, however, are based on the ceremonial counties of England as defined by the Lieutenancies Act of 1997. Regions of Scotland, Wales, and Northern Ireland Like England, the other countries of the United Kingdom have their own regional subdivisions, although with some different terminology. Scotland’s subdivisions are council areas, while Wales has unitary authorities, and Northern Ireland has local government districts. As of 2022, the most-populated Scottish council area was Glasgow City, with over 622,000 inhabitants. In Wales, Cardiff had the largest population among its unitary authorities, and in Northern Ireland, Belfast was the local government area with the most people living there.

As of 2023, the population density in London was by far the highest number of people per square km in the UK, at 5,690. Of the other regions and countries which constitute the United Kingdom, North West England was the next most densely populated area at 533 people per square kilometer. Scotland, by contrast, is the most sparsely populated country or region in the United Kingdom, with only 70 people per square kilometer. UK population over 67 million According to the official mid-year population estimate, the population of the United Kingdom was just almost 67.6 million in 2022. Most of the population lived in England, where an estimated 57.1 million people resided, followed by Scotland at 5.44 million, Wales at 3.13 million and finally Northern Ireland at just over 1.9 million. Within England, the South East was the region with the highest population at almost 9.38 million, followed by the London region at around 8.8 million. In terms of urban areas, Greater London is the largest city in the United Kingdom, followed by Greater Manchester and Birmingham in the North West and West Midlands regions of England. London calling London's huge size in relation to other UK cities is also reflected by its economic performance. In 2021, London's GDP was approximately 494 billion British pounds, almost a quarter of UK GDP overall. In terms of GDP per capita, Londoners had a GDP per head of 56,431 pounds, compared with an average of 33,224 for the country as a whole. Productivity, expressed as by output per hour worked, was also far higher in London than the rest of the country. In 2021, London was around 33.2 percent more productive than the rest of the country, with South East England the only other region where productivity was higher than the national average.

The population of the United Kingdom in 2023 was estimated to be approximately 68.3 million in 2023, with almost 9.48 million people living in South East England. London had the next highest population, at over 8.9 million people, followed by the North West England at 7.6 million. With the UK's population generally concentrated in England, most English regions have larger populations than the constituent countries of Scotland, Wales, and Northern Ireland, which had populations of 5.5 million, 3.16 million, and 1.92 million respectively. English counties and cities The United Kingdom is a patchwork of various regional units, within England the largest of these are the regions shown here, which show how London, along with the rest of South East England had around 18 million people living there in this year. The next significant regional units in England are the 47 metropolitan and ceremonial counties. After London, the metropolitan counties of the West Midlands, Greater Manchester, and West Yorkshire were the biggest of these counties, due to covering the large urban areas of Birmingham, Manchester, and Leeds respectively. Regional divisions in Scotland, Wales and Northern Ireland The smaller countries that comprise the United Kingdom each have different local subdivisions. Within Scotland these are called council areas whereas in Wales the main regional units are called unitary authorities. Scotland's largest Council Area by population is that of Glasgow City at over 622,000, while in Wales, it was the Cardiff Unitary Authority at around 372,000. Northern Ireland, on the other hand, has eleven local government districts, the largest of which is Belfast with a population of around 348,000.

Mid-year (30 June) population density of Lower layer Super Output Areas (LSOAs) in England and Wales based on estimates of the usual resident population.

United Kingdom UK: Population Density: People per Square Km data was reported at 272.898 Person/sq km in 2017. This records an increase from the previous number of 271.134 Person/sq km for 2016. United Kingdom UK: Population Density: People per Square Km data is updated yearly, averaging 235.922 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 272.898 Person/sq km in 2017 and a record low of 218.245 Person/sq km in 1961. United Kingdom UK: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

