Centre for Longitudinal Studies Birth Cohort Studies Data Linkage: National Child Development Study

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Objective for processing

[2 paragraphs unchanged] Seven years later it was decided it would be worthwhile to find [99 words unchanged] to the impact of the COVID-19 pandemic but re-commenced in early 2021. The main survey is now complete, and at the beginning of 2024, those who didn’t complete the main survey as well as cohort members living abroad will be invited to take part in a short web version of the survey. Subsequent sweeps* of the study will likely take place every five years. [25 paragraphs unchanged] 3) The CLS will promote and make possible wider use of this linked data set, through providing wider access to the linked NHS England HES / (HES/ECDS) to CLS NCDS data to the research community via the UK Data Service (UKDS) Secure Lab under sublicensing agreements subject to the following access arrangements. [20 paragraphs unchanged] The anticipated volume / number of licences is 1-2 sub-licences per month. Sub-licences shall not exceed a term of 12 months at a time and may be extended/renewed if appropriate. The sub-licences any sub-licence will remain valid only while UCL retains the right to hold and share the data from NHS England. Sub-licences may be extended/renewed but only under the same terms as just stated. [6 paragraphs unchanged] The funding is provided by the Economic and Social Research Council. The funding is specifically for the study described. Funding is in place until 2025. [5 paragraphs unchanged]

Processing activities

[2 paragraphs unchanged] The CLS will carry out validation of the administrative pseudonymised data received (linked HES/ECDS (HES/ECDS data) and combined the supplied administrative data with the information collected from the participant as part of the NCDS study using the study ID. [11 paragraphs unchanged] CLS does not link HES/ECDS the data data to any other dataset. However, when a researcher accesses the pseudonymised [64 words unchanged] and will only be permitted if CLS DAC approves the project proposal. [1 paragraph unchanged] UCL only request data for those individuals who have given consent to [16 words unchanged] NHS England, for the matching of consenting participants to the NHS England HES/ECDS datasets, the data manager will check for withdrawals and will remove participants who- [5 paragraphs unchanged] Only staff who are permitted to work at the UKDS (and are [68 words unchanged] In addition, all statistical outputs are subject to statistical disclosure control procedure. . Access to the Secure Lab is only available to ESRC accredited researchers who are based at a UK academic institution or an ESRC-funded research centre and are an ESRC Accredited Researchers. institution. PhD and research students can request access but must apply jointly with their supervisors from established organisations. Access will also be available to non-academic, non-commercial research organisations.. Access to the Secure Lab is available to ESRC accredited researchers who are based at a UK academic institution. PhD and research students can request access but must apply jointly with their supervisors from established organisations. Access will also be available to non-academic, non-commercial research organisations. [3 paragraphs unchanged]

Expected output

Following the data quality and validation work, the first output will be the addition of the refreshed data to the existing linked NCDS/HES dataset which is now available for researchers to apply. The data requested under this Agreement will refresh this already rich linked dataset. CLS will publicise the data release on the CLS website. Since approval, CLS has sub-licenced (used) the NCDS/HES to support one methodological project which is detailed below: The reason for not having received more applications is because it took a while to receive the approval and then to get the application process set up at CLS. When this permission was granted for the first time, CLS needed to work on the data to prepare it for deposit at the UKDS. Once, data was deposited, the UKDS also needed a month to make the data available to researchers. This meant that the data was not available for researchers to use 7 months after gaining approval, this also happened during the period in which the pandemic started. CLS has recently delivered a webinar to introduce this dataset to researchers and is currently planning further promotion of this dataset via different platforms, to increase the use of the data. These include, possible collaboration with HDRUK to promote this linked data and a Depositor Case study which will be produced by the UK Data Service and will showcase a project that is currently using CLS’ other study (Next steps) linked to HES data with the intention to bring awareness of the availability and usefulness of this rich linked dataset. CLS at UCL actively promotes the use of their data among the research community through publications and events (e.g. training and workshops on each data set to help researchers better use the data), as well as providing extensive documentation, guidance on the use of the data and so ultimately benefit health and social care. The second output will be a methodological paper titled : “Examining the linkage quality and sample representativeness of the linked 1958 National Child Development Study (NCDS) ”. This is planned to be published by the end of 2022. The target journals for publication are : The second output will be health-related research, alongside methodological papers on the linked dataset, published in peer-reviewed journals. The methodological assessments are expected to finish three years after obtaining the data. Outputs will contain only aggregate-level data with small numbers suppressed in line with HES 1) Journal for Survey Statistics and Methodology special issue on “Recent Advances in Data Integration” analysis guide. CLS researchers doing research and/or methodological work will access this data via the UCL Data Safe Heaven. 2) Public Opinion Quarterly special issue on “Augmenting Surveys with Paradata, Administrative Data, and Contextual Data” The methodological paper titled: “Examining the quality and sample representativeness of linked survey and administrative data Linking the 1958 National Child Development Study to Hospital Episode Statistics data" which used data received under this agreement is now published. 3) International Journal of Population Data Science In this paper, researchers have examined the quality of the linkage in terms of the associations between [21 words unchanged] may be expected to be associated with hospital attendance, and hence with successful HES linkage (self-reported hospital attendance, self-rated general health, self-reported long-term illness). The researchers have additionally evaluated the population representativeness of the linked sample using external data (hospital admission rates in the general population). The findings suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness. Researchers hope that these analyses will both improve the quality and transparency of research using this linked data resource and encourage providers and users of other linked data resources to undertake and publish similarly thorough evaluations. CLS has not yet published any methodological papers reviewing the linkage.. The reasons why no papers have been published since earlier versions of this application were approved is because CLS have a policy of not using data internally for research when not available to external users and the original approval did not allow CLS to share the data with external users. Thus, CLS have focused on getting the necessary permission for sub-licencing the data. successful HES linkage (self-reported hospital attendance, self-rated general health, self-reported long-term illness). The researchers have additionally evaluated the population representativeness of the linked sample using external data (hospital admission rates in the general population). The creation of this refreshed HES/NCDS database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. The findings suggest that the linkage quality of the NCDS/HES CLS actively promotes the use of their data among the research community through publications and briefings, working papers, webinars, social media, public reports, hosted events. As well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care. data is high and that the linked sample maintains an excellent level of population representativeness. Researchers hope that these analyses will both improve the quality and transparency of research using this linked data resource and encourage providers and users of other linked data resources to undertake and publish similarly thorough evaluations. This paper can be found here https://cls.ucl.ac.uk/wp-content/uploads/2017/02/Examining-the-quality-and-sample-representativeness-of-linked-survey-and-administrative-data-CLS-Working-Paper-2022-5.pdf Currently there are two projects using the linked NCDS/HES data for research. Project 1Title: Physical activity, sedentary behaviour, and diet: impact on cardiometabolic and women’s health Project 2 Title: Hospital use over the life course

Expected measurable benefits

Benefits from the linked data- The linked NCDS/HES dataset received under previous versions of this agreement is now available for researchers to use for research that could potentially influence policy and benefit health and social care as well as improve people's lives. The projects listed under the 'Outputs section' are evidence of how this rich dataset can be used to benefit society. The specific benefits to society, using the data accessed through the sub-license, would be stated by each applicant as part of their application (as required in the Accredited research form and Research proposal – e.g. ‘How your findings is expected to benefit society?’, ‘How does the project provide a public benefit?’, ‘Contribution towards public policy or journal publications’). CLS is expected to further require the applicant to describe the benefits of their intended research to health and social care. [1 paragraph unchanged] This data linkage will also facilitate research that CLS anticipate will be [58 words unchanged] of research question that might be put forward to use the linked NCDS/HES linked data. NCDS/HES. In total over 1600 research publications have been produced using NCDS data [16 words unchanged] are some examples of existing publications using NCDS data benefiting public health: [14 paragraphs unchanged] 1. CLS researchers have already added to the existing body of evidence [78 words unchanged] of the longitudinal studies employed to support these conclusions and policy investment recommendations. As an example of longitudinal study data use, the DWP describe: This innovative, research-led policy investment is directing support to front-line professionals aimed at improving educational and mental health outcomes for children whose parents/carers experience worklessness. Proposals based on the findings include: recommendations. As an example of longitudinal study data use, the DWP describe: This innovative, research-led policy investment is directing support to front-line professionals aimed at improving educational and mental health outcomes for children whose parents/carers experience worklessness. Proposals based on the findings include: [15 paragraphs unchanged]

Benefits reported

[3 paragraphs unchanged] No Yielded Benefits can yet be evidenced from the data received and accessed for research under previous version of this agreement, however there are 2 projects accessing this linked data for research on health and we hope that some of these future publications will result in successful yielded benefits. MARCH 2024 COMPLIANCE REPORT UPDATE: First Output: The first output is the NCDS/HES dataset which is now available to the research community. https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8697 And the creation of the NCDS/HES user guide. This document provides researchers with a complete guide to the linked data and information on the application process. Second Output: Methodological work complete - “Examining the quality and sample representativeness of linked survey and administrative data Linking the 1958 National Child Development Study to Hospital Episode Statistics data" which used data received under this agreement is now published. The findings suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness. We hope that these analyses will both improve the quality and transparency of research using this linked data resource and encourage providers and users of other linked data resources to undertake and publish similarly thorough evaluations.” This paper can be found via the link https://cls.ucl.ac.uk/wp-content/uploads/2017/02/Examining-the-quality-and-sample-representativeness-of-linked-survey-and-administrative-data-CLS-Working-Paper-2022-5.pdf Third Output: Sublicence applications- The specific benefits to society of using the data accessed through the sub-license, would be stated by each applicant as part of their application to CLS. E.g. researchers applying to use the data will need to complete a project proposal and among other questions, will need to provide a response to the following question “How are your project findings expected to benefit society?”. Project 1 -“Derived variables of mental health-related health services use in NCDS and BCS70” The same project above is carried out using the data under this agreement. Project 2- “Young people’s barriers to mental health services” On any given day in the United Kingdom 28.5% of children/young people aged 5 to 19 experience mental health problems. Only one in four of these children/young people receive formal support for these problems. There is a lack of knowledge of what happens to those young people not receiving mental health services. Mental health problems have been shown to limit economic, vocational, and social functioning and international studies have found that 50 to 70% of young people who receive services for their mental health problems continue to experience these problems in adulthood. In this current study researchers would like to explore the barriers experienced by young people in obtaining mental health services. Their aim is to use a developmental epidemiological perspective to: a) Explore characteristics (demographic, onset/development/progression mental health problems, environment) of those young people not receiving professional services for their mental health problems and determine if they have access to informal support. b) Examine resilience of young people not receiving mental health services for their problems during unpredictable challenging times (COVID pandemic) c) Employ a statistical technique that is novel to the field of mental health research to adjust for missing data (Missing-Not-At-Random – NMAR modelling) Project 3- Physical activity, sedentary behaviour, and diet: impact on cardiometabolic and women’s health. Many different health outcomes are influenced by lifestyle behaviours such as physical activity, sleep, and diet. This research focuses on two main health clusters: cardiovascular related health and women’s health. Lifestyle behaviours, that are often interlinked and influenced by the social environment, may have both synergistic and specific effects on health across the life course in ways we do not completely understand. The researchers will investigate how lifestyle behaviours (physical activity, sleep, diet, alcohol, smoking) as well as specific components of each behaviour (e.g., activity type, duration, frequency, context, intensity) across the life course influence HES-data derived health outcomes including diagnoses and healthcare use. The researchers have two main work packages in this project. First, they intend to examine cardiometabolic related conditions (diabetes, stroke, heart disease etc) and second, to examine outcomes related to women’s health. These findings will have direct benefits for society as it enables researchers to identify specific components of health behaviours that improve health, whilst having the robustness of HES data. Project 4 – “Hospital use over the life course”. Frailty is a valuable concept, widely used to inform and guide the clinical care of older people. Still, its relevance in people aged less than 65 is not clear. This research examines different approaches to frailty measurement that could be applied across the NHS to see if they usefully identify the risk of future poor outcomes. This project will use nationally representative studies following people since birth (‘birth cohorts’) to make a ‘frailty index’. This is a means of measuring the level of frailty and can be applied to different patient groups and over time. Frailty indices work by identifying common factors associated with frailty and determining how many are present in any individual. This gives rise to a frailty index, which has been shown to link to adverse outcomes in studies of older - and some younger - people. Because data have been collected since birth, we can explore the life course factors that may influence the development of frailty at any age of adulthood. In parallel, this research will test a Hospital Frailty Risk Score (HFRS). This can be automatically created from NHS electronic records for all people admitted to a hospital in England. Researchers will assess the ability of this risk score to predict how long people stay in hospital and whether they die. Creating a frailty index and an HFRS in the same individuals through linked birth cohort and NHS electronic records, researchers can then see how the two different approaches to frailty measurement compare for hospital outcomes such as length of stay and death, but also outcomes such as quality of life, physical or cognitive function. Once the researchers understand the factors associated with frailty in younger adults, they can think about treatments that could be applied over the life course to slow or prevent the development of frailty.

