NIHRDataInsights/HRCSData huggingface dataset:
Title: Developing and disseminating tools for population health improvement: an online interactive atlas for identifying environmental change
Funder: Medical Research Council

Abstract: The UK has some of the world’s best longitudinal data on individuals and their health but, in contrast, longitudinal data on built and physical environments are poor and hard to access for the non-expert. This matters because the environment is an important determinant of health. The MRC/CSO Social & Public Health Sciences Unit have developed an atlas of built and physical environment change from Ordnance Survey (OS) data but this is currently for Scotland only, offers a narrow range of environmental attributes and lacks ‘off-the-shelf’ measures of both environment and change which would make it more useful for the public, policy makers, planners and those evaluating the impacts of environmental interventions on health and health inequalities. The project aims to add to tools for population health improvement by collating, enhancing and sharing easy-to-use information capturing change in the built and physical environment over time, at fine spatial scale for the whole of Great Britain, in order to link data to changes in health and/or health-related behaviours and to evaluate the impacts of environmental interventions on health, behaviour and inequalities.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: GENERIC HEALTH RELEVANCE
Title: Trusted Research Environments (TRE): removing technical and governance barriers to support innovation and interdisciplinary research
Funder: Medical Research Council

Abstract: ‘Non-traditional’ researchers and innovators from different disciplines and organisations, including software developers, AI experts and others from third- and private sector organisations could drive improvements to health systems and outcomes across the United Kingdom. While TREs support privacy by design, access by non-traditional users has been limited and remains largely aspirational. This project will design a governance structure based on the Five Safes model1, to support non-traditional user access. A risk assessment will be completed in collaboration with a public group to develop a prototype solution, which will be tested using key use cases. The prototype will include a user training module designed to equip non-traditional users with the skills, understanding and credibility to access health and social care data in TREs. As well as addressing concerns from the public group, we will address known barriers: 1. Safe People: o Researchers must demonstrate skills and research experience to evidence Safe People credentials.2 This expectation is defined around academic research backgrounds, constraining those from non-academic careers. o Existing training to support accreditation of Safe People, does not consider non- traditional users who need to understand the implications of working within TREs.3 2. Safe Settings: o Virtual Desktop Interfaces (VDI) within the National Safe Haven are designed with standard analytical tools, without a defined governance mechanism or risk assessment for bespoke VDI builds. o The audit and testing regimes do not have a strong focus on software security. 3. Safe Projects: o There is no national requirement for ethical review of projects not deemed ‘health research’. o Existing ethical reviews of research projects do not cover AI or machine learning innovation projects and ethics panels lack AI ethics membership. o Innovation projects are novel, requiring an agile and evolving risk assessment and ethics evaluation. 4. Safe Outputs: o Routes to withdraw software and coding extractions from the TRE are poorly defined. Specific project deliverables are: 1. A new governance model and standards for access to sensitive data that adapts the successful Five Safes framework for non-traditional researchers. 2. New online learning and accreditation modules for non-traditional researchers, to ensure robust standards are clearly achieved before granting access to health and social care data. 3. Evidence of transparent and active public involvement in the development of these new resources, to provide confidence for wider adoption across the UK.


Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;7.4 RESOURCES AND INFRASTRUCTURE (DISEASE MANAGEMENT);8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE;8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Privacy Risk Assessment Methodology
Funder: Medical Research Council