SUMMARYPopulation statistics at the Middle Layer Super Output Area (MSOA) level.Statistics are provided for local populations as a whole, irrespective of gender. The following statistics are provided:Age, split into discrete age bands (Office for National Statistics data, 30th June 2019 population estimates)Ethnicity (Census data, 2011)*Religion (Census data, 2011)**To increase the clarity of the data visualisations, the most frequently reported ethnicities and religions are included, with the less-frequently reported ethnicities and religions combined into suitable groups, respectively. Raw data for each MSOA can be accessed here.Data relating to gender was not included as, at present, only data relating to ‘males’ and ‘females’ are available, which is not inclusive of all genders. Following the 2021 census, data that more accurately reflects all genders are likely to be available.DATA SOURCESPopulation 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.Ethnicity statistics: 2011 Census: QS211EW Ethnic group (detailed), Middle Layer Super Output Areas (MSOAs) and Lower Layer Super Output Areas (LSOAs) in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2013.Religion statistics: 2011 Census: QS210EW Religion (detailed), Middle Layer Super Output Areas (MSOAs) and Lower Layer Super Output Areas (LSOAs) in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2013. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. COPYRIGHT NOTICE© Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2013; © Crown Copyright 2020; © Crown copyright and database right 2021. Data edited for publishing by Ribble Rivers Trust.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The data collection contains population projections for UK ethnic groups and all local area by age (single year of age up to 100+) and sex. Included in the data set are also input data to the cohort component model that was used to project populations into the future-fertility rates, mortality rates, international migration flows and internal migration probabilities. Also included in data set are output data: Number of deaths, births and internal migrants. All data included are for the years 2011 to 2061. We have produced two ethnic population projections for UK local authorities, based on information on 2011 Census ethnic populations and 2010-2011-2012 ethnic components. Both projections align fertility and mortality assumptions to ONS assumptions. Where they differ is in the migration assumptions. In LEEDS L1 we employ internal migration rates for 2001 to 2011, including periods of boom and bust. We use a new assumption about international migration anticipating that the UK may leave the EU (BREXIT). In LEEDS L2 we use average internal migration rates for the 5 year period 2006-11 and the official international migration flow assumptions with a long term balance of +185 thousand per annum.

This project aims to understand and to forecast the ethnic transition in the United Kingdom's population at national and sub-national levels. The ethnic transition is the change in population composition from one dominated by the White British to much greater diversity. In the decade 2001-2011 the UK population grew strongly as a result of high immigration, increased fertility and reduced mortality. Both the Office for National Statistics (ONS) and Leeds University estimated the growth or decline in the sixteen ethnic groups making up the UK's population in 2001. The 2011 Census results revealed that both teams had over-estimated the growth of the White British population and under-estimated the growth of the ethnic minority populations. The wide variation between our local authority projected populations in 2011 and the Census suggested inaccurate forecasting of internal migration. We propose to develop, working closely with ONS as our first external partner, fresh estimates of mid-year ethnic populations and their components of change using new data on the later years of the decade and new methods to ensure the estimates agree in 2011 with the Census. This will involve using population accounting theory and an adjustment technique known as iterative proportional fitting to generate a fully consistent set of ethnic population estimates between 2001 and 2011.

We will study, at national and local scales, the development of demographic rates for ethnic group populations (fertility, mortality, internal migration and international migration). The ten year time series of component summary indicators and age-specific rates will provide a basis for modelling future assumptions for projections. We will, in our main projection, align the assumptions to the ONS 2012-based principal projection. The national assumptions will need conversion to ethnic groups and to local scale. The ten years of revised ethnic-specific component rates will enable us to study the relationships between national and local demographic trends. In addition, we will analyse a consistent time series of local authority internal migration. We cannot be sure, at this stage, how the national-local relationships for each ethnic group will be modelled but we will be able to test our models using the time series.

Of course, all future projections of the population are uncertain. We will therefore work to measure the uncertainty of component rates. The error distributions can be used to construct probability distributions of future populations via stochastic projections so that we can define confidence intervals around our projections. Users of projections are always interested in the impact of the component assumptions on future populations. We will run a set of reference projections to estimate the magnitude and direction of impact of international migrations assumptions (net effect of immigration less emigration), of internal migration assumptions (the net effect of in-migration less out-migration), of fertility assumptions compared with replacement level, of mortality assumptions compared with no change and finally the effect of the initial age distribution (i.e. demographic potential).

The outputs from the project will be a set of technical reports on each aspect of the research, journal papers submitted for peer review and a database of projection inputs and outputs available to users via the web. The demographic inputs will be subject to quality assurance by Edge Analytics, our second external partner. They will also help in disseminating these inputs to local government users who want to use them in their own ethnic projections. In sum, the project will show how a wide range of secondary data sources can be used in theoretically refined demographic...

Population estimates provide statistics on the size and age structure of the population in the UK at country, region, county, and local authority level. They are the official source of estimated population size in between censuses and inform a wide range of official statistics.Persons included are all those people who usually live in an area, regardless of nationality. Arriving international migrants are included in the usually resident population if they remain in the UK for at least a year and emigrants are excluded if they remain outside the UK for at least a year. Students and school boarders are included at their term time address.

Mid-year (30 June) estimates of the usual resident population for Middle layer Super Output Areas (MSOAs) in England and Wales by single year of age and sex.

