This publication provides statistics on the number of unique NHS numbers with an associated national data opt-out. The national data opt-out was introduced on 25 May 2018. It was introduced following recommendations from the National Data Guardian. It indicates that a patient does not want their confidential patient information to be shared for purposes beyond their individual care across the health and care system in England. The service allows individuals to set a national data opt-out or reverse a previously set opt-out. It replaced the previous type 2 opt-outs which patients registered via their GP Practice. Previous type 2 opt-outs have been converted to national data opt-outs each month, until November 2018. This is why the monthly increase in opt-outs decreases from December 2018 onward. This publication includes the number of people who have a national data opt-out, broken down by age, gender and a variety of geographical breakdowns. From June 2020 the methodology for reporting NDOP changed, representing a break in time series. Therefore, caution should be used when comparing data to publications prior to June 2020. The number of deceased people with an active NDOP has been captured and reported for the first time in June 2020. Please note that this publication is no longer released monthly. It is released annually or when the national opt-out rate changes by more than 0.1 per cent. Prior to September 2020 there is a slight inflation of less than 0.05 percent in the number of National Data Opt-outs. This is due to an issue with the data processing, which has been resolved and does not affect data after September 2020. This issue does not disproportionately affect any single breakdown, including geographies. Please take this into consideration when using the data. As of January 2023, index of multiple deprivation (IMD) data has been added to the publication, allowing the total number of opt-outs to be grouped by IMD decile. This data has been included as a new CSV, and has also been added to a new table in the summary file. IMD measures relative deprivation in small areas in England, with decile 1 representing the most deprived areas, and decile 10 representing least deprived. Please note that the figures reported in IMD decile tables will not add up to the national totals. This is because the IMD-LSOA mapping reference data was created in 2019, and any geography codes added since then will not be mapped to an IMD decile. For more information about the reference data used, please view this report: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 Management information describes aggregate information collated and used in the normal course of business to inform operational delivery, policy development or the management of organisational performance. It is usually based on administrative data but can also be a product of survey data. We publish these management information to ensure equality of access and provide wider public value.

Data on patient opt-out information received from GP Practices.

There are two types of opt-out:

• A type 1 opt-out prevents information being shared outside a GP practice for purposes other than direct care.

• A type 2 opt-out prevents information being shared outside of the HSCIC for purposes beyond the individual's direct care.

A more detailed description of opt-outs is available from the HSCIC website

Type 1 and type 2 opts-outs are presented at GP practice level. Type 1 opt-outs are reported as instances (i.e. number of times the opt-out code occurs within GP records, which may include the same patient recorded at more than one practice) and there is no way to de-duplicate this information.

Levels of type 1 opt-outs are therefore likely to be higher than levels of type 2 opt-outs, which are de-duplicated.

