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Background/Aims Psoriatic arthritis (PsA) is a form of inflammatory arthritis affecting up to 30% of those with psoriasis. It has a considerable impact on patients’ functional capacity and quality of life. Studies have shown an increased prevalence of depression in patients with PsA which can contribute to reduced likelihood of disease remission. Smartphone applications have been developed to monitor symptoms in patients with rheumatic diseases. We used experience sampling to assess the relationship between daily PsA symptom burden using the PsAID-9 questionnaire and mood components utilising the validated Mood Zoom (MZ) questionnaire. Methods Participants (n = 25) with PsA aged ≥18 and Results Mean (SD) age was 49.6 (12.05) years. Sixteen had co-morbid psoriasis. Mean (SD) PsAID-9 scores were 3.28 (1.92). There was a bi-directional association between the mood components of MZ and total PsAID-9 score for each day, as shown in Table 1. Conclusion This study has demonstrated a relationship between daily fluctuations in disease burden and mood. This is the first study to pilot a daily assessment of disease impact versus traditional longer interval collections. Whilst these data demonstrate an association between mood and disease symptoms, they do not allow us to make inferences on the causal effect of mood on symptoms and vice versa. Further research into the relationship between mood and disease burden in PsA are warranted. Disclosure D. McGagh: None. N. McGowan: None. Y. Wu: None. C. Hinds: None. K.E.A. Saunders: None. L.C. Coates: None.

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/Aims Several studies have demonstrated an increased prevalence of sleep disorders in patients with psoriatic arthritis (PsA). However, there are few data on the relationship between sleep disturbance and disease activity in PsA. We conducted a cross-sectional study to investigate the prevalence of insomnia symptoms and poor sleep quality in PsA and examine correlation with demographic, clinical and patient-reported outcome measures. Methods Patients were recruited from rheumatology clinics at a single centre. Patients had a diagnosis of PsA, met the CASPAR (ClASsification criteria for Psoriatic ARthritis) criteria and were aged ≥ 18 and ≤ 80 years. They had no other confirmed sleep disorder diagnoses. Disease activity was calculated using the cDAPSA (Clinical Disease Activity in PSoriatic Arthritis) score. Disease impact was measured using a pain visual analogue scale (VAS) and the Psoriatic Arthritis Impact of Disease (PsAID)-12 questionnaire and functional impairment was assessed using the health assessment questionnaire (HAQ). We used the Sleep Condition Indicator (SCI) to assess symptoms of Diagnostic and Statistical Manual of Mental Disorders (DSM)-V insomnia disorder (ID). The SCI is a score from 0-30 with lower scores indicating worse subjective sleep. We used the Pittsburgh Sleep Quality Index (PSQI) to assess subjective sleep quality. Relationships between clinical characteristics and sleep disturbance were examined using Pearson’s correlation coefficients and multivariable logistic regression. Results Thirty participants were enrolled in the study, of which 13 were female. Mean (SD) age of participants was 49 (13.5) years. Overall, the prevalence of probable DSM-V ID (SCI score ≤ 16) was 43.33% (13/30) with a mean (SD) SCI score of 6.80 (4.79). Mean (SD) PSQI score was 6.77 (3.22) with 60% (18/30) classified as subjective poor sleepers. Mean (SD) pain VAS, PsAID-12 and HAQ were 33.89 (28.67), 2.97 (2.05) and 46.12 (38.83), respectively. Mean (SD) cDAPSA score was 12.3 (15). A significant correlation was observed between the SCI score and cDAPSA (r=-0.3809, p = 0.038), and SCI and PsAID-12 scores (r=-0.3809, p = 0.033). No correlation was found between SCI score and HAQ, age, pain VAS, or disease duration. Multivariable logistic regression controlling for age found an association between cDAPSA and probable ID (OR:1.098 95%CI:1.014-1.253). This association did not persist once pain level, gender, PsAID-12 and HAQ scores were controlled for. Conclusion This cross-sectional analysis of patients with PsA demonstrates that sleep disturbance is highly prevalent. Symptoms of insomnia in this small cohort were related to higher baseline disease activity and self-reported disease impact. This study is the first to demonstrate these findings and warrants further exploration in a larger sample size with longitudinal data. It would be of interest to understand the relationship between insomnia and disease activity and whether insomnia is responsive to treating PsA to low disease activity levels. Disclosure D. McGagh: None. N.M. McGowan: None. S. Elahi: None. J. MacDonald: None. K.E.A. Saunders: None. L.C. Coates: None.