Population estimates provide statistics on the size and age structure of the population in the UK at country, region, county, and local authority level. They are the official source of estimated population size in between censuses and inform a wide range of official statistics.Persons included are all those people who usually live in an area, regardless of nationality. Arriving international migrants are included in the usually resident population if they remain in the UK for at least a year and emigrants are excluded if they remain outside the UK for at least a year. Students and school boarders are included at their term time address.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
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. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. 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

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

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...

The median age of the population in London was 35.9 years in 2023, the lowest median age among regions of the United Kingdom. By contrast, South West England had a median age of 43.9, the highest in the UK.

This file provides a rural-urban view of 2001 Output Areas (OA) in England and Wales. The ZIP file contains the Rural Urban Classification in XLSX and CSV format and includes a user guide. The files were originally from the NeSS website. Click on the Download button in the top right corner to download the file.The classification of rural and urban areas is the outcome of a project co-sponsored by:Office for National Statistics (ONS);Department for Environment, Food and Rural Affairs (Defra);Office of the Deputy Prime Minister (now Communities and Local Government);Countryside Agency (CA); andNational Assembly for Wales (NAW).The classification was developed in 2004 by a consortium co-ordinated by Prof. John Shepherd from Birkbeck College. The technical work was lead by Peter Bibby of University of Sheffield and the project also involved the University of Glamorgan and Geowise. The rural and urban classification of Output Areas (this dataset), Super Output Areas and Wards has been provided to enable datasets to be analysed according to the classification. This provides a powerful tool for the development and monitoring of rural and urban policies.Please Note: Output Areas do not have all the same codes as the SOA and Ward level Datasets. For SOAs and Wards the classifications for ‘Villages, Hamlets and Isolated Dwellings’ have been combined.The classification enables each of the 175,434 Output Areas in England and Wales to be classified on the basis of context i.e. whether the surrounding area of a given Output Area is sparsely populated or less sparsely populated. Secondly, the classification enables Output Areas to be distinguished on a morphological basis - as predominantly urban or predominantly town and fringe, predominantly village or predominantly dispersed (which includes Hamlets and Isolated Dwellings). The key for these are shown below. The town and fringe, village, hamlet and isolated dwellings classifications are taken as being rural.2005 Rural and Urban morphology indicator:1 - denotes predominantly urban >10k2 - denotes predominantly town and fringe3 - denotes predominantly village4 - denotes predominantly dispersed (hamlet and isolated dwellings)2005 Rural and Urban context indicator:0 denotes less sparsely populated areas1 denotes sparsely populated areas

A series of flow based classifications of commuting for England and Wales based on MSOA origin-destination data from the 2011 Census. It consists of 9 super-groups and 40 sub-groups. The evidence can be used to target funding for an 'into-work-scheme' to help the most disconnected community. The toolkit allows the policymaker to explore levels of commuting and compare the level of connectivity of each neighbourhood to major employment centres. The underlying rationale for the research is that the toolkit will help deliver efficiencies in public and private sector investment. This is crucial at a time when the government is promoting the need for smarter economic growth but doing so in a challenging context in which public sector resources are scarce and the private sector is risk averse.

Numerous research studies use commuting data, collected through the Census of Population, to understand social, economic and environmental challenges in the UK. This commuting data has been used to understand patterns; answer questions regarding the relationship between housing and labour markets; and to see if travel behaviour is becoming more or less sustainable over time. However, there is lots of untapped potential for such data to be used to evaluate transport policy and investment decisions so resources are more effectively and efficiently targeted to places of need. In applied public policy a major shortcoming has been a lack of use of this data to support investment in transport which has major implications for economic growth. If transport investments are inefficiently targeted, this restricts the capacity of places to grow economies to their full potential. This wastes their resources by over investing in transport capacity in areas where it is not needed. Equally, it has long been argued that efficient investment in transport is crucial if labour market exclusion, particularly the case of deprived communities, is to be tackled. The aim of the research is to inform community transportation policy and investment and the socio-spatial dimensions of travel to work flows over time (2001-2011). Our research develops a toolkit to help decision-makers better target investment in transport capacity and infrastructure. The toolkit includes a series of new classifications of commuting flows from the 2001 and 2011 Censuses. It will include a classification of newly developed official Workplace Zones for England to complement official residential population-based classifications alongside various population, deprivation, investment and infrastructure data. The toolkit will bring these classifications and datasets together online through various mapping and analysis tools to understand the dynamics of commuting between different types of residential and workplace locations over time and combine these datasets and analyses with locally-specific transport investment data. The methodology developed will be applied to England as a whole but we will use the Manchester as a test-case for our analysis and for development of the toolkit. The use of open source approaches to build the toolkit means that other locations will have the framework to develop their own toolkit. The flow and area-based (Workplace Zones) classifications for England will complement official ONS residential-based output area classification and existing indices of deprivation. This will be mapped in relation to key transport investments made in Manchester, using local administrative data and overlay these with the results of commuting analysis to support decision-making regarding future targeted public transport infrastructure investment. The toolkit will be interactive so users can pose policy questions to explore commuting relationships between different places. The strength of this approach is that it will enable policy and decision-makers to test various scenarios for future transport investment depending on problems they have posed. In a hypothetical situation, a policymaker in might ask the question of whether a specific deprived community in their city is more or less connected into a major employment centre than another equally deprived community.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. 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)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (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.For each of the above illnesses, the percentage of each MSOA’s population with that illness 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 area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness 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 each illness, within the relevant age range.For each illness, 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 that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn 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 predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. 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).3. 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.4. 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 obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. 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 outliersDOWNLOADING 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.

