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Background: Trusted Research Environments provide a legitimate basis for data access along with a set of technologies to support implementation of the "five-safes" framework for privacy protection. Lack of standard approaches in achieving compliance with the "five-safes" framework results in a diversity of approaches across different TREs. Data access and analysis across multiple TREs has a range of benefits including improved precision of analysis due to larger sample sizes and broader availability of out-of-sample records, particularly in the study of rare conditions. Knowledge of governance approaches used across UK-TREs is limited., Objective: To document key governance features in major UK-TRE contributing to UK wide analysis and to identify elements that would directly facilitate multi TRE collaborations and federated analysis in future., Method: We summarised three main characteristics across 15 major UK-based TREs: 1) data access environment; 2) data access requests and disclosure control procedures; and 3) governance models. We undertook case studies of collaborative analyses conducted in more than one TRE. We identified an array of TREs operating on an equivalent level of governance. We further identify commonly governed TREs with architectural considerations for achieving an equivalent level of information security management system standards to facilitate multi TRE functionality and federated analytics., Results: All 15 UK-TREs allow pooling and analysis of aggregated research outputs only when they have passed human-operated disclosure control checks. Data access requests procedures are unique to each TRE. We also observed a variability in disclosure control procedures across various TREs with no or minimal researcher guidance on best practices for file out request procedures. In 2023, six TREs (40.0%) held ISO 20071 accreditation, while 9 TREs (56.2%) participated in four-nation analyses., Conclusion: Secure analysis of individual-level data from multiple TREs is possible through existing technical solutions but requires development of a well-established governance framework meeting all stakeholder requirements and addressing public and patient concerns. Formation of a standard model could act as the catalyst for evolution of current TREs governance models to a multi TRE ecosystem within the UK and beyond., Competing Interests: Statement on conflicts of interest: ST, RAL, FT, ES declare funding from Dementias Platform UK 2 - Integrated Dementia Experimental Medicine (MR/T033371/1). RF and ST declare funding from SeRP and all organisations and research programmes using the SeRP platform. ST declared funding from SAIL Databank. Others had no competing interests.

Introduction: Critical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research., Objective: To describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care., Method: To demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales., Results: When applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission., Conclusion: This methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways., Competing Interests: Statement on conflicts of interest: The authors declare they have no conflicts of interest.

Introduction: Early alcohol use has significant association with poor health outcomes. Individual risk factors around early alcohol use have been identified, but a holistic, data-driven investigation into health and household environmental factors on early alcohol use is yet to be undertaken., Objectives: This study aims to investigate the relationship between preceding health events, household exposures and early alcohol use during adolescence using a two-stage data-driven approach., Methods: In stage one, a study population (N = 1,072) were derived from the Millennium Cohort Study (MCS) Wales (born between 2000-2002). MCS data were first linked with electronic-health records. Factors associated with early (<=eleven years old) alcohol use were identified using feature selection and stepwise logistic regression. In stage two, analogous risk factors from MCS were recreated for whole population (N = 59,231) of children (born between 1998-2002 in the Welsh Demographic Service Dataset) using routine data to predict the alcohol-related health events in hospital or GP records., Results: Significant risk factors from stage two included poor maternal mental (adjusted odds ratio [aOR] = 1.31) and physical health (aOR = 1.25), living with someone with alcohol-related problem (aOR = 2.16), single-adult household (aOR = 1.45), ever in deprivation (aOR = 1.66), child's high hyperactivity (aOR = 3.57), and conduct disorder (aOR = 3.26). Children with health events, whose health needs are supported (e.g., are taken to the doctor), are at lower risk of early alcohol use., Conclusion: Health events of the family members and the child can act as modifiable exposures and may therefore inform the development of prevention initiatives. Families with known alcohol problems, living in deprivation, experiencing child behavioural problems and those who are not taken to the doctor are at higher risk of early drinking behaviour and should be prioritised for early years support and interventions to target problem drinking in young people., Competing Interests: Conflict of interest: The authors declare that they have no conflict of interest.

