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.

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 2009 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 were a few sector specific questions.

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 ##

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.

Note: This dataset has been archived as of January 2024, as will not be made available to the public. Information that the data is ward-level aggregated which is volatile and easy to misinterpret the data and the level datasets are misleading if published.

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 dataset presents the percentage of children aged 4 to 5 years in Reception who are classified as overweight or living with obesity. The data is sourced from the National Child Measurement Programme (NCMP), managed by NHS England, and provides a snapshot of early childhood weight status across England. It is a key indicator for monitoring trends in childhood obesity and informing public health interventions aimed at improving children's health outcomes.

Rationale

The rationale behind this indicator is to reduce the proportion of children in Reception who are overweight or obese. Early childhood is a critical period for establishing healthy behaviours, and excess weight at this age is associated with a higher risk of obesity and related health conditions later in life. Monitoring this metric supports targeted prevention strategies and policy development.

Numerator

The numerator is the number of children in Reception (aged 4 to 5 years) with a valid height and weight measurement who are classified as living with obesity or severe obesity, as recorded by the NCMP.

Denominator

The denominator is the total number of children in Reception (aged 4 to 5 years) with a valid height and weight measurement recorded by the NCMP.

Caveats

There is potential for error in the collection, collation, and interpretation of the data. This includes possible bias due to poor response rates or selective opt-out by participants, which may affect the representativeness of the results.

External References

For more information, visit the Fingertips Public Health Profiles.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

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.

This project used a cross-sectional survey method to gather information from GPs practicing in England. As the GP system operates differently in Scotland, Wales and Northern Ireland, these GPs were not included in the study. The survey was developed based on literature related to GP practice in falls prevention, current falls prevention clinical guidelines, completed Australian studies, and the results of pilot on-line survey, in conjunction with feedback from a group of GPs and a focus group of older people. All 211 Community Care Groups (CCGs) in NHS England were approached to support the survey, and 4 CCGs opted out. The survey was provided as both a paper survey to 4000 randomly selected GPs and a further 3,200 GPs were invited to participate via an online version of the same survey (using the Bristol Online Survey software). As advised by GP advisors we sent letter to GP practice managers and included an evidence-based invitation letter (as well as participant information sheet) for GPs, in order to enhance response rate. Survey topics included the perceptions, knowledge and routine practice of GPs in relation to identifying, screening and assessing falls risks in their people, their falls management and referral practices, and barriers and facilitators to them effectively preventing falls in their older people. The study has contributed to the methodological debate about paper versus online survey response rates. In this study the response rate was equally poor for both versions. Response rate was seemingly higher of GPs from CCGs who had actively endorsed participation in the study. · Letter to GP practice managers · Evidence-based letter to GPs · Participant Information sheet · Paper copy of Survey · Spreadsheet of raw data – complete set, paper, online · Publication reference: McIntyre A, Mackenzie L, Harvey M (2018) Engagement of general practitioners in falls prevention and referral to occupational therapists. British Journal of Occupational Therapy, (online) · Copy of presentation given at Royal College of Occupational Therapists’ 2017 conference

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.

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

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: