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BACKGROUND: Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2). METHODS: RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people. FINDINGS: Data were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76-0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1-99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2 at rest (as a percentage); the area under the curve was 0·84 (0·78-0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9-99·7; 1176 of 1183). INTERPRETATION: Both RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2 is available, RECAP-O2 is useful to assess the need for treatment escalation. FUNDING: Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK.

BACKGROUND Molecular point-of-care testing (POCT) used in primary care can inform whether a patient presenting with an acute respiratory infection has influenza. A confirmed clinical diagnosis, particularly early in the disease, could inform better antimicrobial stewardship. Social distancing and lockdowns during the COVID-19 pandemic have disturbed previous pattern of influenza infections in 2021. However data from samples taken in the last quarter of 2022, suggest that influenza represents 36% of sentinel network positive virology; compared to 24% with RSV. Problems with integration into clinical workflow is a known barrier to incorporating technology into routine care. OBJECTIVE To report the impact of point of care testing for influenza on antimicrobial prescribing in primary care. We will additionally describe severe outcomes of infection (hospitalisation and mortality) and how POCT is integrated into primary care workflows. METHODS The impact of point of care testing for influenza on antimicrobial stewardship (PIAMS) in UK primary care is an observational study between December 2022-May 2023, involving ten practices that contribute data to the English sentinel network will take place. Up to 1,000 people who present to participating practices with respiratory symptoms will be swabbed and tested with a rapid molecular POCT analyser in the practice. Antimicrobial prescribing and other study outcomes will be collected by linking information from the POCT analyser with data from the patient’s computerised medical record. We will collect data on how POCT is incorporated into practice using data flow diagrams, unified modelling language use-case diagrams and business process modelling notation. RESULTS We will present the crude and adjusted odds of antimicrobial prescribing (all antibiotics and antivirals) given a POCT diagnosis of influenza, stratifying by whether individuals have a respiratory or other relevant diagnosis (e.g. bronchiectasis). We will also present the rates of hospital referrals and deaths related to influenza infection in PIAMS study practices compared with a set of matched practices in the sentinel network and the rest of the network. We will describe any difference in implementation models in terms of staff involved and workflow. CONCLUSIONS This study will generate data on the impact of POCT testing for influenza in primary care as well as help to inform about the feasibility of incorporating POCT into primary care workflows. It will inform the design of future larger studies about the effectiveness and cost effectiveness of POCT to improve antimicrobial stewardship and any impact on severe outcomes. CLINICALTRIAL Not applicable

BACKGROUND In 2019, cardiovascular disease (CVD) accounted for 14% of total disability-adjusted life years and 30% of all deaths in England. Fast forwarding to 2020, many healthcare systems were on the verge of being overwhelmed by COVID-19, which disproportionately manifested as severe cases in populations affected by non-communicable diseases, most of which are CVD-risk factors. Physical activity interventions (PA-I) have been shown to play an important role in helping to address CVD risk factors and primary care has an essential role to play in supporting patients to improve their health through these types of interventions. OBJECTIVE In this manuscript, we explore the use of PA-I in primary care in England for different demographics of patients with a cardiovascular disease (CVD) risk factor. METHODS An existing ontology was used to capture PA-I and a new ontology was designed to cover several key concepts covering long-term conditions (LTC) indicating CVD risk factors. Readv2 and CTV3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre (RCGP RSC), a sentinel network of over 1800 primary care practices across England covering a population of over 15,000,000 registered patients, was used for data analyses covering a period from April 2017 to March 2021. To estimate the eligible population with a LTC that received a PA-I, the annual mean weekly rate/10,000 for a population with a given attribute (e.g. ethnicity) presenting to primary care with a LTC was used as a proxy for the eligible population for receiving a PA-I. The annual mean weekly rate/10,000 for a population with the same attribute (e.g. ethnicity) that received a PA-I was divided by proxy eligible population to determine the percentage of the population that received a PA-I that could have benefited from one. RESULTS Across all attributes and time periods (with the exception of the 18-39 group, 2017-2019) we see underuse of PA-I with a general trend of decreased use of PA-I after 2017. For the different attributes, the highest percentage of use is as follows: regions - London with ~40% use in 2019; ethnicities - ‘Other’ ethnicity with ~50% use in 2017; IMD - IMD1 (the most deprived quintile) with ~40% use in 2017; gender – females with ~35% use in 2017; age – 18-39 age group with ~100% use from 2017-2018. Additionally, there is marked variation that changes over time: ~2-fold greater use of PA-I in London compared to the Southeast in 2017, which increases to a nearly 4-fold increase by 2020; ~1.5-fold greater use of PA-I for the IMD-1 demographic compared to IMD5 in 2020; and ~3.5-fold greater use of PA-I for the 18-39 group compared to both the 40-64 and 65+ groups. CONCLUSIONS Our findings point to marked underuse and variation in PA-I use in primary care in England for individuals at risk of CVD. These results provide valuable insights that can inform policy and practice to improve population resilience while shifting away from a singular focus on supply-side constraints in healthcare systems.

BACKGROUND The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe’s oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance. OBJECTIVE Describe the cohort profile at the start of the 2021-2022 surveillance season and changes to our surveillance practice. METHODS The RSC’s pseudonymised primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub (ORCHID), a trusted research environment (TRE). We describe the RSC’s cohort profile as of September 2021, divided into a primary care sentinel cohort (PCSC) - collecting virological and serological specimens - and a larger group of syndromic surveillance general practices (SSGP). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control (ECDC) guidance and then compare our cohort sociodemographic characteristics with Office for National Statistics (ONS) data. We describe influenza and COVID-19 vaccine coverage for the 2020-21 season (week 40, 2020 to week 39 2021), the latter differentiated by vaccine brand. Finally, we report COVID-19 related outcomes in terms of hospitalisation, intensive care unit (ICU) admission, and death. RESULTS As a response to COVID-19, RSC grew from just over 500 PCSC practices in 2019 to 1,879 practices (PCSC=938, SSGP=1,203). This represents 28.6% of English general practices and 31% of the population (N=17,560,196). In the reporting period, the PCSC collected >8,000 virology and >23,000 samples. The RSC population was found to be broadly representative of the national population in terms of age, gender, ethnicity, NHS Region, socioeconomic status, obesity and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4m), and COVID-19; reporting dose one, (n=11.9m), two (n=11m) and three (n=0.4m) for the latter as well as brand-specific uptake data (AstraZeneca vaccine [n=11.6m], Pfizer [n=10.8m] and Moderna [N=0.7m]). The median (and interquartile ranges) for COVID Hospitalisation and ICU admissions for COVID, were 1181/week (559-1559/week) and 115/week (50-174/week) respectively. CONCLUSIONS The RSC is broadly representative of the national population, its PCSC is geographically representative. Its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to hospital outcomes and death. The challenge remains to increase virological and serological sampling to monitor the effectiveness, and waning of the increasing range of vaccines available in a timely manner.

BACKGROUND Respiratory syncytial virus (RSV) commonly causes lower respiratory tract infections (LRTI) and hospitalisation in children. In 2019/20, the Europe-wide RSV ComNet standardised study protocol was developed to measure the clinical and socio-economic disease burden of RSV infections amongst children OBJECTIVE We describe adaptations of RSV ComNet standardised study protocol for England, and the challenges of conducting the study during the COVID-19 pandemic. METHODS The study is being conducted by the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), the English national primary care sentinel network. We will invite all general practices within the network that undertake virology sampling (N=248 general practices, registered population 3,056,583) to participate in the study by recruiting eligible patients. Children RESULTS Preliminary results of the data collection between ISO weeks 1 to 41 2021 are described. There was no RSV detected in the winter of 2020/2021 through the study. The first positive RSV swab collected through the sentinel network in England was collected in International Standards Organisation (ISO) week 17, 2021 and then every week since ISO week 25. 16 (16/248-6.5%) of the virology sampling practices volunteered to participate. These 16 practices are high sampling practices collecting the majority of eligible swabs across the sentinel network, namely 200 out of the 457 swabs (43.8%) of which 54 (27%) were positive for RSV. CONCLUSIONS Measures to control the COVID-19 pandemic meant there was no circulating RSV last winter, however RSV has circulated out of season, detected by the sentinel network. Whilst the sentinel network practices have collected 40% (200/500) of the required samples and 27% (54200) were RSV positive. We have demonstrated the feasibility of implementing a European-standardised RSV Disease Burden study protocol in England during a pandemic, and now need to recruit to this adapted protocol. CLINICALTRIAL Not applicable

BACKGROUND Long covid (LC) is defined as fatigue, breathlessness, cognitive dysfunction and a variety of other symptoms occurring after coronavirus-2019 disease (COVID-19). (1,2) Over a million people in the UK are estimated to have prolonged symptoms after COVID-19, with 60% of long covid patients reporting extended symptoms lasting months, and 240,000 people with symptoms limiting day-to-day activity. (3,4) The spectrum of symptoms imply widespread involvement of organs and there is a recognisable pattern of long covid disease resulting from autonomic dysfunction and mast cell disorder. (5) The Office for National Statistics (ONS) suggests that the prevalence of long covid is greater in females, middle-aged people, those from the most deprived areas, and those with an activity-limiting health condition or disability. (4) Symptoms are wide-ranging but fatigue, shortness of breath, and cognitive difficulties (termed ‘brain fog’ by patients) are most commonly reported. (6) In late 2020 there was a release of International Classification of Disease (ICD) and Systematised Nomenclature of Medicine (SNOMED) clinical terms to support long covid coding (termed post-COVID condition) but recording in primary care electronic records varied. (7,8) Primary care data, however, remains the most useful source of epidemiological data outside hospital records and bespoke surveys to understand the symptoms that long covid patients present to primary care following documented COVID-19 infection. (6) There is a need to characterise the prevalence, risk factors and symptom patterns in long covid patients using routine clinical data to understand the symptoms people present with to primary care following COVID-19, and whether presentation and post-acute mortality differs in people who were not hospitalised. This study reports the symptoms, sociodemographic profile, and outcomes of people identified as having long covid in English primary care. Our study has four components: (1) A comparison of clinical symptoms of people with long covid before and after COVID-19. (2) A description of the characteristics of people with long covid compared to those without long covid. (3) A comparison of those with long covid that were hospitalised with COVID-19 versus those that were not. (4) An analysis of all-cause mortality in people with long covid. OBJECTIVE This study was conducted as part of the Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting (RECAP) project. (9–11) RECAP included creating a phenotype for LC via an observational study. The population characteristics, baseline data, and our LC phenotype, were published in the study protocol. (12) The protocol also set out the details of the comparisons undertaken in this study. These were: (1) Undertaking a before and after comparison of the number of symptoms identified by ONS as more common in LC; (2) Comparing sociodemographic, comorbid and exposure characteristics of people coded by their GP with LC with people who were not; (3) Comparing characteristics of people with LC who had their index infection in hospital with those who had a community infection; (4) An analysis of all-cause mortality in people with LC. The study period included COVID-19 cases between 1st March 2020 and 1st April 2021, with a follow-up period of a further six months, up to latest 30th September 2021. METHODS We used routine data from the nationally representative Primary Care Sentinel Cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (N=7.4million), applying a pre-defined long covid phenotype and grouped by whether the index illness was in hospital or community. We conducted a before and after analysis of pre-specified long covid symptoms identified by the Office of National Statistics, comparing symptoms presented between one and six months after their index infection matched with the same months one year previously. We conducted logistic regression analysis, quoting odds ratios with 95% confidence intervals, reporting differences between those with an index community infection compared to those who had been hospitalised, and separately associations with all-cause mortality. RESULTS 5.6% (n=428,588) and 1.8% (n=7,600) of patients respectively had a coded diagnosis of COVID-19 and diagnosis or referral for long covid. People coded as having long covid were significantly more likely to have presented to primary care after diagnosis than people without (odds ratios 2.66 [2.46-2.88] for those with index community infection and 2.42 [2.03-2.89] for those hospitalised). Following an index community infection, patients were more likely to present with non-specific symptoms (odds ratio 3.44 [3.00-3.95], p CONCLUSIONS The low percentage (1.8%) of people recorded as having long covid following COVID-19 reflects either low prevalence or under-recording. The characteristics and comorbidities of those presenting with long covid following a community infection are different from those who were hospitalised with their index infection. This study provides insights into the presentation of long covid in primary care and implications for workload.

BackgroundAccurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).MethodsProspective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.FindingsData were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, degree of breathlessness, fatigue, and SpO2 at rest (AUC: 0.843), Validation NPV of ‘low risk’ 99.4%.InterpretationBoth RECAP models are a valid tool in the assessment of COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored at home and SpO2 is available, RECAP-O2 is useful to assess the need for further treatment escalation.Research in context panelEvidence before the studyThis study was conceived during the first COVID-19 wave in the UK (March - April 2020), as members of the research team contributed to the development of national clinical guidelines for COVID-19 management in the community and to the Oxford COVID-19 rapid review to track signs and symptoms of COVID-19 internationally. The review was carried out according to Cochrane Collaboration standards for rapid reviews and identified systematic reviews and large-scale observational studies describing the signs and symptoms of COVID-19. Evidence gathered showed worsening of COVID-19 symptoms around the 7th day of disease and challenges in identifying patients with higher likelihood of severity to increase their monitoring. To this end, tools such NEWS2 have been used in the UK to assess COVID-19 patients in primary care, but they do not capture the characteristics of COVID-19 infection and/or are not suitable for community remote assessment. Several COVID-19 risk scores have been developed. QCOVID provides a risk of mortality considering patients’ existing risk factors but does not include acute signs and symptoms. ISARIC 4C Deterioration model has been specifically developed for hospital settings. In England, the NHS has implemented the Oximetry @home strategy to monitor patients with acute COVID-19 deemed at risk (older than 64 years old or with comorbidities) by providing pulse oximeters; however, the criteria for monitoring or for escalation of care have not been validated. There is, therefore, the need to develop a risk prediction score to establish COVID-19 patients’ risk of deterioration to be used in the community for both face to face or remote consultation.Added value of this studyWe developed and validated two COVID-19 specific risk prediction scores. One to be used in the initial remote assessment of patients with acute COVID-19 to assess need for monitoring (RECAP-GP). The second one to assess the need for further treatment escalation and includes peripheral saturation of oxygen among the model predictors (RECAP-O2). To our knowledge, this is the first COVID-19 specific risk prediction score to assess and monitor COVID-19 patients’ risk of deterioration remotely. This will be a valuable resource to complement the use of oximetry in the community clinical decision-making when assessing a patient with acute COVID-19.Implications of all available evidenceTo manage pandemic waves and their demand on healthcare, acute COVID-19 patients require close monitoring in the community and prompt escalation of their treatment. Guidance available so far relies on unvalidated tools and clinician judgement to assess deterioration. COVID-19 specific community-based risk prediction scores such as RECAP may contribute to reducing the uncertainty in the assessment and monitoring of COVID-19 patients, increase safety in clinical practice and improve outcomes by facilitating appropriate treatment escalation.

Objectives To mitigate risk of mortality from coronavirus 2019 infection (COVID-19), the UK government recommended ‘shielding’ of vulnerable people through self-isolation for 12 weeks. Methods A retrospective cohort study using a nationally representative English primary care database comparing people aged >= 40 years who were recorded as being advised to shield using a fixed ratio of 1:1, matching to people with the same diagnoses not advised to shield (n = 77,360 per group). Time-to-death was compared using Cox regression, reporting the hazard ratio (HR) of mortality between groups. A sensitivity analysis compared exact matched cohorts (n = 24,752 shielded, n = 61,566 exact matches). Results We found a time-varying HR of mortality between groups. In the first 21 days, the mortality risk in people shielding was half those not (HR = 0.50, 95%CI:0.41–0.59. p

Objectives To monitor changes in seroprevalence of SARS-CoV-2 antibodies in populations over time and between different demographic groups. Methods A subset of practices in the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network provided serum samples, collected when volunteer patients had routine blood tests. We tested these samples for SARS-CoV-2 antibodies using Abbott (Chicago, USA), Roche (Basel, Switzerland) and/or Euroimmun (Luebeck, Germany) assays, and linked the results to the patients’ primary care computerised medical records. We report seropositivity by region and age group, and additionally examined the effects of gender, ethnicity, deprivation, rurality, shielding recommendation and smoking status. Results We estimated seropositivity from patients aged 18-100 years old, which ranged from 4.1% (95% CI 3.1–5.3%) to 8.9% (95% CI 7.8–10.2%) across the different assays and time periods. We found higher Euroimmun seropositivity in younger age groups, people of Black and Asian ethnicity (compared to white), major conurbations, and non-smokers. We did not observe any significant effect by region, gender, deprivation, or shielding recommendation. Conclusions Our results suggest that prior to the vaccination programme, most of the population remained unexposed to SARS-CoV-2.

Background Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. Objective This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. Methods We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. Results Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). Conclusions The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

BackgroundThe SARS-CoV-2 pandemic has passed its first peak in Europe.AimTo describe the mortality in England and its association with SARS-CoV-2 status and other demographic and risk factors.Design and settingCross-sectional analyses of people with known SARS-CoV-2 status in the Oxford RCGP Research and Surveillance Centre (RSC) sentinel network.MethodPseudonymised, coded clinical data were uploaded from volunteer general practice members of this nationally representative network (n = 4 413 734). All-cause mortality was compared with national rates for 2019, using a relative survival model, reporting relative hazard ratios (RHR), and 95% confidence intervals (CI). A multivariable adjusted odds ratios (OR) analysis was conducted for those with known SARS-CoV-2 status (n = 56 628, 1.3%) including multiple imputation and inverse probability analysis, and a complete cases sensitivity analysis.ResultsMortality peaked in week 16. People living in households of ≥9 had a fivefold increase in relative mortality (RHR = 5.1, 95% CI = 4.87 to 5.31, PPPP = 0.0056) had higher odds of mortality.ConclusionThe first SARS-CoV-2 peak in England has been associated with excess mortality. Planning for subsequent peaks needs to better manage risk in males, those of black ethnicity, older people, people with learning disabilities, and people who live in multi-occupancy dwellings.

© 2020 The Authors Objectives: Few studies report contributors to the excess mortality in England during the first wave of coronavirus disease 2019 (COVID-19) infection. We report the absolute excess risk (AER) of mortality and excess mortality rate (EMR) from a nationally representative COVID-19 sentinel surveillance network including known COVID-19 risk factors in people aged 45 years and above. Methods: Pseudonymised, coded clinical data were uploaded from contributing primary care providers (N = 1,970,314, ≥45years). We calculated the AER in mortality by comparing mortality for weeks 2 to 20 this year with mortality data from the Office for National Statistics (ONS) from 2018 for the same weeks. We conducted univariate and multivariate analysis including preselected variables. We report AER and EMR, with 95% confidence intervals (95% CI). Results: The AER of mortality was 197.8/10,000 person years (95%CI:194.30–201.40). The EMR for male gender, compared with female, was 1.4 (95%CI:1.35–1.44, p

BACKGROUND The Oxford–Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) are commencing their 54th season of collaboration at a time when SARS-CoV-2 infections are likely to be cocirculating with the usual winter infections. OBJECTIVE The aim of this study is to conduct surveillance of influenza and other monitored respiratory conditions and to report on vaccine uptake and effectiveness using nationally representative surveillance data extracted from primary care computerized medical records systems. We also aim to have general practices collect virology and serology specimens and to participate in trials and other interventional research. METHODS The RCGP RSC network comprises over 1700 general practices in England and Wales. We will extract pseudonymized data twice weekly and are migrating to a system of daily extracts. First, we will collect pseudonymized, routine, coded clinical data for the surveillance of monitored and unexpected conditions; data on vaccine exposure and adverse events of interest; and data on approved research study outcomes. Second, we will provide dashboards to give general practices feedback about levels of care and data quality, as compared to other network practices. We will focus on collecting data on influenza-like illness, upper and lower respiratory tract infections, and suspected COVID-19. Third, approximately 300 practices will participate in the 2020-2021 virology and serology surveillance; this will include responsive surveillance and long-term follow-up of previous SARS-CoV-2 infections. Fourth, member practices will be able to recruit volunteer patients to trials, including early interventions to improve COVID-19 outcomes and point-of-care testing. Lastly, the legal basis for our surveillance with PHE is Regulation 3 of the Health Service (Control of Patient Information) Regulations 2002; other studies require appropriate ethical approval. RESULTS The RCGP RSC network has tripled in size; there were previously 100 virology practices and 500 practices overall in the network and we now have 322 and 1724, respectively. The Oxford–RCGP Clinical Informatics Digital Hub (ORCHID) secure networks enable the daily analysis of the extended network; currently, 1076 practices are uploaded. We are implementing a central swab distribution system for patients self-swabbing at home in addition to in-practice sampling. We have converted all our primary care coding to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) coding. Throughout spring and summer 2020, the network has continued to collect specimens in preparation for the winter or for any second wave of COVID-19 cases. We have collected 5404 swabs and detected 623 cases of COVID-19 through extended virological sampling, and 19,341 samples have been collected for serology. This shows our preparedness for the winter season. CONCLUSIONS The COVID-19 pandemic has been associated with a groundswell of general practices joining our network. It has also created a permissive environment in which we have developed the capacity and capability of the national primary care surveillance systems and our unique public health institute, the RCGP and University of Oxford collaboration.

BACKGROUND National Health Service (NHS) England supports social prescribing in order to address social determinants of health, which account for approximately 80% of all health outcomes. Nevertheless, data on ongoing social prescribing activities are lacking. Although NHS England has attempted to overcome this problem by recommending 3 standardized primary care codes, these codes do not capture the social prescribing activity to a level of granularity that would allow for fair attribution of outcomes to social prescribing. OBJECTIVE In this study, we explored whether an alternative approach to coding social prescribing activity, specifically through a social prescribing ontology, can be used to capture the social prescriptions used in primary care in greater detail. METHODS The social prescribing ontology, implemented according to the Web Ontology Language, was designed to cover several key concepts encompassing social determinants of health. Readv2 and Clinical Terms Version 3 codes were identified using the NHS Terms Browser. The Royal College of General Practitioners Research Surveillance Centre, a sentinel network of over 1000 primary care practices across England covering a population of more than 4,000,000 registered patients, was used for data analyses for a defined period (ie, January 2011 to December 2019). RESULTS In all, 668 codes capturing social prescriptions addressing different social determinants of health were identified for the social prescribing ontology. For the study period, social prescribing ontology codes were used 5,504,037 times by primary care practices of the Royal College of General Practitioners Research Surveillance Centre as compared to 29,606 instances of use of social prescribing codes, including NHS England’s recommended codes. CONCLUSIONS A social prescribing ontology provides a powerful alternative to the codes currently recommended by NHS England to capture detailed social prescribing activity in England. The more detailed information thus obtained will allow for explorations about whether outputs or outcomes of care delivery can be attributed to social prescriptions, which is essential for demonstrating the overall value that social prescribing can deliver to the NHS and health care systems.

BACKGROUND Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. OBJECTIVE This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. METHODS We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. RESULTS Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). CONCLUSIONS The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

BACKGROUND Routinely recorded primary care data have been used for many years by sentinel networks for surveillance. More recently, real world data have been used for a wider range of research projects to support rapid, inexpensive clinical trials. Because the partial national lockdown in the United Kingdom due to the coronavirus disease (COVID-19) pandemic has resulted in decreasing community disease incidence, much larger numbers of general practices are needed to deliver effective COVID-19 surveillance and contribute to in-pandemic clinical trials. OBJECTIVE The aim of this protocol is to describe the rapid design and development of the Oxford Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) and its first two platforms. The Surveillance Platform will provide extended primary care surveillance, while the Trials Platform is a streamlined clinical trials platform that will be integrated into routine primary care practice. METHODS We will apply the FAIR (Findable, Accessible, Interoperable, and Reusable) metadata principles to a new, integrated digital health hub that will extract routinely collected general practice electronic health data for use in clinical trials and provide enhanced communicable disease surveillance. The hub will be findable through membership in Health Data Research UK and European metadata repositories. Accessibility through an online application system will provide access to study-ready data sets or developed custom data sets. Interoperability will be facilitated by fixed linkage to other key sources such as Hospital Episodes Statistics and the Office of National Statistics using pseudonymized data. All semantic descriptors (ie, ontologies) and code used for analysis will be made available to accelerate analyses. We will also make data available using common data models, starting with the US Food and Drug Administration Sentinel and Observational Medical Outcomes Partnership approaches, to facilitate international studies. The Surveillance Platform will provide access to data for health protection and promotion work as authorized through agreements between Oxford, the Royal College of General Practitioners, and Public Health England. All studies using the Trials Platform will go through appropriate ethical and other regulatory approval processes. RESULTS The hub will be a bottom-up, professionally led network that will provide benefits for member practices, our health service, and the population served. Data will only be used for SQUIRE (surveillance, quality improvement, research, and education) purposes. We have already received positive responses from practices, and the number of practices in the network has doubled to over 1150 since February 2020. COVID-19 surveillance has resulted in tripling of the number of virology sites to 293 (target 300), which has aided the collection of the largest ever weekly total of surveillance swabs in the United Kingdom as well as over 3000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology samples. Practices are recruiting to the PRINCIPLE (Platform Randomised trial of INterventions against COVID-19 In older PeopLE) trial, and these participants will be followed up through ORCHID. These initial outputs demonstrate the feasibility of ORCHID to provide an extended national digital health hub. CONCLUSIONS ORCHID will provide equitable and innovative use of big data through a professionally led national primary care network and the application of FAIR principles. The secure data hub will host routinely collected general practice data linked to other key health care repositories for clinical trials and support enhanced in situ surveillance without always requiring large volume data extracts. ORCHID will support rapid data extraction, analysis, and dissemination with the aim of improving future research and development in general practice to positively impact patient care. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/19773

Background There are few primary care studies of the COVID-19 pandemic. We aimed to identify demographic and clinical risk factors for testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre primary care network. Methods We analysed routinely collected, pseudonymised data for patients in the RCGP Research and Surveillance Centre primary care sentinel network who were tested for SARS-CoV-2 between Jan 28 and April 4, 2020. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive SARS-CoV-2 tests within this surveillance network. Findings We identified 3802 SARS-CoV-2 test results, of which 587 were positive. In multivariable analysis, male sex was independently associated with testing positive for SARS-CoV-2 (296 [18·4%] of 1612 men vs 291 [13·3%] of 2190 women; adjusted odds ratio [OR] 1·55, 95% CI 1·27–1·89). Adults were at increased risk of testing positive for SARS-CoV-2 compared with children, and people aged 40–64 years were at greatest risk in the multivariable model (243 [18·5%] of 1316 adults aged 40–64 years vs 23 [4·6%] of 499 children; adjusted OR 5·36, 95% CI 3·28–8·76). Compared with white people, the adjusted odds of a positive test were greater in black people (388 [15·5%] of 2497 white people vs 36 [62·1%] of 58 black people; adjusted OR 4·75, 95% CI 2·65–8·51). People living in urban areas versus rural areas (476 [26·2%] of 1816 in urban areas vs 111 [5·6%] of 1986 in rural areas; adjusted OR 4·59, 95% CI 3·57–5·90) and in more deprived areas (197 [29·5%] of 668 in most deprived vs 143 [7·7%] of 1855 in least deprived; adjusted OR 2·03, 95% CI 1·51–2·71) were more likely to test positive. People with chronic kidney disease were more likely to test positive in the adjusted analysis (68 [32·9%] of 207 with chronic kidney disease vs 519 [14·4%] of 3595 without; adjusted OR 1·91, 95% CI 1·31–2·78), but there was no significant association with other chronic conditions in that analysis. We found increased odds of a positive test among people who are obese (142 [20·9%] of 680 people with obesity vs 171 [13·2%] of 1296 normal-weight people; adjusted OR 1·41, 95% CI 1·04–1·91). Notably, active smoking was linked with decreased odds of a positive test result (47 [11·4%] of 413 active smokers vs 201 [17·9%] of 1125 non-smokers; adjusted OR 0·49, 95% CI 0·34–0·71). Interpretation A positive SARS-CoV-2 test result in this primary care cohort was associated with similar risk factors as observed for severe outcomes of COVID-19 in hospital settings, except for smoking. We provide evidence of potential sociodemographic factors associated with a positive test, including deprivation, population density, ethnicity, and chronic kidney disease.

Background The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) have successfully worked together on the surveillance of influenza and other infectious diseases for over 50 years, including three previous pandemics. With the emergence of the international outbreak of the coronavirus infection (COVID-19), a UK national approach to containment has been established to test people suspected of exposure to COVID-19. At the same time and separately, the RCGP RSC’s surveillance has been extended to monitor the temporal and geographical distribution of COVID-19 infection in the community as well as assess the effectiveness of the containment strategy. Objectives The aims of this study are to surveil COVID-19 in both asymptomatic populations and ambulatory cases with respiratory infections, ascertain both the rate and pattern of COVID-19 spread, and assess the effectiveness of the containment policy. Methods The RCGP RSC, a network of over 500 general practices in England, extract pseudonymized data weekly. This extended surveillance comprises of five components: (1) Recording in medical records of anyone suspected to have or who has been exposed to COVID-19. Computerized medical records suppliers have within a week of request created new codes to support this. (2) Extension of current virological surveillance and testing people with influenza-like illness or lower respiratory tract infections (LRTI)—with the caveat that people suspected to have or who have been exposed to COVID-19 should be referred to the national containment pathway and not seen in primary care. (3) Serology sample collection across all age groups. This will be an extra blood sample taken from people who are attending their general practice for a scheduled blood test. The 100 general practices currently undertaking annual influenza virology surveillance will be involved in the extended virological and serological surveillance. (4) Collecting convalescent serum samples. (5) Data curation. We have the opportunity to escalate the data extraction to twice weekly if needed. Swabs and sera will be analyzed in PHE reference laboratories. Results General practice clinical system providers have introduced an emergency new set of clinical codes to support COVID-19 surveillance. Additionally, practices participating in current virology surveillance are now taking samples for COVID-19 surveillance from low-risk patients presenting with LRTIs. Within the first 2 weeks of setup of this surveillance, we have identified 3 cases: 1 through the new coding system, the other 2 through the extended virology sampling. Conclusions We have rapidly converted the established national RCGP RSC influenza surveillance system into one that can test the effectiveness of the COVID-19 containment policy. The extended surveillance has already seen the use of new codes with 3 cases reported. Rapid sharing of this protocol should enable scientific critique and shared learning. International Registered Report Identifier (IRRID) DERR1-10.2196/18606

BACKGROUND The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) have successfully worked together on the surveillance of influenza and other infectious diseases for over 50 years, including three previous pandemics. With the emergence of the international outbreak of the coronavirus infection (COVID-19), a UK national approach to containment has been established to test people suspected of exposure to COVID-19. At the same time and separately, the RCGP RSC’s surveillance has been extended to monitor the temporal and geographical distribution of COVID-19 infection in the community as well as assess the effectiveness of the containment strategy. OBJECTIVE The aim of this study is the surveillance of COVID-19 in both asymptomatic populations and ambulatory cases with respiratory infections to ascertain both the rate and pattern of COVID-19 spread and to assess the effectiveness of the containment policy. METHODS The RCGP RSC, a network of over 500 general practices in England, extract pseudonymized data weekly. This extended surveillance comprises of five components: (1) Recording in medical records of anyone suspected to have or who has been exposed to COVID-19. Computerized medical records suppliers have within a week of request created new codes to support this. (2) Extension of current virological surveillance and testing people with influenza-like illness or lower respiratory tract infections (LRTI)—with the caveat that people suspected to have or who have been exposed to COVID-19 should be referred to the national containment pathway and not seen in primary care. (3) Serology sample collection across all age groups. This will be an extra blood sample taken from people who are attending their general practice for a scheduled blood test. The 100 general practices currently undertaking annual influenza virology surveillance will be involved in the extended virological and serological surveillance. (4) Collecting convalescent serum samples. (5) Data curation. We have the opportunity to escalate the data extraction to twice weekly if needed. Swabs and sera will be analyzed in PHE reference laboratories. RESULTS General practice clinical system providers have introduced an emergency new set of clinical codes to support COVID-19 surveillance. Additionally, practices participating in current virology surveillance are now taking samples for COVID-19 surveillance from low-risk patients presenting with LRTIs. Within the first 2 weeks of setup of this surveillance, we have identified 3 cases: 1 through the new coding system, the other 2 through the extended virology sampling. CONCLUSIONS We have rapidly converted the established national RCGP RSC influenza surveillance system into one that can test the effectiveness of the COVID-19 containment policy. The extended surveillance has already seen the use of new codes with 3 cases reported. Rapid sharing of this protocol should enable scientific critique and shared learning. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18606

Background: There are few epidemiological studies of community cases in the current coronavirus-2019 (COVID-19) pandemic. We report on the first 500 COVID-19 cases identified through United Kingdom primary care surveillance and describe risk factors for testing COVID-19 positive. Methods: The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), is a nationally representative primary care sentinel network sharing pseudonymised data, including virological test data for COVID-19. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive COVID-19 tests within this surveillance programme. Findings: We identified 3,802 COVID-19 results between 28/01/20 and 04/04/2020, 587 were positive. Greater odds of testing COVID-19 positive included: working-age people (40-64years) and older age, (≥75 years) versus 0-17 year olds (adjusted odds ratio [aOR] 5.26, 95%CI:3.26-8.49 and 5.17,95%CI:2.99-8.92, respectively); male gender (aOR 1.56, 95%CI:1.28-1.90); black and mixed ethnicity compared with white (aOR 4.55, 95%CI:2.55-8.10 and 1.84 95%CO:1.1-3.14, respectively)); urban compared with rural areas (aOR 4.58, 95%CI:3.57-5.88); people with chronic kidney disease (CKD) (aOR 1.88, 95%CI:1.29-2.75) and increasing body mass index (aOR 1.02, 95%CI:1.00-1.03). People in the least deprived deprivation quintile had lower odds of a positive test (aOR 0.49 95%CI:0.36-0.65) as did current smokers (aOR 0.53, 95%CI:0.38-0.74). Interpretation: A positive COVID-19 test result in primary care was associated with similar risk factors for severe outcomes seen in hospital settings, with the exception of smoking. We provide early evidence of potential sociodemographic factors associated with a positive test, including ethnicity, deprivation, population density, and CKD. Funding Statement: Public Health England provides the core funding for RCGP RSC, no specific funding was provided for this analysis. Declaration of Interests: The authors have no competing interests. SdeL is the Director of the Oxford RCGP RSC, RB, JS, FF, EK and GH are part funded by PHE; and CO and AC by a Wellcome Biomedical resources grant (212763/Z/18/Z). JD is funded by Wellcome Trust (216421/Z/19/Z). Ethics Approval Statement: This study was approved by the RCGP RSC study approval committee and was classified as a study of “usual practice”. Therefore, no further ethical approval was required.

Background: There was excess mortality from the first wave of coronavirus 2019 infection (COVID-19), which mainly affected older people. To mitigate risk, the UK government recommended ‘shielding’ of vulnerable people through self-isolation for 12 weeks. We investigated the impact of primary care-reinforced shielding advice on all-cause mortality. Methods: We conducted a retrospective cohort study using a nationally representative English primary care database. We compare people aged >=40years who were recorded as being advised to shield using a fixed ratio of 1:1, matching (a mixture of exact and propensity score matching) to people with the same diagnoses not advised to shield (n=77,360 per group). Time-to-death was compared using Cox regression, reporting the hazard ratio (HR) of mortality between groups. A sensitivity analysis compared exact matched cohorts (n=24,752 shielded, n=61,566 exact matches). Findings: Over the follow-up period, we found a time-varying HR of mortality between groups. In the first 21 days, the mortality risk in people shielding was half those not (HR=0.50, 95%CI:0.41-0.59. p

Background: The SARS-CoV-2 pandemic has passed its first peak in many European countries. We describe the mortality peak in England and the association of known SARS-CoV-2 status and other demographic and risk factors with mortality. Methods: Pseudonymised, coded clinical data were uploaded from volunteer primary care providers who were members of a nationally representative sentinel network ((N=4,413,734) including SARS-CoV-2 status data (n=56,628, 1.3%). We compared all-cause mortality with the national rate for 2019, using a relative survival model, reporting relative hazard ratios (RHR) and 95% confidence intervals (CI). We conducted cross-sectional analyses of those with known SARS-CoV-2 status. We used multiple imputation to adjust for missing data and included an inverse probability analysis. We also complete cases analysis; presenting results as odds ratios (OR) with 95%CI. Results: Mortality peaked in week 16 of 2020. People residing in households of 9 or more had a five-fold increase in relative mortality (RHR:5.1,95%CI:4.87-5.31, p

Tranexamic acid reduces surgical bleeding and decreases mortality in patients with traumatic extracranial bleeding. Intracranial bleeding is common after traumatic brain injury (TBI) and can cause brain herniation and death. We aimed to assess the effects of tranexamic acid in patients with TBI.This randomised, placebo-controlled trial was done in 175 hospitals in 29 countries. Adults with TBI who were within 3 h of injury, had a Glasgow Coma Scale (GCS) score of 12 or lower or any intracranial bleeding on CT scan, and no major extracranial bleeding were eligible. The time window for eligibility was originally 8 h but in 2016 the protocol was changed to limit recruitment to patients within 3 h of injury. This change was made blind to the trial data, in response to external evidence suggesting that delayed treatment is unlikely to be effective. We randomly assigned (1:1) patients to receive tranexamic acid (loading dose 1 g over 10 min then infusion of 1 g over 8 h) or matching placebo. Patients were assigned by selecting a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was head injury-related death in hospital within 28 days of injury in patients treated within 3 h of injury. We prespecified a sensitivity analysis that excluded patients with a GCS score of 3 and those with bilateral unreactive pupils at baseline. All analyses were done by intention to treat. This trial was registered with ISRCTN (ISRCTN15088122), ClinicalTrials.gov (NCT01402882), EudraCT (2011-003669-14), and the Pan African Clinical Trial Registry (PACTR20121000441277).Between July 20, 2012, and Jan 31, 2019, we randomly allocated 12 737 patients with TBI to receive tranexamic acid (6406 [50·3%] or placebo [6331 [49·7%], of whom 9202 (72·2%) patients were treated within 3 h of injury. Among patients treated within 3 h of injury, the risk of head injury-related death was 18·5% in the tranexamic acid group versus 19·8% in the placebo group (855 vs 892 events; risk ratio [RR] 0·94 [95% CI 0·86-1·02]). In the prespecified sensitivity analysis that excluded patients with a GCS score of 3 or bilateral unreactive pupils at baseline, the risk of head injury-related death was 12·5% in the tranexamic acid group versus 14·0% in the placebo group (485 vs 525 events; RR 0·89 [95% CI 0·80-1·00]). The risk of head injury-related death reduced with tranexamic acid in patients with mild-to-moderate head injury (RR 0·78 [95% CI 0·64-0·95]) but not in patients with severe head injury (0·99 [95% CI 0·91-1·07]; p value for heterogeneity 0·030). Early treatment was more effective than was later treatment in patients with mild and moderate head injury (p=0·005) but time to treatment had no obvious effect in patients with severe head injury (p=0·73). The risk of vascular occlusive events was similar in the tranexamic acid and placebo groups (RR 0·98 (0·74-1·28). The risk of seizures was also similar between groups (1·09 [95% CI 0·90-1·33]).Our results show that tranexamic acid is safe in patients with TBI and that treatment within 3 h of injury reduces head injury-related death. Patients should be treated as soon as possible after injury.National Institute for Health Research Health Technology Assessment, JP Moulton Charitable Trust, Department of Health and Social Care, Department for International Development, Global Challenges Research Fund, Medical Research Council, and Wellcome Trust (Joint Global Health Trials scheme).For the Arabic, Chinese, French, Hindi, Japanese, Spanish and Urdu translations of the abstract see Supplementary Material.