Linking survey and administrative data offers the possibility of combining the strengths, and mitigating the weaknesses, of both. Such linkage is therefore an extremely promising basis for future empirical research in social science. For ethical and legal reasons, linking administrative data to survey responses will usually require obtaining explicit consent. It is well known that not all respondents give consent. Past research on consent has generated many null and inconsistent findings. A weakness of the existing literature is that little effort has been made to understand the cognitive processes of how respondents make the decision whether or not to consent. The overall aim of this project was to improve our understanding about how to pursue the twin goals of maximizing consent and ensuring that consent is genuinely informed. The ultimate objective is to strengthen the data infrastructure for social science and policy research in the UK. Specific aims were: 1. To understand how respondents process requests for data linkage: which factors influence their understanding of data linkage, which factors influence their decision to consent, and to open the black box of consent decisions to begin to understand how respondents make the decision. 2. To develop and test methods of maximising consent in web surveys, by understanding why web respondents are less likely to give consent than face-to-face respondents. 3. To develop and test methods of maximising consent with requests for linkage to multiple data sets, by understanding how respondents process multiple requests. 4. As a by-product of testing hypotheses about the previous points, to test the effects of different approaches to wording consent questions on informed consent. Our findings are based on a series of experiments conducted in four surveys using two different studies: The Understanding Society Innovation Panel (IP) and the PopulusLive online access panel (AP). The Innovation Panel is part of Understanding Society: the UK Household Longitudinal Study. It is a probability sample of households in Great Britain used for methodological testing, with a design that mirrors that of the main Understanding Society survey. The Innovation Panel survey was conducted in wave 11, fielded in 2018. The Innovation Panel data are available from the UK Data Service (SN: 6849, http://doi.org/10.5255/UKDA-SN-6849-12). Since the Innovation Panel sample size (around 2,900 respondents) constrained the number of experimental treatment groups we could implement, we fielded a parallel survey with additional experiments, using a different sample. PopulusLive is a non-probability online panel with around 130,000 active sample members, who are recruited through web advertising, word of mouth, and database partners. We used age, gender and education quotas to match the sample composition of the Innovation Panel. A total of nine experiments were conducted across the two sample sources. Experiments 1 to 5 all used variations of a single consent question, about linkage to tax data (held by HM Revenue and Customs, HMRC). Experiments 6 and 7 also used single consent questions, but respondents were either assigned to questions on tax or health data (held by the National Health Service, NHS) linkage. Experiments 8 and 9 used five different data linkage requests: tax data (held by HMRC), health data (held by the NHS), education data (held by the Department for Education in England, DfE, and equivalent departments in Scotland and Wales), household energy data (held the Department for Business, Energy and Industrial Strategy, BEIS), and benefit and pensions data (held by the Department for Work and Pensions, DWP). The experiments, and the survey(s) on which they were conducted, are briefly summarized here: 1. Easy vs. standard wording of consent request (IP and AP). Half the respondents were allocated to the ‘standard’ question wording, used previously in Understanding Society. The balance was allocated to an ‘easy’ version, where the text was rewritten to reduce reading difficulty and to provide all essential information about the linkage in the question text rather than an additional information leaflet. 2. Early vs. late placement of consent question (IP). Half the respondents were asked for consent early in the interview, the other half were asked at the end. 3. Web vs. face-to-face interview (IP). This experiment exploits the random assignment of IP cases to explore mode effects on consent. 4. Default question wording (AP). Experiment 4 tested a default approach to giving consent, asking respondents to “Press ‘next’ to continue” or explicitly opt out, versus the standard opt-in consent procedure. 5. Additional information question wording (AP). This experiment tested the effect of offering additional information, with a version that added a third response option (“I need more information before making a decision”) to the standard ‘yes’ or no’ options. 6. Data linkage domain (AP). Half the respondents were assigned to a question asking for consent to link to HMRC data; the other half were asked for linkage to NHS data. 7. Trust priming (AP).This experiment was crossed with the data linkage domain experiment, and focused on the effect of priming trust on consent. Half the sample saw an additional statement: “HMRC / The NHS is a trusted data holder” on an introductory screen prior to the consent question. This was followed by an icon symbolizing data security: a shield and lock symbol with the heading “Trust”. The balance was not shown the additional statement or icon. 8. Format of multiple consents (AP). For one group, the five consent questions were each presented on a separate page, with respondents consenting to each in turn. For the second group the questions were all presented on one page; however, the respondent still had to answer each consent question individually. For the third group all five data requests were presented on a single page and the respondent answered a single yes/no question, whether they consented to all the linkages or not. 9. Order of multiple consents (AP). One version asked the five consent questions in ascending order of sensitivity of the request (based on previous data), with NHS asked first. The other version reversed the order, with consent to linkage to HMRC data asked first. For all of the experiments described above, we examined the rates of consent. We also tested comprehension of the consent request, using a series of knowledge questions about the consent process. We also measured subjective understanding, to get a sense of how much respondents felt they understood about the request. Finally, we also ascertained subjective confidence in the decision they had made. In additional to the experiments, we used digital audio-recordings of the IP11 face-to-face interviews (recorded with respondents’ permission) to explore how interviewers communicate the consent request to respondents, whether and how they provide additional information or attempt to persuade respondents to consent, and whether respondents raise questions when asked for consent to data linkage. Key Findings Correlates of consent: (1) Respondents who have better understanding of the data linkage request (as measured by a set of knowledge questions) are also more likely to consent. (2) As in previous studies, we find no socio-demographic characteristics that consistently predict consent in all samples. The only consistent predictors are positive attitudes towards data sharing, trust in HMRC, and knowledge of what data HMRC have. (3) Respondents are less likely to consent to data linkage if the wording of the request is difficult and the question is asked late in the questionnaire. Position has no effect on consent if the wording is easy; wording has no effect on consent if the position is early. (4) Priming respondents to think about trust in the organisations involved in the data linkage increases consent. (5) The only socio-demographic characteristic that consistently predicts objective understanding of the linkage request is education. Understanding is positively associated with the number of online data sharing behaviours (e.g., posting text or images on social media, downloading apps, online purchases or banking) and with trust in HMRC. (6) Easy wording of the consent question increases objective understanding of the linkage request. Position of the consent question in the questionnaire has no effect on understanding. The consent decision process: (7) Respondents decide about the consent request in different ways: some use more reflective decision-making strategies, others use less reflective strategies. (8) Different decision processes are associated with very different levels of consent, comprehension, and confidence in the consent decision. (9) Placing the consent request earlier in the survey increases the probability of the respondent using a reflective decision-making process. Effects of mode of data collection on consent: (10) As in previous studies, respondents are less likely to consent online than with an interviewer. (11) Web respondents have lower levels of understanding than face-to-face respondents. (12) There is no difference by mode in respondents’ confidence in their decisions. (13) Web respondents report higher levels of concern about data security than face-to-face respondents. (14) Web respondents are less likely to use reflective strategies to make their decision than face-to-face respondents, and instead more likely to make habit-based decisions. (15) Easier wording of the consent request does not reduce mode effects on rates of consent. (16) Respondents rarely ask questions and interviewers rarely provide additional information. Multiple consent requests: (17) The format in which a sequence of consent requests is asked does not seem to matter. (18) The order of multiple consent requests affects consent rates, but not in a

