AbstractWe discovered that the official list of clinical codes for pregnancy during the COVID-19 pandemic identified some unlikely pregnancies (for example, in older men), principally due to a code describing a specific fetal position (“knee presentation”), which notably lacks “fetal” in the code description. This is an informative example of commonly overlooked problems in creating and using clinical data.

Abstract Background/Aims Previous data highlighted a sharp decrease in new diagnoses of rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (axSpA) during the early COVID-19 pandemic. Little is known about whether these diagnoses have rebounded as the NHS recovers from the pandemic, or how deficits in inflammatory arthritis diagnoses compare with connective tissue diseases (CTD) and vasculitis. Using data for 18 million adults in England, we quantified differences between observed and expected rates of new diagnoses for 10 rheumatic diseases up to March 2025. Methods With NHS England approval, we analysed primary care and hospital admission data for 18.1 million adults in England using the OpenSAFELY platform. We evaluated temporal trends in incidence rates for RA, PsA, axSpA, undifferentiated inflammatory arthritis, Sjogren’s disease, systemic lupus erythematosus, systemic sclerosis, myositis, giant cell arteritis (GCA), and ANCA vasculitis between April 1, 2016, and March 31, 2025. Differences between observed and expected incidence rates after the onset of the pandemic (from March 2020 to March 2025) were quantified using seasonal autoregressive integrated moving-average models. Results The largest and most persistent deficit in new diagnoses since March 2020 was evident for PsA, with 15,036 (24.5%) fewer diagnoses than expected (95% CI: 13,176, 16,896). As of March 2025, diagnosis rates for PsA remained substantially below pre-pandemic levels. For RA, there were 12,139 (8.5%) fewer diagnoses than expected (95% CI: 8,081, 16,197). In contrast, axSpA diagnoses have increased above pre-pandemic trends, with 2,410 (8.5%) more diagnoses than expected since March 2020 (95% CI: 762, 4,057). For CTDs, there were 5,530 (10.1%) fewer diagnoses than expected (95% CI: 4,098, 6,962), driven by a marked decrease in new diagnoses of Sjogren’s disease, which only returned to pre-pandemic rates in 2024. For vasculitis, there were 6,589 (12.4%) fewer diagnoses than expected (95% CI: 5,422, 7,757), accounted for by declining GCA diagnosis rates since the pandemic, whereas ANCA vasculitis diagnoses have remained broadly stable. Conclusion There continues to be a marked and disproportionate impact of the pandemic on diagnosis rates for many rheumatic diseases in England. For inflammatory arthritis, this has been most evident for PsA, with 25% fewer diagnoses than expected since the pandemic; contrasting a post-pandemic increase in axSpA diagnoses. For CTDs and vasculitis, the pandemic has disproportionately impacted Sjogren’s disease and GCA. Importantly, we have shown that it is possible to monitor the epidemiology of rheumatic diseases in England in near real-time using data in OpenSAFELY. This could inform strategies to enhance case detection and reduce unwarranted variation in care. Disclosure M.D. Russell: Honoraria; AbbVie, Biogen, Galapagos, Johnson & Johnson, Lilly, Menarini, Novartis, Pfizer, UCB and Viforpharma. Grants/research support; Sandoz UK. A. Schaffer: None. B. MacKenna: Other; Employed by NHS England working on medicines policy and clinical lead for primary care medicines data. A. Mahto: None. K. Bechman: Honoraria; Galapagos, UCB and Viforpharma. Grants/research support; NIHR. C. Wincup: None. A.I. Rutherford: None. P. Gordon: None. E. Nikiphorou: None. S. Steer: None. S. Patel: None. M. Dey: None. K. Biddle: None. S. Patel: None. M. Gibson: None. E. Alveyn: Other; Received support for attending meetings from UCB. V. Allen: None. S. Bacon: None. A. Mehrkar: Consultancies; Has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. Other; Represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GDPPR; former employee and interim Chief Medical Officer of NHS Digital. B. Goldacre: Grants/research support; Bennett Foundation, Laura & John Arnold Foundation, NIHR, NHS England, Mohn-Westlake Foundation, Wellcome, Good Thinking Foundation, HDRUK, Health Foundation, WHO, MRC, Asthma UK, BLF, Nat. Core Study. Other; Previously a Non-Executive Director at NHS Digital; also receives personal income from speaking and writing for lay audiences on the misuse of science. S. Norton: None. A.P. Cope: None. E. Price: None. J.B. Galloway: Honoraria; Abbvie, Biovitrum, BMS, Celgene, Chugai, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Sanofi, Sobi and UCB. Grants/research support; Sandoz UK.

