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.

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).


As in previous years, the 2010 survey contained different versions of the core questionnaire for each of the four main sectors (acute, ambulance, mental health and primary care). The majority of the content is the same across the different versions of the core questionnaire but there are a few sector-specific questions. A few questions were dropped and some added for 2010 - see Appendix 9 of the Guidance Notes document for details of changes.


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.

Sector-specific questions include:...

Proportion of children aged 10 to 11 years classified as overweight or living with obesity. For population monitoring purposes, a child’s body mass index (BMI) is classed as overweight or obese where it is on or above the 85th centile or 95th centile, respectively, based on the British 1990 (UK90) growth reference data. The population monitoring cut offs for overweight and obesity are lower than the clinical cut offs (91st and 98th centiles for overweight and obesity) used to assess individual children; this is to capture children in the population in the clinical overweight or obesity BMI categories and those who are at high risk of moving into the clinical overweight or clinical obesity categories. This helps ensure that adequate services are planned and delivered for the whole population.

Rationale There is concern about the rise of childhood obesity and the implications of obesity persisting into adulthood. The risk of obesity in adulthood and risk of future obesity-related ill health are greater as children get older. Studies tracking child obesity into adulthood have found that the probability of children who are overweight or living with obesity becoming overweight or obese adults increases with age[1,2,3]. The health consequences of childhood obesity include: increased blood lipids, glucose intolerance, Type 2 diabetes, hypertension, increases in liver enzymes associated with fatty liver, exacerbation of conditions such as asthma and psychological problems such as social isolation, low self-esteem, teasing and bullying.

It is important to look at the prevalence of weight status across all weight/BMI categories to understand the whole picture and the movement of the population between categories over time.

The National Institute of Health and Clinical Excellence have produced guidelines to tackle obesity in adults and children - http://guidance.nice.org.uk/CG43.

1 Guo SS, Chumlea WC. Tracking of body mass index in children in relation to overweight in adulthood. The American Journal of Clinical Nutrition 1999;70(suppl): 145S-8S.

2 Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Preventative Medicine 1993;22:167-77.

3 Starc G, Strel J. Tracking excess weight and obesity from childhood to young adulthood: a 12-year prospective cohort study in Slovenia. Public Health Nutrition 2011;14:49-55.

Definition of numerator Number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP with a BMI classified as overweight or living with obesity, including severe obesity (BMI on or above the 85th centile of the UK90 growth reference).

Definition of denominator The number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP.

Caveats Data for local authorities may not match that published by NHS England which are based on the local authority of the school attended by the child or based on the local authority that submitted the data. There is a strong correlation between deprivation and child obesity prevalence and users of these data may wish to examine the pattern in their local area. Users may wish to produce thematic maps and charts showing local child obesity prevalence. When presenting data in charts or maps it is important, where possible, to consider the confidence intervals (CIs) around the figures. This analysis supersedes previously published data for small area geographies and historically published data should not be compared to the latest publication. Estimated data published in this fingertips tool is not comparable with previously published data due to changes in methods over the different years of production. These methods changes include; moving from estimated numbers at ward level to actual numbers; revision of geographical boundaries (including ward boundary changes and conversion from 2001 MSOA boundaries to 2011 boundaries); disclosure control methodology changes. The most recently published data applies the same methods across all years of data. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of children with a high BMI for age/sex which it is not possible to control for). There is not a good measure of response bias and the degree of selective opt out, but participation rates (the proportion of eligible school children who were measured) may provide a reasonable proxy; the higher the participation rate, the less chance there is for selective opt out, though this is not a perfect method of assessment. Participation rates for each local authority are available in the https://fingertips.phe.org.uk/profile/national-child-measurement-programme/data#page/4/gid/8000022/ of this profile.

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.

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).


For the second edition (August 2015) the variable Occgrp was removed from the data at the depositor’s request. The data can be analysed by occupational group using the variable Occ_da.
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.

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

The general practice census is collected each year and records numbers and details of GPs in England along with information on their practices, staff, patients and the services they provide. This publication is one of three that make up the NHS Staff 1996 - 2006 publication. The other two are: Non-Medical staff 1996 - 2006 Medical and Dental staff 1996 - 2006 General Practice staff, 30 September 2006 - Detailed Results The detailed results contain further data tables as at September 2006 for England, broken down by Strategic Health Authority area and selected statistics by Primary Care Trust. Each table can be downloaded using the following links: Selected GP statistics by Primary Care Trust Table 1a - All GPs: headcount by type Table 1b - All GPs: full time equivalents by type Table 2 - All GPs (excluding GP registrars & GP retainers), by ageband Table 3 - All GPs (excluding GP registrars & GP retainers), by country of primary medical qualification group Table 4 - Practice staff by type Table 5 - Registered GP patients by ageband Table 6 - GP Partnerships (excluding GP registrars & GP retainers), by size Table 7 - Analysis of GMS Partnership Opt-Outs Table 8 - Patient registration transactions