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

PsA is a complex, heterogeneous disease that can place a large burden on patients’ psychological and physical well-being. The multifaceted nature of PsA poses a significant assessment challenge, both in randomized control trials and in clinical practice. In recent years, there has been much progress in the development of unidimensional and composite measures of disease activity, as well as of questionnaires that capture the patient’s perspective of the condition. Despite these advances, there remains uncertainty around which tools to implement within a research setting. This review aims to summarize the currently available clinical and patient-derived assessment tools, providing a practical and informative resource for the assessment of PsA. This review will also explore recent advancements in digital approaches to the assessment of rheumatological conditions. This will highlight the potential for digitalization in the assessment and monitoring of PsA, outlining innovative means of capturing disease activity and treatment response.

The aims of treatment for psoriatic arthritis (PsA) are to control inflammation, normalise functions and impacts on patients and prevent complications of the disease and its treatment. Over the past decade, treatment options for PsA have expanded with the availability of many more novel therapeutic agents. However, the treatment decisions and pathways for the use of these drugs are not always straightforward. There is a need to tailor the choice of medication to the individual patient, taking into account the type of their disease and consideration of other factors such as their co-morbidities. A treat to target approach is recommended with the aim to get the patient into a state of remission or low disease activity (whichever target is chosen). Both European League Against Rheumatism (EULAR) and American College of Rheumatology (ACR) have recently published updated guidance in 2018-2019. In this section, we will summarise the evidence for therapies in PsA and review the similarities and differences in these two sets of recommendations.

The control of coronavirus 2019 disease (COVID-19) relies on implementing an effective testing and tracing strategy; the ideal would be a move to using rapid point-of-care tests (POCTs) in the community, to detect both active infections and to identify those already immune, as being tested in Liverpool.1 To date, largely laboratory-based virology testing (reverse transcription polymerase chain reaction (PCR) to detect viral ribonucleic acid from naso/oropharyngeal swabs) remains the gold standard for diagnosis. The role of COVID-19 antibody testing is limited, with few studies published about the diagnostic accuracy of POCTs that might be used in primary care. This article aims to summarise the current state of knowledge about the potential role of COVID-19 antibody testing in primary care. Following infection, we develop immunoglobulins (Ig) against SARS-CoV-2 proteins, including the diagnostically important nucleoprotein and/or spike proteins (N- and S-proteins, respectively). The median time of detection of anti- SARS-CoV-2 IgG is day 14 post-onset of symptoms (POS) (for IgM and IgA it is day 5), and it is detectable in 90% of individuals by then.2,3 Anti-N-protein immunoglobulin class G (IgG) is the most sensitive current target for serological detection, albeit its combined detection with anti-S improves sensitivity.4–6 IgA antibodies, implicated in mucosal surface defence, are detectable from the first day POS.3 A waning in IgG response has been reported,7 with potentially severe implications for long-term immunity depending upon the alternative effectiveness of memory T-cell-mediated immunity. There are suggestions that seropositive individuals are only at a lower risk of re-infection compared with seronegative individuals.8 There is no definitive evidence present about the protective effect, if any, of anti-SARS-CoV-2 antibody against reinfection and/or COVID-19 disease. Individuals previously infected with COVID-19 can serorevert and some who have seroconverted do not necessarily stop shedding the …

Background: The BNT162b2 mRNA (Pfizer-BioNTech) and ChAdOx1 (Oxford-AstraZeneca) COVID-19 vaccines have demonstrated high efficacy against infection in phase 3 clinical trials and are now being used in national vaccination programmes in the UK and several other countries. There is an urgent need to study the ‘real-world’ effects of these vaccines. The aim of our study was to estimate the effectiveness of the first dose of these COVID-19 vaccines in preventing hospital admissions. Methods: We conducted a prospective cohort study using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) database comprising of linked vaccination, primary care, Real-Time Polymerase Chain Reaction (RT-PCR) testing, hospitalisation and mortality records for 5.4 million people in Scotland (covering ~99% of population). A time-dependent Cox model and Poisson regression models were fitted to estimate effectiveness against COVID-19 related hospitalisation (defined as 1- Adjusted Hazard Ratio) following the first dose of vaccine. Findings: The first dose of the BNT162b2 vaccine was associated with a vaccine effect of 85% (95% confidence interval [CI] 76 to 91) for COVID-19 related hospitalisation at 28-34 days post-vaccination. Vaccine effect at the same time interval for the ChAdOx1 vaccine was 94% (95% CI 73 to 99). Results of combined vaccine effect for prevention of COVID-19 related hospitalisation were comparable when restricting the analysis to those aged ≥80 years (81%; 95% CI 65 to 90 at 28-34 days post-vaccination). Interpretation: A single dose of the BNT162b2 mRNA and ChAdOx1 vaccines resulted in substantial reductions in the risk of COVID-19 related hospitalisation in Scotland. Funding: UK Research and Innovation (Medical Research Council); Research and Innovation Industrial Strategy Challenge Fund; Health Data Research UK. Conflict of Interest: AS is a member of the Scottish Government Chief Medical Officer’s COVID-19Advisory Group and the New and Emerging Respiratory Virus Threats (NERVTAG) Risk Stratification Subgroup. CRS declares funding from the MRC, NIHR, CSO and 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 Scientific Advisory Group on Emergencies (SAGE) subgroup on ethnicity and acknowledges funding from a NRS Senior Clinical Fellowship, MRC and CSO. All other authors report no conflicts of interest. Ethical Approval: Approvals were obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number: 12/SS/0201) and Public Benefit and Privacy Panel for Health and Social Care (reference number: 1920-0279).

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 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.

BackgroundThere is an urgent need for epidemiological research in primary care to develop risk assessment processes for patients presenting with COVID-19, but lack of a standardised approach to data collection is a significant barrier to implementation.AimTo collate a list of relevant symptoms, assessment items, demographics, and lifestyle and health conditions associated with COVID-19, and match these data items with corresponding SNOMED CT clinical terms to support the development and implementation of consultation templates.Design & settingPublished and preprint literature for systematic reviews, meta-analyses, and clinical guidelines describing the symptoms, assessment items, demographics, and/or lifestyle and health conditions associated with COVID-19 and its complications were reviewed. Corresponding clinical concepts from SNOMED CT, a widely used structured clinical vocabulary for electronic primary care health records, were identified.MethodGuidelines and published and unpublished reviews (N= 61) were utilised to collate a list of relevant data items for COVID-19 consultations. The NHS Digital SNOMED CT Browser was used to identify concept and descriptive identifiers. Key implementation challenges were conceptualised through a Normalisation Process Theory (NPT) lens.ResultsIn total, 32 symptoms, eight demographic and lifestyle features, 25 health conditions, and 20 assessment items relevant to COVID-19 were identified, with proposed corresponding SNOMED CT concepts. These data items can be adapted into a consultation template for COVID-19. Key implementation challenges include: 1) engaging with key stakeholders to achieve ’buy in’; and 2) ensuring any template is usable within practice settings.ConclusionConsultation templates for COVID-19 are needed to standardise data collection, facilitate research and learning, and potentially improve quality of care for COVID-19.

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