The minimum data set (MDS) collection for health visiting (HV) provides the number of full time equivalent (FTE) health visitors employed by all NHS organisations, Local Authorities and Social Enterprises. It collects information from Area Teams (ATs) that employ health visitors but do not use the Electronic Staff Record (ESR), such as local authorities and some social enterprises. Those NHS organisations using ESR have their HV numbers fed directly into the collection.

The MDS collection for HV differs from the monthly workforce statistics published by the Health and Social Care Information Centre as it is solely focused on health visitors and includes those who are employed by non-NHS organisations and those organisations that do not use ESR over and above those NHS organisations included in the standard monthly workforce statistics.

Context

This data was scraped from the NHS jobs website. The dataset will be valuable to those who seek to understand the the job market of doctors for the NHS within the UK.

Content

This data only includes jobs that were deemed as being related to doctors from March 2019 to October 2019. Fields relating to a json file derive from a json file embedded in the HTML code. These fields are typically easier to work with as they contain less user inputted data.

Acknowledgements

The NHS.

Inspiration

1. What variables constitute to salary?
2. Can salary be predicted?
3. What important bits of information can be extracted from the job descriptions?

Context

Mental Health Services Monthly Statistics

This publication provides the most timely picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England. These are experimental statistics which are undergoing development and evaluation. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is made available later in our Mental Health Bulletin: Annual Report publication series.

• COVID-19 and the production of statistics

Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being referred to hospital and more appointments being carried out via phone/telemedicine/email. Therefore, data should be interpreted with care over the COVID-19 period.

Content

Time period covered Feb 1, 2020 - April 31, 2020

Area covered England

Acknowledgements

reference: Mental Health Services Monthly Statistics

Author: Community and Mental Health Team, NHS Digital
Responsible Statistician: Tom Poupart, Principal Information Analyst
Public Enquiries: Telephone: 0300 303 5678
Email: enquiries@nhsdigital.nhs.uk
Press enquiries should be made to: Media Relations Manager: Telephone: 0300 303 3888

Published by NHS Digital part of the Government Statistical Service 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.

You may re-use this document/publication (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence v3.0.
To view this licence visit To view this licence visit www.nationalarchives.gov.uk/doc/open-government-licence www.nationalarchives.gov.uk/doc/open-government-licence or write to the Information Policy Team, The National Archives, or write to the Information Policy Team, The National Archives, Kew, Richmond, Surrey, TW9 4DU Kew, Richmond, Surrey, TW9 4DU; or email: psi@nationalarchives.gsi.gov.uk or email: psi@nationalarchives.gsi.gov.uk

Cover by-

Inspiration

This dataset is to solve the challenge- UNCOVER COVID-19 Challenge, United Network for COVID Data Exploration and Research. This data is scraped in hopes of solving the task - Mental health impact and support services.

Task Details Can we predict changes in demand for mental health services and how can we ensure access? (by region, social/economic/demographic factors, etc). Are there signs of shifts in mental health challenges across demographies, whether improvements or declines, as a result of COVID-19 and the various measures implement to contain the pandemic?

Legacy unique identifier: P01748

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for November 2017. The CSDS is a patient-level dataset providing information relating to publicly funded community services for children, young people and adults. These services can include district nursing services, school nursing services, health visiting services and occupational therapy services, among others. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young People's Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available from https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/children-and-young-people-s-health-services-data-set References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use this form to provide us with any feedback or suggestions for improving the report. Update 6 April 2018: Please note since the removal of the age restriction to include adult data in CSDS, some of our Data Quality measures may not take into account items intended for children only. We are currently reviewing these measures and will look to reflect this in future reports.

Legacy unique identifier: P01752

Out of Area Mental Health Placements

Data from NHS & Independent Providers in England

By data.world's Admin [source]

About this dataset

This dataset provides a comprehensive look into the Out of Area Placements (OAPs) happening in the mental health services in England. It gives insight on placements from both NHS and independent providers, giving an overall picture of how these placements are happening across the country.

By taking a closer look at this report we can gain understanding into what is going on with OAPs around us – like which questions are being asked, breakdowns of how it’s divided and number to back it up. With this data we can better understand issues that affect our community and do our part to help support those in need

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How to use the dataset

This dataset provides information on out of area placements in mental health services in England from both NHS and independent providers. The dataset contains data related to the number placements, as well as breakdowns by region and provider. With this data you can explore the trends for out of area placements in your region and compare those trends with national level figures.

This guide will show you how to get started exploring this dataset.

Step 1: Understand The Data Set Structure

The first step for getting started is to get a good understanding of the structure of the dataset itself in order to better understand what types of questions we can ask our data with. This dataset has several columns which have been listed below:

• Publication Type : This column provides information on what type of report is being referenced such as statistical bulletin or key facts & figures etc

• Publication Period: This column represents a period within a year moment which periods are expressed by either month, quarter or financial year etc..

• Publication Date: This column informs us when the publication was made available online expressed as a date format e.g 2018-04-02)

• Question : Here we will find measurements such as people waiting an average or median length times such that they answer certain question asked by officials.

• Breakdown1, BreakDown1Code, ‘Breakdown1Description’ : These columns provide extra context into specific highlights from results in further detail eg Breakdowns include areas like Age Group ,Nationality (for immigration statistics) gender for population statistics etc... where code values may appear something like “OAP_AGE_All” and descriptions appear like “Waiting Times All Ages respectively .

• BreakDown2, BreakDown2Code, 'Breakdown2Description':These are data attributes similar top BreakDown 1 but at even more granular level eg Doctor Specialty/Department, Treatment Type, Indicators (for regional/local analysis), Countries ..etc . It's important not note here that breakdown 2 has deeper break down against Breakdown 1 depending further detail asked while investigating deeper under specified parameters /results .Eg You might want drill down ages into age groups 0–4, 5–14 ,15-29....etc excluding 65+ corresponding breakdown codes might be OAP_AGE_0

Research Ideas

• Creating insight into regional differences in mental health out of area placements in order to identify if more funding is needed and implement programs to address the predisposing risk factors for those regions with higher out of area placement rates.
• Comparing the amount of expenditure allocated on out of area placements between different areas and provinces, so that extra funding may be given to areas which need it more.
• Examining the correlation between changes in funding or policy and its effects on out of area placements at both a national and local level, in order to assess whether certain policies are successful or not at curbing them such as introducing preventative measures before placement outside an individual's region is necessary

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a...

With NHS database selections covering CEO's, Directors and Management contacts, responsive marketing lists can be purchased by job role, seniority level, size and region.

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. The percentage point difference between the rate of employment in the general population of working age (16-64) and the rate of employment amongst adults of working age with a long-term condition. This indicator measures the extent to which people with long-term conditions are able to live as normal a life as possible by looking at their levels of employment. Legacy unique identifier: P01748

With NHS management database selections covering CEO's, Directors and Management level decision makers, responsive contact data can be purchased by job role, seniority level, size and region.

Psychology Services - All staff survey: national Oracle database of NHSScotland psychology services workforce. Statistical information to describe the clinical workforce employed in NHSScotland Psychology services. Data includes NHS Board, professional group, target age of patients treated, area of work, tier of operation, band, gender and age. As from May 2010 these statistics can be designated as National Statistics products. This publication will be released quarterly from June 2011. Source agency: ISD Scotland (part of NHS National Services Scotland) Designation: National Statistics Language: English Alternative title: Workforce Planning for Psychology Services in NHSScotland

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.

This synthetic dataset includes 16,276 patients admitted for drug overdose from 2016 to 2022, featuring comprehensive patient demographics, comorbidities coded by ICD-10 and SNOMED-CT, and detailed admission data from the index event onward. Information on clinical outcomes, primary diagnoses, psychiatric referrals, and all treatments (e.g., fluids, blood products, procedures) is included.

The dataset was generated using the SDV package's HMA1 synthesizer. The real data was pre-processed, with metadata defining schema, primary/foreign keys, and inter-table relationships, guiding the synthesizer in learning data structure and dependencies. This approach produced synthetic data that mirrors the original’s statistical properties, supporting privacy-preserving analysis and model training.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Study: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1997-2023: Secure Access includes data files from the NHS Digital HES database for those cohort members who provided consent to health data linkage in the Age 50 sweep. The HES database contains information about all hospital admissions in England. The following linked HES data are available:

1) Accident and Emergency (A&E)
The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2020 (inclusive). It includes major A&E departments, single speciality A&E departments, minor injury units and walk-in centres in England.

2) Admitted Patient Care (APC)
The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2023 (inclusive).

3) Critical Care (CC)
The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2023 (inclusive).

4) Out Patient (OP)
The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2023 (inclusive).

5) Emergency Care Dataset (ECDS)
The ECDS lists the emergency care appointments between 01-04-2020 and 31-03-2023 (inclusive).

6) Consent data
The consents dataset describes consent to linkage, and is current at the time of deposit.

CLS/ NHS Digital Sub-licence agreement
NHS Digital has given CLS permission for onward sharing of the NCDS/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital requires that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the NCDS/HES data dictionary, which will subsequently be made available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project. Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.

CLS Hospital Episode Statistics data access update July 2025

From March 2027, HES data linked to all four CLS studies will no longer be available via the UK Data Service. For projects ending before March 2027, uses should continue to apply via UKDS. However, if access to a wider range of linked Longitudinal Population Studies data is needed, UKLLC might be more suitable. For projects ending after March 2027, users must apply via UKLLC.

