Suchergebnisse

ObjectivesWe aimed to support the work of CO-CONNECT by meaningfully involving patient and public members across all project work packages. In addition, we aimed to ensure that the team members and outputs are connected to public perspectives and that public voices are adequately represented and embedded throughout CO-CONNECT.
ApproachWe have two public members on our leadership team who co-lead our Patient and Public Involvement and Engagement (PPIE) work stream with support from academics. They convened a "Public User Group" (PUG) of five public members from across the United Kingdom who regularly contribute to all aspects of CO-CONNECT. Our PPIE work was co-produced with our public members and a PPIE strategy was developed to ensure meaningful involvement throughout CO-CONNECT. At the beginning of the project, we developed an information pack for our public members to provide insight into CO-CONNECT and the importance of their role.
ResultsTo ensure complete transparency with the public, our PUG members attend and actively contribute to all team meetings within CO-CONNECT. This provides opportunities for public voices to be heard and acted upon in relation to questions about the use of, and access to, healthcare data in healthcare research. PUG members have contributed to the development of the CO-CONNECT website including providing information for biographies to increase public awareness of the involvement of public members in CO-CONNECT. They have written blogs and been interviewed for newsletter articles on the important of public involvement in research. Together we have created videos discussing their experience of being involved with CO-CONNECT and created a set of "Frequently Asked Questions" to provide more information about CO-CONNECT for the public-facing website.
ConclusionThe PPIE work within CO-CONNECT has created an innovative approach to ensuring public voices are heard and acted upon within data linkage networks. This model has the potential to be used in future projects to ensure inclusive and meaningful involvement of patient and public members in healthcare research.

ObjectiveThe global COVID pandemic has highlighted the need for the reporting of enhanced serology data from laboratory testing data. Qualitative results alone do not provide detailed data to understand levels of SARS-CoV-2 seroprevalence within a populace. To enable this, a minimum data set is required to support standardised data reporting.
ApproachThe CO-CONNECT project is concerned with the response to COVID within the UK and how datasets can be made Findable, Accessible, Interoperable and Reusable (FAIR). One aspect of this is a focus upon improving granular levels of COVID serology testing data being reported by national laboratories to the National Pathology Exchange (NPEx). A study was conducted to ascertain the essential data elements that could formulate a COVID Serology Data Standard (CSDS). Three NHS laboratories acting as pilot studies and NPEx have been actively involved with the development of the CSDS, together with clinicians and academics from the CO-CONNECT project.
ResultsFeedback from clinicians, academics and NPEx has been collected, together with NHS laboratory feasibility analysis to derive a minimum set of data elements that form a proposed data standard to standardise the reporting of laboratory COVID serology data for the UK. The proposed CSDS comprises twelve data elements in total, grouped into four main areas, (1) subject identifier and test date and time, (2) analyser types, test kits and samples, (3) qualitative results and (4) quantitative results. To further support the standardisation effort, a proposed Health Level Seven (HL7) message structure has been created to enable the reporting of the CSDS data elements to NPEx.
ConclusionThe CSDS has been created to help augment levels of reported granular data for laboratory COVID serology testing. If adopted, this could enable healthcare professionals to better understand the calibration of assays across multiple analyser types and thus antibody response levels.

ObjectivesTo assess a range of tools and methods to support Trusted Research Environments (TREs) to assess output from AI methods for potentially identifiable information, investigate the legal and ethical implications and controls, and produce a set of guidelines and recommendations to support all TREs with export controls of AI algorithms.
ApproachTREs provide secure facilities to analyse confidential personal data, with staff checking outputs for disclosure risk before publication. Artificial intelligence (AI) has high potential to improve the linking and analysis of population data, and TREs are well suited to supporting AI modelling. However, TRE governance focuses on classical statistical data analysis. The size and complexity of AI models presents significant challenges for the disclosure-checking process. Models may be susceptible to external hacking: complicated methods to reverse engineer the learning process to find out about the data used for training, with more potential to lead to re-identification than conventional statistical methods.
ResultsGRAIMatter is:

Quantitatively assessing the risk of disclosure from different AI models exploring different models, hyper-parameter settings and training algorithms over common data types
Evaluating a range of tools to determine effectiveness for disclosure control
Assessing the legal and ethical implications of TREs supporting AI development and identifying aspects of existing legal and regulatory frameworks requiring reform.
Running 4 PPIE workshops to understand their priorities and beliefs around safeguarding and securing data
Developing a set of recommendations including

suggested open-source toolsets for TREs to use to measure and reduce disclosure risk
descriptions of the technical and legal controls and policies TREs should implement across the 5 Safes to support AI algorithm disclosure control
training implications for both TRE staff and how they validate researchers



ConclusionGRAIMatter is developing a set of usable recommendations for TREs to use to guard against the additional risks when disclosing trained AI models from TREs.

Funding Information: AS, JM, and CR are members of the Scottish Government Chief Medical Officer's COVID-19 Advisory Group. JM is a member of the New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG) and AS is a member of the NERVTAG Risk Stratification Subgroup and an unfunded member of Astra-Zeneca's COVID-19 Strategic Consultancy Group: Thrombocytopenia Taskforce. JM is a member of the Scientific Advisory Group on Emergencies (SAGE) and chairs the COVID Scottish National Incident Management Team and the Scientific Committee of the European Centre for Disease Prevention and Control/WHO-funded IMOVE-COVID-19 group. CM reports research funding from Medical Research Council (MRC), Health Data Research UK, National Institute for Health Research (NIHR), and Scottish Chief Scientist Office (CSO). SJS reports research funding from Wellcome Trust, MRC, NIHR, and Scottish CSO. CRS declares funding from the MRC, NIHR, Scottish CSO, and the New Zealand Ministry for Business, Innovation and Employment and Health Research Council during the conduct of this study. SVK is co-chair of the Scottish Government's Expert Reference Group on COVID-19 and ethnicity, is a member of the SAGE subgroup on ethnicity, and acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship, MRC, and Scottish CSO. CR is a member of the Scientific Pandemic Influenza Group on Modelling and the Medicines and Healthcare Products Regulatory Agency Vaccine Benefit and Risk Working Group. JLKM is a member of the COVID Scottish National Incident Management Team. SdL has received funding through his University from AstraZeneca. FDRH acknowledges part support from the NIHR Applied Research Collaboration Oxford Thames Valley and the NIHR Oxford University Hospital Biomedical Research Centre. All other authors declare no competing interests. Funding Information: EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE?The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director-General Health and Social Care. We thank Dave Kelly from Albasoft for his support with making primary care data available and James Pickett, Wendy Inglis-Humphrey, Vicky Hammersley, Maria Georgiou, Laura Gonzalez Rienda, Pam McVeigh, Amanda Burridge, Sumedha Asnani-Chetal, and Afshin Dastafshan for their support with project management and administration. We acknowledge the support of the EAVE II Patient Advisory Group. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK?HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). Funding Information: EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government Director-General Health and Social Care. We thank Dave Kelly from Albasoft for his support with making primary care data available and James Pickett, Wendy Inglis-Humphrey, Vicky Hammersley, Maria Georgiou, Laura Gonzalez Rienda, Pam McVeigh, Amanda Burridge, Sumedha Asnani-Chetal, and Afshin Dastafshan for their support with project management and administration. We acknowledge the support of the EAVE II Patient Advisory Group. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK—HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). ; Peer reviewed ; Publisher PDF

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and the Scottish Government's director-general of Health and Social Care. FDRH acknowledges part support from the National Institutes of Health Research (NIHR) School for Primary Care Research, the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford, and the NIHR Oxford Biomedical Research Centre. We thank Dave Kelly from Albasoft for his support with making primary care data available, and James Pickett, Wendy Inglis-Humphrey, Vicky Hammersley, Maria Georgiou, and Laura Gonzalez Rienda for their support with project management and administration. SVK acknowledges funding from an NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and Scottish Government Chief Scientist Office (SPHSU13). ; Peer reviewed ; Publisher PDF

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and the Scottish Government's director-general of Health and Social Care. FDRH acknowledges part support from the National Institutes of Health Research (NIHR) School for Primary Care Research, the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford, and the NIHR Oxford Biomedical Research Centre. SVK acknowledges funding from an NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and Scottish Government Chief Scientist Office (SPHSU13). ; Background The BNT162b2 mRNA (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines have shown high efficacy against disease in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. Studying the real-world effects of these vaccines is an urgent requirement. The aim of our study was to investigate the association between the mass roll-out of the first doses of these COVID-19 vaccines and hospital admissions for COVID-19. Methods We did a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19—EAVE II—database comprising linked vaccination, primary care, real-time reverse transcription-PCR testing, and hospital admission patient records for 5·4 million people in Scotland (about 99% of the population) registered at 940 general practices. Individuals who had previously tested positive were excluded from the analysis. A time-dependent Cox model and Poisson regression models with inverse propensity weights were fitted to estimate effectiveness against COVID-19 hospital admission (defined as 1–adjusted rate ratio) following the first dose of vaccine. Findings Between Dec 8, 2020, and Feb 22, 2021, a total of 1 331 993 people were vaccinated over the study period. The mean age of those vaccinated was 65·0 years (SD 16·2). The first dose of the BNT162b2 mRNA vaccine was associated with a vaccine effect of 91% (95% CI 85–94) for reduced COVID-19 hospital admission at 28–34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 88% (95% CI 75–94). Results of combined vaccine effects against hospital admission due to COVID-19 were similar when restricting the analysis to those aged 80 years and older (83%, 95% CI 72–89 at 28–34 days post-vaccination). Interpretation Mass roll-out of the first doses of the BNT162b2 mRNA and ChAdOx1 vaccines was associated with substantial reductions in the risk of hospital admission due to COVID-19 in Scotland. There remains the possibility that some of the observed effects might have been due to residual confounding. Funding UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK. ; proof ; Peer reviewed

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and the Scottish Government's director-general of Health and Social Care. FDRH acknowledges part support from the National Institutes of Health Research (NIHR) School for Primary Care Research, the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford, and the NIHR Oxford Biomedical Research Centre. SVK acknowledges funding from an NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and Scottish Government Chief Scientist Office (SPHSU13). ; Background The BNT162b2 mRNA (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines have shown high efficacy against disease in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. Studying the real-world effects of these vaccines is an urgent requirement. The aim of our study was to investigate the association between the mass roll-out of the first doses of these COVID-19 vaccines and hospital admissions for COVID-19. Methods We did a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19—EAVE II—database comprising linked vaccination, primary care, real-time reverse transcription-PCR testing, and hospital admission patient records for 5·4 million people in Scotland (about 99% of the population) registered at 940 general practices. Individuals who had previously tested positive were excluded from the analysis. A time-dependent Cox model and Poisson regression models with inverse propensity weights were fitted to estimate effectiveness against COVID-19 hospital admission (defined as 1–adjusted rate ratio) following the first ...

Background: The UK COVID-19 vaccination programme has prioritised vaccination of those at the highest risk of COVID-19 mortality and hospitalisation. The programme was rolled out in Scotland during winter 2020–21, when SARS-CoV-2 infection rates were at their highest since the pandemic started, despite social distancing measures being in place. We aimed to estimate the frequency of COVID-19 hospitalisation or death in people who received at least one vaccine dose and characterise these individuals. Methods: We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) national surveillance platform, which contained linked vaccination, primary care, RT-PCR testing, hospitalisation, and mortality records for 5·4 million people (around 99% of the population) in Scotland. Individuals were followed up from receiving their first dose of the BNT162b2 (Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines until admission to hospital for COVID-19, death, or the end of the study period on April 18, 2021. We used a time-dependent Poisson regression model to estimate rate ratios (RRs) for demographic and clinical factors associated with COVID-19 hospitalisation or death 14 days or more after the first vaccine dose, stratified by vaccine type. Findings: Between Dec 8, 2020, and April 18, 2021, 2 572 008 individuals received their first dose of vaccine—841 090 (32·7%) received BNT162b2 and 1 730 918 (67·3%) received ChAdOx1. 1196 (<0·1%) individuals were admitted to hospital or died due to COVID-19 illness (883 hospitalised, of whom 228 died, and 313 who died due to COVID-19 without hospitalisation) 14 days or more after their first vaccine dose. These severe COVID-19 outcomes were associated with older age (≥80 years vs 18–64 years adjusted RR 4·75, 95% CI 3·85–5·87), comorbidities (five or more risk groups vs less than five risk groups 4·24, 3·34–5·39), hospitalisation in the previous 4 weeks (3·00, 2·47–3·65), high-risk occupations (ten or more previous COVID-19 tests vs less than ten previous COVID-19 tests 2·14, 1·62–2·81), care home residence (1·63, 1·32–2·02), socioeconomic deprivation (most deprived quintile vs least deprived quintile 1·57, 1·30–1·90), being male (1·27, 1·13–1·43), and being an ex-smoker (ex-smoker vs non-smoker 1·18, 1·01–1·38). A history of COVID-19 before vaccination was protective (0·40, 0·29–0·54). Interpretation: COVID-19 hospitalisations and deaths were uncommon 14 days or more after the first vaccine dose in this national analysis in the context of a high background incidence of SARS-CoV-2 infection and with extensive social distancing measures in place. Sociodemographic and clinical features known to increase the risk of severe disease in unvaccinated populations were also associated with severe outcomes in people receiving their first dose of vaccine and could help inform case management and future vaccine policy formulation. Funding: UK Research and Innovation (Medical Research Council), Research and Innovation Industrial Strategy Challenge Fund, Scottish Government, and Health Data Research UK.

Funding: This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (HDRUK2020.146). EAVE II is funded by the Medical Research Council (MC_PC_19075) and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004). BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. ConCOV is supported by the Medical Research Council (MR/V028367/1); Health Data Research UK (HDR-9006) which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK which is funded by the Economic and Social Research Council (grant ES/S007393/1). ; Introduction : The novel coronavirus SARS-CoV-2, which emerged in December 2019, has caused millions of deaths and severe illness worldwide. Numerous vaccines are currently under development of which a few have now been authorised for population-level administration by several countries. As of 20 September 2021, over 48 million people have received their first vaccine dose and over 44 million people have received their second vaccine dose across the UK. We aim to assess the uptake rates, effectiveness, and safety of all currently approved COVID-19 vaccines in the UK. Methods and analysis : We will use prospective cohort study designs to assess vaccine uptake, effectiveness and safety against clinical outcomes and deaths. Test-negative case–control study design will be used to assess vaccine effectiveness (VE) against laboratory confirmed SARS-CoV-2 infection. Self-controlled case series and retrospective cohort study designs will be carried out to assess vaccine safety against mild-to-moderate and severe adverse events, respectively. Individual-level pseudonymised data from primary care, secondary care, laboratory test and death records will be linked and analysed in secure research environments in each UK nation. Univariate and multivariate logistic regression models will be carried out to estimate vaccine uptake levels in relation to various population characteristics. VE estimates against laboratory confirmed SARS-CoV-2 infection will be generated using a generalised additive logistic model. Time-dependent Cox models will be used to estimate the VE against clinical outcomes and deaths. The safety of the vaccines will be assessed using logistic regression models with an offset for the length of the risk period. Where possible, data will be meta-analysed across the UK nations. Ethics and dissemination : We obtained approvals from the National Research Ethics Service Committee, Southeast Scotland 02 (12/SS/0201), the Secure Anonymised Information Linkage independent Information Governance Review Panel project number 0911. Concerning English data, University of Oxford is compliant with the General Data Protection Regulation and the National Health Service (NHS) Digital Data Security and Protection Policy. This is an approved study (Integrated Research Application ID 301740, Health Research Authority (HRA) Research Ethics Committee 21/HRA/2786). The Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub meets NHS Digital's Data Security and Protection Toolkit requirements. In Northern Ireland, the project was approved by the Honest Broker Governance Board, project number 0064. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. UA, CM, AA-L, and AFF acknowledge funding from Chief Scientist Office Rapid Research in COVID-19 programme (COV/SAN/20/06) and Health Data Research UK (measuring and understanding multimorbidity using routine data in the UK—HDR-9006; CFC0110). SVK acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government's Chief Scientist Office (SPHSU17). SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). ; Background The UK COVID-19 vaccination programme has prioritised vaccination of those at the highest risk of COVID-19 mortality and hospitalisation. The programme was rolled out in Scotland during winter 2020–21, when SARS-CoV-2 infection rates were at their highest since the pandemic started, despite social distancing measures being in place. We aimed to estimate the frequency of COVID-19 hospitalisation or death in people who received at least one vaccine dose and characterise these individuals. Methods We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) national surveillance platform, which contained linked vaccination, primary care, RT-PCR testing, hospitalisation, and mortality records for 5·4 million people (around 99% of the population) in Scotland. Individuals were followed up from receiving their first dose of the BNT162b2 (Pfizer–BioNTech) or ChAdOx1 nCoV-19 (Oxford–AstraZeneca) COVID-19 vaccines until admission to hospital for COVID-19, death, or the end of the study period on April 18, 2021. We used a time-dependent Poisson regression model to estimate rate ratios (RRs) for demographic and clinical factors ...