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.).

Community Acquired Pneumonia (CAP) is the leading cause of infectious death and the third leading cause of death globally. Disease severity and outcomes are highly variable, dependent on host factors (such as age, smoking history, frailty and comorbidities), microbial factors (the causative organism) and what treatments are given. Clinical decision pathways are complex and despite guidelines, there is significant national variability in how guidelines are adhered to and patient outcomes.

For clinicians treating pneumonia in the hospital setting, care of these patients can be challenging. Key decisions include the type of antibiotics (oral or intravenous), the appropriate place of care (home, hospital or intensive care), and when it is appropriate to stop antibiotics. Decision support tools to help inform clinical management would be highly valuable to the clinical community.

This dataset is synthetic, formed from statistical modelling using real patient data, and represents a population with significant diversity in terms of patient demography, socio-economic status, CAP severity, treatments and outcomes. It can be used to develop code for deployment on real data, train data analysts and increase familiarity with this disease and its management.

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

EHR. 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 & 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”. This synthetic dataset has been modelled to reflect data collected from this EHR.

Scope: A synthetic dataset which has been statistically modelled on all hospitalised patients admitted to UHB with Community Acquired Pneumonia. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care including timings, admissions, escalation of care to ITU, discharge outcomes, physiology readings (heart rate, blood pressure, AVPU score and others), blood results and drug prescribing and administration.

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

I am writing to confirm that I have now completed my search for the information which you requested. Request You asked us: I’m writing to submit a request under the Freedom of Information Act. For each of the last 24 months for which data is available, please could you provide the number of NHS Pension Scheme Members who have opted out of the NHS Pension Scheme, along with any ‘reason for opting out’ data that is collected, such as affordability, Annual Allowance / LTA etc. If possible, please provide a breakdown of the monthly figures between: a) Medical Staff under capacity code 03 b) GPs, specifically b) Dental Staff under capacity code 08 d) General Dental Practitioners, Dental Registrars. Consultant Dentists and Dental Surgeons, specifically c) other The NHS Business Services Authority (NHSBSA) received your request on 23 April 2025. We have handled your request under the Freedom of Information Act (FOIA) 2000. Our response The NHSBSA does not hold the requested information split by the categories as specified in your request; however, we do publish a monthly report which provides data about employees in the Employee Staff Record (ESR) choosing to opt-out of the NHS Pension Scheme. https://www.nhsbsa.nhs.uk/information-about-nhs-pensions/nhs-pensions-opt-out-data Publishing this response Please note that this information will be published on our Freedom of Information disclosure log at: https://opendata.nhsbsa.net/dataset/foi-02775 Your personal details will be removed from the published response.

The NHS Fetal Anomaly Ultrasound Screening Programme (FA2) data provides insights into the coverage and effectiveness of the fetal anomaly ultrasound scan conducted between 18 and 20 weeks of pregnancy. This data includes the percentage of eligible pregnant women who received the scan, which is designed to detect a range of fetal anomalies such as Down's syndrome, Edwards' syndrome, Patau's syndrome, and various physical conditions like spina bifida and congenital heart disease.

Rationale The FA2 data helps assess the programme's reach and identify areas for improvement, ensuring that expectant mothers receive timely and accurate information about their baby's health. This information is crucial for healthcare providers to monitor and enhance the quality of prenatal care services.

Definition of numerator The number of eligible pregnant women who received the fetal anomaly ultrasound scan between 18 and 20 weeks of pregnancy.

