Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects.

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges.

Cancer incidence and mortality in 23 000 patients with type 1 diabetes in the UK: Long-term follow-up.

Type 2 diabetes is associated with raised risk of several cancers, but for type 1 diabetes risk data are fewer and inconsistent We assembled a cohort of 23 473 UK patients with insulin-treated diabetes diagnosed at ages <30, almost all of whom will have had type 1 diabetes, and for comparison 5058 diagnosed at ages 30 to 49, of whom we estimate two-thirds will have had type 2, and followed them for an average of 30 years for cancer incidence and mortality compared with general population rates. Patients aged <30 at diabetes diagnosis had significantly raised risks only for ovarian (standardised incidence ratio = 1.58; 95% confidence interval 1.16-2.11; P < .01) and vulval (3.55; 1.94-5.96; P < .001) cancers, with greatest risk when diabetes was diagnosed at ages 10-14. Risks of cancer overall (0.89; 0.84-0.95; P < .001) and sites including lung and larynx were significantly diminished. Patients diagnosed with diabetes at ages 30 to 49 had significantly raised risks of liver (1.76;1.08-2.72) and kidney (1.46;1.03-2.00) cancers, and reduced risk of cancer overall (0.89; 0.84-0.95). The raised ovarian and vulval cancer risks in patients with type 1 diabetes, especially with diabetes diagnosed around pubertal ages, suggest possible susceptibility of these organs at puberty to metabolic disruption at diabetes onset. Reduced risk of cancer overall, particularly smoking and alcohol-related sites, might reflect adoption of a healthy lifestyle.

The impact of the COVID-19 pandemic on cardiovascular disease prevention and management.

How the Coronavirus Disease 2019 (COVID-19) pandemic has affected prevention and management of cardiovascular disease (CVD) is not fully understood. In this study, we used medication data as a proxy for CVD management using routinely collected, de-identified, individual-level data comprising 1.32 billion records of community-dispensed CVD medications from England, Scotland and Wales between April 2018 and July 2021. Here we describe monthly counts of prevalent and incident medications dispensed, as well as percentage changes compared to the previous year, for several CVD-related indications, focusing on hypertension, hypercholesterolemia and diabetes. We observed a decline in the dispensing of antihypertensive medications between March 2020 and July 2021, with 491,306 fewer individuals initiating treatment than expected. This decline was predicted to result in 13,662 additional CVD events, including 2,281 cases of myocardial infarction and 3,474 cases of stroke, should individuals remain untreated over their lifecourse. Incident use of lipid-lowering medications decreased by 16,744 patients per month during the first half of 2021 as compared to 2019. By contrast, incident use of medications to treat type 2 diabetes mellitus, other than insulin, increased by approximately 623 patients per month for the same time period. In light of these results, methods to identify and treat individuals who have missed treatment for CVD risk factors and remain undiagnosed are urgently required to avoid large numbers of excess future CVD events, an indirect impact of the COVID-19 pandemic.

Towards a standardised cross-sectoral data access agreement template for research: a core set of principles for data access within trusted research environments.

Introduction: Trusted Research Environments (TREs) are secure computing environments that provide access to data for approved researchers to use in studies that can save and improve lives. TREs rely on Data Access Agreements (DAAs) to bind researchers and their organisations to the terms and conditions of accessing the infrastructure and data use. However, DAAs can be overly lengthy, complex, and can contain outdated terms from historical data sharing agreements for physical exchange of data. This is often cited as a cause of significant delays to legal review and research projects starting. Objectives: The aim was to develop a standardised DAA optimised for data science in TREs across the UK and framed around the 'Five Safes framework' for trustworthy data use. The DAA is underpinned by principles of data access in TREs, the development of which is described in this paper. Methods: The Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance led the development of a core set of data access principles. This was informed by a benchmarking exercise of DAAs used by established TREs and consultation with public members and stakeholders. Results: We have defined a core set of principles for TRE data access that can be mapped to a common set of DAA terms for UK-based TREs. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes. Public views obtained through public involvement and engagement (PIE) activities are also reported. Conclusions: These principles provide the foundation for a standardised UK TRE DAA template, designed to support the growing ecosystem of TREs. By providing a familiar structure and terms, this template aims to build trust among data owners and the UK public and to provide clarity to researchers on their obligations to protect the data. Widespread adoption is intended to accelerate health data research by enabling faster approval of projects, ultimately enabling more timely and effective research.