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Datasets: + HES-ID to MPS-ID HES Accident and Emergency; + HES-ID to MPS-ID HES Admitted Patient Care; + HES-ID to MPS-ID HES Outpatients

Objective for processing

The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council (ESRC) Centre, based at the Department of Quantitative Social Science, UCL Institute of Education. It is responsible for three of Britain's internationally renowned birth cohort studies, the 1958 National Child Development Study, the 1970 British Cohort Study (BCS70) and the Millennium Cohort Study (MCS). All these studies are 'birth' studies, following the groups of participants from cradle to grave. As such, this group of studies is unique and has, and still is, providing a wealth of information used in the policy decisions affecting society's health and well-being. The Centre for Longitudinal Studies (CLS) at University College London (UCL) requires access to NHS England data for the purpose of the National Child Development Study (NCDS). In 1958 doctors and scientists were concerned at the high rate of [45 words unchanged] on the family background of the mother, the pregnancy and labour, and about the baby at birth and during the first week of the baby’s life. Seven years later it was decided it would be worthwhile to find [82 words unchanged] took place in 2002/3. The Age 62 survey commenced in January 2020 but is currently when it was paused due to the impact of the COVID-19 pandemic. It is expected to re-commence pandemic but re-commenced in early 2021. Subsequent sweeps sweeps* of the study will likely take place every five years. During the 2008 (Aged 50) survey, CLS at UCL obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from 6,529 cohort members who at the time were in England. *The term ‘sweep’ is used to refer to a round of data collection in the longitudinal study. Linking health data from Hospital Episodes Statistics (HES) to the Next Steps survey data has greatly increased the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyle aspects, such as; drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data has enriched these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which have helped UCL better understand how health conditions could be better treated or supported. NCDS has collected information about cohort members’ education and employment, economic circumstances, family life, physical and emotional health and wellbeing, social participation and attitudes. Data about health behaviours are more accurate when obtained from administrative records because of misreporting of complex health conditions, under-reporting of health problems or due to perceived sensitivities around certain behaviours and lifestyle aspects. So, this offers an interesting methodological opportunity to validate the data collected in the survey and vice versa. This Agreement sets out three distinct elements for which relevant information is subsequently given for each: At this stage the aim of the research is to; 1) The CLS will receive linked data for the cohort and will use that data to validate, enhance and improve the quality of the cohort data, in this way creating a uniquely rich administrative/survey-linked data set. 1. validate and improve the quality of the cohort data The following NHS England data will be accessed: 2. produce methodological papers describing the quality of the data and its benefit to health and social care • Hospital Episode Statistics 3. develop and create a useful and rich HES linked NCDS Aged 50 data set. o Admitted Patient Care 4. Advance learning in the research community by providing access to the linked NHS Digital HES / CLS' NCDS Age 50 data to the research community via the UK Data Service (UKDS) Secure Lab, through a sub-licensing agreement agreed between UCL and NHS Digital. o Accident & Emergency CLS at UCL has not granted any sublicenses as of yet. This is because they are at the data preparation stage – ensuing that the data they provide to researchers has been suitably minimised and grouped to be of the best use for the projects. CLS at UCL envisage this will commence in the Autumn of 2020 (September/October) and there will be approximately 2-3 sublicensees per month. No sub-licences have been granted as of 28/08/2020. o Critical Care THE SUB-LICENCE: o Outpatients UCL are permitted to include onward sharing of the linked HES and Next Steps data with the UK Data Service (UKDS), where data can be accessed by accredited researchers in a Secure Research Environment, known as Secure Lab, following a “Sub-licensing model”. • Emergency Care Data Set (ECDS) The UKDS is funded by the Economic and Social Research Council (ESRC) with contributions from the University of Essex, the University of Manchester and Jisc (Jisc is a United Kingdom not-for-profit company whose role is to support post-16 and higher education, and research, by providing relevant and useful advice, digital resources and network and technology services, while researching and developing new technologies and ways of working). The UKDS provides access to high-quality data to meet the data needs of researchers, students and teachers from all sectors including academia and central and local government. Linking health data from Hospital Episodes Statistics (HES)/Emergency Care Data Set (ECDS) to the NCDS survey data has greatly increased the possibilities for using the cohort to study how health outcomes impact the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and social or economic determinations of health behaviours such as drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The UKDS is based at, and hosted by, the University of Essex. Although the researchers at the UKDS are substantively employed by the University of Essex, only staff who are permitted to work at the UKDS will access the data. The successful inclusion of HES/ECDS data has enriched these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this - e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which have helped CLS better understand how health conditions could be better treated or supported. Under the “Sub-licensing model”, NHS Digital shares data with UCL, who are in turn licensed to share these data with other organisations, subject to agreed controls, scoped in this agreement between NHS Digital and UCL. In line with this onward sharing model, the data sharing controls in place between NHS Digital and UCL are replicated between UCL and the other organisations. UCL, which houses CLS at the UCL Institute of Education, is fully accountable for the actions of the parties involved in subsequent data share and use. The agreement mirrors the Data Sharing Framework Contract in place between NHS Digital and UCL. It also requests information about the research proposal, benefits to health and/or social care, organisational security assurance and terms and conditions regarding onward sharing of data, responsibilities and processing activities etc. Data about health behaviours are more accurate when obtained from administrative records because of misreporting of complex health conditions, under-reporting of health problems or due to perceived sensitivities around certain behaviours and social or economic determinations of health. This offers an interesting methodological opportunity to validate the data collected in the survey and vice versa. Under the sub-licensing model CLS will deposit the linked data with UK Data Service, who will serve as a data repository. Access to the deposited data will be granted to approved researchers within a Secure Research Environment on behalf of UCL, as outlined in this document. NHS Digital will retain the ability to directly audit UKDS’s compliance with the outlined and agreed data access arrangements. The level of data required is identifiable - necessary to enable linkage of the data with data collected from other sources, including the participants themselves. There will be no charge applied to licence supplied by CLS. The data will be minimised as follows: The territory of use in the sub-licence will be the same or narrower than the territory of use stated in this DSA, namely UK - Limited to a study cohort of approximately 7,591. Of these, UCL only linked those members of the cohort who took part in the Age 50 Survey in 2008 and gave their permission to add information from health records held by the NHS. In this sharing model of the linked data, UCL will be a data controller, determining the purposes for which and the manner in which the linked data are processed. The UKDS will be the data processor, as they will be processing the data on behalf of UCL. This includes holding the linked data in a secure environment, screening for completeness of applications for data access, providing training for use of linked data securely, entering into contractual agreements with approved researchers, extraction of approved data and setting up access systems, and approving statistical outputs, following a statistical disclosure control procedure. 2) The CLS will use this data set to produce methodological papers on the quality of the data (e.g. around measurement, and representativeness) and research papers helping to showcase its benefits for healthcare, adult social care, or the promotion of health. The approved organisations and researchers, who are granted an access to the linked data via the UKDS Secure Lab, agree to terms and conditions of use, their rights and responsibilities as users of the linked data, as defined by the UKDS. In addition to the agreements signed with the UKDS, the organisation of the researcher applying to use the linked data will enter into a Licence agreement with UCL. Access will be restricted to CLS researchers who meet the following requirements: ORGANISATIONAL AGREEMENTS i. The researcher must be substantively employed in the CLS by UCL; UCL will provide a sub-licence to UK organisations undertaking research that will be of benefit to the public (this will be assessed in the project proposal form submitted to the UKDS and to UCL). Applicants (potential licencees) will need to show in Schedule 1 Section 18 (a) and (b) that the provision of the sub licencing will be in the public interest and that that the data will be used either (i) for the provision of health care or adult social care; or (ii) for the promotion of health. UCL will not provide data access to commercial organisations for research for commercial purposes. Additionally, the CLS Licence agreement, Schedule 1 Section 18, will assess the project proposal against its assessment criteria to determine the details of the project, the people who will be accessing the data, and what the data will be requested. Applicants will need to be accredited researchers or agree to undertake training and become accredited, prior to accessing the data. Additionally, applicant’s organisation will need to provide evidence that they have IG and security assurances in place (covered in Schedule 1, Section 15 of the CLS Licence agreement). Members of the CLS Data Access Committee (DAC) will review and decide if the evidence provided satisfy the criteria requirements. ii. The researcher must have completed NHS England’s Data Security Awareness course; Applicants (licensees) and their organisations will have to sign two agreements to obtain a sub-licence, one with the UKDS and another with UCL, in both cases the licensee will agree with the terms stated in the Confidentiality Section of the UCL Licence Agreement and with the Confidentiality Terms stated in the Secure Access Agreement which will be signed with the UKDS. By signing these agreements the licensee agrees to adhere to these terms, including respecting the privacy of health services users data they will receive. Licensees are also be reminded of the penalties they are likely to incur if they do not comply with the terms they have agreed. In addition to the above, the UKDS agreement stipulates that data users must complete mandatory training before they are allowed to access the data. iii. The researcher must have submitted a project proposal for review by the CLS Data Access Committee (DAC) and the CLS DAC must have approved the access request; To ensure the security of the linked information, shared with UCL by NHS Digital, and subsequently shared by CLS at UCL with the UKDS, where data could be accessed by approved researchers in a Secure Lab, UCL envisage the following controls employed at the different steps of the process of depositing, approving and sharing of the linked information: iv. Once, approved, the researcher will sign a licence agreement with CLS and will then be granted access to the relevant subset of data via the UCL Data Safe Haven (DSH). • An agreement between NHS Digital and CLS to onwardly share linked HES and UCL information under “Sub-licencing model”, which outlines the terms and conditions of use of the linked data via the UKDS Service Secure Lab as the data repository, and the full accountability of UCL (housing CLS at the UCL Institute of Education) to the actions of the parties involved in subsequent access to the linked data. The level of data required is pseudonymised. • An agreement between CLS at UCL (as a data controller) and UKDS (as a data processor), which outlines the terms and conditions under which the linked data can be accessed via the UKDS Secure Lab. The data will be minimised to variables and potential cases relevant to the purpose. • An agreement between UKDS and the approved researcher, which outlines the terms and conditions of use of the linked data in the UKDS’s Secure Lab. 3) The CLS will promote and make possible wider use of this linked data set, through providing wider access to the linked NHS England HES / CLS NCDS data to the research community via the UK Data Service (UKDS) Secure Lab under sublicensing agreements subject to the following access arrangements. • An Agreement between UCL at UCL and the organisation requesting to use the linked data via the UKDS, which outlines the terms and conditions of use of the linked data. With the exception of individuals substantively employed by UCL in the CLS, all other access will be via the UKDS only. Access to the data via the UKDS will only be granted to third party researchers who meet the following requirements: i. The researcher must be registered with the UKDS; ii. The researcher must successfully apply for approval by the CLS DAC via the process outlined below. The process for applying for approval is as follows: i. The researcher submits an application, including an 'Accredited Researcher application form' and 'Research proposal', to the UKDS. ii. The UKDS screens the application and either rejects or forwards the application to the CLS at UCL. iii. The CLS checks the organisational Information Governance and security assurance evidence provided and either requests further evidence, if the evidence of provided does not meet the requirements (as outlined in the sub-licence), or submits the application for CLS DAC approval. iv. The CLS DAC assesses both project documents (UKDS project proposal and the “UCL Licence agreement”) and makes a decision to approve it, not approve it, or require further information. CLS DAC considerations include an assessment of the expected benefits to health care, adult social care or the promotion of health. Should an application be rejected, a researcher can apply again with a revised application. v. If the CLS DAC approves the project: a. CLS informs UKDS that the project has been approved. b. The CLS authorised representative signs the “UCL License agreement” and sends it back to the UKDS to be forwarded to the researcher. vi. UKDS informs the researcher that their project was approved; sends them a countersigned copy of the “UCL Licence agreement’ and makes the data available to them via Secure access to linked data at the Safe Centre at the UKDS (hosted at the University of Essex) or via the researcher's own institutional desktop PC, depending on the sensitivity/impact level of the data being requested. The agreement with the UKDS is signed at the point of deposit of the data. The data manager will prepare the data for deposit and as part of the depositing of the data, the Deposit Licence agreement is signed. vii. CLS DAC will publish the information about any data dissemination on the CLS website, including the name of the organisation to which data was provided, purpose (summary of the project) and what data was released. (NB: If CLS DAC does not approve the project, no data will be disseminated). The data will be provided to the researcher via their own project folder, which will contain only the data that the researcher needs to see for their project. The research-linked data provided to researchers are pseudonymised and de-identified, and will never contain identifiable information such as name, address, date of birth, NHS or NI number. [1 paragraph unchanged] The data held at UCL will be deleted if the data sharing agreement between NHS Digital and UCL were to cease. If it were to cease, the license agreement between UCL and the licensee organisation will be terminated. Disclosure control checks are carried before any research publication. UCL legal basis for processing (acquiring, linking and sharing) personal data is for a public task under GDPR (article 6(1)(e)) i.e. processing is necessary for the performance of a task carried out in the public interest (as is made explicit to participants in the information leaflets provided). UCL also process special categories of personal data for research under GDPR (article 9(2)(j)) i.e. processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. In addition, for ethical reasons and under the Common Law Duty of Confidentiality, UCL sought permission from cohort members to access and link their routine health records to their survey data, and to the onward sharing of this linked data in pseudonymised form (via a secure setting with appropriate safeguards). The data sharing controls in place between NHS England and UCL are replicated between UCL and the other organisations. UCL, which houses CLS at the UCL Institute of Education, is fully accountable for the actions of the parties involved in subsequent data share and use. The UCL Licence agreement mirrors the Data Sharing Framework Contract in place between NHS England and UCL. All data processed under the sub-license will be completed using the same legal basis as mentioned above, namely GDPR (article 6(1)(e))and GDPR (article 9(2)(j)). The CLS Licence agreement will require licensees to provide the Legal Basis of their request to link health data via CLS and therefore CLS Data Access Committee (DAC) will only grant approval to applications from researchers within public bodies who have a legal basis to process data under GDPR. There will be no charge applied to licenses supplied by UCL. The data disseminated to UCL will be accessed by substantive employees of UCL who will work on the data to make it research ready, will pseudonymise the data and deposit it at the UKDS for researchers applying to use for specific projects. The pseudonymised data will also be accessed by CLS employees to conduct methodological and research work via the UCL Data Safe Heaven. NHS England will retain the ability to directly audit UKDS's compliance with the outlined and agreed data access arrangements. Access to the deposited data can be remote access via the secure lab or physically present at UKDS, depending on which environment is more suitable for the researcher. The anticipated volume / number of licences is 1-2 sub-licences per month. Sub-licences shall not exceed a term of 12 months at a time and may be extended/renewed if appropriate. The sub-licences remain valid only while UCL retains the right to hold and share the data from NHS England. The territory of use in the sub-licence will be the same or narrower than the territory of use stated in this data sharing agreement, namely the UK. The UKDS (hosted by University of Essex) is a processor acting under the instructions of UCL. UKDS’ role is limited to holding the linked data in a secure environment; screening for completeness of applications for data access; providing training for use of linked data securely; entering into contractual agreements with approved researchers; extraction of approved data and setting up access systems, and approving statistical outputs, following a statistical disclosure control procedure. UCL will maintain an agreement with UKDS which outlines the terms and conditions under which the linked data can be accessed via the UKDS Secure Lab. UCL will not provide data access to commercial organisations for research or for commercial purposes. UCL is the controller as the organisation responsible for ensuring that the data will only be processed for the purpose described above. The lawful basis for processing personal data under the UK GDPR is Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller; The lawful basis for processing special category data under UK GDPR is Article 9(2)(j) processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. In addition, for ethical reasons and under the Common Law Duty of Confidentiality, UCL sought permission from cohort members to access and link their routine health records to their survey data, and to the onward sharing of this linked data in pseudonymised form (via a secure setting with appropriate safeguards). The funding is provided by the Economic and Social Research Council. The funding is specifically for the study described. Funding is in place until 2025. The University of Essex is a processor acting under the instructions of UCL. The University of Essex hosts the UKDS and their role is limited to storing the linked pseudonymised data and facilitating access to third-party researchers who have the necessary approvals from the CLS DAC and an active sublicense agreement with UCL. Only staff who are permitted to work at the UKDS (and are substantively employed by the University of Essex) will process the data. Should any substantively employed researchers from the University of Essex wish to use the UKDS data, they will be required to apply via the sub-licence route, the same as other researchers from other organisations. Amazon Web Services (AWS) is a processor acting under the instructions of UCL. AWS’ role is limited to secure backup of data stored in UCL’s Data Safe Haven. UCL uses offsite data centre services provided by VIRTUS data centre. VIRTUS does not have access to the data. The UKDS is funded by the Economic and Social Research Council (ESRC) with contributions from the University of Essex, the University of Manchester and Jisc (Jisc is a United Kingdom not-for-profit company whose role is to support post-16 and higher education, and research, by providing relevant and useful advice, digital resources and network and technology services, while researching and developing new technologies and ways of working). The UKDS provides access to high quality data to meet the data needs of researchers, students and teachers from all sectors including academia and central and local government.

Processing activities

No new data is being requested and no further data is being sent to NHS Digital under this version of the Agreement. The CLS Team is based at UCL. The CLS at UCL will transfer data to NHS England. The data will consist of identifying details (specifically NHS Number, Date of Birth, full name, Postcode, Sex and a unique person ID) for the cohort to be linked with NHS England data. 1. CLS team have supplied NHS Digital with identifiers of cohort members who have consented to this data linkage, including full name, sex, postcode, date of birth, NHS number (if known) and study ID (study-specific pseudonymised identifier). NHS England data will provide the relevant records from the HES and ECDS datasets to UCL. The data will contain no direct identifying data items but will contain a unique person ID which can be used to link the data with other record level data already held by the recipient. 2. NHS Digital have linked the identifiable study data to HES data. NHS Digital have removed identifiers from linked dataset and returned the pseudonymised dataset to the CLS team at UCL with the study ID. The CLS will carry out validation of the administrative pseudonymised data received (linked HES/ECDS data) and combined the supplied administrative data with the information collected from the participant as part of the NCDS study using the study ID. The data disseminated to UCL will be accessed by substantive employees of UCL who have been appropriately trained in data protection and confidentiality. The data will be held at the secure server in the UCL Data Safe Haven (DSH) and accessed remotely by CLS staff. Once the linked survey-administrative data files have been created, the CLS may perform other activities to prepare the data for use, such as coding and cleaning, derivation of summary variables and compilation of data documentation. 3. CLS carried out validation of the administrative pseudonymised data received (linked HES data) and combined the supplied administrative data with the information collected from the participant as part of the Next Steps study using the study ID. The CLS will use these data to create an analysis file which does not contain any identifiable data. Once the linked survey-administrative data files have been created, CLS may perform other activities to prepare the data for use, such as coding and cleaning, derivation of summary variables and compilation of data documentation. The CLS creates derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.) and compare NCDS survey data with data from hospital statistics in order to compare and validate the data collected in CLS surveys. 4. CLS researchers used these data to create an analysis file, which do not contain any identifiable data. The CLS will securely transfer the analysis file to the UKDS (hosted by University of Essex). 5. CLS created derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.) and compare Next Steps survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys. The data will be stored on servers at both UCL and the UK Data Archive based at the University of Essex. The UK Data Archive will store the pseudonymised analysis file only. CLS researchers who need to access the data to produce methodological papers on the quality of the data (eg around measurement, representativeness) and research papers helping to showcase its benefits for health and social care will need to submit an application to CLS DAC detailing their project proposal. Upon DAC approval, a pseudonymised dataset will be provided to the researcher. The data will be held at the secure server in the UCL Data Safe Haven (DSH). Amazon Web Services provides cloud hosting services to UCL and will store the data as contracted by UCL. The UCL DSH is certified to ISO 27001:2013 and is compliant with NHS Digital's Data Security and Protection Toolkit. Research teams using the DSH complete annual training and regularly review data access arrangements ensuring data is only limited to those authorised to access it. UCL Computing Regulations are based on the premise that access to resources is generally forbidden unless expressly permitted. All data transfers from the DSH require approval and are carried out through secure portals which are fully audited. Access to the UCL DSH is via remote desktop and requires multi-factor authentication. In addition to a strong password each user has to use a six digit number generated by a smartphone app or physical token at each login. Passwords must be changed at regular intervals, and unused accounts are automatically disabled after a fixed period. Once inside the environment, robust access control ensures that researchers can only examine information that they are approved to use. The data will be accessed by authorised personnel via remote access. The data will remain on the servers at UCL, AWS and the University of Essex at all times. Identifiers are held separately from attribute characteristics. HES data is not be relinked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study id, and then in turn destroy their data. Personnel are prohibited from downloading or copying data to local devices. Addition of the sub-licence: The data will not leave the UK at any time. The process of accessing the linked data via the UKDS Service Secure Lab include the following steps: Access is restricted to authorised individuals within the CLS at UCL, individuals employed by the UK Data Archive at the University of Essex and access to appropriately minimised subsets of the pseudonymised analysis file will be granted to authorised third parties under sublicense. • Registration with the UKDS Service. All personnel accessing the data have been appropriately trained in data protection and confidentiality. • Submission of an application, including an ‘Accredited Researcher application form’ and ‘Research proposal’. CLS does not link HES/ECDS data to any other dataset. However, when a researcher accesses the pseudonymised data via the UKDS, they may wish to access this data in combination with other pseudonymised datasets which are also available at the UKDS. If a researcher wishes to use the data in combination with other datasets they may do so by using the two datasets together in a secure environment. However, they will need to explicitly mention this in their application to CLS and will only be permitted if CLS DAC approves the project proposal. • Screening of application by UKDS for completeness. At UCL, identifiers will be held separately from attribute characteristics. HES/ECDS data will not be relinked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study ID and then in turn destroy their data. Once a researcher has registered and UKDS has screened / approved the application: UCL only request data for those individuals who have given consent to link their health records to their survey data. Before preparing the file to be sent to NHS England, for the matching of consenting participants to the NHS England HES/ECDS datasets, the data manager will check for withdrawals and will remove participants who- 1) UKDS sends project proposal (researcher application forms) to CLS-UCL (for Data Access Committee (DAC) approval). Applicants are required to demonstrate they have security assurance in place (System Level Security Policy/ISO Certificate/DSPT). · Have asked CLS to withdraw their consent to health data linkage 2) CLS-UCL sends the CLS License Agreement for Linked NHS Digital data to researchers to be completed and signed by their organisation (this includes the benefits to health and social care and evidence of organisational security assurances) not covered by UKDS application. · Have asked CLS to delete their data 3) Researcher/their organisation representative will send the CLS License Agreement for Linked NHS Digital completed and signed back to CLS-UCL. The file will only contain details of those who consented to their health data being linked to NCDS study data and have not subsequently withdrawn their consent or requested that their data be deleted. 4) CLS-UCL will: a) check the organisational Information Governance and security assurance evidence provided as per Section 15 Organisational Security Assurance of the CLS Licence agreement, b) send the project for CLS DAC approval. The HES/ECDS data provided by NHS England, which are linked to the CLS cohort members, are processed by the CLS data management team to minimise the risk of disclosure when linked to the CLS survey data. This is achieved by removing highly identifiable variables and altering other variables by top-coding or truncating them. 5) Should evidence of organisational Information Governance and security assurance provided not meet the requirements (as outlined in the sub-license) CLS will request the applicant to provide further evidence, and will only submit the project to CLS DAC for approval when evidence provided is satisfactory. Approval for data access will only be granted to applicant organisations that meet the security assurance requirement. The data provided to the UK Data Archive will be pseudonymised and will be accessed only via the UKDS secure lab. Data accessed in this way cannot be downloaded. This means that researchers will have access to a screen view only and it is not possible to remove data from the environment. Once researchers and their projects are approved, they can analyse the data remotely from their organisational desktop, or by using the UKDS Safe Room. Specialised staff will apply statistical control techniques to ensure the delivery of safe statistical results. 6) CLS DAC will assess both documents (UKDS project proposal + CLS Licence agreement) and make a decision to approve it or not approve it or require further information. Should an application be rejected, a researcher can apply again with a revised application. Only staff who are permitted to work at the UKDS (and are substantively employed by University of Essex) will process the data for sub-licensing. Note that any data accessed through the UKDS Secure Lab can only be accessed under secure conditions and cannot be downloaded. The linked data provided to approved researchers may be subject to sub-setting of variables (and, if necessary, cases) to minimize disclosure risks and ensure that no individual or organisation can be identified from the results. In addition, all statistical outputs are subject to statistical disclosure control procedure. Access to the Secure Lab is only available to researchers who are based at a UK academic institution or an ESRC-funded research centre and are an ESRC Accredited Researchers. PhD and research students can request access but must apply jointly with their supervisors from established organisations. 7) In the CLS License agreement, CLS DAC will, among other things assess the benefits for health and social care statement, and decide whether it is satisfied with the answer. 8) Once CLS DAC approves the project: a) CLS will inform UKDS that project has been approved. b) CLS representative should sign the CLS License agreement noting the DAC reference number on the License document and send it back to the organisation of the applicant (the Principal Investigator for the study requiring access will sign the agreement - they will be an authorised signatory for their organisation). c) CLS DAC will provide information about any data dissemination to NHS Digital, including the name of the organisation to which data was provided, purpose (summary of the project) and what data was released. (NB. If CLS DAC doesn't approve the project, no data will be disseminated). 9) If CLS DAC is not satisfied with the evidence provided by the applicant about the benefits to health and social care, then CLS DAC can ask the applicant to provide additional information and the project can be re-submitted for CLS DAC approval on the next CLS DAC meeting or via Chair approval. 10) UKDS will inform the researcher that their project was approved and make the data available to them via Secure access to linked data at the Safe Centre at the UK Data Service (hosted at the University of Essex) or via the researcher’s own institutional desktop PC, depending on the sensitivity/impact level of the data being requested . Note that any data accessed through the UKDS Secure Lab can only be accessed under secure conditions and cannot be downloaded. The linked data provided to approved researchers may be subject to sub-setting of variables (and if necessary cases) to minimize disclosure risks and ensure that no individual or organisation can be identified from the results. In addition, all statistical outputs are subject to statistical disclosure control procedure. Access to the Secure Lab is only available to researchers who are be based at a UK academic institution or an ESRC-funded research centre, and be an ESRC Accredited Researcher. PhD and research students can request access but must apply jointly with their supervisors from established organisations. [1 paragraph unchanged] UKDS SECURE DATA HANDLING PROCEDURES The UKDS home working policy does not involve Bring your own device (BYOD). Researchers applying to use the data (stored at the UKDS Secure lab) remotely from home, will only be given permission to access the data if they agree that they will use their work organisation office computer remotely and from there access to the Secure lab. The UK Data Service has received government technical accreditation and has been certified for its secure data handling procedures under the international standard for information security (ISO 27001). To maintain this certification, regular internal and external audits are undertaken. UKDS also hires a government-approved company to conduct internal and external penetration testing of its Secure Lab systems. The data can be accessed by authorised CLS personnel via remote access on UCL-issued devices from their work organisation office or from home. The data will always remain on the servers at UCL CLS. Personnel are prohibited from downloading or copying data to local devices. More widely, the UKDS employs an Information Security Management System (ISMS), to ensure compliance with the ISO accreditation. The Secure Lab falls into this system, and a number of documented processes are regularly maintained and reviewed to ensure these processes are robust, relevant, and fit-for-purpose. The ISMS is overseen by an Information Security Management Group (ISMG), which regularly meets and approves changes to procedures. UKDS DATA ACCESS MECHANISMS As an ESRC resource centre, CLS shares its survey data with the research community via the UKDS under safeguarded or controlled access mechanisms, dependent on the likelihood and potential impact of disclosure. Data with higher risk of disclosure is treated with an appropriate degree of security and management. CLS data fall into the following categories, which are defined by the likelihood and potential impact of disclosure: • Tier 1: data with low level of disclosure: e.g. participant self-reported survey data. These data are made available through the UKDS End User Licence and have a low impact of disclosure; • Tier 2a: data that is potentially disclosive: e.g. medium level and coarse geographies or sensitive information about cohort members. These data are made available through the UKDS Special Licence and have a medium impact of disclosure; • Tier 2: data that are too detailed, sensitive or confidential to be made available under the standard End User Licence or Special Licence, such as detailed geographical indicators or fine-grained individual level linked data. These data have a high impact of disclosure and are made available through the UKDS Secure Access. Access mechanisms to NHS Digital HES data linked to CLS cohort studies via the UKDS The HES data provided to CLS by NHS Digital, which are linked to the CLS cohort members, have been processed by the CLS data management team to minimise the risk of disclosivity when linked to the CLS survey data. This has been achieved by removing highly identifiable variables and altering other variables by top-coding or truncating them. Following this processing, the final health datasets have been classified under Tier 2. It is therefore CLS’s intention to deposit these Tier 2 linked HES data with the UK Data Service under the UKDS Secure Access, and provide access to this information for approved researchers, following the process and contractual arrangements, outlined above and described in more detail below, following an agreed between NHS Digital, CLS and UKDS onward sharing model. The UKDS’s ‘Controlled access to data’ specification is supplied to NHS Digital as part of this application amendment. The data provided will be pseudonymised, and will be accessed only via the UKDS secure lab. Downloading the data is not possible. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

Expected output

Following the data quality and validation work, the first output- output will be the creation addition of the refreshed data to the existing linked NCDS/HES dataset which is now available for researchers to apply. The most recent HES data requested in under this application Agreement will refresh this already rich linked dataset. CLS and the UKDS will publicise the data release at both on the CLS and UKDS websites. website. CLS has not yet sub-licenced the available NCDS/HES data as CLS only recently got permission for sub-licensing this data. CLS has recently delivered a webinar to introduce this dataset to researchers and hope to start receiving applications soon. Since approval, CLS has sub-licenced (used) the NCDS/HES to support one methodological project which is detailed below: The reason for not having received more applications is because it took a while to receive the approval and then to get the application process set up at CLS. When this permission was granted for the first time, CLS needed to work on the data to prepare it for deposit at the UKDS. Once, data was deposited, the UKDS also needed a month to make the data available to researchers. This meant that the data was not available for researchers to use 7 months after gaining approval, this also happened during the period in which the pandemic started. CLS has recently delivered a webinar to introduce this dataset to researchers and is currently planning further promotion of this dataset via different platforms, to increase the use of the data. These include, possible collaboration with HDRUK to promote this linked data and a Depositor Case study which will be produced by the UK Data Service and will showcase a project that is currently using CLS’ other study (Next steps) linked to HES data with the intention to bring awareness of the availability and usefulness of this rich linked dataset. A first application to use the data is currently in progress, details are provided below: The second output will be a methodological paper titled : “Examining the linkage quality and sample representativeness of the linked 1958 National Child Development Study (NCDS) ”. This is planned to be published by the end of 2022. The target journals for publication are : Project Title: 'Linkage of National Longitudinal Cohort Studies and Administrative Data: A Mutually Beneficial Arrangement' 1) Journal for Survey Statistics and Methodology special issue on “Recent Advances in Data Integration” This research will be addressing the following three methodological research questions, which will be directly reflected in the project work packages. 2) Public Opinion Quarterly special issue on “Augmenting Surveys with Paradata, Administrative Data, and Contextual Data” 1. How can linked administrative data aid the handling of missing cohort data? 3) International Journal of Population Data Science In this paper, researchers have examined the quality of the linkage in terms of the associations between key cohort member sociodemographic characteristics and successful linkage, and compared the levels of successful linkage within strata of NCDS variables which may be expected to be associated with hospital attendance, and hence with successful HES linkage (self-reported hospital attendance, self-rated general health, self-reported long-term illness). The researchers have additionally evaluated the population representativeness of the linked sample using external data (hospital admission rates in the general population). The findings suggest that the linkage quality of the NCDS-HES data is high and that the linked sample maintains an excellent level of population representativeness. Researchers hope that these analyses will both improve the quality and transparency of research using this linked data resource and encourage providers and users of other linked data resources to undertake and publish similarly thorough evaluations. 2. How can linked cohort data improve our understanding of the quality of administrative data? CLS has not yet published any methodological papers reviewing the linkage.. The reasons why no papers have been published since earlier versions of this application were approved is because CLS have a policy of not using data internally for research when not available to external users and the original approval did not allow CLS to share the data with external users. Thus, CLS have focused on getting the necessary permission for sub-licencing the data. 3. How can linked cohort data help address residual confounding in analyses of administrative data The creation of this refreshed HES/NCDS database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. The UKDS will approve statistical outputs, following a statistical disclosure control procedure and HES analysis guide for the data accessed via the UKDS secure lab. CLS actively promotes the use of their data among the research community through publications and briefings, working papers, webinars, social media, public reports, hosted events. As well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care. CLS has not yet published any methodological papers reviewing the linkage. CLS expects to carry out these methodological and research assessments two/three years after the approval of this extension. This is because CLS have a policy of not using data internally for research when not available to external users and the original approval did not allow CLS to share the data with external users. Thus, CLS have focused on getting the necessary permission for sub-licencing the data. The creation of this HES/NCDS Aged 50 database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. CLS actively promotes the use of their data among the research community through publications and briefings , working papers, webinars, social media, public reports, hosted events. As well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care.