Background: Population-level information on dispensed medication provides insight on the distribution of treated morbidities, particularly if linked to other population-scale data at an individual-level., Objective: To evaluate the impact of COVID-19 on dispensing patterns of medications., Methods: Retrospective observational study using population-scale, individual-level dispensing records in Wales, UK. Total dispensed drug items for the population between 1 st January 2016 and 31 st December 2019 (3-years, pre-COVID-19) were compared to 2020 with follow up until 27 th July 2021 (COVID-19 period). We compared trends across all years and British National Formulary (BNF) chapters and highlighted the trends in three major chapters for 2019-21: 1-Cardiovascular system (CVD); 2-Central Nervous System (CNS); 3-Immunological & Vaccine. We developed an interactive dashboard to enable monitoring of changes as the pandemic evolves., Result: Amongst all BNF chapters, 73,410,543 items were dispensed in 2020 compared to 74,121,180 items in 2019 demonstrating -0.96% relative decrease in 2020. Comparison of monthly patterns showed average difference (D) of -59,220 and average Relative Change (RC) of -0.74% between the number of dispensed items in 2020 and 2019. Maximum RC was observed in March 2020 (D = +1,224,909 and RC = +20.62), followed by second peak in June 2020 (D = +257,920, RC = +4.50%). A third peak was observed in September 2020 (D = +264,138, RC = +4.35%). Large increases in March 2020 were observed for CVD and CNS medications across all age groups. The Immunological and Vaccine products dropped to very low levels across all age groups and all months (including the March dispensing peak)., Conclusions: Reconfiguration of routine clinical services during COVID-19 led to substantial changes in community pharmacy drug dispensing. This change may contribute to a long-term burden of COVID-19, raising the importance of a comprehensive and timely monitoring of changes for evaluation of the potential impact on clinical care and outcomes., Competing Interests: Statement on conflicts of interest: The author(s) declare(s) that they have no competing interests.

Introduction: COVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society., Objectives: To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK., Methods: We conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24 th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28 th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R 2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24 th January-30 th April 2020 and 1 st May-28 th July 2020) to assess algorithm performance., Results: 1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell's C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes., Conclusions: The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population., Competing Interests: Conflicts of interest: AS is a member of the Scottish Government’s COVID-19 Chief Medical Officer’s Advisory Group and its Standing Committee on Pandemics; he is also a member of NERVTAG’s Risk Stratification Subgroup. KK is member of NERVTAG subgroup and member of the Scientific Advisory Group for Emergencies (SAGE). JHC reports grants from National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) and other research councils, during the conduct of the study. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. JHC is chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in COVID-19 infection. RAL is a member of the Welsh Government COVID-19 Technical Advisory Group.

Introduction: The excessive consumption of alcohol is detrimental to long term health and increases the likelihood of hospital admission. However, definitions of alcohol-related hospital admission vary, giving rise to uncertainty in the effect of alcohol on alcohol-related health care utilization., Objectives: To compare diagnostic codes on hospital admission and discharge and to determine the ideal combination of codes necessary for an accurate determination of alcohol-related hospital admission., Methods: Routine population-linked e-cohort data were extracted from the Secure Anonymised Information Linkage (SAIL) Databank containing all alcohol-related hospital admissions (n,= 92,553) from 2006 to 2011 in Wales, United Kingdom. The distributions of the diagnostic codes recorded at admission and discharge were compared. By calculating a misclassification rate (sensitivity-like measure) the appropriate number of coding fields to examine for alcohol-codes was established., Results: There was agreement between admission and discharge codes. When more than ten coding fields were used the misclassification rate was less than 1%., Conclusion: With the data at present and alcohol-related codes used, codes recorded at admission and discharge can be used equivalently to identify alcohol-related admissions. The appropriate number of coding fields to examine was established: fewer than ten is likely to lead to under-reporting of alcohol-related admissions. The methods developed here can be applied to other medical conditions that can be described using a certain set of diagnostic codes, each of which can be a known sole cause of the condition and recorded in multiple positions in e-cohort data., Competing Interests: Conflict of interests: The authors declare that they do not have any conflict of interest.

Introduction: The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care., Objectives: We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers., Methods: SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors., Results: From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years., Conclusion: We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research., Competing Interests: Statement on Conflicts of Interest: None declared.

Background: The Secure Anonymised Information Linkage (SAIL) Databank is a national data safe haven of de identified datasets principally about the population of Wales, made available in anonymised form to researchers across the world. It was established to enable the vast arrays of data collected about individuals in the course of health and other public service delivery to be made available to answer important questions that could not otherwise be addressed without prohibitive effort. The SAIL Databank is the bedrock of other funded centres relying on the data for research., Approach: SAIL is a data repository surrounded by a suite of physical, technical and procedural control measures embodying a proportionate privacy-by-design governance model, informed by public engagement, to safeguard the data and facilitate data utility. SAIL operates on the UK Secure Research Platform (SeRP), which is a customisable technology and analysis platform. Researchers access anonymised data via this secure research environment, from which results can be released following scrutiny for disclosure risk. SAIL data are being used in multiple research areas to evaluate the impact of health and social exposures and policy interventions., Discussion: Lessons learned and their applications include: managing evolving legislative and regulatory requirements; employing multiple, tiered security mechanisms; working hard to increase analytical capacity efficiency; and developing a multi-faceted programme of public engagement. Further work includes: incorporating new data types; enabling alternative means of data access; and developing further efficiencies across our operations., Conclusion: SAIL represents an ongoing programme of work to develop and maintain an extensive, whole population data resource for research. Its privacy-by-design model and UK SeRP technology have received international acclaim, and we continually endeavour to demonstrate trustworthiness to support data provider assurance and public acceptability in data use. We strive for further improvement and continue a mutual learning process with our contemporaries in this rapidly developing field., Competing Interests: Conflicts of Interest: The authors declare that they have no known conflicts of interest.