GREENER principles for environmentally sustainable computational science.

The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

Data capture and sharing in the COVID-19 pandemic: a cause for concern.

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Development and evaluation of rapid data-enabled access to routine clinical information to enhance early recruitment to the national clinical platform trial of COVID-19 community treatments.

BACKGROUND: The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24-48 h of their test result. METHODS: A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24-48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology. RESULTS: Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached. CONCLUSIONS: This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions. TRIAL REGISTRATION: PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22 . 26/03/2020 ISRCTN registry: ISRCTN86534580 . 20/03/2020 REC number: 20/SC/058 IRAS number: 281958.

The international Perinatal Outcomes in the Pandemic (iPOP) study: protocol.

Preterm birth is the leading cause of infant death worldwide, but the causes of preterm birth are largely unknown. During the early COVID-19 lockdowns, dramatic reductions in preterm birth were reported; however, these trends may be offset by increases in stillbirth rates. It is important to study these trends globally as the pandemic continues, and to understand the underlying cause(s). Lockdowns have dramatically impacted maternal workload, access to healthcare, hygiene practices, and air pollution - all of which could impact perinatal outcomes and might affect pregnant women differently in different regions of the world. In the international Perinatal Outcomes in the Pandemic (iPOP) Study, we will seize the unique opportunity offered by the COVID-19 pandemic to answer urgent questions about perinatal health. In the first two study phases, we will use population-based aggregate data and standardized outcome definitions to: 1) Determine rates of preterm birth, low birth weight, and stillbirth and describe changes during lockdowns; and assess if these changes are consistent globally, or differ by region and income setting, 2) Determine if the magnitude of changes in adverse perinatal outcomes during lockdown are modified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, or other social and economic markers, obtained from publicly available datasets. We will undertake an interrupted time series analysis covering births from January 2015 through July 2020. The iPOP Study will involve at least 121 researchers in 37 countries, including obstetricians, neonatologists, epidemiologists, public health researchers, environmental scientists, and policymakers. We will leverage the most disruptive and widespread "natural experiment" of our lifetime to make rapid discoveries about preterm birth. Whether the COVID-19 pandemic is worsening or unexpectedly improving perinatal outcomes, our research will provide critical new information to shape prenatal care strategies throughout (and well beyond) the pandemic.

Health data poverty: an assailable barrier to equitable digital health care.

Data-driven digital health technologies have the power to transform health care. If these tools could be sustainably delivered at scale, they might have the potential to provide everyone, everywhere, with equitable access to expert-level care, narrowing the global health and wellbeing gap. Conversely, it is highly possible that these transformative technologies could exacerbate existing health-care inequalities instead. In this Viewpoint, we describe the problem of health data poverty: the inability for individuals, groups, or populations to benefit from a discovery or innovation due to a scarcity of data that are adequately representative. We assert that health data poverty is a threat to global health that could prevent the benefits of data-driven digital health technologies from being more widely realised and might even lead to them causing harm. We argue that the time to act is now to avoid creating a digital health divide that exacerbates existing health-care inequalities and to ensure that no one is left behind in the digital era.

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

BACKGROUND: Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D). METHODS: To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D). RESULTS: None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background. CONCLUSIONS: This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

HDR UK supporting mobilising computable biomedical knowledge in the UK.