Background:Long COVID continues to affect millions of adults and contribute to substantial economic burden across Europe. Ethnic inequalities in Long COVID, and the reasons underlying these, are poorly understood. We aimed to investigate ethnic differences in the incidence of diagnosed Long COVID in England using linked national primary care data. Methods:With approval from NHS England, we used linked health record data from England, 2020-2022, accessed through the OpenSAFELY platform. We applied Cox regression to compare incidence of diagnosed Long COVID in primary care across self-reported ethnicity in five groups. We explored potential explanations for these differences by 1) adjusting for sociodemographic and health-related factors, 2) restricting to those tested or hospitalised with COVID-19, 3) stratifying into 16 ethnic sub-groups. Findings:Our sample comprised 17,848,825 adults, of whom 16,970 (0.1%) had a diagnosis of Long COVID recorded in primary care. Hazard ratios (95% confidence intervals) for Long COVID compared with the white group were 1.04 (0.98-1.11) for the South Asian group, 0.84 (0.75-0.94) for the Black group, 0.97 (0.84-1.13) for the Mixed Ethnicity group, and 0.63 (0.55-0.72) for Other ethnic groups, which remained similar when adjusting for sociodemographic and health-related factors and among those tested or hospitalised for COVID-19. Disaggregating into 16 ethnic sub-groups revealed heterogeneity within groups, for example, compared with the White British group, hazard ratios were 1.21 (1.00-1.47) for the Bangladeshi group and 1.09 (0.99-1.21) for the Pakistani group, but 0.77 (0.70-0.86) for the Indian group; and 1.15 (0.95-1.40) for the Black Caribbean group but 0.61 (0.51-0.72) for the Black African group. Interpretation:Differences in Long COVID diagnoses across broad ethnic groups mask important sub-group inequalities, offering insight into underlying mechanisms and approaches to better target Long COVID services. Funding:The OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY and this analysis have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC_PC_20030, MC_PC_20059) [2020-2022] and the Data and Connectivity strand (MC_PC_20058) [2021-2022]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_PC_20051) [2021-2024] and MRC (MR/V040235/1) [2021-24].

BACKGROUND:We aimed to explore the occurrence and persistence of symptoms, diagnoses and prescribing after COVID-19 among populations from earlier (wave 2) and later (wave 4) in the pandemic. METHODS:With the approval of NHS England, we analysed data from English primary care using The Phoenix Partnership SystmOne through the OpenSAFELY data analytics platform. Individuals with community-diagnosed COVID-19 September 2020-January 2021 (wave 2) were matched to contemporary (2020-2021) and historical (2017-2018) comparators. Individuals with community COVID-19 December 2021-March 2022 (wave 4) were matched to contemporary comparators (last follow-up 31 March 2023). Occurrence of each of (1) long-COVID symptoms; (2) primary-care diagnoses and (3) new prescriptions was analysed at any time during 1 year after COVID-19 and at: 4-12 weeks, 12 weeks-6 months and 6 months-12 months after COVID-19 to assess persistence. RESULTS:902 885 COVID-19 cases (wave 2) matched to 4 449 265 contemporary (no-COVID-19) comparators. 1 553 160 COVID-19 cases (wave 4) matched to 7 624 770 contemporary comparators. Positive wave 2 associations after COVID-19 were observed for hair loss (OR 1.57, 95% CI 1.48 to 1.66), mobility impairment (1.41, 1.35 to 1.48), fatigue (1.46, 1.42 to 1.49), cognitive impairment (1.39, 1.34 to 1.44) and loss of taste or smell (1.38, 1.31 to 1.46). At 6-12 months reporting persisted for mobility impairment, fatigue and cognitive impairment. There were small increases in new prescriptions for NSAIDs (1.24, 1.23 to 1.26), drugs to treat infections (1.24, 1.23 to 1.25) and musculoskeletal problems (1.23, 1.22 to 1.25). Wave 4 associations were generally weaker than Wave 2. CONCLUSIONS:Long-COVID symptoms and new prescribing generally reduce over time and are potentially less problematic following less severe illness. Fatigue/cognitive/mobility symptoms persist following COVID-19.

