Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study.

OBJECTIVE: This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes. METHODS: We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom. RESULTS: We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants. SIGNIFICANCE: We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper.

BACKGROUND: Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product. OBJECTIVE: This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit. METHODS: We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text. RESULTS: We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders. CONCLUSIONS: By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Public Views on Models for Accessing Genomic and Health Data for Research: Mixed Methods Study.

BACKGROUND: The literature abounds with increasing numbers of research studies using genomic data in combination with health data (eg, health records and phenotypic and lifestyle data), with great potential for large-scale research and precision medicine. However, concerns have been raised about social acceptability and risks posed for individuals and their kin. Although there has been public engagement on various aspects of this topic, there is a lack of information about public views on data access models. OBJECTIVE: This study aimed to address the lack of information on the social acceptability of access models for reusing genomic data collected for research in conjunction with health data. Models considered were open web-based access, released externally to researchers, and access within a data safe haven. METHODS: Views were ascertained using a series of 8 public workshops (N=116). The workshops included an explanation of benefits and risks in using genomic data with health data, a facilitated discussion, and an exit questionnaire. The resulting quantitative data were analyzed using descriptive and inferential statistics, and the qualitative data were analyzed for emerging themes. RESULTS: Respondents placed a high value on the reuse of genomic data but raised concerns including data misuse, information governance, and discrimination. They showed a preference for giving consent and use of data within a safe haven over external release or open access. Perceived risks with open access included data being used by unscrupulous parties, with external release included data security, and with safe havens included the need for robust safeguards. CONCLUSIONS: This is the first known study exploring public views of access models for reusing anonymized genomic and health data in research. It indicated that people are generally amenable but prefer data safe havens because of perceived sensitivities. We recommend that public views be incorporated into guidance on models for the reuse of genomic and health data.

A case study of the Secure Anonymous Information Linkage (SAIL) Gateway: a privacy-protecting remote access system for health-related research and evaluation.

With the current expansion of data linkage research, the challenge is to find the balance between preserving the privacy of person-level data whilst making these data accessible for use to their full potential. We describe a privacy-protecting safe haven and secure remote access system, referred to as the Secure Anonymised Information Linkage (SAIL) Gateway. The Gateway provides data users with a familiar Windows interface and their usual toolsets to access approved anonymously-linked datasets for research and evaluation. We outline the principles and operating model of the Gateway, the features provided to users within the secure environment, and how we are approaching the challenges of making data safely accessible to increasing numbers of research users. The Gateway represents a powerful analytical environment and has been designed to be scalable and adaptable to meet the needs of the rapidly growing data linkage community.

Incidence of Campylobacter and Salmonella infections following first prescription for PPI: a cohort study using routine data.

OBJECTIVES: To examine the incidence of Campylobacter and Salmonella infection in patients prescribed proton pump inhibitors (PPIs) compared with controls. METHODS: Retrospective cohort study using anonymous general practitioner (GP) data. Anonymised individual-level records from the Secure Anonymised Information Linkage (SAIL) system between 1990 and 2010 in Wales were selected. Data were available from 1,913,925 individuals including 358,938 prescribed a PPI. The main outcome measures examined included incidence of Campylobacter or Salmonella infection following a prescription for PPI. RESULTS: The rate of Campylobacter and Salmonella infections was already at 3.1-6.9 times that of non-PPI patients even before PPI prescription. The PPI group had an increased hazard rate of infection (after prescription for PPI) of 1.46 for Campylobacter and 1.2 for Salmonella, compared with baseline. However, the non-PPI patients also had an increased hazard ratio with time. In fact, the ratio of events in the PPI group compared with the non-PPI group using the prior event rate ratio was 1.17 (95% CI 0.74-1.61) for Campylobacter and 1.00 (0.5-1.5) for Salmonella. CONCLUSIONS: People who go on to be prescribed PPIs have a greater underlying risk of gastrointestinal (GI) infection beforehand and they have a higher prevalence of risk factors before PPI prescription. The rate of diagnosis of infection is increasing with time regardless of PPI use, and there is no evidence that PPI is associated with an increase in diagnosed GI infection. It is likely that factors associated with the demographic profile of the patient are the main contributors to increased rate of GI infection for patients prescribed PPIs.

Commentary on 'disability outcome measures in multiple sclerosis clinical trials'.