Introduction: Excessive alcohol consumption has adverse effects on health and there is a recognised need for the longitudinal analysis of population data to improve our understanding of the patterns of alcohol use, harms to consumers and those in their immediate environment. The UK has a number of linkable, longitudinal databases that if assembled properly could support valuable research on this topic., Aims and Objectives: This paper describes the development of a broad set of cross-linked cohorts, e-cohorts, surveys and linked electronic healthcare records (EHRs) to construct an alcohol-specific analytical platform in the United Kingdom using datasets on the population of Wales.The objective of this paper is to provide a description of existing key datasets integrated with existing, routinely collected electronic health data on a secure platform, and relevant derived variables to enable population-based research on alcohol-related harm in Wales. We illustrate our use of these data with some exemplar research questions that are currently under investigation., Methods: Record-linkage of routine and observational datasets. Routine data includes hospital admissions, general practice, and cohorts specific to children. Two observational studies were included. Routine socioeconomic descriptors and mortality data were also linked., Conclusion: We described a record-linked, population-based research protocol for alcohol related harm on a secure platform. As the datasets used here are available in many countries, ELAStiC provides a template for setting up similar initiatives in other countries. We have also defined a number of alcohol specific variables using routinely-collected available data that can be used in other epidemiological studies into alcohol related outcomes. With over 10 years of longitudinal data, it will help to understand alcohol-related disease and health trajectories across the lifespan., Competing Interests: Conflict of interest: The authors declare that they do not have any conflict of interest.

Background: In longitudinal health research, combining the richness of cohort data to the extensiveness of routine data opens up new possibilities, providing information not available from one data source alone. In this study, we set out to extend information from a longitudinal birth cohort study by linking to the cohort child's routine primary and secondary health care data. The resulting linked datasets will be used to examine health outcomes and patterns of health service utilisation for a set of common childhood health problems. We describe the experiences and challenges of acquiring and linking electronic health records for participants in a national longitudinal study, the UK Millennium Cohort Study (MCS)., Method: Written parental consent to link routine health data to survey responses of the MCS cohort member, mother and her partner was obtained for 90.7% of respondents when interviews took place at age seven years in the MCS. Probabilistic and deterministic linkage was used to link MCS cohort members to multiple routinely-collected health data sources in Wales and Scotland., Results: Overall linkage rates for the consented population using country-specific health service data sources were 97.6% for Scotland and 99.9% for Wales. Linkage rates between different health data sources ranged from 65.3% to 99.6%. Issues relating to acquisition and linkage of data sources are discussed., Conclusions: Linking longitudinal cohort participants with routine data sources is becoming increasingly popular in population data research. Our results suggest that this is a valid method to enhance information held in both sources of data., Competing Interests: Statement on conflicts of interest: None to declare.

Introduction: Due to various regulatory barriers, it is increasingly difficult to move pseudonymised routine health data across platforms and among jurisdictions. To tackle this challenge, we summarized five approaches considered to support a scientific research project focused on the risk of the new non-vitamin K Target Specific Oral Anticoagulants (TSOACs) and collaborated between the Farr institute in Wales and Scotland., Approach: In Wales, routinely collected health records held in the Secure Anonymous Information Linkage (SAIL) Databank were used to identify the study cohort. In Scotland, data was extracted from national dataset resources administered by the eData Research & Innovation Service (eDRIS) and stored in the Scottish National Data Safe Haven. We adopted a federated data and multiple analysts approach, but arranged simultaneous accesses for Welsh and Scottish analysts to generate study cohorts separately by implementing the same algorithm. Our study cohort across two countries was boosted to 6,829 patients towards risk analysis. Source datasets and data types applied to generate cohorts were reviewed and compared by analysts based on both sites to ensure the consistency and harmonised output., Discussion: This project used a fusion of two approaches among five considered. The approach we adopted is a simple, yet efficient and cost-effective method to ensure consistency in analysis and coherence with multiple governance systems. It has limitations and potentials of extending and scaling. It can also be considered as an initialisation of a developing infrastructure to support a distributed team science approach to research using Electronic Health Records (EHRs) across the UK and more widely., Competing Interests: Conflict of Interest Statement: The authors have no conflicts of interest to declare.