This release provides insights into self-reported health in England and Wales in 2021, broken down by age and sex. Key findings are presented at country, regional and local authority level. Additional analyses compare general health to the 2011 Census and examines the relationship between deprivation and health at a national decile (England) or quintile (Wales) level can be found here.

In 2021 and 2011, people were asked “How is your health in general?”. The response options were:

• Very good
• Good
• Fair
• Bad
• Very bad

Age specific percentage

Age-specific percentages are estimates of disability prevalence in each age group, and are used to allow comparisons between specified age groups. Further information is in the glossary.

Age-standardised percentage

Age-standardised percentages are estimates of disability prevalence in the population, across all age groups. They allow for comparison between populations over time and across geographies, as they account for differences in the population size and age structure. Further information is in the glossary.

Details on usage of Age-standardised percentage can be found here

Count

The count is the number of usual residents by general health status from very good to very bad, sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included..

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

Index of Multiple Deprivation and Welsh Index of Multiple Deprivation

National deciles and quintiles of area deprivation are created through ranking small geographical populations known as Lower layer Super Output Areas (LSOAs), based on their deprivation score from most to least deprived. They are then grouped into 10 (deciles) or 5 (quintiles) divisions based on the subsequent ranking. We have used the 2019 IMD and WIMD because this is the most up-to-date version at the time of publishing.

Population

The population is the number of usual residents of each sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included.

Usual resident

For Census 2021, a usual resident of the UK is anyone who, on census day, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

This study uses the ARDL model and semi-macro data to conduct regression analysis on the relationship between immigrant share and unemployment rate and draws the following conclusions: First, overall, the increase of immigration will decrease the unemployment rate in the U.K. in the short-term; however, the long-term effect may be zero. The effect of immigration on the unemployment rate may vary subtly depending on the local economic development or population density. Specifically, in less economically developed or sparsely populated areas, an increase in the immigrant share may improve employment in the long term. Conversely, in regions with higher population density or RGDP, although immigration will improve employment in the short term, the long-term effects on employment are likely to be negative. The uploaded zip file includes the raw data folder as well as the data files for analysis. After executing the latter CSV file named post_2021_7 into EViews 10, the overall regression analysis of immigrants on the unemployment rates can be obtained by following the steps of the panel ARDL approach. The results of the analysis of the impact of immigrants on the unemployment rates in areas with lower RGDP, the impact of immigrants on the unemployment rates in areas with higher RGDP, the impact of immigrants on the unemployment rates in areas with low population density and the impact of immigrants on the unemployment rates in areas with high population density can be obtained by using the files named post_2021_7_red_rgdp_1_2, post_2021_7_green_rgdp_1_2, post_2021_7_red_pop_1_2 and post_2021_7_green_pop_1_2, separately.

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

Dataset population: Workday population aged 16 to 74

Daytime/workday population

England and Wales (Workday Population)

Workday population is where the usually resident population is re-distributed to their places of work, while those not in work are recorded at their usual residence.

The workday population of an area is defined as all usual residents aged 16 and above who are in employment and whose workplace is in the area, and all other usual residents of any age who are not in employment but are resident in the area. People who work mainly at or from home or do not have a fixed place of work are included in their area of their usual residence. The following population groups are excluded from the workday population of an area:

• Those living in England and Wales but working in Scotland, Northern Ireland, outside the UK or on offshore installations,
• Those with a place of work in England and Wales but who are not usually resident in England and Wales, and
• Short-term residents

England and Wales (Workplace Population)

Workplace population is where the usually resident population is re-distributed to their main place of work, but those not working are excluded.

Hours worked

The number of hours that a person aged 16 and over, in employment in the week before the census, worked in their main job. This includes paid and unpaid overtime.

Full-time working is defined as working 31 hours or more per week, and Part-time working is defined as working 30 hours or less per week.