ADR UK (Administrative Data Research UK) is a partnership transforming the way researchers access the UK’s wealth of public sector data, to enable better informed policy decisions that improve people’s lives. By linking together data held by different parts of government, and by facilitating safe and secure access for accredited researchers to these newly joined-up data sets, ADR UK is creating a sustainable body of knowledge about how our society and economy function – tailored to give decision makers the answers they need to solve important policy questions. ADR UK is made up of three national partnerships (ADR Scotland, ADR Wales, and ADR NI) and the Office for National Statistics (ONS), which ensures data provided by UK government bodies is accessed by researchers in a safe and secure form with minimal risk to data holders or the public. The partnership is coordinated by a UK-wide Strategic Hub, which also promotes the benefits of administrative data research to the public and the wider research community, engages with UK government to secure access to data, and manages a dedicated research budget. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. To find out more, visit adruk.org or follow @ADR_UK on Twitter. ADR UK is funding the creation of a research-ready database linking health, education and social care data for all children in England for the first time. ECHILD stands for Education and Child Health Insights from Linked Data. The study involves the linking of around 14 million children’s records, which will be used to better understand how education affects children’s health and how health affects children’s education. The ECHILD project is led by University College London in collaboration with the London School of Hygiene & Tropical Medicine and the Institute for Fiscal Studies, in partnership with NHS Digital and the Department for Education, working with the Office for National Statistics (ONS).

What is this research project about? We will investigate to what extent exposure to air pollution during pregnancy and the first five years of life and poor housing conditions (such as overcrowding and damp/mould) contribute to hospital admissions for respiratory tract infections (RTIs) in children less than five years old. Why are we doing this research? RTIs, including bronchiolitis, pneumonia and croup, are the most common reason for hospital admission in young children in the UK. These admissions are stressful for children, their parents and costly for the National Health Service (NHS). Being admitted to hospital with an RTI during the first few years of life is also associated with the development of chronic respiratory problems, such as asthma, in later childhood. Previous research has found that children from poor backgrounds are more likely to need an RTI admission, but it is not clear which aspects of children's living conditions make the largest contribution to RTI hospital admissions. In this study, we will examine whether exposure to air pollution in the womb or during early childhood, and poor housing conditions are associated with a child's risk of being admitted to hospital with an RTI. Also, we will look at how many RTI admissions could be prevented in the UK if we reduced air pollution and/or improved housing conditions for families with young children. How are we going to do it? We will use data collected from birth certificates, linked to maternity records and hospital admission data for all children born in England between 2005 and 2014, and Scotland between 1997 and 2019: 8 million children in total. We will link in data about children's air pollution exposure during pregnancy and childhood, building characteristics, and information about housing and socio-economic background from the 2011 Census. All data will be kept on secure servers and linked using methods that protect the identities of mothers and children. We will use these data to examine whether exposure to air pollution and poor housing conditions are associated with an increased risk of being admitted to hospital with an RTI during the first five years of life. We will use statistical methods that allow us to take into account whether children have other underlying risk factors for RTI hospital admissions, such as chronic health problems. We will also make sure that other researchers can access these datasets to carry out maternal and child health research in the future.

Aim: Our aim is to improve the usefulness of an established UK resource, the National Neonatal Research Database (NNRD) for parents and for researchers so that they can view data and conduct studies to improve the care of preterm and sick newborn babies more quickly, efficiently and at lower cost than presently. About 1 in 7 (100,000 each year) newborn babies is admitted to a NHS neonatal unit. Neonatal problems and the care received, affect life-long health and well-being. Background: We established the NNRD, a unique, award-winning resource, in collaboration with parents, doctors, nurses, other healthcare professionals, and researchers to improve care, treatments and outcomes for preterm and sick babies admitted to NHS neonatal units. The NNRD contains comprehensive data, updated quarterly, from the electronic medical notes of all babies admitted to NHS neonatal units in England, Scotland and Wales. Imperial College London hosts the NNRD securely on a computer server. No data that can identify any individual baby are included. The data include details of diseases, daily treatments and outcomes. To-date the NNRD has information on about one million babies; around 25,000 new babies are added each quarter. Why this work is needed: We established the NNRD because a key challenge in newborn care is the need for up-to-date, timely and accurate data for research to improve, evaluate and develop new treatments. Data are required for all of the many types of studies needed, such as improving understanding of diseases, their causes and the care provided, and to develop new medicines. Different types of studies often need similar data (e.g. age, sex, weight, disease) but traditionally, researchers collect these again and again for each new purpose. This is expensive, wastes time and increases the risk of errors. Studies can fail because data availability or quality are poor. Data also need to be up-to-date otherwise information may be misleading. For example, information on health outcomes of very preterm babies that are widely used in the UK to counsel parents and guide clinical practice was derived from research conducted over 20 years ago and no longer reflects circumstances today. The NNRD provides a single source of up-to-date data for research and other purposes. This work is needed to improve the NNRD and make it more useful. Our objectives, and what we will do: We will automate processes to check accuracy and add new data into the NNRD that we currently perform manually. At present anyone who wants to use the NNRD must request our assistance, which inevitably incurs a delay. We will identify common types of information that researchers, parents and clinicians would find useful to obtain from the NNRD. This might be to determine the number of patients with particular conditions that are admitted to neonatal units. We will also obtain views on the way in which they would like to see the results (e.g. tables or graphs). This will help us develop web-based tools to enable parents and researchers to answer common questions themselves. We will make these tools available on our website. We will also develop ways to process NNRD data so that we can apply new techniques that can help identify patterns such as where particular types of disease occur and provide clues to their causes. Additionally, we will train young scientists in handling complex health data. Why this partnership is needed: We have formed a partnership because our objectives require skills across different organisations and disciplines. Our partnership brings clinical neonatologists, academic researchers and data scientists together with the national information technology lead for the health and care system in England (NHS Digital), expertise in data tools (Strategic Intelligence Alliance for Health; SIA), the national charity for preterm and sick newborn babies (Bliss) and the national institute for health data, Health Data Research (UK HDR-UK).

Over 20 million people in the UK live with rheumatic and musculoskeletal diseases (RMD), and inflammatory arthritis (IA) is a major subdivision of RMD causing joint inflammation leading to damage. IA causes long-term pain, disability and incurs substantial personal and societal costs. There is also an estimated 59% increase in diagnosed IA cases between 2004 and 2020 in the UK which has important implications for health services. Rheumatology departments accounted for approximately 9% of the average NHS trusts total medication spend in 2019/2020. There are still significant unmet needs in the IA patient pathway, especially in IA detection and flare management. IA presents with non-specific symptoms and there is currently no diagnostically definitive single biomarker for IA. Early detection is critical but challenging, and delay in detection and late referral often result in loss of the window of opportunity when effective treatment should start and delays can lead to disability and associated unemployment. For patients who are diagnosed with IA, IA outcomes and activities such as flare-up are very heterogeneous in their manifestations between individual patients. Real-world data from The National Early Inflammatory Arthritis Audit showed inequality in care for rheumatology patients from minority ethnic groups. A lower proportion of ethnic minority patients achieved disease remission compared to white patients. UN4 Finally, weather is another contributing factor of IA flare heterogeneity. Despite significant unmet needs, RMD, especially IA, is still an underexplored area of real-world ML application in comparison with other diseases. Existing ML studies do not fit for purpose of early detection in practice as they are not trained based on the data available at the point of early detection. Furthermore, although there are studies showing potential determinants of IA, there is no research, or any machine learning methods that can identify the undetected determinants-combination that can offer a useful level of prediction of IA. This is because current ML approaches still cannot handle the underlying relationships among heterogenous datasets with different data types, modalities, contexts, cohorts and levels of incompleteness. On the other hand, existing ML methods in IA, and healthcare in general, still rely on a "one-size-fits-all" paradigm rendering generic learning algorithms, suboptimal on the individual level especially as IA is known to be heterogenous in nature from the time of diagnosis. Although there are methods for explainable ML local, there is limited research to quantify and explain model prediction uncertainty and its usability in practice. For a physician to use and trust ML predictions it is critical to understand the uncertainty associated with these predictions for the individual patient. Although successful translation requires bringing together expertise and stakeholders from many disciplines, the development of ML solutions is currently occurring in silos, and there is a lack of holistic and scalable ML development pipeline. Despite all the limitations of current ML, there are huge opportunities to advance ML, especially in rheumatology applications, because rheumatology has already been leading the way in the use of virtual clinics and remote monitoring in the UK. It is now time to advance ML using data generated for real early detection and personalised management of IA. Our vision: The proposed project will develop useful and responsible machine learning methods to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. We will develop a holistic and scalable approach through an interdisciplinary team addressing the pressing healthcare challenges of inflammatory arthritis and the limitations of machine learning to accelerate real-world ML application in healthcare.

Autonomous systems, such as medical systems, autonomous aerial and road vehicles, and manufacturing and agricultural robots, promise to extend and expand human capacities. But their benefits will only be harnessed if people have trust in the human processes around their design, development, and deployment. Enabling designers, engineers, developers, regulators, operators, and users to trace and allocate responsibility for the decisions, actions, failures, and outcomes of autonomous systems will be essential to this ecosystem of trust. If a self-driving car takes an action that affects you, you will want to know who is responsible for it and what are the channels for redress. If you are a doctor using an autonomous system in a clinical setting, you will want to understand the distribution of accountability between you, the healthcare organisation, and the developers of the system. Designers and engineers need clarity about what responsibilities fall on them, and when these transfer to other agents in the decision-making network. Manufacturers need to understand what they would be legally liable for. Mechanisms to achieve this transparency will not only provide all stakeholders with reassurance, they will also increase clarity, confidence, and competence amongst decision-makers. The research project is an interdisciplinary programme of work - drawing on the disciplines of engineering, law, and philosophy - that culminates in a methodology to achieve precisely that tracing and allocation of responsibility. By 'tracing responsibility' we mean the process of tracking the autonomous system's decisions or outcomes back to the decisions of designers, engineers, or operators, and understanding what led to the outcome. By 'allocating responsibility' we mean both allocating role responsibilities to different agents across the life-cycle and working out in advance who would be legally liable and morally responsible for different system decisions and outcomes once they have occurred. This methodology will facilitate responsibility-by-design and responsibility-through-lifecycle. In practice, the tracing and allocation of responsibility for the decisions and outcomes of AS is very complex. The complexity of the systems and the constant movement and unpredictability of their operational environments makes individual causal contributions difficult to distinguish. When this is combined with the fact that we delegate tasks to systems that require ethical judgement and lawful behaviour in human beings, it also gives rise to potential moral and legal responsibility gaps. The more complex and autonomous the system is, the more significant the role that assurance will play in tracing and allocating responsibility, especially in contexts that are technically and organisationally complex. The research project tackles these challenges head on. First, we clarify the fundamental concepts of responsibility, the different kinds of responsibility in play, the different agents involved, and where 'responsibility gaps' arise and how they can be addressed. Second, we build on techniques used in the technical assurance of high-risk systems to reason about responsibility in the context of uncertainty and dynamism, and therefore unpredictable socio-technical environments. Together, these strands of work provide the basis for a methodology for responsibility-by-design and responsibility-through-lifecycle that can be used in practice by a wide range of stakeholders. Assurance of responsibility will be achieved that not only identifies which agents are responsible for which outcomes and in what way throughout the lifecycle, and explains how this identification is achieved, but also establishes why this tracing and allocation of responsibility is well-justified and complete.