Abstract: The effective use of data is expected to transform society but use of personal data creates privacy risks. Currently privacy risk management is vague, resulting in a variety of Trusted Research Environments with no consistent guidance for privacy risk assessment, mitigation and management. This is challenging for interdisciplinary research where complex health and social care datasets under different domains of control need to be combined. Today’s privacy impact assessment methods are complicated, demanding and not widely used in practice, leading to inconsistent results. A common way of analysing privacy risks is needed to establish effective cross-council research networks and ensure privacy risk can be managed consistently and efficiently. We aim to lay the foundations for a standard privacy risk assessment framework that can describe and automatically assess privacy risk for safe data federations. The objectives are to 1) define use cases and data patterns for advanced analytics, 2) identify privacy risk factors 3) define a risk tier classification framework 4) assess privacy risks for use cases (public health and integrated care) using cyber security risk modelling and simulation, and 5) develop, evaluate and disseminate the framework and lessons learnt through engagement with experts and the public. The framework for comparative assessment of different privacy risks will provide a reference to enable organisations to assess the overall risk levels. We will then investigate how to extend ISO 27005 information security risk management concepts and processes to privacy risk management. We will investigate important types of privacy risk from the framework (e.g. re-identification); threats that can cause privacy risks (e.g., linking); patterns of assets to identify threats (e.g. aggregation of datasets); environments that affect the likelihood of privacy threats (e.g. environment affecting the risk of re-identification); adversarial conditions (e.g. motivations, capabilities and opportunity); and controls (e.g. homomorphic encryption, parquet encryption, secure enclaves, contracts) that can lower the likelihood of threats occurring or mitigate the impact of the risk. Three work packages will address user needs, privacy risk framework and implementation. WP1 “Use Cases, Evaluation & Stakeholder Engagement” will analyse use cases, requirements, conduct evaluation and capture/disseminate lessons learnt to maximise impact. WP2 “Privacy Risk Framework Specification” will identify privacy risks factors and development the privacy assessment framework. WP3 “Privacy Risk Modelling & Simulation” will model risk factors and assess use cases using ISO27005.


Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;7.4 RESOURCES AND INFRASTRUCTURE (DISEASE MANAGEMENT);8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE;8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Trusted Research Environment and Enclave for Hosting Open Original Science Exploration (TREEHOOSE)
Funder: Medical Research Council

Abstract: TREs are necessary for the ethical and secure management of sensitive data. Traditionally, they require large, up-front capital, investment in specialist infrastructure and potentially procurement delays. The need for increased power and flexibility in TREs has driven increased adoption of cloud services in place of on-premise equipment. Cloud has matured in provision and security to the point where several security conscious organisations (e.g. Police, Government, Defence, Health) are employing them to allow for scaling efficiencies and on-demand access to leading edge hardware. Designing and operating a TRE in the cloud requires considerable custom work, with a challenging learning curve for operations staff. The implementation, maintenance and development of TREs requires unique skillsets for which there is a current shortage. Upskilling of new and existing staff is crucial for the continued growth and development of Data Science in the UK. Recent technical advances in Trusted Computing and cloud equipment such as AWS Nitro Enclaves allows the creation of a cryptographic ‘enclave’, within which code can execute free from tampering or inspection by the host environment: a ‘black box’, into which sensitive data can be passed, producing output which is captured and scrutinised before release to the user, without exposing the code itself to analysis or potential reverse-engineering by the TRE operators. This also provides enhanced, cryptographically-verified reproducibility of the container environment in which the code was executed. We have been running a TRE for over a decade and have recently collaborated with AWS to develop a Cloud TRE which is now in production. TREEHOOSE will share open-source code and documentation for both operating and performing research within a modern cloud TRE with enclave facilities. This will • Reduce the learning curve for TRE operators and users alike. • Reduce the cost of migration to the cloud for other TREs. • Aid portability of code between TREs. • Make federation more straightforward through the use of common cloud deployments. TREEHOOSE will also upskill project team members for the benefit of future research projects. None of this work is possible nor sensible without engagement with the public and patients as they are the final arbiters of acceptable reuse of their data. We will embed workshops on trustworthiness of cloud computing with research data and report on our findings.


Research activity codes: 2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);4.5 RESOURCES AND INFRASTRUCTURE (DETECTION);5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT);8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMatter)
Funder: Medical Research Council

Abstract: TREs are widely, and increasingly being used to support statistical analysis of sensitive data across a range of sectors (e.g., education, police, tax and health) as they enable secure and transparent research whilst protecting data confidentiality. There is increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These more complex AI models require more information to describe and reproduce, increasing the possibility that sensitive information regarding secure data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all researchers to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. We aim to address this problem by developing a set of usable recommendations for TREs to use to guard against the additional risks when disclosing trained AI models from TREs. We will draw upon our internationally recognised expertise in TREs, AI, data governance, disclosure control, data security and confidentiality, law and ethics. WP1: Quantitative Assessment of Risk: a detailed empirical study to evaluate vulnerabilities of a selection of machine learning models. We will explore different models, hyper-parameter settings and training algorithms over common data types. WP2: Controls and Evaluation of Tools: evaluation of effectiveness of a range of tools for addressing vulnerabilities identified in WP1 technically (do they accurately quantify disclosure risks) and organisationally (what is their impact on TRE output checking, and in assisting researchers to produce checkable ‘safe’ outputs). WP3: Legal and Ethical Implications: a legal and ethical analysis of information from WP1/2 to develop a framework for regulation of AI models developed in a protected environment, and identification of aspects of existing legal and regulatory frameworks requiring reform to facilitate the use and export of AI models from protected environments. WP4: PPIE: 4 workshops run by lay Co-Is seeking input from public/patients on our approach. We will produce lay summaries of project outputs and support and train our researcher team onhow best to work with the public. WP5: Green Paper: all WPs will collaborate on drafting a green paper and seek input from the wider community though a consultancy period. Our international collaborator will provide a perspective external to the UK.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY);8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE;8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Degron - Precision control of protein dosage in vivo
Funder: Medical Research Council