Expected measurable benefits

NCDS surveys include questions relating to health outcomes and hospitalisations. CLS will [33 words unchanged] the data quality and comparability of two important data sources. This will help both improve the quality and transparency of research using this linked data resource and encourage providers and users of other linked data resources to undertake and publish similarly thorough evaluations. This will also be of benefit to research investigating health and social care. This data linkage will also facilitate research that CLS anticipate will be carried out on the effects [32 words unchanged] community services interfacing with schools through informing policy to improve healthy lifestyles. It is difficult to predict in advance the type of research question that might be put forward to use the linked NCDS/HES linked data. In total over 1600 research publications have been produced using NCDS data since 2008 - of which a very significant proportion are focused on aspects of health. Below are some examples of existing publications using NCDS data benefiting public health: Below are examples of existing publications using NCDS data benefiting public health in the areas of pregnancy health, birth, breastfeeding, vitamin D, obesity, diabetes, respiratory disease and others. The research papers below have been written using data from the CLS cohorts and COVID19 surveys: ARCHER, G., XUN, W.W., STUCHBURY, R., NICHOLAS, O. and SHELTON, N. (2020) Are ‘healthy cohorts’ real-world relevant? Comparing the National Child Development Study (NCDS) with the ONS Longitudinal Study (LS). Longitudinal and Life Course Studies, 20(20), 1-24. David Bann, Aase Villadsen, Jane Maddock, View ORCID ProfileAlun Hughes, George B. Ploubidis, Richard J. Silverwood, Praveetha Patalay (2020), ‘Changes in the behavioural determinants of health during the coronavirus (COVID-19) pandemic: gender, socioeconomic and ethnic inequalities in 5 British cohort studies’. AVENDANO, M., DE COULON, A. and NAFILYAN, V. (2020) Does longer compulsory schooling affect mental health? Evidence from a British reform. Journal of Public Economics, 183, 104137. Using data from five nationally representative British cohort studies (born 2000-2, 1989-90, 1970, 1958, and 1946), researchers investigated sleep, physical activity (exercise), diet, and alcohol intake (N=14,297). Using measures of each behaviour reported before and during lockdown, researchers investigated change in each behaviour, and whether such changes differed by age/cohort, gender, ethnicity, and socioeconomic position (SEP; childhood social class, education attainment, and adult reporting of financial difficulties). The results showed changes in these outcomes occurred in both directions, i.e. shifts from the middle part of the distribution to both declines and increases in sleep, exercise, and alcohol use. For all outcomes, older cohorts were less likely to report changes in behaviours compared with younger cohorts. In the youngest cohort (born 2001), the following shifts were more evident: increases in exercise, fruit and vegetable intake, sleep duration, and less frequent alcohol consumption. Widening inequalities in sleep during lockdown were more frequent amongst females, socioeconomically disadvantaged groups, and ethnic minorities. For other outcomes, inequalities were largely similar before and during lockdown, yet ethnic minorities were increasingly likely during lockdown to undertake less exercise and consume lower amounts of fruit and vegetables. BANN, D., FITZSIMONS, E. and JOHNSON, W.. (2020) Determinants of the population health distribution, or why are risk factor-body mass index associations larger at the upper end of the BMI distribution? International Journal of Epidemiology, dyz245, 13 January 2020. doi: https://doi.org/10.1101/2020.07.29.20164244 BRICARD, D., JUSOT, F., TRANNOY, A. and TUBEUF, S. (2020) Inequality of opportunities in health and death: an investigation over the lifespan in Great Britain. International Journal of Epidemiology, Upcoming, 2020. Constantin-Cristian Topriceanu, Andrew Wong, James C Moon, View ORCID ProfileAlun D Hughes, David Bann, Nish Chaturvedi, Praveetha Patalay, Gabriella Conti, Gabriella Captur (2020), ‘Inequality in access to health and care services during lockdown’ – Findings from the COVID-19 survey in five UK national longitudinal studies Researchers studied whether COVID19 further deepened existing health inequalities. Access to health services and adequate care is influenced by sex, ethnicity, socio-economic position (SEP) and burden of co-morbidities. However, it is unknown whether the COVID-19 pandemic further deepened these already existing health inequalities. Participants were from five longitudinal age-homogenous British cohorts (born in 2001, 1990, 1970, 1958 and 1946). A web and telephone-based survey provided data on cancelled surgical or medical appointments, and the number of care hours received during the UK COVID-19 national lockdown. Using binary or ordered logistic regression, researchers evaluated whether these outcomes differed by sex, ethnicity, SEP and having a chronic illness. The findings showed that Females and those with a chronic illness experienced significantly more cancellations during lockdown. Ethnic minorities and those with a chronic illness required a higher number of care hours during the lockdown . Age was not independently associated with either outcome in meta-regression. SEP was not associated with cancellation or care hours. CARAYOL, M., ALBERTUS, G., FANTIN, R., LANG, T., KELLY-IRVING, M., GROSCLAUDE, P. and DELPIERRE, C. (2020) Nutritional lifestyle patterns and cancer: confounding effect of social determinants across the life course in women from the 1958 British birth cohort study. Longitudinal and Life Course Studies, 20(20), 1-22. https://doi.org/10.1101/2020.09.12.20191973 DAVID BATTY, G., DEARY, I.J., HAMER, M., FRANK, P. and BANN, D. (2020) Association of Childhood Psychomotor Coordination With Survival Up to 6 Decades Later. JAMA Network Open, 3(4), e204031. GAGNÉ, T, SCHOON, I and SACKER, A. (2020) Health and voting over the course of adulthood: Evidence from two British birth cohorts. SSM - Population Health, 10(April 2020), 100531. MADDOCK, J., CASTILLO-FERNANDEZ, J., WONG, A., COOPER, R., RICHARDS, M., ONG, K.K., PLOUBIDIS, G.B., GOODMAN, A., KUH, D., BELL, J.T. and HARDY, R. (2020) DNA methylation age and physical and cognitive ageing. Journals of Gerontology: Series A, 75(3), 504–511. NING, K., GONDEK, D., PATALAY, P. and PLOUBIDIS, G.B.. (2020) The association between early life mental health and alcohol use behaviours in adulthood: A systematic review. PLOS ONE, 15(2), e0228667. ØIESTAD, B.E., HILDE, G., TVETER, A.T., PEAT, G.G., THOMAS, M.J., DUNN, K.M. and GROTLE, M. (2020) Risk factors for episodes of back pain in emerging adults. A systematic review. European Journal of Pain, 24(1), 19-38. PINTO PEREIRA, S, DE STAVOLA, B.L, ROGERS, N.T, HARDY, R, COOPER, R and POWER, C. (2020) Adult obesity and mid-life physical functioning in two British birth cohorts: investigating the mediating role of physical inactivity. International Journal of Epidemiology, published online(6 March 2020), dyaa014. SIRONI, M, PLOUBIDIS, G.B and GRUNDY, E.M. (2020) Fertility History and Biomarkers Using Prospective Data: Evidence From the 1958 National Child Development Study. Demography, 57, 529-558. ANDERSON, D.J., CHUNG, H.-F., SEIB, C.A., DOBSON, A.J., KUH, D. , BRUNNER, E.J., CRAWFORD, S.L., AVIS, N.E., GOLD, E.B., GREENDALE, G.A., MITCHELL, E.S., WOODS, N.F., YOSHIZAWA, T. and MISHRA, G.D. (2019) Obesity, smoking, and risk of vasomotor menopausal symptoms: a pooled analysis of eight cohort studies. American Journal of Obstetrics and Gynecology, published online, 6 November 2019. BANN, D., FLUHARTY, M., HARDY, R. and SCHOLES, S. (2019) Socioeconomic inequalities in blood pressure: co-ordinated analysis of 147,775 participants from repeated birth cohort and cross-sectional datasets, 1989 to 2016. MedRxiv, Posted, 21 December 2019. BALDWIN, J.R. and DANESE, A.. (2019) Pathways from childhood maltreatment to cardiometabolic disease: a research review. Adoption & Fostering, 43(3), 329-339. BATTY, G.D., DEARY, I., HAMER, M., RITCHIE, S. and BANN, D. (2019) Childhood coordination and survival up to six decades later: extended follow-up of participants in the National Child Development Study. MedRxiv, posted, 13 August 2019. BERGER, E, CASTAGNE, R, CHADEAU-HYAM, M, BOCHUD, M, D'ERRICO, A, GANDINI, M, KARIMI, M, KIVIMAKI, M, KROGH, V, MARMOT, M, PANICO, S, PREISIG, M, RICCERI, F, SACERDOTE, C, STEPTOE, A, STRINGHINI, S, TUMINO, R, VINEIS, P, DELPIERRE, C and KELLY-IRVING, M. (2019) Multi-cohort study identifies social determinants of systemic inflammation over the life course. Nature Communications, 10, 773. BLACK, N, JOHNSTON, D.W., PROPPER, C and SHIELDS, M.A.. (2019) The effect of school sports facilities on physical activity, health and socioeconomic status in adulthood. Social Science & Medicine, 220, 120-128. BOUNTZIOUKA,V, CUMBERLAND,P.M and RAHI,J.S. (2017) Trends in Visual Health Inequalities in Childhood Through Associations of Visual Function With Sex and Social Position Across 3 UK Birth Cohorts. JAMA Ophthalmology, 135(9), 954-961. [1 paragraph unchanged] ANDERSON, L.R. (2018) Adolescent mental health and behavioural problems, and intergenerational social mobility: A decomposition of health selection effects. Social Science & Medicine, 197, 153-160. IMPACT: Research using NCDS Cohort Study data has contributed to better understanding the relationship between early life eczema and asthma later in life. ARCHER, G , PINTO PEREIRA, S and POWER, C. (2017) Child maltreatment as a predictor of adult physical functioning in a prospective British birth cohort. BMJ Open, 7(10), e017900. Detailed information about the study can be found here: BALBO, N. (2018) The Mental Toll of Being Connected. What kind of impact is social media having on adolescent health? Population Europe Policy Brief No. 19, Nov 2018. Berlin: Max Planck Society. https://www.mdpi.com/1660-4601/15/7/1415 BATTY, D.G., PLOUBIDIS, G.B., GOODMAN, A and BANN, D. (2018) Association of nursery and early school attendance with later health behaviours, biomedical risk factors, and mortality: evidence from four decades of follow-up of participants in the 1958 birth cohort study. Journal of Epidemiology and Community Health, 72(7), 658-663 BANN, D, JOHNSON, W, LI, L, KUH, D and HARDY, R. (2018) Socioeconomic inequalities in childhood and adolescent body-mass index, weight, and height from 1953 to 2015: an analysis of four longitudinal, observational, British birth cohort studies. Lancet Public Health, 3(4), e194-e203. LLEWELLYN, A, SIMMONDS, M, OWEN, C.G and WOOLACOTT, N. (2016) Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obesity Reviews, 17(1), 56-67. IMPACT: Research using NCDS Cohort Study data has enabled a long-run investigation of socioeconomic inequalities in BMI, and to more recent data than previously available. Detailed information about the study can be found here: https://www.sciencedirect.com/science/article/pii/S2468266718300458?via%3Dihub BARBOSA-SOLÍS, C, KELLY-IRVING, M, FANTIN, R, DARNAUDÉRY, M, TORRISANI, J, LANG, T and DELPIERRE, C. (2015) Adverse childhood experiences and physiological wear-and-tear in midlife: Findings from the 1958 British birth cohort. Proceedings of the National Academy of Sciences of the United States of America, 112(7), E738–E746. Using the Millennium Cohort Study (MCS), another existing study being researched at the CLS, as an example below are examples highlighting each point, which are expected to be replicated: BERRY, D.J, HESKETH, K, POWER, C and HYPPONEN, E. (2011) Vitamin D status has a linear association with seasonal infections and lung function in British adults. British Journal of Nutrition, 106(9), 1433-14440. The CLS have already demonstrated yielded measurable benefits within the Health and Social care system with various studies to date. Whilst a single output is not expected to change healthcare policy or practice, the variety of outputs that are expected, are planned to add to the existing body of evidence supporting improvements to healthcare policy and practice in the long term. MONTGOMERY, S.M and EKBOM, A. (2002) Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. British Medical Journal, 324, 26-27. 1. CLS researchers have already added to the existing body of evidence supporting various scientific publications. Based on ESRC-supported research and using both Millennium Cohort and Understanding Society data, the Department for Work and Pensions (DWP) has launched a policy initiative aimed at supporting parents/carers and families who experience worklessness and economic disadvantage, with the objective of improving educational attainment and mental health outcomes for children and young people. This policy announcement uniquely recognises young peoples educational attainment and mental health as primary pillars of future employability, and the importance of the longitudinal studies employed to support these conclusions and policy investment recommendations. As an example of longitudinal study data use, the DWP describe: This innovative, research-led policy investment is directing support to front-line professionals aimed at improving educational and mental health outcomes for children whose parents/carers experience worklessness. Proposals based on the findings include: Redefining the Troubled Families Programme “to encourage a greater emphasis on tackling worklessness and issues associated with it”. - Strengthening support to help reduce relationship distress between parents/carers, whether together or separated, announcing an innovative new programme, backed initially by £30 million (April 2017), with an additional 30 million added in the November 2017 Budget Statement. Using data from the Millennium Cohort Study (MCS), CLS researchers and collaborators have investigated the prevalence of mental ill-health during childhood and adolescence, up to age 14. Their research has aimed to identify the factors associated with mental ill-health and the groups most at risk and in need of support. In addition, their studies have examined the distinction between poor mental health and poor wellbeing. This research has received widespread public attention and achieved a variety of impacts on policy and practice. Following on from this work, CLS researchers are exploring the prevalence of mental ill-health among the cohort in their mid-teens, using MCS data, collected at age 17. 2. Research evidence used for government briefing papers. CLS academics, wrote accessible briefing papers available on the link below, which were shared with government departments. The extensive media coverage about the research drew huge interest from policymakers, practitioners, parents, and educators. The research rapidly became part of the bloodstream of public discussion of young people’s mental health. https://cls.ucl.ac.uk/briefings_impact/ The researchers hosted an ESRC Festival of Social Science event, attended by policymakers and third sector, participated in the Public Health England Special Interest Group on young peoples mental health, and presented at a Conservative Party roundtable on youth mental health, and at the Public Health England (PHE) annual conference. To help communicate the distinction between mental health and wellbeing, the researchers translated complex statistical analysis into an infographic showing risk and protective factors at age 11. This was used in several contexts and by practitioners in public health, including PHE, the Department for Education and many local government and child mental health training programmes. Concrete policy impacts include decisions to increase children and young peoples (CYP) mental health services capacity and changes to PHE strategies for tackling CYP mental health. For example, due to this work, PHE has expanded its focus from one that had previously been on mental health, to also include mental wellbeing. It also adapted the framework it uses to identify how to best support young people, by extending the range of multi-level and multi-setting risk and protective factors associated with mental health and wellbeing. This work has also framed discussion among policymakers at the Department for Education. The research based on MCS age 14 data highlighted for the first time the extent of mental ill-health among young people across the UK. Through widespread dissemination in the media, it was instrumental in bringing the scale of the issue to public consciousness. Coverage included front-page headlines and follow-up features in a variety of national newspapers, and high-profile interviews in the broadcast media. As NHS England’s National Mental Health Director stated to The Guardian in response to the research: “After decades in the shadows, children’s mental health is finally in the spotlight” 3. Attracting public attention to the wider issues surrounding the research. There was also extensive coverage for the findings on changes in mental health over time, and on the links between parental break-up and children’s mental health. For example: The Times -Quarter of girls are depressed at 14 in mental health crisis BBC News - Quarter of 14-year-old girls ‘have signs of depression’ The data linkage in this Agreement is expected to facilitate research that CLS anticipate is expected to be carried out on the effects of social or economic determinations of health, and environmental factors on the evolution of the wellbeing, health, and development of family members. This could be of direct benefit to the NHS and to community services interfacing with schools through informing policy to improve healthy lifestyles.