In order to fully understand and explore the effectiveness of any intervention for the management of multiple sclerosis (MS), it is important to have robust, valid, reliable, and universally applied measures. The recent article, 'Disability outcome measures in multiple sclerosis clinical trials' by Cohen, Reingold, Polman and Wolinsky (2012), explores this issue in regards to the effective measurement of MS-related disability, and the utilisation of patient-reported outcome measures, whilst highlighting the need for collaboration between the academic and clinical communities. Although it is important to examine disability measures, it is also equally important to recognise that physical function is only one aspect of a person's experience; for example, quality of life and psychological well-being are also important aspects to assess. The application of e-health technologies and patient registers could be a useful method of gaining additional information, using patient-reported outcomes. This commentary explores these issues in relation to points raised by the Cohen et al. paper.

The feasibility of collecting information from people with Multiple Sclerosis for the UK MS Register via a web portal: characterising a cohort of people with MS.

BACKGROUND: A UK Register of people with Multiple Sclerosis has been developed to address the need for an increased knowledge-base about MS. The Register is being populated via: a web-based portal; NHS neurology clinical systems; and administrative data sources. The data are de-identified and linked at the individual level. At the outset, it was not known whether people with MS would wish to participate in the UK MS Register by personally contributing their data to the Register via a web-based system. Therefore, the research aim of this work was to build an internet-mounted recruitment and consenting technology for people with Multiple Sclerosis, and to assess its feasibility as a questionnaire delivery platform to contribute data to the UK MS Register, by determining whether the information provided could be used to describe a cohort of people with MS. METHODS: The web portal was developed using VB.net and JQuery with a Microsoft SQL 2008 database. UK adults with MS can self-register and enter data about themselves by completing validated questionnaires. Descriptive statistics were used to characterise the respondents. RESULTS: The web portal was launched in May 2011, and in first three months 7,279 individuals registered on the portal. The ratio of men to women was 1:2.4 (n = 5,899), the mean self-reported age at first symptoms was 33.8 (SD 10.5) years, and at diagnosis 39.6 (SD 10.3) years (n = 4,401). The reported types of MS were: 15% primary progressive, 63% relapsing-remitting, 8% secondary progressive, and 14% unknown (n = 5,400). These characteristics are similar to those of the prevalent MS population. Employment rates, sickness/disability rates, ethnicity and educational qualifications were compared with the general UK population. Information about the respondents' experience of early symptoms and the process of diagnosis, plus living arrangements are also reported. CONCLUSIONS: These initial findings from the MS Register portal demonstrate the feasibility of collecting data about people with MS via a web platform, and show that sufficient information can be gathered to characterise a cohort of people with MS. The innovative design of the UK MS register, bringing together three disparate sources of data, is creating a rich resource for research into this condition.

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions. Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits. Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models. Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

The prescribed duration algorithm: utilising 'free text' from multiple primary care electronic systems.

PURPOSE: To develop and test an algorithm that translates total dose and daily regimen, inputted as 'free text' on a prescription, into numerical values to calculate the prescribed treatment duration. METHOD: The algorithm was developed using antibiotic prescriptions (n = 711,714) from multiple primary care computer systems. For validation, the prescribed treatment duration of an independent sample of antibiotic scripts was calculated in two ways: (a) computer algorithm, (b) manually reviewed by a researcher blinded to the results of (a). The outputs of the two methods were compared and the level of agreement assessed, using confidence intervals for differences in proportions. This was repeated on sample of antidepressant scripts to test generalisability of the algorithm. RESULTS: For the antibiotic prescriptions, the algorithm processed 98.5% with an accuracy of 99.8% and the manual review processed 98.5% with 98.9% accuracy. The differences between these proportions are 0.0% (95%CI of -0.9, 0.9%) and 1.0% (95%CI of -0.1, 2.3%), respectively. For the antidepressant prescriptions, the algorithm processed 91.5% with an accuracy of 96.6% compared to the manual review with 96.4% processed and 99.8% accuracy; difference between these proportions is 4.9% (95%CI of 2.0, 8.0%) and 3.2% (95%CI of 1.6, 5.3%), respectively. CONCLUSION: The algorithm proved to be applicable and efficient for assessing prescribed duration, with sensitivity and specificity values close to the manual review, but with the added advantage that the computer can process large volume of scripts rapidly and automatically.

Protocol for a population-based ankylosing spondylitis (PAS) cohort in Wales.

BACKGROUND: To develop a population-based cohort of people with ankylosing spondylitis (AS) in Wales using (1) secondary care clinical datasets, (2) patient-derived questionnaire data and (3) routinely-collected information in order to examine disease history and the health economic cost of AS. METHODS: This data model will include and link (1) secondary care clinician datasets (i.e. electronic patient notes from the rheumatologist) (2) patient completed questionnaires (giving information on disease activity, medication, function, quality of life, work limitations and health service utilisation) and (3) a broad range of routinely collected data (including; GP records, in-patient hospital admission data, emergency department data, laboratory/pathology data and social services databases). The protocol involves the use of a unique and powerful data linkage system which allows datasets to be interlinked and to complement each other. DISCUSSION: This cohort can integrate patient supplied, primary and secondary care data into a unified data model. This can be used to study a range of issues such as; the true economic costs to the health care system and the patient, factors associated with the development of severe disease, long term adverse events of new and existing medication and to understand the disease history of this condition. It will benefit patients, clinicians and health care managers. This study forms a pilot project for the use of routine data/patient data linked cohorts for other chronic conditions.

Public involvement & engagement in the work of a data safe haven: a case study of the SAIL Databank.

BACKGROUND: The SAIL Databank is a data safe haven established in 2007 at Swansea University (Wales). It was set up to create new opportunities for research using routinely-collected health and other public service datasets in linkable anonymised form. SAIL forms the bedrock of other Population Data Science initiatives made possible by the data and safe haven environment. AIM: The aim of this paper is to provide an overview of public involvement & engagement in connection with the SAIL Databank and related Population Data Science initiatives. APPROACH: We have a public involvement & engagement policy for SAIL in the context of Population Data Science. We established a Consumer Panel to provide advice on the work of SAIL and associated initiatives, including on proposed uses of SAIL data. We reviewed the topics discussed and provide examples of advice to researchers. We carried out a survey with members on their experiences of being on the Panel and their perceptions of the work of SAIL. We have a programme of wider public engagement and provide illustrations of this work. DISCUSSION: We summarise what this paper adds and some lessons learned. In the rapidly developing area of Population Data Science it is important that people feel welcome, that they are encouraged to ask questions and are provided with digestible information and adequate consideration time. Citizens have provided us with valuable anticipated and unanticipated opinions and novel viewpoints. We seek to take a pragmatic approach, prioritising the communication modes that allow maximum public input commensurate with the purpose of the activity. CONCLUSION: This paper has set out our policy, rationale, scope and practical approaches to public involvement & engagement for SAIL and our related Population Data Science initiatives. Although there will be jurisdictional, cultural and organizational differences, we believe that the material covered in this paper will be of interest to other data focused enterprises across the world.

Personal and societal costs of multiple sclerosis in the UK: A population-based MS Registry study.

OBJECTIVES: To investigate through survey and data linkage, healthcare resource use and costs (except drugs), including who bears the cost, of multiple sclerosis in the United Kingdom by disease severity and type. METHODS: The United Kingdom Multiple Sclerosis Register deployed a cost of illness survey, completed by people with multiple sclerosis and linked this with data within the United Kingdom Multiple Sclerosis Register and from their hospital records. Resource consumption was categorised as being medical or non-medical and costed by National Health Service and social services estimates for 2018. RESULTS: We calculated £509,003 in non-medical costs over a year and £435,488 in medical costs generated over 3 months. People with multiple sclerosis reported self-funding 75% of non-medical costs with non-medical interventions having long-term potential benefits. Costs increased with disability as measured by patient-reported Expanded Disability Status Score and Multiple Sclerosis Impact Scale, with Multiple Sclerosis Impact Scale physical being a more powerful predictor of costs than the patient-reported Expanded Disability Status Score. Two distinct groups were identified: medical and non-medical interventions (n = 138); and medical interventions only (n = 399). The medical and non-medical group reported increased disease severity and reduced employment but incurred 80% more medical costs per person than the medical-only group. CONCLUSIONS: The importance of disability in driving costs is illustrated with balance between medical and non-medical costs consistent with the United Kingdom health environment. People with multiple sclerosis and their families fund a considerable proportion of non-medical costs but non-medical interventions with longer term impact could affect future medical costs.

Residential Anonymous Linking Fields (RALFs): a novel information infrastructure to study the interaction between the environment and individuals' health.

BACKGROUND: The necessity of aggregating health data over areas can impede our understanding of health determinants. METHODS: We demonstrate the possibility of creating anonymous links between individual residences and the local environment using digital map data and a data linkage system. RESULTS: Digital map data were used successfully to anonymously link 1.3 million addresses to the local environment. The data linkage system allows detailed environment data surrounding each residence to be linked both to each resident therein and to their medical records. CONCLUSIONS: Local environment data specific to each house can be effectively and anonymously linked to the population registered with the National Health Service. Our integrated approach potentially enables flexible fine-scale, large-area observational studies of communities and health.

The SAIL Databank: building a national architecture for e-health research and evaluation.