Definition of denominator The total number of eligible pregnant women who were offered the fetal anomaly ultrasound scan between 18 and 20 weeks of pregnancy.

Caveats

Eligibility Criteria: The data only includes pregnant women who meet specific eligibility criteria, such as those with a fetal crown rump length (CRL) measurement between 45.0mm and 84.0mm (11 weeks plus 2 days to 14 weeks plus 1 day of pregnancy). Women who fall outside these parameters are excluded. Exclusions: Certain groups are excluded from the data, such as women who miscarry, opt for termination, transfer out before testing, or have pregnancies of a higher order than twins. Technical Limitations: There may be instances where it is not technically possible to measure the nuchal translucency (NT), leading to exclusions from the data. Service Capacity: The capacity of ultrasound departments to accommodate women presenting later in pregnancy can affect the data. If the service cannot offer the combined test to women presenting between 13 weeks plus 1 day and 14 weeks plus 1 day, these women can be excluded. Informed Choice: The data includes women who decline the screening after being offered, which can impact the overall coverage rates.

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Commission for Health Improvement (CHI), in conjunction with the Department of Health (DH), appointed Aston University to develop and pilot a new national National Health Service (NHS) staff survey, commencing in 2003, and to establish an advice centre and web site to support that process. Administration of the programme was taken over by the Healthcare Commission in time for the 2004 series. On the 1st April 2009, the Care Quality Commission (CQC) was formed which replaced the Healthcare Commission (users should note that some of the surveys in the series conducted prior to this date will still be attributed to the Healthcare Commission). In 2011 the Department of Health took over management of the survey. Since 2013 NHS England (NHSE) have been in charge of the survey programme. Researchers at Aston University were responsible for the initial development of the survey questionnaire instrument, and for the setting up of the NHS National Staff Survey Advice Centre. From 2011, Picker Institute Europe took over from Aston University as survey contractors. All organisations concerned worked in partnership to consult widely with NHS staff about the content of the new national survey. The work was conducted under the guidance of a stakeholder group, which contained representatives from the staff side, CQC, DH, human resources directors, Strategic Health Authorities and the NHS workforce.

Aims and conduct of the survey
The purpose of the annual NHS staff survey is to collect staff views about working in their local NHS trust. The survey has been designed to replace trusts' own annual staff surveys, the DH '10 core questions', and the HC 'Clinical Governance Review' staff surveys. It is intended that this one annual survey will cover the needs of HC, DH and trusts. Thus, it provides information for deriving national performance measures (including star ratings) and to help the NHS, at national and local level, work towards the 'Improving Working Lives' standard. The design also incorporates questions relating to the 'Positively Diverse Programme'. Trusts will be able to use the findings to identify how their policies are working in practice. The survey enables organisations, for the first time, to benchmark themselves against other similar NHS organisations and the NHS as a whole, on a range of measures of staff satisfaction and opinion. From 2013, the NHS Staff Survey went out to all main trust types - social enterprises, clinical commissioning groups and clinical support units were able to opt themselves in to the survey. Organisations were allowed to conduct the survey electronically and to submit data for an entire census or extended sample of their organisation. Previously the sample was restricted to 850 staff.

The collection of data (i.e. the survey fieldwork) is conducted by a number of independent survey contractors (see documentation for individual survey information). The contractors are appointed directly by each NHS trust in England and are required to follow a set of detailed guidance notes supplied by the Advice Centre (see web site link above), which covers the methodology required for the survey. For example, this includes details on how to draw the random sample, the requirements for printing of questionnaires, letters to be sent to respondents, data entry and submission. At the end of the fieldwork, the data are then sent to the Advice Centre. From the data submitted, each participating NHS trust in England receives a benchmarked 'Feedback Report' from the Advice Centre, which also produces (on behalf of the Department of Health) a series of detailed spreadsheets which report details of each question covered in the survey for each participating trust in England, and also a 'Key Findings' summary report covering the survey findings at national level. Further information about the survey series and related publications are available from the Advice Centre web site (see link above).


Main Topics:
Topics covered in the survey include: work-life balance; appraisal; training, learning and development; team working; health and safety; errors and incidents witnessed; job characteristics and arrangements; management and supervision; perceptions of organisation worked for; harassment, bullying and violence; and respondents' demographic characteristics.

Thank you for your request for information as outlined below: Please could you supply the updated data for ‘Monthly opt-out data and the reasons for opting out’ as was previously published on pg.49 of the NHS-Employers-submissionDDRB-202122. I.e. for each month since June 22, please provide the numbers who have opted out for the reasons of: 1) Total 2) Affordability 3) Annual Allowance/LTA 4) Contributing to another pension scheme 5) In receipt of a fixed or enhanced protection certificate 6) Other 7) Secured retirement income via other means 8) Temporary opt out due to other financial priorities 9) Would prefer not to say

Child Obesity and Excess Weight data from the National Child Measurement Programme (NCMP, published by Public Health England).