Benefits reported

-The The first yielded benefit is the creation of the refreshed linked NCDS/HES dataset. dataset which will include most recent data. The dataset dataset, which covers up to the year 2017 is now available for researchers to use via the UKDS. A link to the dataset is provided here https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8697 -CLS CLS has also produced the an NCDS/HES user guide. This document provides researchers with a complete guide to the linked data and information on the application process - process. See: http://doc.ukdataservice.ac.uk/doc/8697/mrdoc/pdf/ncds_hes_user_guide_v1.pdf http://doc.ukdataservice.ac.uk/doc/8697/mrdoc/pdf/ncds_hes_user_guide_v1.pdf CLS delivered a webinar "An Introduction to linked health administrative data in four cohort studies" on the 11th of February 2021 to introduce the HES linked datasets and provide information to researchers on how to access them. -Webinar- An introduction to linked health administrative data in four cohort studies. CLS delivered a webinar on the 11th February 2021 to introduce the HES linked datasets and provide information to researchers on how to access them. -Yielded benefits from the survey Yielded benefits from the COVID survey carried out by CLS with the four cohort studies between May 2020 and February 2021. Two research papers have been written using data from the CLS cohorts and COVID19 surveys David Bann, Aase Villadsen, Jane Maddock, View ORCID ProfileAlun Hughes, George B. Ploubidis, Richard J. Silverwood, Praveetha Patalay (2020) Changes in the behavioural determinants of health during the coronavirus (COVID-19) pandemic: gender, socioeconomic and ethnic inequalities in 5 British cohort studies. Using data from five nationally representative British cohort studies (born 2000-2, 1989-90, 1970, 1958, and 1946), researchers investigated sleep, physical activity (exercise), diet, and alcohol intake (N=14,297). Using measures of each behaviour reported before and during lockdown, researchers investigated change in each behaviour, and whether such changes differed by age/cohort, gender, ethnicity, and socioeconomic position (SEP; childhood social class, education attainment, and adult reporting of financial difficulties). The results showed changes in these outcomes occurred in both directions, i.e. shifts from the middle part of the distribution to both declines and increases in sleep, exercise, and alcohol use. For all outcomes, older cohorts were less likely to report changes in behaviours compared with younger cohorts. In the youngest cohort (born 2001), the following shifts were more evident: increases in exercise, fruit and vegetable intake, sleep duration, and less frequent alcohol consumption. Widening inequalities in sleep during lockdown were more frequent amongst females, socioeconomically disadvantaged groups, and ethnic minorities. For other outcomes, inequalities were largely similar before and during lockdown, yet ethnic minorities were increasingly likely during lockdown to undertake less exercise and consume lower amounts of fruit and vegetables doi: https://doi.org/10.1101/2020.07.29.20164244 Constantin-Cristian Topriceanu, Andrew Wong, James C Moon, View ORCID ProfileAlun D Hughes, David Bann, Nish Chaturvedi, Praveetha Patalay, Gabriella Conti, Gabriella Captur (2020) Inequality in access to health and care services during lockdown – Findings from the COVID-19 survey in five UK national longitudinal studies Researchers studied whether COVID19 further deepened existing health inequalities. Access to health services and adequate care is influenced by sex, ethnicity, socio-economic position (SEP) and burden of co-morbidities. However, it is unknown whether the COVID-19 pandemic further deepened these already existing health inequalities. Participants were from five longitudinal age-homogenous British cohorts (born in 2001, 1990, 1970, 1958 and 1946). A web and telephone-based survey provided data on cancelled surgical or medical appointments, and the number of care hours received during the UK COVID-19 national lockdown. Using binary or ordered logistic regression, researchers evaluated whether these outcomes differed by sex, ethnicity, SEP and having a chronic illness. The findings showed that Females and those with a chronic illness experienced significantly more cancellations during lockdown. Ethnic minorities and those with a chronic illness required a higher number of care hours during the lockdown . Age was not independently associated with either outcome in meta-regression. SEP was not associated with cancellation or care hours. https://doi.org/10.1101/2020.09.12.20191973 In total over 1600 research publications have been produced using NCDS data since 2008 - of which a very significant proportion are focused on aspects of health. Below are some examples of existing publications using NCDS data benefiting public health: ABO-ZAID, G, SHARPE, R.A., FLEMING, L.E., DEPLEDGE, M and OSBORNE, N.J.. (2018) Association of Infant Eczema with Childhood and Adult Asthma: Analysis of Data from the 1958 Birth Cohort Study. International Journal of Environmental Research and Public Health, 15(7), 1415. IMPACT: Research using NCDS Cohort Study data has contributed to better understanding the relationship between early life eczema and asthma later in life. Detailed information about the study can be found here: https://www.mdpi.com/1660-4601/15/7/1415 BANN, D, JOHNSON, W, LI, L, KUH, D and HARDY, R. (2018) Socioeconomic inequalities in childhood and adolescent body-mass index, weight, and height from 1953 to 2015: an analysis of four longitudinal, observational, British birth cohort studies. Lancet Public Health, 3(4), e194-e203. IMPACT: Research using NCDS Cohort Study data has enabled a long-run investigation of socioeconomic inequalities in BMI, and to more recent data than previously available. Detailed information about the study can be found here: https://www.sciencedirect.com/science/article/pii/S2468266718300458?via%3Dihub BEAUMONT, R.N, WARRINGTON, N.M, CAVADINO, A, TYRRELL, J, NODZENSKI, M, HORIKOSHI, M, GELLER, F, MYHRE, R, RICHMOND, R.C, PATERNOSTER, L, BRADFIELD, J.P, KREINER-MØLLER, E, HUIKARI, V, METRUSTRY, S, LUNETTA, K.L, PAINTER, J.N, HOTTENGA, J-J, ALLARD, C, BARTON, S.J, ESPINOSA, A, MARSH, J.A, POTTER, C, ZHANG, G, ANG, W, BERRY, D.J, BOUCHARD, L, DAS, S, EARLY GROWTH GENETICS (EGG) CONSORTIUM, HAKONARSON, H, HEIKKINEN, J, HELGELAND, Ø, HOCHER, B, HOFMAN, A, INSKIP, H.M, JONES, S.E, KOGEVINAS, M, LIND, P.A, MARULLO, L, MEDLAND, S.E, MURRAY, A, MURRAY, J.C, NJØLSTAD, P.R, NOHR, E.A, REICHETZEDER, C, RING, S.M, RUTH, K.S, SANTA-MARINA, L, SCHOLTENS, D.M, SEBERT, S, SENGPIEL, V, TUKE, M.A, VAUDEL, M, WEEDON, M.N, WILLEMSEN, G, WOOD, A.R, YAGHOOTKAR, H, MUGLIA, L.J, BARTELS, M, RELTON, C.L, PENNELL, C.E, CHATZI, L, ESTIVILL, X, HOLLOWAY, J.W, BOOMSMA, D.I, MONTGOMERY, G.W, MURABITO, J.M, SPECTOR, T.D, POWER, C, JÄRVELIN, M-R, BISGAARD, H, GRANT, S.F, SØRENSEN, T.I, JADDOE, V.W, JACOBSSON, B, MELBYE, M, MCCARTHY, M.I, HATTERSLEY, A.T, HAYES, M.G, FRAYLING, T.M, HIVERT, M-F, FELIX, J.F, HYPPÖNEN, E, LOWE, W.L, EVANS, D.M, LAWLOR, D.A, FEENSTRA, B and FREATHY, R.M. (2018) Genome-wide association study of offspring birth weight in 86,577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Human Molecular Genetics, 27(4), 742-756. Detailed information about the study can be found here: https://academic.oup.com/hmg/article/27/4/742/4788598 Power, C., & Matthews, S. (1997). Origins of health inequalities in a national population sample. The Lancet, 350(9091), 1584-1589 HYPPONEN, E and POWER, C. (2007) Hypovitaminosis D in British adults at age 45 y: nationwide cohort study of dietary and lifestyle predictors. American Journal of Clinical Nutrition, 85(3), 860-868. CLARK C, RODGERS B, CALDWELL T, POWER C and STANSFELD S. (2007) Childhood and adulthood psychological ill health as predictors of midlife affective and anxiety disorders: the 1958 British birth cohort. Archives of General Psychiatry, 64(6), 668-78.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Datasets: + Emergency Care Data Set (ECDS)

Objective for processing

*** TO CORRECT THE INVOICE Previous iterations of this DSA have covered the dissemination of HES data and its linkage to linked to a subset of the 1958 National Child Development Study (NCDS) as part of the Next Steps longitudinal study. This amendment request (version 1) is for the addition of a sub-licence to allow onward sharing of the linked HES data with the UK Data Services (UKDS) where data can be accessed by accredited researchers. Further detail of this is outlined below: [32 paragraphs unchanged] UCL legal basis for processing (acquiring, linking and sharing) personal data is [97 words unchanged] survey data, and to the onward sharing of this linked data in pseudo-anonymised pseudonymised form (via a secure setting with appropriate safeguards). [1 paragraph unchanged] The data disseminated to UCL will be accessed by substantive employees of UCL who will work on the data to make it research ready, will pseudo-anonymise pseudonymise the data and deposit it at the UKDS for researchers applying to use for specific projects. The pseudo-anonymised pseudonymised data will also be accessed by CLS employees to conduct methodological and research work via the UCL Data Safe Heaven.

Processing activities

[3 paragraphs unchanged] The data disseminated to UCL will be accessed by substantive employees of UCL who have been appropriately trained in data protection and confidentiality. The data will be held at the secure server in the UCL Data Safe Haven (DSH) and accessed remotely by CLS staff. [4 paragraphs unchanged] CLS researchers who need to access the data to produce methodological papers on the quality of the data (eg around measurement, representativeness) and research papers helping to showcase its benefits for health and social care will need to submit an application to CLS DAC detailing their project proposal. Upon DAC approval, a pseudonymised dataset will be provided to the researcher. The data will be held at the secure server in the UCL Data Safe Haven (DSH). The UCL DSH is certified to ISO 27001:2013 and is compliant with NHS Digital's Data Security and Protection Toolkit. Research teams using the DSH complete annual training and regularly review data access arrangements ensuring data is only limited to those authorised to access it. UCL Computing Regulations are based on the premise that access to resources is generally forbidden unless expressly permitted. All data transfers from the DSH require approval and are carried out through secure portals which are fully audited. Access to the UCL DSH is via remote desktop and requires multi-factor authentication. In addition to a strong password each user has to use a six digit number generated by a smartphone app or physical token at each login. Passwords must be changed at regular intervals, and unused accounts are automatically disabled after a fixed period. Once inside the environment, robust access control ensures that researchers can only examine information that they are approved to use. [17 paragraphs unchanged] c) CLS DAC will publish the provide information about any data dissemination on the to NHS Digital release register, Digital, including the name of the organisation to which data was provided, purpose (summary of the project) and what data was released. (NB. If CLS DAC doesn't approve the project, no data will be disseminated). name of the organisation to which data was provided, purpose (summary of the project) and what data was released. (NB. If CLS DAC doesn't approve the project, no data will be disseminated). [17 paragraphs unchanged]

Expected output

Following the data quality and validation work, the first output will be output- the creation of the linked NCDS/HES data set. dataset is now available for researchers to apply. The most recent HES data requested in this application will add an important layer to refresh this already rich linked dataset. CLS and the UKDS will publicise the data as well as providing release at both the means for data quality checking. CLS and UKDS websites. CLS is currently working on the data to make it ‘research ready’. The HES/NCDS dataset is nearly ready and it will be made available for researchers to apply via the UKDS once this application for sublicensing and is approved. CLS and the UKDS will publicise the data release at both the CLS and UKDS websites. CLS has not yet sub-licenced the available NCDS/HES data as CLS only recently got permission for sub-licensing this data. CLS has recently delivered a webinar to introduce this dataset to researchers and hope to start receiving applications soon. A first application to use the data is currently in progress, details are provided below: Project Title: 'Linkage of National Longitudinal Cohort Studies and Administrative Data: A Mutually Beneficial Arrangement' This research will be addressing the following three methodological research questions, which will be directly reflected in the project work packages. 1. How can linked administrative data aid the handling of missing cohort data? 2. How can linked cohort data improve our understanding of the quality of administrative data? 3. How can linked cohort data help address residual confounding in analyses of administrative data [1 paragraph unchanged] CLS has not yet published any methodological papers reviewing the linkage. CLS [29 words unchanged] for research when not available to external users and the original approval didn’t did not allow CLS to share the data with external users. Thus, CLS have focused on getting the necessary permission for onward sharing (sub-licence) sub-licencing the data. [2 paragraphs unchanged]

Benefits reported

Not stated in the previous version; added here.