OBJECTIVES:We aimed to estimate how rheumatology outpatient hospital attendances have changed since the COVID-19 pandemic and determine demographic characteristics associated with observed changes. METHODS:Using three primary and secondary care electronic health record datasets in England (with the approval of NHS England), Scotland and Wales, we identified people with a diagnosis of RA before 1 April 2019. We determined the proportion of people with rheumatology hospital outpatient appointments each month [April 2019 to December 2022 (Wales and Scotland), November 2023 (England)] and quantified changes using interrupted time-series analysis. We used logistic regression to determine characteristics associated with having fewer appointments compared with 2019. RESULTS:We identified 145 065, 3813 and 13 637 people coded with RA in England, Scotland and Wales, respectively. At the start of the COVID-19 pandemic the number of rheumatology outpatient appointments dropped sharply across all nations. In England and Scotland, the percentage of monthly appointments has continued to decline. In Wales, while there was a gradual recovery, rheumatology services have not returned to pre-pandemic levels. In contrast, the number of appointments for other specialties has recovered in all nations. People with no rheumatology outpatient appointments were more often aged over 80, male and living in rural areas. Ethnic minorities, those living in more deprived and urban areas had fewer appointments after the start of the pandemic compared with 2019. CONCLUSION:For the first time, we compared healthcare use across three UK nations and found rheumatology outpatient appointments had not recovered to pre-COVID-19 pandemic levels, particularly in Scotland and England.

IntroductionGonadotropin-releasing hormone (GnRH) agonists are the most commonly used form of androgen deprivation therapy (ADT) for advanced prostate cancer, often prescribed with radiotherapy or chemotherapy. This study examined national trends in the prescribing of injectable GnRH agonists in England from 2015 to 2024, by formulation type and demographic factors (age, ethnicity, and deprivation). We hypothesised that the use of longer-acting formulations has been increasing over time, specifically during the COVID-19 pandemic when access to face-to-face appointments was limited, and that variation over time and by demographics could inform future service delivery.MethodsWith the approval of NHS England, we conducted a cohort study using the OpenSAFELY-TPP database of 24 million adults. Monthly prescription counts and rates (per 100,000 men with prostate cancer) were visualised over time. Generalised linear models were used to estimate the impact of the COVID-19 pandemic.ResultsThe cohort included 390,265 men with prostate cancer (mean age 69.8 years, SD 13.5). Overall, 1,535,725 prescriptions were issued to 208,010 participants (53%). Monthly prescription counts increased by 40%, from 11,787 in 2015 to 16,697 in 2024, while rates declined from 8453 to 7721. During 2020-2021, prescribing of 1- and 3-monthly formulations decreased, whereas 6-monthly formulations increased from 437 per month (245 per 100,000 men) in 2019 to 755 (349 per 100,000 men) in 2024, an excess of 29%.ConclusionsBefore the pandemic, 6-monthly formulations were rarely prescribed. Their uptake during the pandemic suggested a shift towards longer-acting formulations, reducing treatment burden. Declining GnRH rates may reflect earlier diagnosis and evolving treatment guidelines. Divergence between prescription counts and rates, and variation by demographic factors, reflected challenges faced by healthcare systems.