BACKGROUND: Vast quantities of electronic data are collected about patients and service users as they pass through health service and other public sector organisations, and these data present enormous potential for research and policy evaluation. The Health Information Research Unit (HIRU) aims to realise the potential of electronically-held, person-based, routinely-collected data to conduct and support health-related studies. However, there are considerable challenges that must be addressed before such data can be used for these purposes, to ensure compliance with the legislation and guidelines generally known as Information Governance. METHODS: A set of objectives was identified to address the challenges and establish the Secure Anonymised Information Linkage (SAIL) system in accordance with Information Governance. These were to: 1) ensure data transportation is secure; 2) operate a reliable record matching technique to enable accurate record linkage across datasets; 3) anonymise and encrypt the data to prevent re-identification of individuals; 4) apply measures to address disclosure risk in data views created for researchers; 5) ensure data access is controlled and authorised; 6) establish methods for scrutinising proposals for data utilisation and approving output; and 7) gain external verification of compliance with Information Governance. RESULTS: The SAIL databank has been established and it operates on a DB2 platform (Data Warehouse Edition on AIX) running on an IBM 'P' series Supercomputer: Blue-C. The findings of an independent internal audit were favourable and concluded that the systems in place provide adequate assurance of compliance with Information Governance. This expanding databank already holds over 500 million anonymised and encrypted individual-level records from a range of sources relevant to health and well-being. This includes national datasets covering the whole of Wales (approximately 3 million population) and local provider-level datasets, with further growth in progress. The utility of the databank is demonstrated by increasing engagement in high quality research studies. CONCLUSION: Through the pragmatic approach that has been adopted, we have been able to address the key challenges in establishing a national databank of anonymised person-based records, so that the data are available for research and evaluation whilst meeting the requirements of Information Governance.

The SAIL databank: linking multiple health and social care datasets.

BACKGROUND: Vast amounts of data are collected about patients and service users in the course of health and social care service delivery. Electronic data systems for patient records have the potential to revolutionise service delivery and research. But in order to achieve this, it is essential that the ability to link the data at the individual record level be retained whilst adhering to the principles of information governance. The SAIL (Secure Anonymised Information Linkage) databank has been established using disparate datasets, and over 500 million records from multiple health and social care service providers have been loaded to date, with further growth in progress. METHODS: Having established the infrastructure of the databank, the aim of this work was to develop and implement an accurate matching process to enable the assignment of a unique Anonymous Linking Field (ALF) to person-based records to make the databank ready for record-linkage research studies. An SQL-based matching algorithm (MACRAL, Matching Algorithm for Consistent Results in Anonymised Linkage) was developed for this purpose. Firstly the suitability of using a valid NHS number as the basis of a unique identifier was assessed using MACRAL. Secondly, MACRAL was applied in turn to match primary care, secondary care and social services datasets to the NHS Administrative Register (NHSAR), to assess the efficacy of this process, and the optimum matching technique. RESULTS: The validation of using the NHS number yielded specificity values > 99.8% and sensitivity values > 94.6% using probabilistic record linkage (PRL) at the 50% threshold, and error rates were < 0.2%. A range of techniques for matching datasets to the NHSAR were applied and the optimum technique resulted in sensitivity values of: 99.9% for a GP dataset from primary care, 99.3% for a PEDW dataset from secondary care and 95.2% for the PARIS database from social care. CONCLUSION: With the infrastructure that has been put in place, the reliable matching process that has been developed enables an ALF to be consistently allocated to records in the databank. The SAIL databank represents a research-ready platform for record-linkage studies.

Health Data Linkage for UK Public Interest Research: Key Obstacles and Solutions.

INTRODUCTION: Analysis of linked health data can generate important, even life-saving, insights into population health. Yet obstacles both legal and organisational in nature can impede this work. APPROACH: We focus on three UK infrastructures set up to link and share data for research: the Administrative Data Research Network, NHS Digital, and the Secure Anonymised Information Linkage Databank. Bringing an interdisciplinary perspective, we identify key issues underpinning their challenges and successes in linking health data for research. RESULTS: We identify examples of uncertainty surrounding legal powers to share and link data, and around data protection obligations, as well as systemic delays and historic public backlash. These issues require updated official guidance on the relevant law, approaches to linkage which are planned for impact and ongoing utility, greater transparency between data providers and researchers, and engagement with the patient population which is both high-profile and carefully considered. CONCLUSIONS: Health data linkage for research presents varied challenges, to which there can be no single solution. Our recommendations would require action from a number of data providers and regulators to be meaningfully advanced. This illustrates the scale and complexity of the challenge of health data linkage, in the UK and beyond: a challenge which our case studies suggest no single organisation can combat alone. Planned programmes of linkage are critical because they allow time for organisations to address these challenges without adversely affecting the feasibility of individual research projects.