-The first yielded benefit is the creation of the linked NCDS/HES dataset. The dataset is now available for researchers to use via the UKDS. A link to the dataset is provided here https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8697

-CLS has also produced the NCDS/HES user guide. This document provides researchers with complete guide to the linked data and information on the application process -

http://doc.ukdataservice.ac.uk/doc/8697/mrdoc/pdf/ncds_hes_user_guide_v1.pdf

-Webinar- An introduction to linked health administrative data in four cohort studies.

CLS delivered a webinar on the 11th February 2021 to introduce the HES linked datasets and provide information to researchers on how to access them.

-Yielded benefits from the survey

Yielded benefits from the COVID survey carried out by CLS with the four cohort studies between May 2020 and February 2021.

Two research papers have been written using data from the CLS cohorts and COVID19 surveys

David Bann, Aase Villadsen, Jane Maddock, View ORCID ProfileAlun Hughes, George B. Ploubidis, Richard J. Silverwood, Praveetha Patalay (2020) Changes in the behavioural determinants of health during the coronavirus (COVID-19) pandemic: gender, socioeconomic and ethnic inequalities in 5 British cohort studies.

Using data from five nationally representative British cohort studies (born 2000-2, 1989-90, 1970, 1958, and 1946), researchers investigated sleep, physical activity (exercise), diet, and alcohol intake (N=14,297). Using measures of each behaviour reported before and during lockdown, researchers investigated change in each behaviour, and whether such changes differed by age/cohort, gender, ethnicity, and socioeconomic position (SEP; childhood social class, education attainment, and adult reporting of financial difficulties). The results showed changes in these outcomes occurred in both directions, i.e. shifts from the middle part of the distribution to both declines and increases in sleep, exercise, and alcohol use. For all outcomes, older cohorts were less likely to report changes in behaviours compared with younger cohorts. In the youngest cohort (born 2001), the following shifts were more evident: increases in exercise, fruit and vegetable intake, sleep duration, and less frequent alcohol consumption. Widening inequalities in sleep during lockdown were more frequent amongst females, socioeconomically disadvantaged groups, and ethnic minorities. For other outcomes, inequalities were largely similar before and during lockdown, yet ethnic minorities were increasingly likely during lockdown to undertake less exercise and consume lower amounts of fruit and vegetables

doi: https://doi.org/10.1101/2020.07.29.20164244

Constantin-Cristian Topriceanu, Andrew Wong, James C Moon, View ORCID ProfileAlun D Hughes, David Bann, Nish Chaturvedi, Praveetha Patalay, Gabriella Conti, Gabriella Captur (2020) Inequality in access to health and care services during lockdown – Findings from the COVID-19 survey in five UK national longitudinal studies

Researchers studied whether COVID19 further deepened existing health inequalities. Access to health services and adequate care is influenced by sex, ethnicity, socio-economic position (SEP) and burden of co-morbidities. However, it is unknown whether the COVID-19 pandemic further deepened these already existing health inequalities. Participants were from five longitudinal age-homogenous British cohorts (born in 2001, 1990, 1970, 1958 and 1946). A web and telephone-based survey provided data on cancelled surgical or medical appointments, and the number of care hours received during the UK COVID-19 national lockdown. Using binary or ordered logistic regression, researchers evaluated whether these outcomes differed by sex, ethnicity, SEP and having a chronic illness. The findings showed that Females and those with a chronic illness experienced significantly more cancellations during lockdown. Ethnic minorities and those with a chronic illness required a higher number of care hours during the lockdown . Age was not independently associated with either outcome in meta-regression. SEP was not associated with cancellation or care hours. https://doi.org/10.1101/2020.09.12.20191973

In total over 1600 research publications have been produced using NCDS data since 2008 - of which a very significant proportion are focused on aspects of health. Below are some examples of existing publications using NCDS data benefiting public health:

ABO-ZAID, G, SHARPE, R.A., FLEMING, L.E., DEPLEDGE, M and OSBORNE, N.J.. (2018) Association of Infant Eczema with Childhood and Adult Asthma: Analysis of Data from the 1958 Birth Cohort Study. International Journal of Environmental Research and Public Health, 15(7), 1415.

IMPACT: Research using NCDS Cohort Study data has contributed to better understanding the relationship

between early life eczema and asthma later in life.

Detailed information about the study can be found here:

https://www.mdpi.com/1660-4601/15/7/1415

BANN, D, JOHNSON, W, LI, L, KUH, D and HARDY, R. (2018) Socioeconomic inequalities in childhood and

adolescent body-mass index, weight, and height from 1953 to 2015: an analysis of four longitudinal, observational, British birth cohort studies. Lancet Public Health, 3(4), e194-e203.

IMPACT: Research using NCDS Cohort Study data has enabled a long-run investigation of socioeconomic

inequalities in BMI, and to more recent data than previously available.

Detailed information about the study can be found here:

https://www.sciencedirect.com/science/article/pii/S2468266718300458?via%3Dihub

BEAUMONT, R.N, WARRINGTON, N.M, CAVADINO, A, TYRRELL, J, NODZENSKI, M, HORIKOSHI, M, GELLER, F, MYHRE, R, RICHMOND, R.C, PATERNOSTER, L, BRADFIELD, J.P, KREINER-MØLLER, E, HUIKARI, V, METRUSTRY, S, LUNETTA, K.L, PAINTER, J.N, HOTTENGA, J-J, ALLARD, C, BARTON, S.J, ESPINOSA, A, MARSH, J.A, POTTER, C, ZHANG, G, ANG, W, BERRY, D.J, BOUCHARD, L, DAS, S, EARLY GROWTH GENETICS (EGG) CONSORTIUM, HAKONARSON, H, HEIKKINEN, J, HELGELAND, Ø, HOCHER, B, HOFMAN, A, INSKIP, H.M, JONES, S.E, KOGEVINAS, M, LIND, P.A, MARULLO, L, MEDLAND, S.E, MURRAY, A, MURRAY, J.C, NJØLSTAD, P.R, NOHR, E.A, REICHETZEDER, C, RING, S.M, RUTH, K.S, SANTA-MARINA, L, SCHOLTENS, D.M, SEBERT, S, SENGPIEL, V, TUKE, M.A, VAUDEL, M, WEEDON, M.N, WILLEMSEN, G, WOOD, A.R, YAGHOOTKAR, H, MUGLIA, L.J, BARTELS, M, RELTON, C.L, PENNELL, C.E, CHATZI, L, ESTIVILL, X, HOLLOWAY, J.W, BOOMSMA, D.I, MONTGOMERY, G.W, MURABITO, J.M, SPECTOR, T.D, POWER, C, JÄRVELIN, M-R, BISGAARD, H, GRANT, S.F, SØRENSEN, T.I, JADDOE, V.W, JACOBSSON, B, MELBYE, M, MCCARTHY, M.I, HATTERSLEY, A.T, HAYES, M.G, FRAYLING, T.M, HIVERT, M-F, FELIX, J.F, HYPPÖNEN, E, LOWE, W.L, EVANS, D.M, LAWLOR, D.A, FEENSTRA, B and FREATHY, R.M. (2018) Genome-wide association study of offspring birth weight in 86,577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics. Human Molecular Genetics, 27(4), 742-756.

Detailed information about the study can be found here: https://academic.oup.com/hmg/article/27/4/742/4788598

Power, C., & Matthews, S. (1997). Origins of health inequalities in a national population sample. The Lancet,

350(9091), 1584-1589

HYPPONEN, E and POWER, C. (2007) Hypovitaminosis D in British adults at age 45 y: nationwide cohort study of dietary and lifestyle predictors. American Journal of Clinical Nutrition, 85(3), 860-868.

CLARK C, RODGERS B, CALDWELL T, POWER C and STANSFELD S. (2007) Childhood and adulthood psychological ill health as predictors of midlife affective and anxiety disorders: the 1958 British birth cohort. Archives of General Psychiatry, 64(6), 668-78.

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Objective for processing

*** TO CORRECT THE INVOICE [36 paragraphs unchanged]

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The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council (ESRC) Centre, based at the Department of Quantitative Social Science, UCL Institute of Education. It is responsible for three of Britain's internationally renowned birth cohort studies, the 1958 National Child Development Study, the 1970 British Cohort Study and the Millennium Cohort Study (MCS). All these studies are 'birth' studies, following the groups of participants from cradle to grave. As such, this group of studies is unique and has, and still is, providing a wealth of information used in the policy decisions affecting society's health and well-being. This application is for data to be linked to a subset of the 1958 National Child Development Study (NCDS). Previous iterations of this DSA have covered the dissemination of HES data and its linkage to linked to a subset of the 1958 National Child Development Study (NCDS) as part of the Next Steps longitudinal study. This amendment request (version 1) is for the addition of a sub-licence to allow onward sharing of the linked HES data with the UK Data Services (UKDS) where data can be accessed by accredited researchers. Further detail of this is outlined below: In 1958 doctors and scientists were concerned at the high rate of infant death and ill health in Britain. There were an alarming number of stillbirths and children dying in the first few weeks of life. And so the National Child Development Study (NCDS) began as the Perinatal Mortality Survey. Nearly 17,500 babies were studied. Information was collected on the family background of the mother, her pregnancy and labour, and about her baby at birth and during its first week of life. The Centre for Longitudinal Studies (CLS) is an Economic and Social Research Council (ESRC) Centre, based at the Department of Quantitative Social Science, UCL Institute of Education. It is responsible for three of Britain's internationally renowned birth cohort studies, the 1958 National Child Development Study, the 1970 British Cohort Study (BCS70) and the Millennium Cohort Study (MCS). All these studies are 'birth' studies, following the groups of participants from cradle to grave. As such, this group of studies is unique and has, and still is, providing a wealth of information used in the policy decisions affecting society's health and well-being. Seven years later it was decided that it would be worthwhile to find the families included in the original birth survey and see what had happened to the babies since they were born – how healthy they were, how they were getting on at school, and so on. This second survey was carried out in 1965. Since then there have been eight other major surveys, attempting to trace all those born in the week of the original 1958 survey – in 1969, 1974, 1981, 1991, 1999/2000, 2004/5, 2008/9 and most recently in 2013. In addition, a major ‘bio-medical’ survey took place in 2002/3. In 1958 doctors and scientists were concerned at the high rate of infant death and ill health in Britain. There were an alarming number of stillbirths and children dying in the first few weeks of life. So, the National Child Development Study (NCDS) began as the Perinatal Mortality Survey. Nearly 17,500 babies were studied. Information was collected on the family background of the mother, the pregnancy and labour, and about the baby at birth and during the first week of the baby’s life. During the 2008 (Aged 50) survey, CLS obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from 6529 cohort members who at the time were in England. Seven years later it was decided it would be worthwhile to find families included in the original birth survey and see what had happened to the babies since they were born – how healthy they were, how they were getting on at school, and so on. This second survey was carried out in 1965. Since then there have been eight other major surveys, attempting to trace all those born in the week of the original 1958 survey – in 1969, 1974, 1981, 1991, 1999/2000, 2004/5, 2008/9 and 2013. In addition, a major ‘bio-medical’ survey took place in 2002/3. The Age 62 survey commenced in January 2020 but is currently paused due to the impact of the COVID-19 pandemic. It is expected to re-commence in early 2021. Subsequent sweeps of the study will likely take place every five years. Linking health data from Hospital Episodes Statistics (HES) to the NCDS survey data will greatly increase the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyles choices such as drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data will enrich these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which could help us better understand how health conditions could be better treated or supported. During the 2008 (Aged 50) survey, CLS at UCL obtained informed consent from cohort members for their health data to be linked to the data collected in the study. In total consent was obtained from 6,529 cohort members who at the time were in England. Data about health behaviours may be more accurate if obtained from administrative records as a result of misreporting of complex health conditions, under-reporting of particular health problems or due to perceived sensitivities around certain behaviours and lifestyle choices. So this also offers a methodological opportunity to validate the data collected in the survey and vice versa. Linking health data from Hospital Episodes Statistics (HES) to the Next Steps survey data has greatly increased the possibilities for using the cohort to study how health outcomes impact on the individual and aspects of their life such as work, relationships and family life and, likewise, how health outcomes relate to the individual behaviours and lifestyle aspects, such as; drug and alcohol use, sexual health, diet and exercise, which are all documented as part of the study. The successful inclusion of HES data has enriched these data by revealing which cohort members have been admitted to or attended hospital and the reasons for this, e.g. drug and alcohol treatment, accident and emergency, maternity and mental health services which have helped UCL better understand how health conditions could be better treated or supported. Data about health behaviours are more accurate when obtained from administrative records because of misreporting of complex health conditions, under-reporting of health problems or due to perceived sensitivities around certain behaviours and lifestyle aspects. So, this offers an interesting methodological opportunity to validate the data collected in the survey and vice versa. [3 paragraphs unchanged] 3. develop and create a useful and rich HES linked NCDS Aged 50 dataset data set. 4. Advance learning in the research community by providing access to the linked NHS Digital HES / CLS' NCDS Age 50 data to the research community via the UK Data Service (UKDS) Secure Lab, through a sub-licensing agreement agreed between UCL and NHS Digital. CLS at UCL has not granted any sublicenses as of yet. This is because they are at the data preparation stage – ensuing that the data they provide to researchers has been suitably minimised and grouped to be of the best use for the projects. CLS at UCL envisage this will commence in the Autumn of 2020 (September/October) and there will be approximately 2-3 sublicensees per month. No sub-licences have been granted as of 28/08/2020. THE SUB-LICENCE: UCL are permitted to include onward sharing of the linked HES and Next Steps data with the UK Data Service (UKDS), where data can be accessed by accredited researchers in a Secure Research Environment, known as Secure Lab, following a “Sub-licensing model”. The UKDS is funded by the Economic and Social Research Council (ESRC) with contributions from the University of Essex, the University of Manchester and Jisc (Jisc is a United Kingdom not-for-profit company whose role is to support post-16 and higher education, and research, by providing relevant and useful advice, digital resources and network and technology services, while researching and developing new technologies and ways of working). The UKDS provides access to high-quality data to meet the data needs of researchers, students and teachers from all sectors including academia and central and local government. The UKDS is based at, and hosted by, the University of Essex. Although the researchers at the UKDS are substantively employed by the University of Essex, only staff who are permitted to work at the UKDS will access the data. Under the “Sub-licensing model”, NHS Digital shares data with UCL, who are in turn licensed to share these data with other organisations, subject to agreed controls, scoped in this agreement between NHS Digital and UCL. In line with this onward sharing model, the data sharing controls in place between NHS Digital and UCL are replicated between UCL and the other organisations. UCL, which houses CLS at the UCL Institute of Education, is fully accountable for the actions of the parties involved in subsequent data share and use. The agreement mirrors the Data Sharing Framework Contract in place between NHS Digital and UCL. It also requests information about the research proposal, benefits to health and/or social care, organisational security assurance and terms and conditions regarding onward sharing of data, responsibilities and processing activities etc. Under the sub-licensing model CLS will deposit the linked data with UK Data Service, who will serve as a data repository. Access to the deposited data will be granted to approved researchers within a Secure Research Environment on behalf of UCL, as outlined in this document. NHS Digital will retain the ability to directly audit UKDS’s compliance with the outlined and agreed data access arrangements. There will be no charge applied to licence supplied by CLS. The territory of use in the sub-licence will be the same or narrower than the territory of use stated in this DSA, namely UK In this sharing model of the linked data, UCL will be a data controller, determining the purposes for which and the manner in which the linked data are processed. The UKDS will be the data processor, as they will be processing the data on behalf of UCL. This includes holding the linked data in a secure environment, screening for completeness of applications for data access, providing training for use of linked data securely, entering into contractual agreements with approved researchers, extraction of approved data and setting up access systems, and approving statistical outputs, following a statistical disclosure control procedure. The approved organisations and researchers, who are granted an access to the linked data via the UKDS Secure Lab, agree to terms and conditions of use, their rights and responsibilities as users of the linked data, as defined by the UKDS. In addition to the agreements signed with the UKDS, the organisation of the researcher applying to use the linked data will enter into a Licence agreement with UCL. ORGANISATIONAL AGREEMENTS UCL will provide a sub-licence to UK organisations undertaking research that will be of benefit to the public (this will be assessed in the project proposal form submitted to the UKDS and to UCL). Applicants (potential licencees) will need to show in Schedule 1 Section 18 (a) and (b) that the provision of the sub licencing will be in the public interest and that that the data will be used either (i) for the provision of health care or adult social care; or (ii) for the promotion of health. UCL will not provide data access to commercial organisations for research for commercial purposes. Additionally, the CLS Licence agreement, Schedule 1 Section 18, will assess the project proposal against its assessment criteria to determine the details of the project, the people who will be accessing the data, and what the data will be requested. Applicants will need to be accredited researchers or agree to undertake training and become accredited, prior to accessing the data. Additionally, applicant’s organisation will need to provide evidence that they have IG and security assurances in place (covered in Schedule 1, Section 15 of the CLS Licence agreement). Members of the CLS Data Access Committee (DAC) will review and decide if the evidence provided satisfy the criteria requirements. Applicants (licensees) and their organisations will have to sign two agreements to obtain a sub-licence, one with the UKDS and another with UCL, in both cases the licensee will agree with the terms stated in the Confidentiality Section of the UCL Licence Agreement and with the Confidentiality Terms stated in the Secure Access Agreement which will be signed with the UKDS. By signing these agreements the licensee agrees to adhere to these terms, including respecting the privacy of health services users data they will receive. Licensees are also be reminded of the penalties they are likely to incur if they do not comply with the terms they have agreed. In addition to the above, the UKDS agreement stipulates that data users must complete mandatory training before they are allowed to access the data. To ensure the security of the linked information, shared with UCL by NHS Digital, and subsequently shared by CLS at UCL with the UKDS, where data could be accessed by approved researchers in a Secure Lab, UCL envisage the following controls employed at the different steps of the process of depositing, approving and sharing of the linked information: • An agreement between NHS Digital and CLS to onwardly share linked HES and UCL information under “Sub-licencing model”, which outlines the terms and conditions of use of the linked data via the UKDS Service Secure Lab as the data repository, and the full accountability of UCL (housing CLS at the UCL Institute of Education) to the actions of the parties involved in subsequent access to the linked data. • An agreement between CLS at UCL (as a data controller) and UKDS (as a data processor), which outlines the terms and conditions under which the linked data can be accessed via the UKDS Secure Lab. • An agreement between UKDS and the approved researcher, which outlines the terms and conditions of use of the linked data in the UKDS’s Secure Lab. • An Agreement between UCL at UCL and the organisation requesting to use the linked data via the UKDS, which outlines the terms and conditions of use of the linked data. The researcher accessing the data via the UKDS Secure Lab will not be able to download any data. Once the researcher has finished their research, the UKDS will delete the data folder with the tailored dataset for the specific project. The data held at UCL will be deleted if the data sharing agreement between NHS Digital and UCL were to cease. If it were to cease, the license agreement between UCL and the licensee organisation will be terminated. UCL legal basis for processing (acquiring, linking and sharing) personal data is for a public task under GDPR (article 6(1)(e)) i.e. processing is necessary for the performance of a task carried out in the public interest (as is made explicit to participants in the information leaflets provided). UCL also process special categories of personal data for research under GDPR (article 9(2)(j)) i.e. processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. In addition, for ethical reasons and under the Common Law Duty of Confidentiality, UCL sought permission from cohort members to access and link their routine health records to their survey data, and to the onward sharing of this linked data in pseudo-anonymised form (via a secure setting with appropriate safeguards). All data processed under the sub-license will be completed using the same legal basis as mentioned above, namely GDPR (article 6(1)(e))and GDPR (article 9(2)(j)). The CLS Licence agreement will require licensees to provide the Legal Basis of their request to link health data via CLS and therefore CLS Data Access Committee (DAC) will only grant approval to applications from researchers within public bodies who have a legal basis to process data under GDPR. The data disseminated to UCL will be accessed by substantive employees of UCL who will work on the data to make it research ready, will pseudo-anonymise the data and deposit it at the UKDS for researchers applying to use for specific projects. The pseudo-anonymised data will also be accessed by CLS employees to conduct methodological and research work via the UCL Data Safe Heaven.