Background: Living with children has been associated with greater risks of SARS-CoV-2 infection, COVID-19 hospitalisation, and COVID-19 death. We examined how these associations varied during 2021–22 and according to the COVID-19 vaccination status of adults. Methods: We carried out a population-based cohort study, with the approval of NHS England. Primary care data and pseudonymously-linked hospital and death records from England, between 20th December 2020 and 21st February 2022, were used for adults (≥18 years) registered at a general practice on 20th December 2020. Adjusted hazard ratios (HRs) for SARS-CoV-2 infection, COVID-19 hospitalisation, or COVID-19 death, by presence of children in the household were calculated. Results: The cohort included 9,417,278 adults aged ≤65 years and 2,866,602 adults aged >65 years. Adults aged ≤65 years living with children of any age (versus no children) had greater risks of SARS-CoV-2 infection and COVID-19 hospitalisation (but not COVID-19 death), both when schools were open and closed (e.g. HR=1.50, 95% CI:1.49-1.51, for SARS-CoV-2 infection in the ‘Omicron dominant’ period, when schools were open, in adults living with children aged 0–11 years only). These associations also existed for adults aged >65 years, and there was some evidence that adults living with children also had greater risks of COVID-19 death. Vaccinated adults living with children had greater risks of SARS-CoV-2 infection, but lower risks of COVID-19 hospitalisation and death, than unvaccinated adults not living with children. Conclusions: In an era of widespread adult vaccination, adults living with children remained at increased risk of SARS-CoV-2 infection and COVID-19 hospitalisation.

BACKGROUND:During the first year of the COVID-19 pandemic, diagnosis rates were reported to have declined for several autoimmune rheumatic diseases in numerous countries. It remains unclear whether diagnosis rates have since returned to pre-pandemic levels. This study aimed to evaluate diagnosis rates across autoimmune rheumatic diseases in the pre-pandemic and post-pandemic periods, with comparisons by disease, age group, sex, ethnicity, and socioeconomic status. METHODS:With NHS England approval, this population-level, observational cohort study used primary care and hospital admission data from all adults (aged ≥18 years) in England registered with general practices using TPP health record software, via the OpenSAFELY platform. Incident diagnosis rates for ten autoimmune rheumatic diseases were evaluated between April 1, 2016, and March 31, 2025. Expected diagnosis rates after pandemic onset (March, 2020) were modelled using Prophet time-series forecasting and compared with observed rates. FINDINGS:23 353 040 adults had data available for analysis and were included in the reference population. Mean age was 46·2 years (SD 18·8). 11 776 490 (50·4%) of 23 353 040 individuals were female and 11 576 545 (49·6%) were male; 18 581 585 (79·6%) were White, 1 923 140 (8·2%) were Asian or Asian British, 700 025 (3·0%) were Black or Black British, 332 490 (1·4%) were mixed ethnicity, 691 475 (3·0%) were Chinese or other ethnicity, and 1 124 325 (4·8%) had unknown ethnicity. In the first year of the pandemic, diagnosis rates sharply declined across all autoimmune rheumatic diseases except for small vessel vasculitis. As of March, 2025, cumulative reductions in diagnoses remained for psoriatic arthritis (-12 320 diagnoses, 95% prediction interval [PI] -12 780 to -11 860; percentage difference -24·9%, 95% PI -25·6 to -24·2), rheumatoid arthritis (-11 690 diagnoses, -13 280 to -10 110; -9·8%, -11·0 to -8·6), and giant cell arteritis (-5660 diagnoses, -6130 to -5200; -15·2%, -16·3 to -14·1). By contrast, axial spondyloarthritis diagnoses increased above pre-pandemic levels (2040 diagnoses, 1670 to 2410; 8·9%, 7·1 to 10·7), with diagnosis rates in women exceeding those in men from 2023 onwards. Early diagnostic deficits in connective tissue diseases were followed by compensatory increases during the pandemic recovery period. INTERPRETATION:Persistent reductions in diagnoses remain evident for psoriatic arthritis, rheumatoid arthritis, and giant cell arteritis 5 years after the onset of the pandemic; contrasting a post-pandemic increase in axial spondyloarthritis diagnoses, particularly in women. Data-driven disease surveillance could be used to identify drivers of these changes and address the long-term impact of delayed diagnosis. FUNDING:None.