Abstract: Genome editing has transformed our ability to make mouse models of human disease. However, genetic manipulation occurs at the DNA level, whereas proteins mediate most biological functions. This limits the ability to understand disease mechanisms, identify therapeutic targets and model therapeutic interventions. This cluster will address this problem using degron-tagging technology to degrade target proteins in a rapid, tuneable and reversible manner. Although several degrons have been developed in cultured mammalian cells, how they will behave in the more complex in vivo environment is unclear. Our workplan will proceed in two phases. During phase I, we will build genetic reporters to evaluate and compare three degron systems. We will test existing ligands and synthesise novel derivatives to identify compounds with favourable pharmacokinetic and pharmacodynamic properties in vivo. Our aim is to develop protocols for depletion across tissues, diseases and time-scales in line with network priorities. During phase II, we will work with the MLC to establish a national hub for degron technologies, disseminating knowledge from Phase 1 across the network via exemplar projects, and to the wider community via training courses and an online information portal. Targeted protein degradation is an area of intense research. A cluster investment would enable the latest advances to be rapidly translated into advanced human disease models. The speed of degradation opens up a new acute temporal scale for downstream phenotypic analysis which is inaccessible using existing approaches, and will better model the effects of protein degrader drugs. Reversibility will determine whether and at what critical time-points therapeutic interventions can achieve clinical benefit. Our vision is that degron technologies will complement, extend, and in many cases replace recombinase approaches, simultaneously reducing and refining animal use while producing data that are easier to interpret.


Research activity codes: 1.3 CHEMICAL AND PHYSICAL SCIENCES
Health categories: GENERIC HEALTH RELEVANCE
Title: University of Sheffield Advanced Cell and Gene Therapies MSc
Funder: Medical Research Council

Abstract: This course combines the research strengths of the University of Sheffield’s Faculty of Medicine, Dentistry and Health, academic and industry partners, and leading international scientists. The course teaches genetic factors associated with rare inherited diseases and how gene and cell therapies can be adopted as treatment options, as well as the principles of gene and cell therapies. Students also explore the development of specific and efficient medical treatments tailored to individual patients. Students develop essential skills for each step of the translational pathway of cell and gene therapy products, including: discovery, vector design, GLP pre-clinical regulatory studies, GMP manufacturing and analytics, process innovation, quality control, qualified person (QP), safety, clinical design, commercialisation and market authorisation. The course will provide up-to-date knowledge of advances in the field, offering a comprehensive road map on the required steps to take an advanced therapy project from discovery to clinical trial and then marketing authorisation.


Research activity codes: 5.2 CELLULAR AND GENE THERAPIES
Health categories: GENERIC HEALTH RELEVANCE
Title: MAP-care: Multimorbid Ageing Primary Palliative Care in Ghana, Malawi and Zimbabwe.
Funder: Medical Research Council