Processing activities

Only individuals, working under appropriate supervision on behalf of data controller(s) / processor(s) within this agreement, who are subject to the same policies, procedures and sanctions as substantive employees will have access to the data and only for the purposes described in this document. No new data is being requested and no further data is being sent to NHS Digital under this version of the Agreement. The CLS Team is based at UCL. Identifiers will be held separately from attribute characteristics. HES data will not be re-linked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study ID which is used to destroy the data. 1. CLS team have supplied NHS Digital with identifiers of cohort members who have consented to this data linkage, including full name, sex, postcode, date of birth, NHS number (if known) and study ID (study-specific pseudonymised identifier). 1. CLS team will supply NHS Digital with identifiers of cohort members who have consented to this data linkage, including full name, sex, postcode, date of birth and unique ID (study-specific pseudonymised identifier). 2. NHS Digital have linked the identifiable study data to HES data. NHS Digital have removed identifiers from linked dataset and returned the pseudonymised dataset to the CLS team at UCL with the study ID. 2. NHS Digital will link the identifiable study data to HEs data. NHS Digital will then remove identifiers from the linked dataset and return to the CLS team at UCL with the study ID. 3. CLS carried out validation of the administrative pseudonymised data received (linked HES data) and combined the supplied administrative data with the information collected from the participant as part of the Next Steps study using the study ID. 3. CLS will carry out validation of the administrative data received (linked HES data) and will combine the supplied administrative data with the information collected from the participant as part of the NCDS study using the study ID. Once the linked survey-administrative data files have been created, CLS may perform other activities to prepare the data for use, such as coding and cleaning, derivation of summary variables and compilation of data documentation. Once the linked survey-administrative data files have been created, CLS may perform other activities to prepare the data for use by other researchers, such as coding and cleaning, derivation of summary variables and compilation of data documentation but as above. 4. CLS researchers used these data to create an analysis file, which do not contain any identifiable data. 4. CLS researchers will use these data to create an analysis file that will not contain any identifiable data. 5. CLS created derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.) and compare Next Steps survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys. 5. CLS will create derived variables that summarise study members’ hospitalisation and health histories (e.g. hospital admissions and re-admissions, incidence of common diseases, children’s ailments etc.), and will compare NCDS survey data with data from hospital statistics, in order to compare and validate the data collected in CLS surveys. Identifiers are held separately from attribute characteristics. HES data is not be relinked to the identifiable data which is held separately from the survey responses. Re-identification will only happen at the occasion of a request, made from a cohort member, for withdrawal from the study, and this includes removal of data. Where a participant wishes to withdraw from the study, the identifiable data is used to locate the study id, and then in turn destroy their data. UCL are prohibited from linking the identifiable data they hold with data disseminated from NHS Digital. The only exception to this condition would be where a participant wishes to withdraw from the study, the identifiable data would be used to locate the study id and then in turn to destroy the data. Addition of the sub-licence: UCL will not share the linked HES/NCDS Aged 50 data with third parties. The process of accessing the linked data via the UKDS Service Secure Lab include the following steps: • Registration with the UKDS Service. • Submission of an application, including an ‘Accredited Researcher application form’ and ‘Research proposal’. • Screening of application by UKDS for completeness. Once a researcher has registered and UKDS has screened / approved the application: 1) UKDS sends project proposal (researcher application forms) to CLS-UCL (for Data Access Committee (DAC) approval). Applicants are required to demonstrate they have security assurance in place (System Level Security Policy/ISO Certificate/DSPT). 2) CLS-UCL sends the CLS License Agreement for Linked NHS Digital data to researchers to be completed and signed by their organisation (this includes the benefits to health and social care and evidence of organisational security assurances) not covered by UKDS application. 3) Researcher/their organisation representative will send the CLS License Agreement for Linked NHS Digital completed and signed back to CLS-UCL. 4) CLS-UCL will: a) check the organisational Information Governance and security assurance evidence provided as per Section 15 Organisational Security Assurance of the CLS Licence agreement, b) send the project for CLS DAC approval. 5) Should evidence of organisational Information Governance and security assurance provided not meet the requirements (as outlined in the sub-license) CLS will request the applicant to provide further evidence, and will only submit the project to CLS DAC for approval when evidence provided is satisfactory. Approval for data access will only be granted to applicant organisations that meet the security assurance requirement. 6) CLS DAC will assess both documents (UKDS project proposal + CLS Licence agreement) and make a decision to approve it or not approve it or require further information. Should an application be rejected, a researcher can apply again with a revised application. 7) In the CLS License agreement, CLS DAC will, among other things assess the benefits for health and social care statement, and decide whether it is satisfied with the answer. 8) Once CLS DAC approves the project: a) CLS will inform UKDS that project has been approved. b) CLS representative should sign the CLS License agreement noting the DAC reference number on the License document and send it back to the organisation of the applicant (the Principal Investigator for the study requiring access will sign the agreement - they will be an authorised signatory for their organisation). c) CLS DAC will publish the information about any data dissemination on the NHS Digital release register, including the name of the organisation to which data was provided, purpose (summary of the project) and what data was released. (NB. If CLS DAC doesn't approve the project, no data will be disseminated). 9) If CLS DAC is not satisfied with the evidence provided by the applicant about the benefits to health and social care, then CLS DAC can ask the applicant to provide additional information and the project can be re-submitted for CLS DAC approval on the next CLS DAC meeting or via Chair approval. 10) UKDS will inform the researcher that their project was approved and make the data available to them via Secure access to linked data at the Safe Centre at the UK Data Service (hosted at the University of Essex) or via the researcher’s own institutional desktop PC, depending on the sensitivity/impact level of the data being requested . Note that any data accessed through the UKDS Secure Lab can only be accessed under secure conditions and cannot be downloaded. The linked data provided to approved researchers may be subject to sub-setting of variables (and if necessary cases) to minimize disclosure risks and ensure that no individual or organisation can be identified from the results. In addition, all statistical outputs are subject to statistical disclosure control procedure. Access to the Secure Lab is only available to researchers who are be based at a UK academic institution or an ESRC-funded research centre, and be an ESRC Accredited Researcher. PhD and research students can request access but must apply jointly with their supervisors from established organisations. No further onward sharing can occur beyond the sub-licence. UKDS SECURE DATA HANDLING PROCEDURES The UK Data Service has received government technical accreditation and has been certified for its secure data handling procedures under the international standard for information security (ISO 27001). To maintain this certification, regular internal and external audits are undertaken. UKDS also hires a government-approved company to conduct internal and external penetration testing of its Secure Lab systems. More widely, the UKDS employs an Information Security Management System (ISMS), to ensure compliance with the ISO accreditation. The Secure Lab falls into this system, and a number of documented processes are regularly maintained and reviewed to ensure these processes are robust, relevant, and fit-for-purpose. The ISMS is overseen by an Information Security Management Group (ISMG), which regularly meets and approves changes to procedures. UKDS DATA ACCESS MECHANISMS As an ESRC resource centre, CLS shares its survey data with the research community via the UKDS under safeguarded or controlled access mechanisms, dependent on the likelihood and potential impact of disclosure. Data with higher risk of disclosure is treated with an appropriate degree of security and management. CLS data fall into the following categories, which are defined by the likelihood and potential impact of disclosure: • Tier 1: data with low level of disclosure: e.g. participant self-reported survey data. These data are made available through the UKDS End User Licence and have a low impact of disclosure; • Tier 2a: data that is potentially disclosive: e.g. medium level and coarse geographies or sensitive information about cohort members. These data are made available through the UKDS Special Licence and have a medium impact of disclosure; • Tier 2: data that are too detailed, sensitive or confidential to be made available under the standard End User Licence or Special Licence, such as detailed geographical indicators or fine-grained individual level linked data. These data have a high impact of disclosure and are made available through the UKDS Secure Access. Access mechanisms to NHS Digital HES data linked to CLS cohort studies via the UKDS The HES data provided to CLS by NHS Digital, which are linked to the CLS cohort members, have been processed by the CLS data management team to minimise the risk of disclosivity when linked to the CLS survey data. This has been achieved by removing highly identifiable variables and altering other variables by top-coding or truncating them. Following this processing, the final health datasets have been classified under Tier 2. It is therefore CLS’s intention to deposit these Tier 2 linked HES data with the UK Data Service under the UKDS Secure Access, and provide access to this information for approved researchers, following the process and contractual arrangements, outlined above and described in more detail below, following an agreed between NHS Digital, CLS and UKDS onward sharing model. The UKDS’s ‘Controlled access to data’ specification is supplied to NHS Digital as part of this application amendment. The data provided will be pseudonymised, and will be accessed only via the UKDS secure lab. Downloading the data is not possible. All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data).

Expected output

Following the data quality and validation work, the first output will be the creation of the linked NCDS/HES dataset. data set. The HES data will add an important layer to this already rich data as well as providing the means for data quality checking. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. CLS is currently working on the data to make it ‘research ready’. The HES/NCDS dataset is nearly ready and it will be made available for researchers to apply via the UKDS once this application for sublicensing and is approved. CLS and the UKDS will publicise the data release at both the CLS and UKDS websites. The creation of this HES/NCDS Aged 50 database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. The onward sharing to researchers via an agreed mechanism will be subject to a further application to NHS Digital. The second output will be methodological papers published in peer reviewed journals reviewing the linkage and validating the data from the two data sources. These methodological assessments are expected to finish two years after obtaining the data. Outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. The UKDS will approve statistical outputs, following a statistical disclosure control procedure and HES analysis guide for the data accessed via the UKDS secure lab. The outputs in the long term from this dataset are difficult to quantify, but the CLS currently has a searchable bibliography on it's website with over 3,600 publications based on data from the 1958, 1970, Next steps and millennium cohort studies. CLS has not yet published any methodological papers reviewing the linkage. CLS expects to carry out these methodological and research assessments two/three years after the approval of this extension. This is because CLS have a policy of not using data internally for research when not available to external users and the original approval didn’t allow CLS to share the data with external users. Thus, CLS have focused on getting the necessary permission for onward sharing (sub-licence) the data. CLS actively promotes the use of their data among the research community through publications and events, as well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care. The creation of this HES/NCDS Aged 50 database and the methodological papers are the first steps in establishing a robust research database which will be of benefit to health and social care. CLS actively promotes the use of their data among the research community through publications and briefings , working papers, webinars, social media, public reports, hosted events. As well as providing extensive documentation, guidance, training and workshops on each data set to help researchers better use the data and so ultimately benefit health and social care.