BACKGROUND:Pharmacy First, a national community pharmacy service, launched in January 2024 to improve access to primary care for patients with minor conditions facing backlogs caused by the COVID-19 pandemic. Pharmacies are required to share details about their consultations with general practices. AIM:To describe how and what clinical activity was recorded in general practice during the first year of the Pharmacy First service. DESIGN AND SETTING:With the approval of NHS England, we conducted a retrospective cohort study between 31 January 2024 and 30 January 2025 using OpenSAFELY-TPP, a secure platform for analysing pseudonymised GP records from practices using TPP software. METHOD:We described patient demographics, Pharmacy First consultation trends, and the clinical conditions and medications coded with the consultations. RESULTS:A total of 402,165 Pharmacy First consultations were recorded for 340,710 patients from a general population of 26,142,380 registered patients in OpenSAFELY-TPP. Acute pharyngitis (28.9%) and uncomplicated urinary tract infection (28%) were the most frequently recorded conditions. By January 2025, 36.3% of recorded Pharmacy First consultations had a clinical condition, medication, or both. Females, younger adults and those living in more deprived areas were observed more often in Pharmacy First records compared to the general population. CONCLUSION:Increasing recording of the Pharmacy First community pharmacy service was observed in general practice records during its first year, particularly among younger and more deprived populations. However, variation in structured recording of consultation details may limit evaluation.

Electronic health records (EHRs) are a rich source of data which can be used to analyse health outcomes using computable phenotypes. With the approval of NHS England we used the OpenSAFELY secure analytics platform to design and assess phenotypes to classify three key respiratory viruses – respiratory syncytial virus (RSV), influenza, and COVID-19 – in English coded health data between September 2016 and August 2024. We compared specific and sensitive phenotypes to one another and to publicly available surveillance data. Cases from both phenotypes showed similar seasonal patterns to surveillance data. Sensitive phenotypes led to increased risk of misclassification than specific phenotypes for mild cases. For severe cases the risk of misclassification was higher in infants than for older adults, irrespective of the phenotype used. The phenotypes presented here offer a solution to classifying respiratory viruses from coded health records in the absence of testing information. ### Competing Interest Statement REC holds personal shares in AstraZeneca. ### Funding Statement EP was funded by National Institute for Health and Care Research (NIHR); grant number: NIHR303287. RME was supported by the Medical Research Council (MR/X033260/1), and the National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Health Analytics & Modelling, a partnership between the UK Health Security Agency, Imperial College London and LSHTM (grant code NIHR207404). CWG is supported by a Wellcome Career Development Award (225868/Z/22/Z). REC is funded by the Medical Research Council (MR/X033260/1), and the NIHR Health and Social Care Delivery Research programme (NIHR158218). EPKP is funded by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation (NIHR207408), a partnership between UK Health Security Agency and the London School of Hygiene and Tropical Medicine. EPKP has also received funding as a consultant for Tulane University as part of the Safe in Pregnancy and Childhood Study, supported by the Safety Platform for Emergency Vaccines, Task Force for Global Health, and The Coalition for Epidemic Preparedness Innovations. EPKP received reimbursements for providing input on an expert report for the UK COVID-19 Inquiry. WJH is funded by grants from the Wellcome Trust (311535/Z/24/Z) and NIHR (NIHR209347, NIHR206900). He also contributes to the NHS OpenSAFELY Data Analytics Service, funded by NHS England. Funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed are those of the author(s) and not necessarily those of the NIHR, UK Health Security Agency or the Department of Health and Social Care. The OpenSAFELY platform is principally funded by grants from NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z [2020-2024] and 311535/Z/24/Z [2025-2031]); The Medical Research Council (MRC) (MR/V015737/1 [2020-2021]). Additional contributions to OpenSAFELY have been funded by grants from: Medical Research Council (MRC) via the National Core Study programme Longitudinal Health and Wellbeing strand (MC\_PC\_20030, MC\_PC\_20059 [2020-2022]) and the Data and Connectivity strand (MC\_PC\_20058 [2021-2022]); The National Institute for Health Research (NIHR) and the Medical Research Council (MRC) via the CONVALESCENCE programme (COV-LT-0009, MC\_PC\_20051 [2021-2024]); NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Health Research Authority (IRAS ID: 340706) and London School of Hygiene & Tropical Medicine Ethics (29879). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data were linked, stored and analysed securely using the OpenSAFELY platform, https://www.opensafely.org/, as part of the NHS England OpenSAFELY COVID-19 service. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included. No GP data from patients who have registered a Type-1 Opt out with their GP surgery were included in this study. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. Primary care records managed by the GP software provider, TPP, were linked to hospital episode statistics (HES) and ONS death data through OpenSAFELY. NHS England is the data controller of the NHS England OpenSAFELY COVID-19 Service; TPP is the data processor; all study authors using OpenSAFELY have the approval of NHS England (83). This implementation of OpenSAFELY is hosted within the TPP environment which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant (84). Patient data has been pseudonymised for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the NHS England OpenSAFELY COVID-19 service is via a virtual private network (VPN) connection; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for anonymisation of results such as statistical disclosure control for low cell counts (85). The service adheres to the obligations of the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018. The service previously operated under notices initially issued in February 2020 by the the Secretary of State under Regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 (COPI Regulations), which required organisations to process confidential patient information for COVID-19 purposes; this set aside the requirement for patient consent (86). As of 1 July 2023, the Secretary of State has requested that NHS England continue to operate the Service under the COVID-19 Directions 2020 (87). In some cases of data sharing, the common law duty of confidence is met using, for example, patient consent or support from the Health Research Authority Confidentiality Advisory Group (88). Taken together, these provide the legal bases to link patient datasets using the service. GP practices, which provide access to the primary care data, are required to share relevant health information to support the public health response to the pandemic, and have been informed of how the service operates. All code is shared openly for review and re-use under MIT open license in the following repository: https://github.com/opensafely/disparities-comparison.

The interleukin-6 (IL-6) inhibitors tocilizumab and sarilumab have been repurposed for COVID-19 treatment. However, discrepancies exist across global and national COVID-19 guidelines, with limited data on the comparative effectiveness between these therapeutics especially during the delta/omicron periods. With the approval of NHS England and Public Health Scotland, we compared their effectiveness among adults hospitalised with COVID-19 using electronic health records data through the OpenSAFELY-TPP (England) and EAVE II (Scotland) platforms. Following the target trial emulation framework, 10,487 patients treated between July 2021 and February 2022, when both drugs were frequently prescribed, were included. In England, 1150 (20.1%) of 5710 participants receiving tocilizumab died by day 28 compared with 820 (20.4%) of 4025 participants receiving sarilumab (adjusted hazard ratio [aHR] 1.07, 95% CI 0.96-1.19). In Scotland, 114 (29.4%) of 388 participants receiving tocilizumab died by day 28 compared with 97 (27.0%) of 359 participants receiving sarilumab (aHR 0.92, 95% CI 0.68-1.23). There was no evidence of a difference in time to hospital discharge between the groups, and no credible effect modification by variant of concern, vaccination status, age, sex, ethnicity, body mass index, or comorbidities. Our findings provide supportive evidence for both drugs as alternative therapeutic options in COVID-19 in-patient management.