Computable biomedical knowledge (CBK) represents an evolving area of health informatics, with potential for rapid translational patient benefit. Health Data Research UK (HDR UK) is the national Institute for Health Data Science, whose aim is to unite the UK's health data to enable discoveries that improve people's lives. The three main components include the UK HDR Alliance of data custodians, committed to making health data available for research and innovation purposes for public benefit while ensuring safe use of data and building public trust, the HDR Hubs, as centres of expertise for curating data and providing expert domain-specific services, and the HDR Innovation Gateway ('Gateway'), providing discovery, accessibility, security and interoperability services. To support CBK developments, HDR UK is encouraging use of open data standards for research purposes, with guidance around areas in which standards are emerging, aims to work closely with the international CBK community to support initiatives and aid with evaluation and collaboration, and has established a phenomics workstream to create a national platform for dissemination of machine readable and computable phenotypical algorithms to reduce duplication of effort and improve reproducibility in clinical studies.

Receipt of Adjuvant Chemotherapy in Stage II Colon Cancer and Overall Survival: A National Cancer Database Study.

BACKGROUND: There are variations in the use of adjuvant chemotherapy (AC) in stage II colon cancer (CRC). We sought to determine which patients received chemotherapy, what factors were associated with receipt of AC, and how this impacted overall survival. METHODS: Using the National Cancer Database, patients with stage II CRC who underwent surgical resection were selected; patients who received radiation or neoadjuvant chemotherapy were excluded. High-risk features (HRFs) were defined as pathological tumor stage IV, positive surgical margins, and perineural or lymphovascular invasion. Multivariable and subgroup analysis with eight subgroups stratified in the presence of HRFs, age, and the Charlson-Deyo score was performed. RESULTS: Of 77,739 patients identified with stage II CRC, 18.3% received AC. Younger, healthier patients with HRFs had the highest chemotherapy receipt rate (46.7%), whereas patients without HRFs, ≥ 75 y, and with the Charlson-Deyo score of 2+ had the lowest rate (2.1%). Community cancer centers were more likely to initiate AC (odds ratio = 1.24 P < 0.01) especially among healthy HRF-negative patients and younger patients. No significant racial differences in AC use were observed. AC was associated with improved overall survival in subgroups with HRFs (hazard ratio [HR]: 0.81 P < 0.001; HR: 0.75 P < 0.001; HR: 0.65 P = 0.03; HR: 0.55, P < 0.001) but not in patients without HRFs. CONCLUSIONS: AC receipt rates differed depending on patient age and type of institution delivering care. AC was associated with survival benefits only in patients with HRFs regardless of age. These findings are clinically relevant to inform appropriate use of AC in stage II CRC.

A genome-wide association study suggests new evidence for an association of the NADPH Oxidase 4 (NOX4) gene with severe diabetic retinopathy in type 2 diabetes.

PURPOSE: Diabetic retinopathy is the most common eye complication in patients with diabetes. The purpose of this study is to identify genetic factors contributing to severe diabetic retinopathy. METHODS: A genome-wide association approach was applied. In the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) datasets, cases of severe diabetic retinopathy were defined as type 2 diabetic patients who were ever graded as having severe background retinopathy (Level R3) or proliferative retinopathy (Level R4) in at least one eye according to the Scottish Diabetic Retinopathy Grading Scheme or who were once treated by laser photocoagulation. Controls were diabetic individuals whose longitudinal retinopathy screening records were either normal (Level R0) or only with mild background retinopathy (Level R1) in both eyes. Significant Single Nucleotide Polymorphisms (SNPs) were taken forward for meta-analysis using multiple Caucasian cohorts. RESULTS: Five hundred and sixty cases of type 2 diabetes with severe diabetic retinopathy and 4,106 controls were identified in the GoDARTS cohort. We revealed that rs3913535 in the NADPH Oxidase 4 (NOX4) gene reached a p value of 4.05 × 10-9 . Two nearby SNPs, rs10765219 and rs11018670 also showed promising p values (p values = 7.41 × 10-8 and 1.23 × 10-8 , respectively). In the meta-analysis using multiple Caucasian cohorts (excluding GoDARTS), rs10765219 and rs11018670 showed associations for diabetic retinopathy (p = 0.003 and 0.007, respectively), while the p value of rs3913535 was not significant (p = 0.429). CONCLUSION: This genome-wide association study of severe diabetic retinopathy suggests new evidence for the involvement of the NOX4 gene.