Expected measurable benefits

[2 paragraphs unchanged] Below are examples of existing publications using NCDS data benefiting public health in the areas of pregnancy health, birth, breastfeeding, vitamin D, obesity, diabetes, respiratory disease. disease and others. Delpierre, C., Fantin, R., Barboza-Solis, C., Lepage, B., Darnaudéry, and M., Kelly-Irving, M. (2016) The early life nutritional environment and early life stress as potential pathways towards the metabolic syndrome in mid-life? A lifecourse analysis using the 1958 British Birth cohort. BMC Public Health. 2016 Aug 18; 16(1):815. Epub 2016 Aug 18. ARCHER, G., XUN, W.W., STUCHBURY, R., NICHOLAS, O. and SHELTON, N. (2020) Are ‘healthy cohorts’ real-world relevant? Comparing the National Child Development Study (NCDS) with the ONS Longitudinal Study (LS). Longitudinal and Life Course Studies, 20(20), 1-24. AVENDANO, M., DE COULON, A. and NAFILYAN, V. (2020) Does longer compulsory schooling affect mental health? Evidence from a British reform. Journal of Public Economics, 183, 104137. BANN, D., FITZSIMONS, E. and JOHNSON, W.. (2020) Determinants of the population health distribution, or why are risk factor-body mass index associations larger at the upper end of the BMI distribution? International Journal of Epidemiology, dyz245, 13 January 2020. BRICARD, D., JUSOT, F., TRANNOY, A. and TUBEUF, S. (2020) Inequality of opportunities in health and death: an investigation over the lifespan in Great Britain. International Journal of Epidemiology, Upcoming, 2020. CARAYOL, M., ALBERTUS, G., FANTIN, R., LANG, T., KELLY-IRVING, M., GROSCLAUDE, P. and DELPIERRE, C. (2020) Nutritional lifestyle patterns and cancer: confounding effect of social determinants across the life course in women from the 1958 British birth cohort study. Longitudinal and Life Course Studies, 20(20), 1-22. DAVID BATTY, G., DEARY, I.J., HAMER, M., FRANK, P. and BANN, D. (2020) Association of Childhood Psychomotor Coordination With Survival Up to 6 Decades Later. JAMA Network Open, 3(4), e204031. GAGNÉ, T, SCHOON, I and SACKER, A. (2020) Health and voting over the course of adulthood: Evidence from two British birth cohorts. SSM - Population Health, 10(April 2020), 100531. MADDOCK, J., CASTILLO-FERNANDEZ, J., WONG, A., COOPER, R., RICHARDS, M., ONG, K.K., PLOUBIDIS, G.B., GOODMAN, A., KUH, D., BELL, J.T. and HARDY, R. (2020) DNA methylation age and physical and cognitive ageing. Journals of Gerontology: Series A, 75(3), 504–511. NING, K., GONDEK, D., PATALAY, P. and PLOUBIDIS, G.B.. (2020) The association between early life mental health and alcohol use behaviours in adulthood: A systematic review. PLOS ONE, 15(2), e0228667. ØIESTAD, B.E., HILDE, G., TVETER, A.T., PEAT, G.G., THOMAS, M.J., DUNN, K.M. and GROTLE, M. (2020) Risk factors for episodes of back pain in emerging adults. A systematic review. European Journal of Pain, 24(1), 19-38. PINTO PEREIRA, S, DE STAVOLA, B.L, ROGERS, N.T, HARDY, R, COOPER, R and POWER, C. (2020) Adult obesity and mid-life physical functioning in two British birth cohorts: investigating the mediating role of physical inactivity. International Journal of Epidemiology, published online(6 March 2020), dyaa014. SIRONI, M, PLOUBIDIS, G.B and GRUNDY, E.M. (2020) Fertility History and Biomarkers Using Prospective Data: Evidence From the 1958 National Child Development Study. Demography, 57, 529-558. ANDERSON, D.J., CHUNG, H.-F., SEIB, C.A., DOBSON, A.J., KUH, D. , BRUNNER, E.J., CRAWFORD, S.L., AVIS, N.E., GOLD, E.B., GREENDALE, G.A., MITCHELL, E.S., WOODS, N.F., YOSHIZAWA, T. and MISHRA, G.D. (2019) Obesity, smoking, and risk of vasomotor menopausal symptoms: a pooled analysis of eight cohort studies. American Journal of Obstetrics and Gynecology, published online, 6 November 2019. BANN, D., FLUHARTY, M., HARDY, R. and SCHOLES, S. (2019) Socioeconomic inequalities in blood pressure: co-ordinated analysis of 147,775 participants from repeated birth cohort and cross-sectional datasets, 1989 to 2016. MedRxiv, Posted, 21 December 2019. BALDWIN, J.R. and DANESE, A.. (2019) Pathways from childhood maltreatment to cardiometabolic disease: a research review. Adoption & Fostering, 43(3), 329-339. BATTY, G.D., DEARY, I., HAMER, M., RITCHIE, S. and BANN, D. (2019) Childhood coordination and survival up to six decades later: extended follow-up of participants in the National Child Development Study. MedRxiv, posted, 13 August 2019. BERGER, E, CASTAGNE, R, CHADEAU-HYAM, M, BOCHUD, M, D'ERRICO, A, GANDINI, M, KARIMI, M, KIVIMAKI, M, KROGH, V, MARMOT, M, PANICO, S, PREISIG, M, RICCERI, F, SACERDOTE, C, STEPTOE, A, STRINGHINI, S, TUMINO, R, VINEIS, P, DELPIERRE, C and KELLY-IRVING, M. (2019) Multi-cohort study identifies social determinants of systemic inflammation over the life course. Nature Communications, 10, 773. BLACK, N, JOHNSTON, D.W., PROPPER, C and SHIELDS, M.A.. (2019) The effect of school sports facilities on physical activity, health and socioeconomic status in adulthood. Social Science & Medicine, 220, 120-128. BOUNTZIOUKA,V, CUMBERLAND,P.M and RAHI,J.S. (2017) Trends in Visual Health Inequalities in Childhood Through Associations of Visual Function With Sex and Social Position Across 3 UK Birth Cohorts. JAMA Ophthalmology, 135(9), 954-961. ABO-ZAID, G, SHARPE, R.A., FLEMING, L.E., DEPLEDGE, M and OSBORNE, N.J.. (2018) Association of Infant Eczema with Childhood and Adult Asthma: Analysis of Data from the 1958 Birth Cohort Study. International Journal of Environmental Research and Public Health, 15(7), 1415. ANDERSON, L.R. (2018) Adolescent mental health and behavioural problems, and intergenerational social mobility: A decomposition of health selection effects. Social Science & Medicine, 197, 153-160. ARCHER, G , PINTO PEREIRA, S and POWER, C. (2017) Child maltreatment as a predictor of adult physical functioning in a prospective British birth cohort. BMJ Open, 7(10), e017900. BALBO, N. (2018) The Mental Toll of Being Connected. What kind of impact is social media having on adolescent health? Population Europe Policy Brief No. 19, Nov 2018. Berlin: Max Planck Society. BATTY, D.G., PLOUBIDIS, G.B., GOODMAN, A and BANN, D. (2018) Association of nursery and early school attendance with later health behaviours, biomedical risk factors, and mortality: evidence from four decades of follow-up of participants in the 1958 birth cohort study. Journal of Epidemiology and Community Health, 72(7), 658-663 [4 paragraphs unchanged] In it’s nearly sixty years research the NCDS cohort has been responsible for proving beyond doubt that mothers who smoked heavily during pregnancy harmed the health and reduced the weight and height of their children, continuing on to damage English and maths scores at 16 years old. The study also informed the debate about the best place to deliver babies, indicating that mothers should only opt for home births when very early transfer to hospital is possible at the first sign of need and where highly experienced midwives and doctors are available. The study repeatedly demonstrated the need for steps to promote the health of pregnant mothers and facilities for safe childbirth. This led to the modernisation of maternity services with ready availability of high quality obstetrics on the one hand and better and more personal care for all. The case was made for adequate numbers of hospital beds and abolition of the lottery of where to give birth. Research has also made use of the longitudinal nature of the NCDS to examine the long-term effects of breastfeeding. For example, Rudnicka et al (2007) demonstrate that, compared with those who were bottle-fed with formula milk, children who were breastfed for more than a month had a reduced waist circumference and waist/hip ratio, and lower odds of obesity as adults in their mid-forties. RUDNICKA, A. R, OWEN, C. G and STRACHAN, D. P. (2007) The effect of breast feeding on cardio-respiratory risk factors in adulthood. Pediatrics, 119(5), E1107-15. Delpierre, Fantin, Barboza-Solis, Lepage, Darnaudéry, and M. Kelly-Irving (2016) examined the influence of both the early nutritional environment, and the psychosocial environment, on the subsequent risk of metabolic syndrome (MetS) in midlife. Early nutritional environment, represented by mother’s pre-pregnancy BMI, was associated with the risk of MetS in midlife. An important mechanism involves a mother-to-child BMI transmission, independent of birth or perinatal conditions, socioeconomic characteristics and health behaviors over the lifecourse. However this mechanism was not sufficient for explaining the influence of mother’s pre-pregnancy BMI which implies the need to further explore other mechanisms in particular the role of genetics and early nutritional environment. Adverse Childhood Experiences (ACEs) (identified through categories such as child in care, physical neglect, offenders, parental separation, mental illness, alcohol abuse) was not independently associated with MetS. However, the authors suggest that other early life stressful events such as emergency caesarean deliveries and poor socioeconomic status during childhood may contribute as determinants of MetS (Delpierre, C., Fantin, R., Barboza-Solis, C., Lepage, B., Darnaudéry, and M., Kelly-Irving, M. (2016) The early life nutritional environment and early life stress as potential pathways towards the metabolic syndrome in mid-life? A lifecourse analysis using the 1958 British Birth cohort. BMC Public Health. 2016 Aug 18; 16(1):815. Epub 2016 Aug 18). Early negative circumstances during childhood, collected prospectively in the British birth cohort 1958, could be associated with physiological wear-and-tear in midlife as measured by allostatic load. This relationship was largely explained by health behaviors, body mass index, and socioeconomic status in adulthood, but not entirely. The results suggested that a biological link between adverse childhood exposures and adult health may be plausible. The authors’ findings contribute to the development of more adapted public health interventions, both at a societal and individual level (BARBOSA-SOLÍS, C, KELLY-IRVING, M, FANTIN, R, DARNAUDÉRY, M, TORRISANI, J, LANG, T and DELPIERRE, C. (2015) Adverse childhood experiences and physiological wear-and-tear in midlife: Findings from the 1958 British birth cohort. Proceedings of the National Academy of Sciences of the United States of America, 112(7), E738–E746). In meta-analysis, including the NCDS, Llewellyn, Simmonds, Owen, and Woolacott (2016) investigated the ability of childhood body mass index (BMI) to predict obesity-related morbidities in adulthood. The authors found that high childhood BMI was associated with an increased incidence of adult diabetes, coronary heart disease (CHD) and a range of cancers, but not stroke or breast cancer. The accuracy of childhood BMI to predict any adult morbidity was low. Only 31% of future diabetes and 22% of future hypertension and CHD occurred in children aged 12 or over classified as being overweight or obese. Only 20% of all adult cancers occurred in children classified as being overweight or obese. Childhood obesity was associated with moderately increased risks of adult obesity-related morbidity, but the increase in risk was not large enough for childhood BMI to be a good predictor of the incidence of adult morbidities as the majority of adult obesity-related morbidity occurred in adults who were of healthy weight in childhood. Therefore, the authors suggest, targeting obesity reduction solely at obese or overweight children may not substantially reduce the overall burden of obesity-related disease in adulthood (LLEWELLYN, A, SIMMONDS, M, OWEN, C.G and WOOLACOTT, N. (2016) Childhood obesity as a predictor of morbidity in adulthood: a systematic review and meta-analysis. Obesity Reviews, 17(1), 56-67. Using cross-sectional data from the NCDS biomedical survey, Berry, Hesketh, Power and Hypponen (2011) found that vitamin D status had a linear relationship with respiratory infections and lung function, but randomised controlled trials are warranted to investigate the role of vitamin D supplementation on respiratory health and to establish the underlying mechanisms (BERRY, D.J, HESKETH, K, POWER, C and HYPPONEN, E. (2011) Vitamin D status has a linear association with seasonal infections and lung function in British adults. British Journal of Nutrition, 106(9), 1433-14440). Montgomery and Ekbom (2002) tested the hypothesis that maternal smoking during pregnancy increases both the risk of early onset type 2 diabetes and non­diabetic obesity in offspring. The association of diabetes with maternal smoking during pregnancy (independent of finer-grain measures of mothers' smoking in 1974, own smoking at age 16, and other potential confounding factors) suggested that it is a true risk factor for early adult onset diabetes. Cigarette smoking as a young adult was also independently associated with an increased risk of subsequent diabetes. In utero exposures due to smoking during pregnancy may increase the risk of both diabetes and obesity through programming, resulting in lifelong metabolic dysregulation, possibly due to fetal malnutrition or toxicity. The odds ratios for obesity without type 2 diabetes are more modest than those for diabetes and the scope for confounding may be greater. Smoking during pregnancy may represent another important determinant of metabolic dysregulation and type 2 diabetes in offspring. The authors stress that smoking during pregnancy should always be strongly discouraged (MONTGOMERY, S.M and EKBOM, A. (2002) Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. British Medical Journal, 324, 26-27). Research using this cohort has also shed light on cancer and leukaemia in childhood, behavioural disorder, educational delay and disability. Linking Hospital Episodes Statistics (HES) to the NCDS survey data will greatly increase the potential of this unique dataset which has already been benefiting health outcomes for nearly 60 years. Our society is changing fast. This cohort study will be used to chart and understand how society has changed over the years, and how life experiences are different for each generation. They help understand the impact of societal trends such as the ageing population and the growth in lone-parent and step-families, and changes such as growing employment insecurity. This study helps understand that change. Evidence from this cohort study have contributed to many policy decisions in diverse areas – such as increasing the duration of maternity leave, raising the school leaving age, updating breast feeding advice given to parents. Further examples of benefits to health can be found on the study website athttps://ncds.info/home/what-have-we-learned/

Benefits reported

Stated in the previous version and removed here.

Yielded Benefits is not a requirement for new applications.