Abstract: PHASE 1 DEVELOPMENT (Months 0-12) i) To establish an African patient and public involvement palliative care network to inform research procedures and dissemination throughout the programme. ii) To determine primary care staff, patient and family views of costs, barriers, facilitators and necessary support to achieve primary person-centred palliative older care for multimorbid serious illness. iii) To develop a logic model and novel model of care to integrate value-based care, patient-centred outcome measurement, a decision support tool and staff mentorship. iv) To refine a cost tool to measure levels of catastrophic expenditure in the context of progressive multimorbidity, suitable for use in LMIC. PHASE 2 FEASIBILITY (Months 13-18) v) To assess stakeholders' views on acceptability of the novel model of care and ways to refine to the local context to enhance use. vi) To appraise implementation and sustainability of the novel model of care. Sequential mixed-methods cross-national study applying MRC guidelines for development and testing of complex interventions (Phase 1 development and Phase 2 early testing procedures). The MRC complex intervention process guidance will identify the "interdisciplinary mix" (professional and lay) of stakeholders in the health system to engage in the development of the logic model to define and implement the novel care model, and to test fidelity. Phase 1 will apply Photobox methods for PPI development ,and qualitative in-depth interviews to gain staff (n=10-12 per country) patient/family dyads' views (up to n=15). The costing tool will be revised using the qualitative data and n=7-10 dyad cognitive interviews. Phase 2 will deliver the model of care at 1 rural primary care clinic in each country with before/after data questionnaire data collected from n=36 patients per site, and up to n=15 patient/family dyads in-depth interviews and focus group with n=10 staff.


Research activity codes: 7.2 END OF LIFE CARE
Health categories: GENERIC HEALTH RELEVANCE
Title: Systems thinking approach to developing an integrated and patient-centred intervention model for multimorbidity care in primary care settings in India
Funder: Medical Research Council

Abstract: We will directly build on and expand findings from two recently funded studies (year 2019-2020) from our group to assess the pattern and distribution of multimorbidity in Kerala, India. Our current proposal will use a systems thinking approach and causal loop model to conceptualise how health systems manage patients with multi-morbidity in primary health care settings in India. Evidence will be sought from the literature by conducting an updated systematic review on benefits of existing interventions for patients with multi-morbidity in LMIC. An interdisciplinary research team of health system researchers, epidemiologists, and social scientists will conduct the study in two phases: (1) Identification of potential interventions for managing multimorbidity in primary care and (2) development of the final intervention tools for integrated management of multimorbidity evaluation in a future study. In the first phase, potential interventions will be identified and proposed to address gaps in the current system from patients, providers and health system perspectives. A causal loop modelling will be employed to identify feedback loops and evaluate impacts of the potential interventions at the level of patients and care providers. We will also look into ways in which the care for people with multiple chronic conditions can be organised and integrated within the community through community health workers. In the second phase the causal loop analysis results will be linked to decision making on intervention implementation and appropriate tools for the intervention will be developed. The interventions will be informed by our previous work in the area of clinical handover, evaluation methods for complex health system interventions, and primary care coordination for global risk reduction in primary care settings in India. The developed intervention package and the tools for implementation will be piloted and evaluated formally in a future study.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE
Title: Multimorbidity in Africa - Increasing Understanding of the Patient Experience and Epidemiology (MAfricaEE)
Funder: Medical Research Council

Abstract: Multimorbidity, the presence of 2 or more long-term conditions, is an increasing global health challenge. Although multimorbidity is increasingly common in low and middle-income countries (LMICs), we have insufficient: 1. information on patterns, trends or determinants of multimorbidity in LMICs 2. understanding of patient/health professional perspectives of multimorbidity and barriers/facilitators to optimal care in LMICs. Such information is crucial to inform health and social services in LMIC contexts. The collaboration is led by the University of Glasgow, which has particular expertise in multimorbidity research, using both data science and qualitative methods, along with partners in Malawi, the MRC Units in Gambia and Uganda and the London School of Hygiene and Tropical Medicine. Our project involves mixed methods research that will promote capacity building, and involve both primary data collection and secondary analyses of existing cohort and qualitative data to provide important new insights into the challenges posed by multimorbidity. Key research objectives include: 1. Increasing understanding of prevalence and pattern of multimorbidity across different datasets in Malawi/Uganda/Gambia 2. Examining cross-sectional relationship between demographic and lifestyle factors with prevalence and pattern of multimorbidity in Malawi/Uganda/Gambia 3. Exploring patient and professional experiences of the effects of multimorbidity on the work of self-management and barriers and facilitators to optimal care While the work is primarily exploratory, it will promote capacity building and development of a new collaboration looking at multimorbidity in Sub Saharan Africa. The project will conclude with a research symposium in Malawi involving the research teams and key stakeholders to share experiences of multimorbidity research, opportunities and key challenges, to underpin future work in this sphere.


Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: GENERIC HEALTH RELEVANCE