NCMP data is an annual survey of children attending state schools, which is the denominator for percentages. Figures are based on child residence postcode. Data is shown for Lincolnshire and Districts, Wards, and NHS Clinical Commissioning Group (CCG).

The data shows children at risk of obesity and excess weight (which includes overweight and obesity). It uses population monitoring criteria, not clinical assessments which might give lower prevalence rates. NCMP data covers state schools but does not include independent sector children, and some larger children may opt out.

The data is updated annually. Source: Public Health England (PHE) National Obesity Observatory.

This OMOP CDM is built from a flow of primary care data from Cheshire and Merseyside GPs who have signed the ICB Data Sharing Agreement for Population Health. Patients who have signalled that they wish to opt out of their records being shared for secondary uses (i.e. uses beyond Direct Patient Care) are removed as per national data opt-out policy. The source data is refreshed weekly (Sunday evenings) and the data set includes a long list of fields relating to: NHS number, allergies, medications issued, Repeat medications, Covid-19 status, Active and Past Problems, GP Results, Vitals & Measurements (height/weight, BP, physiological function result), Lifestyle factors (smoking and alcohol), GP encounters, vaccinations and immunisations, Contraindications, OTC and Prophylactic Therapy, Family History, Child Health, Diabetes Diagnosis, Chronic Disease Monitoring.

Investment in the earliest stages of life is increasingly recognised to improve health across the life-course, beginning with the health of parents before pregnancy, in embryonic life, through to infancy, childhood, and into adulthood. eLIXIR BiSL combines information from routine maternity and neonatal health records and blood samples at two acute NHS Trust hospitals, along with mental health and primary care data. The study is able to address relationships between maternal and child physical health, and to investigate interactions with mental health. Participants are predominantly residents of South London, in areas with high levels of deprivation and ethnic diversity.

The BiSL data-linkage project uses opt-out consent to collect routine maternity and neonatal clinical patient data (GSTT and KCH NHS Trusts), mental health data from the SLaM CRIS platform, and primary care data from the LDN platform, for those registered with a GP in Lambeth. We hold the approval to also link with emergency and admissions data (HES), national fertility data (HFEA), and immunisation records (NIMS), as well as expanding primary care data to other boroughs in South London, namely: Southwark, Lewisham, and Bromley; the process to link these new data sources is currently ongoing.

At present, eLIXIR holds over 50,000 records. All records are deidentified, including masking of identifying information in open-text fields and use of pseudonymised identifiers. The data refresh process occurs every 6 months, and each update includes all retrospective data since conception of the cohort (October 2018), thus building a dynamic cohort.

The BiSL team includes members King’s College London Faculty of Life Sciences and Medicine and the Institute of Psychiatry, Psychology and Neurosciences (IoPPN), along with services users and patient representatives.

The eLIXIR Born in South London project has now been successfully awarded a MRC Longitudinal Population Study Grant which will enable us to operate for the next 5 years and continue building this dynamic mother-child database. BiSL is part of the MIREDA Study Partnership bringing together birth cohort data across the UK.

Abstract copyright UK Data Service and data collection copyright owner.

In 2003, the offer of screening for foetal abnormality and particularly Down’s syndrome became a routine part of antenatal care in the UK for the first time. The 2003 NICE antenatal care guidelines state that 'all pregnant women should be offered screening for Down’s syndrome with a policy that provides a minimum detection rate of 75% with a false-positive rate no greater than 3% by 2007', indicating a move to first trimester screening technologies which achieve this greater level of accuracy.

Thus the findings of this study of the only NHS site in England offering combined first trimester screening in a one-stop clinic setting are of particular relevance at this time and has provided the opportunity to look at the implications of an IHT (Innovative Health Technology) prior to wide-scale implementation in the UK. The study findings raise questions about the implications for non-directiveness and informed decision-making of the resulting routinisation of screening, and the shift from an ‘opt-in’ to an ‘opt-out’ service.

The implications of the introduction of a routine offer of screening for Down’s syndrome in the first trimester of pregnancy raises new issues for women and their partners, for the organisation and management of screening and for society. The development of prenatal screening technologies is a contested and politically charged arena with ethical and public policy considerations.

In the light of the above concerns, the study aimed to explore: