CPRD GOLD linked Cancer Patient Experience Survey (CPES) data include information from patients who have responded to the CPES about their cancer journey from their initial GP visit prior to diagnosis, through diagnosis and treatment and to the ongoing management of their cancer.

Data relating to any adverse reactions which occur within the first fifteen minutes after administration of the coronavirus (COVID-19) vaccination.

REal-time Assessment of Community Transmission (REACT-1) is one of the largest population surveillance studies in the world. It started in May 2020 to measure the prevalence of SARS-CoV-2 in the general population in England. Each month around 150,000 people completed a questionnaire and returned a PCR test.

The study tracked the progress of infection across England. It was commissioned by the Department of Health and Social Care and was carried out between May 2020 and March 2022 in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.

The partner study, REal-time Assessment of Community Transmission (REACT-2), started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study is also described in the Innovation Gateway.

For further information and the study questionnaires, please see the REACT study website.

We present the full meta-data catalogue in the Innovation Gateway. Researchers external to Imperial College London can apply to access subsets of anonymised data from REACT participants. To access the full REACT dataset, researchers require an affiliation or collaboration with Imperial College London.

NHS national data sets collect information from care records, systems and organisations on specific areas of health and care.

2011 Census data for England and Wales, linked to Mortality Data, Hospital Episode Statistics (HES) data, and GP Extraction Service (GPES) data for Pandemic Planning and Research Data.

Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.

The Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (March 2025) includes responses from 1,594,707 participants.

The current data available contains responses from our baseline health questionnaire only and an indicator if a blood sample was provided and stored. The current data is split into 2 categories: • participant data - which contains baseline demographic information collected across all consented participants • questionnaire data - which contains self-reported health information, details about participants' household, socioeconomic status, work and education history, and family history • Clinical Measurements Data which contains clinical data from participants.

Additional linked datasets are available: - ‘Linked NHS England Health Records Data which contains linked clinical data from NHS England for 1,151,453 participants. - ‘Genotype Array Data’ which includes genotype array data on 707,522 variants from a subset of 651,050 participants - Clinical Measurements Data which contains clinical data from 1,169,699 participants.

The data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.

We aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.

The UK Cystic Fibrosis Registry is a national, secure, centralized database sponsored and managed by the Cystic Fibrosis Trust, with UK National Health Service (NHS) research ethics approval and consent from each person for whom data are collected.

Every ten years since 1801 the nation has set aside one day for the census - a count of all people and households. It is the most complete source of information about the population that we have. The latest census was held on Sunday 21 March 2021.

Every effort is made to include everyone, and that is why the census is so important. It is the only survey which provides a detailed picture of the entire population, and is unique because it covers everyone at the same time and asks the same core questions everywhere. This makes it easy to compare different parts of the country.

The information the census provides allows central and local government, health authorities and many other organisations to target their resources more effectively and to plan housing, education, health and transport services for years to come.

In England and Wales, the census is planned and carried out by the Office for National Statistics. Elsewhere in the UK, responsibility lies with the National Records of Scotland and the Northern Ireland Statistics and Research Agency.

A usual resident is anyone who on Census Day, 21 March 2021 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.

The ONS have three processes for checking and resolving duplicate responses so that the main census data should simply be one record for each person:

1. The ONS resolve duplicates coming in for the same postcode using a process called Resolve Multiple Responses (RMR). For instance, if two people both fill in a form for their whole household, or someone from a household also submits an individual response unknown to the main submission. They have rules for checking they are duplicates, and rules for which to keep.

2. The ONS also do an over coverage check on a sample basis for duplicates across the rest of the country, and then factor the findings into their coverage estimation calculations. This sampling focuses on the types of population which are more likely to be duplicated (people who have indicated they have a second residence on the census, students aged 18-25, armed forces personnel, children, adults enumerated at a communal establishment, etc.) but also samples from the remaining population.

3. The ONS ask parents to fill in basic demographic information for any children who are away studying, and when they get to the question on their term-time address, if they answer that the term-time address is elsewhere, we then use that to filter those out-of-term students out of the main database. Then when that student does respond actually at their term-time address, they only include them there.

Note: variables RELAT06, RELAT11, RELAT16, RELAT21, RELAT26, GENDER_IDENTITY are not available in the data

The NNRD is a national resource holding real-world clinical data captured in the course of care on all admissions to NHS neonatal units in England, Wales, Scotland and the Isle of Man. Neonatal units submit data through their Electronic Patient Record system supplier. At present, there is information on around one million babies and 10 million days of care in the NNRD.

The NNRD is available to support audit, evaluations, bench-marking, quality improvement and clinical, epidemiological, health services and policy research to improve patient care and outcomes. Data in the NNRD comprise the Neonatal Data Set (ISB1595), an approved NHS Information Standard and include demographic details, daily records of interventions and treatments throughout the neonatal inpatient stay, information on diagnoses and outcomes, and follow-up health status at age two years.

The Neonatal Data Analysis Unit was founded to support the management and development of the National Neonatal Research Database (NNRD) established in 2007 by Professor Modi, and related research.

More information can be found at https://www.imperial.ac.uk/neonatal-data-analysis-unit

Record-level patient data set of patients attending outpatient clinics at NHS hospitals in England. A record represents one appointment.

Record-level patient data set of patients admitted for treatment and receiving Critical Care (intensive care or high dependency care) at NHS hospitals in England. A record represents one episode of Critical Care.

The National Neonatal Research Database is an award-winning resource, a dynamic relational database containing information extracted from the electronic patient records of babies admitted to NHS neonatal units in England, Wales and Scotland (Northern Ireland is currently addressing regulatory requirements for participation). The NNRD-AI is a version of the NNRD curated for machine learning and artificial intelligence applications.

A team led by Professor Neena Modi at the Chelsea and Westminster Hospital campus of Imperial College London established the NNRD in 2007 as a resource to support clinical teams, managers, professional organisations, policy makers, and researchers who wish to evaluate and improve neonatal care and services. Recently, supported by an award from the Medical Research Council, the neonatal team and collaborating data scientists at the Institute for Translational Medicine and Therapeutics, Data Science Group at Imperial College London, created NNRD-AI.

The NNRD-AI is a subset of the full NNRD with around 200 baby variables, 100 daily variables and 450 additional aggregate variables. The guiding principle underpinning the creation of the NNRD-AI is to make available data that requires minimal input from domain experts. Raw electronic patient record data are heavily influenced by the collection process. Additional processing is required to construct higher-order data representations suitable for modelling and application of machine learning/artificial intelligence techniques. In NNRD-AI, data are encoded as readily usable numeric and string variables. Imputation methods, derived from domain knowledge, are utilised to reduce missingness. Out of range values are removed and clinical consistency algorithms applied. A wide range of definitions of complex major neonatal morbidities (e.g. necrotising enterocolitis, bronchopulmonary dysplasia, retinopathy of prematurity), aggregations of daily data and clinically meaningful representations of anthropometric variables and treatments are also available.

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham 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 & an expanded 250 ITU bed capacity during COVID. 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”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Summary of SARS-CoV-2 lineages and mutations

Record-level patient data set of patients admitted for treatment at NHS hospitals in England, including delivery and birth data. A record represents one episode.

The English Longitudinal Study of Ageing (ELSA) is a panel study of a representative cohort of men and women living in England aged ≥50 years. The study collects objective and subjective measures of physical and mental health, wellbeing, finances and attitudes around ageing and how these change over time.

ELSA was designed as a sister study to the Health and Retirement Study in the USA and is multidisciplinary in orientation, involving the collection of economic, social, psychological, cognitive, health, biological and genetic data. The original sample was drawn from households that had previously responded to the Health Survey for England (HSE) between 1998 and 2001. A pilot study was conducted in 2001 before main fieldwork began in March 2002. The same group of respondents have been interviewed at two-yearly interviews to measure changes in their health, economic and social circumstances. Younger age groups are replaced or refreshed to retain the panel. The sample has been refreshed using HSE participants in waves 3, 4, 6, 7 and 9. Data are collected using computer-assisted personal interviews and self-completion questionnaires, with additional nurse visits for the assessment of biomarkers every 4 years. The original sample consisted of 11,391 members ranging in age from 50 to 100 years. ELSA is harmonized with ageing studies in other countries to facilitate international comparisons, and is linked to financial and health registry data.

More than 18,000 people have taken part in the study since it started in 2002, with the same people re-interviewed every two years. Data from ELSA participants informs policy across all aspects of ageing including health and social care, retirement and pensions policy, and social and civic participation.

The NSHD is the oldest of the British Birth Cohort studies investigating life course determinants of healthy ageing. Data collected over 70 years include questionnaire, physical and cognitive function, clinical phenotyping and biosamples.

A randomised controlled trial of GP practice staff training and high risk patient identification and flagging to reduce the occurrence of severe asthma related events (hospitalisations and deaths).

UK LLC is the national Trusted Research Environment (TRE) for longitudinal research. Led by the Universities of Bristol and Edinburgh, it is a collaborative endeavour that includes many of the UK's most established longitudinal studies.