Lp-PLA2 activity is associated with increased risk of diabetic retinopathy: a longitudinal disease progression study.

AIMS/HYPOTHESIS: The aim of the study was to examine the association between lipoprotein-associated phospholipase A2 (Lp-PLA2) activity levels and incident diabetic retinopathy and change in retinopathy grade. METHODS: This was a cohort study of diabetic participants with serum collected at baseline and routinely collected diabetic retinal screening data. Participants with type 2 diabetes from the GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) cohort were used. This cohort is composed of individuals of white Scottish ancestry from the Tayside region of Scotland. Survival analysis accounting for informative censoring by modelling death as a competing risk was performed for the development of incident diabetic retinopathy from a disease-free state in a 3 year follow-up period (n = 1364) by stratified Lp-PLA2 activity levels (in quartiles). The same analysis was performed for transitions to more severe grades. RESULTS: The hazard of developing incident diabetic retinopathy was 2.08 times higher (95% CI 1.64, 2.63) for the highest quartile of Lp-PLA2 activity compared with the lowest. Higher Lp-PLA2 activity levels were associated with a significantly increased risk for transitions to all grades. The hazards of developing observable (or more severe) and referable (or more severe) retinopathy were 2.82 (95% CI 1.71, 4.65) and 1.87 (95% CI 1.26, 2.77) times higher for the highest quartile of Lp-PLA2 activity compared with the lowest, respectively. CONCLUSIONS/INTERPRETATION: Higher Lp-PLA2 levels are associated with increased risk of death and the development of incident diabetic retinopathy, as well as transitions to more severe grades of diabetic retinopathy. These associations are independent of calculated LDL-cholesterol and other traditional risk factors. Further, this biomarker study shows that the association is temporally sensitive to the proximity of the event to measurement of Lp-PLA2.

Human diabetic mesenchymal stem cells from peripheral arterial disease patients promote angiogenesis through unique secretome signatures.

BACKGROUND: Diabetic patients are at increased risk of complications from severe peripheral arterial disease. Mesenchymal stem cells (MSC) may be useful in limiting these complications. Our objective is to test the angiogenic potential of diabetic versus healthy MSCs. METHODS: MSCs' angiogenic potential was tested by endothelial cell (EC) proliferation, migration, and 3-dimensional sprouting. Diabetic conditions were simulated with 5.5, 20, or 40 mM glucose. MSC secretome was quantified by enzyme-linked immunosorbent assay. RESULTS: Human aortic ECs were most sensitive to glucose conditions and were used for all MSC experiments. Diabetic MSCs had greater 3-dimensional invasion than healthy MSCs (P<.05), but EC sprouting was decreased in high glucose conditions in both diabetic and healthy MSCs. Secretome analysis demonstrated that 20mM glucose stimulated epidermal growth factor (EGF) expression in diabetic and healthy MSCs, but that diabetic MSCs had a unique secretome with increased levels of chemokine (C-X-C motif) ligand 1 (CXCL-1), interleukin six (IL-6), and monocyte chemoattractant protein 1 (MCP-1) (P<.05). CONCLUSION: Despite having similar in vitro angiogenic activity, diabetic MSCs secrete a unique and inflammatory angiogenic signature that may influence MSC survival and function after transplantation in cell therapy applications. Strategies that normalize secretome in diabetic patients may improve the utility of autologous MSCs in this population of patients.

A case study in distributed team science in research using electronic health records.

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.