Latest edition information
For the third

This dataset contains responses from an online survey of 2187 participants primarily located in the UK. All participants stated that they had used the UK National Health Service (NHS) at some time in their lives. The data were collected between December 2018 and August 2019. Participants' views on data sharing - this dataset contains information about people's willingness to share mental and physical health data for research purposes. It also includes information on willingness to share other types of data, such as financial information. The dataset includes participants' responses to questions relating to mental health data sharing, including the trustworthiness of organisations which use such data, how much the presence of different governance measures (such as deidentification, opt-out, etc.) would alter their views, and whether they would be less likely to access NHS mental health services if they knew their data might be shared with researchers. Participants' satisfaction and interaction with UK mental and physical health services - the dataset includes information regarding participants' views on and interaction with NHS services. This includes ratings of satisfaction at first contact and in the previous 12 months, frequency of use, and type of treatment received. Information about participants - the dataset includes information about participants' mental and physical health, including whether or not they have experience with specific mental health conditions, and how they would rate their mental and physical health at the time of the survey. There is also basic demographic information about the participants (e.g. age, gender, location etc.). ## This item has been replaced by the one which can be found at https://hdl.handle.net/10283/4467 ##

To support respiratory research, a synthetic asthma dataset was generated based on a real-world data, originally documenting 381 patients with physician-confirmed asthma who were admitted to secondary care at a single centre in 2019. The dataset is highly detailed, covering demographics, structured physiological data, medication records, and clinical outcomes. The synthetic version extends to 561 patients admitted over a year, offering insights into patient patterns, risk factors, and treatment strategies.

The dataset was created using the Synthetic Data Vault package, specifically employing the GAN synthesizer. Real data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Real world data. Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real-world data upon request.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

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.

Quarterly activity data relating to inpatients (elective) and outpatients. All data items relate to all specialties. Figures are submitted quarterly on the Quarterly Activity Return (QAR) by NHS Trusts and Independent sector providers treating NHS patients.

Outpatient data include:

the number of GP and other referrals for first consultant outpatient appointments, the total number of attendances at consultant outpatient clinics including patients seen for their first appointments as well as those attending for subsequent or follow up appointments, the number of patients who did not attend their outpatient appointment (first or subsequent) and gave no advance warning, or arrived too late to be seen. Elective admission events are:

decisions to admit, patients admitted, patients failed to attend, removals other than admission.

Source: https://www.england.nhs.uk/statistics/statistical-work-areas/hospital-activity/quarterly-hospital-activity/qar-data/

I did few modifications to make the dataset easier to use.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical and mental 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)- Depression (in adults aged 18+)- 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 illness The 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 8 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.

The 1970 British Cohort Study (BCS70) is a longitudinal birth cohort study, following a nationally representative sample of over 17,000 people born in England, Scotland and Wales in a single week of 1970. Cohort members have been surveyed throughout their childhood and adult lives, mapping their individual trajectories and creating a unique resource for researchers. It is one of very few longitudinal studies following people of this generation anywhere in the world.Since 1970, cohort members have been surveyed at ages 5, 10, 16, 26, 30, 34, 38, 42, 46, and 51. Featuring a range of objective measures and rich self-reported data, BCS70 covers an incredible amount of ground and can be used in research on many topics. Evidence from BCS70 has illuminated important issues for our society across five decades. Key findings include how reading for pleasure matters for children's cognitive development, why grammar schools have not reduced social inequalities, and how childhood experiences can impact on mental health in mid-life. Every day researchers from across the scientific community are using this important study to make new connections and discoveries.BCS70 is run by the Centre for Longitudinal Studies (CLS), a research centre in the UCL Institute of Education, which is part of University College London. The content of BCS70 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from BCS70 that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasetsSecure Access versions of BCS70 have more restrictive access conditions than versions available under the standard End User Licence (EUL). In 2012, consent was sought for data linkage of health administrative records from the Hospital Episode Statistics (HES) to survey data for cohort members in the 1970 British Cohort Study (BCS70). The main aim of this data linkage exercise is to enhance the research potential of the study, by combining administrative record with the rich information collected in the surveys. The 1970 British Cohort Study: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1997-2023: Secure Access contains information about all hospital admissions in England. The following linked HES data are available: 1) Accident and Emergency (A&E) The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2019 (inclusive). It includes major A&E departments, single speciality A&E departments, minor injury units and walk-in centres in England. 2) Admitted Patient Care (APC) The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2023 (inclusive). 3) Critical Care (CC) The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2023 (inclusive). 4) Out Patient (OP) The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2023 (inclusive). 5) Emergency Care Dataset (ECDS) The ECDS lists the emergency care appointments between 01-04-2020 and 31-03-2023 (inclusive). 6) Consent data The consents dataset describes consent to linkage, and is current at the time of deposit CLS/ NHS Digital Sub-licence agreement NHS Digital has given CLS permission for onward sharing of the Next Steps/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital requires that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the Next Steps HES data dictionary, which will subsequently make available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project.

Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.

The Licensee shall acknowledge in any publication, whether printed, electronic or broadcast, based wholly or in part on such materials, both the source of the data and UCL. An example of an appropriate acknowledgement can be found here: https://cls.ucl.ac.uk/data-access-training/citing-our-data/.