The other side of the coin: Harm due to the non-use of health-related data.

INTRODUCTION: It is widely acknowledged that breaches and misuses of health-related data can have serious implications and consequently they often carry penalties. However, harm due to the omission of health data usage, or data non-use, is a subject that lacks attention. A better understanding of this 'other side of the coin' is required before it can be addressed effectively. APPROACH: This article uses an international case study approach to explore why data non-use is difficult to ascertain, the sources and types of health-related data non-use, its implications for citizens and society and some of the reasons it occurs. It does this by focussing on issues with clinical care records, research data and governance frameworks and associated examples of non-use. RESULTS AND DISCUSSION: The non-use of health-related data is a complex issue with multiple explanations. Individual instances of data non-use can be associated with harm, but taken together, they can describe a trail of data non-use that may complicate and compound its impacts. There is ample indirect evidence that health data non-use is implicated in the deaths of many thousands of people and potentially £billions in financial burdens to societies. CONCLUSIONS: Harm due to the non-use of health data is difficult to attribute unequivocally and actual proven evidence is sparse. Although it can be elusive, it is nevertheless a real problem with widespread and serious, if largely unquantifiable, consequences. The most effective initiatives to address specific contexts of data non-use will be those that: firstly, understand the pertinent sources, types and reasons for data non-use in a given domain in order to meet the challenges and create appropriate incentives and repercussions; and secondly, are cognisant of the multiple aspects to this complex issue in other domains to keep benefits and limitations in perspective, to move steadily towards socially responsible reuse of data becoming the norm to save lives and resources.

Assessing the medium-term impact of a home-visiting programme on child maltreatment in England: protocol for a routine data linkage study.

INTRODUCTION: Child maltreatment involves acts of omission (neglect) or commission (abuse) often by caregivers that results in potential or actual harm to a child. The Building Blocks trial (ISRCTN23019866) assessed the short-term impact of an intensive programme of antenatal and postnatal visiting by specially trained nurses to support young pregnant women in England. The Building Blocks: 2-6 Study will assess the medium-term impacts of the programme for mothers and children (n=1562), through the linkage of routinely collected data to the trial data, with a particular emphasis on the programme's impact on preventing child maltreatment. METHODS AND ANALYSIS: We have developed a bespoke model of data linkage whereby outcome data for the trial cohort will be retrieved by linked anonymous data abstraction from NHS Digital, Office for National Statistics and the Department for Education's National Pupil Database. Participants will be given reasonable opportunity to opt out of this study prior to data transfer. The information centres will match participants to the information held in their databases using standard identifiers and send extracts to a third-party safe haven. The study will have 80% power to detect a 4% difference (4%vs8%) for the binary primary outcome of child in need status (from birth to key stage 1) at a two-sided 5% alpha level by following up 602 children in each trial arm. Analysis will be by intention to treat using logistic multilevel modelling. A cost-and-consequences analysis will extend the time frame of the economic analysis from the original trial. ETHICS AND DISSEMINATION: The study protocol has been approved by the National Health Service Wales Research Ethics Committee and the Health Research Authority's Confidentiality Advisory Group. Methods of innovative study design and findings will be disseminated through peer-reviewed journals and conferences; results will be of interest to clinical and policy stakeholders in the UK. TRIAL REGISTRATION NUMBER: ISRCTN23019866.

Cohort profile: the housing regeneration and health study.

A cohort comprising residents of a housing regeneration and health programme was created from routinely collected data using a system which allows us to anonymously link housing data to individuals and their health. The regeneration programme incorporating four rolling work packages runs from 2009 to 2014. The main intervention cohort we describe here contains the 18 312 residents of 9051 residences at baseline. The cohort will be followed continuously through routine health data (demographics, mortality, hospital admissions and general practitioner records including prescriptions) with periodic updates of housing regeneration intervention data. Here, we describe the baseline data for the primary health outcomes of emergency hospital admissions for cardiovascular and respiratory conditions and injuries for those aged ≥60 years. We will compare the health of residents within the homes before and after the housing regeneration work has taken place, and we will calculate the change in health service costs with use of hospital and General Practitioners (GP) services. We will also use a difference in differences approach to assess changes in comparison with comparator cohorts. These data will be accessible at the end of the study period in 2016. Further information about this study can be obtained from Ronan Lyons; r.a.lyons@swansea.ac.uk.