Abstract Background During the early phase of the COVID-19 pandemic in England, people with pre-existing conditions at severe clinical risk were advised to drastically reduce face-to-face contacts in a policy known as “shielding”. The impact of shielding in preventing COVID-19 hospitalisations and deaths has not been evaluated nationally using transmission-dynamic modelling. Methods With the approval of NHS England, we present a retrospective cohort evaluation of the shielding policy, drawing data from electronic health records (EHRs) for 24 million patients in England accessed through the OpenSAFELY platform. The study is from 1 January-1 December 2020, prior to vaccination and SARS-CoV-2 variants. We used a dynamic model of SARS-CoV-2 transmission, infection, and hospitalisation, stratified by age and shielding status for the general population (excluding care homes). We estimated transmission rates in the shielding and non-shielding groups using data from the CoMix social contact survey and fitted the model to hospitalisations and deaths in and outside hospital. Results We found the risk of hospitalisation was higher for shielding people at all ages and increased with age. The hospitalisation fatality risk was similar between shielding and non-shielding people from January to June 2020 and greater in shielding people from July 2020 onward. By comparing the observed epidemic to a counterfactual scenario without shielding, we projected that between 7800 and 10,600 hospitalisations and 2300 to 3500 deaths due to COVID-19 were directly averted by the policy, corresponding to reductions of 25% (24, 28%) and 23% (21, 25%), respectively, in the shielding population in England up to 1 December 2020. Including also the indirect effect in the non-shielding population, we projected 14,700 − 21,800 hospitalisations and 3700–5500 deaths due to COVID-19 were averted by the policy in the total population, each corresponding to reductions of 13% (11, 16%). Conclusions Based on our data and assumptions, we estimated the shielding policy reduced severe illness and mortality in clinically-extremely vulnerable shielding patients in England up to 1 December 2020, and, through indirectly-reduced exposure, also in the non-shielding population. Similar policies for other infections could have a comparable public health impact in reducing both mortality and pressure on health services.

OBJECTIVE:To evaluate temporal changes in rates of newly recorded diagnoses for 19 long term conditions in England in relation to the covid-19 pandemic by disease, age group, sex, socioeconomic status, and ethnicity. DESIGN:Population based cohort study. SETTING:Primary care and hospital admission data, with the approval of NHS England. PARTICIPANTS:29 995 025 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform. MAIN OUTCOME MEASURES:Temporal trends in age and sex standardised incident and prevalent diagnosis rates for 19 long term conditions between 1 April 2016 and 30 November 2024. Differences between expected and observed diagnosis rates after the onset of the covid-19 pandemic were compared using seasonal autoregressive integrated moving-average models, based on modelled projections of expected rates from pre-pandemic patterns. RESULTS:All 19 conditions showed a sharp decline in newly recorded diagnoses during the first year of the pandemic, followed by variable recovery. As of November 2024, cumulative reductions in diagnoses remained evident for conditions such as depression (734 800 (27.7%) fewer diagnoses than expected; 95% prediction interval (PI) 703 100 to 766 400), asthma (152 900 (16.4%) fewer diagnoses; 95% PI 137 500 to 168 300), chronic obstructive pulmonary disease (COPD) (90 100 (15.8%) fewer diagnoses; 95% PI 81 400 to 98 900), psoriasis (54 700 (17.1%) fewer diagnoses; 95% PI 50 100 to 59 200), and osteoporosis (54 100 (11.5%) fewer diagnoses; 95% PI 47 100 to 61 100). Conversely, diagnoses of chronic kidney disease have increased by 34.8% above expected levels during the pandemic recovery period, corresponding to 359 000 additional diagnoses (95% PI 333 500 to 384 500). Unadjusted subgroup analyses stratified by ethnicity and socioeconomic status indicated that, after an initial decrease, dementia diagnosis rates have risen above pre-pandemic levels for people of white ethnicity and in less deprived socioeconomic areas, but not for those from other ethnicities and more deprived areas. CONCLUSIONS:Since the covid-19 pandemic, there have been fewer diagnoses than expected for conditions such as depression, asthma, COPD, and osteoporosis, in contrast with a rapid increase in diagnoses of chronic kidney disease since 2022. Unadjusted analyses stratified by ethnicity and socioeconomic status suggest differential patterns of recovery, particularly for individuals with dementia. This study highlights the potential for near real time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and investigate inequities in healthcare.

ObjectivesAntibiotics are effective in treating bacterial infections, but they carry the risks of antimicrobial resistance and effectiveness loss. This study aimed to assess whether antibiotics for common infections are prescribed in a risk-based manner and how this changed during the COVID-19 pandemic.DesignCohort study of common infections and antibiotic prescribing.SettingWith the approval of NHS England, we accessed pseudonymised patient-level electronic health records of primary care data from The Phoenix Partnership through OpenSAFELY.ParticipantsWe included adults registered at general practices in England with a record of common infection, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI) and lower urinary tract infection (UTI), from January 2019 to March 2023. Patients with a record of COVID-19 were excluded.Main outcome measuresPatient-specific risks of infection-related hospital admission were estimated for each infection using risk prediction scores for patients who were not prescribed an antibiotic. The infection cohorts were then grouped into risk deciles, and probabilities of being prescribed an antibiotic were assessed.ResultsWe found 15,719,750 diagnoses of common infections. Of them, 450,215 (2.86%) were hospitalised in the 30 days after the diagnosis and 10,429,060 (66.34%) were prescribed an antibiotic. There were substantial differences in observed rates of hospital admissions between the lowest and highest risk deciles (25-fold difference in URTI). The probability of being prescribed an antibiotic for LRTI or UTI was unrelated to hospital admission risk, and that for URTI was weakly related to hospital admission risk. During the COVID-19 pandemic, the level of risk-based antibiotic prescribing reduced.ConclusionsThere is a need to better target antibiotics in primary care to patients with worse prognosis and strengthen treatment guidelines in personalisation of prescribing.

Objectives To evaluate temporal changes in the incidence and prevalence of 19 long-term conditions in England, quantifying the impact of the COVID-19 pandemic on diagnosis rates by disease, age group, sex, socioeconomic status, and ethnicity. Design Observational cohort study. Setting Primary care and hospital admission data, with the approval of NHS England. Participants 27,132,190 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform. Main outcomes measures Temporal trends in age and sex-standardised incidence and prevalence were evaluated for 19 long-term conditions between April 1, 2016, and November 30, 2024. Differences between expected and observed incidence rates after the onset of the COVID-19 pandemic were compared using seasonal autoregressive integrated moving-average models. Results Between March 2020 and November 2024, persistent large deficits in incident diagnoses were evident for depression (738,068 [28.0%] fewer diagnoses than expected; 95% CI 701,452 to 774,685), asthma (150,708 [16.0%] fewer diagnoses; 95% CI 133,300 to 168,117), COPD (84,084 [15.1%] fewer diagnoses; 95% CI 74,342 to 93,827), osteoporosis (78,891 [16.5%] fewer diagnoses; 95% CI 72,804 to 84,978) and psoriasis (56,231 [17.6%] fewer diagnoses; 95% CI 51,054 to 61,407). Conversely, post-pandemic diagnoses of chronic kidney disease (CKD) have increased by 32.7% above expected levels, corresponding to 325,996 additional diagnoses (95% CI 252,212 to 399,779). Dementia diagnoses have rebounded above pre-pandemic levels for individuals of White ethnicity and less deprived socioeconomic quintiles, but remain lower than expected for individuals from other ethnicities and more deprived communities. Conclusions There has been a lasting and disproportionate impact of the pandemic on conditions including depression, asthma, COPD and osteoporosis, contrasting a post-pandemic surge in CKD diagnoses. Analyses stratified by ethnicity and socioeconomic status reveal inequity in the recovery from the pandemic, particularly for individuals with dementia. Importantly, this study demonstrates the potential for near real-time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and address healthcare disparities. What is already known What this study adds ### Competing Interest Statement The authors declare the following: JBG has received honoraria from Abbvie, Biovitrum, BMS, Celgene, Chugai, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, Roche, Sanofi, Sobi and UCB; and grant funding from Sandoz UK. MDR has received honoraria from AbbVie, Galapagos, Johnson & Johnson, Lilly, Menarini, Novartis, UCB and Viforpharma; grant funding from Sandoz UK; advisory board fees from Biogen; consultation fees from Pfizer; and support for attending educational meetings from Lilly, Pfizer, Johnson & Johnson and UCB. APC has received grant funding from BMS; consulting fees from Galvani/GSK, BMS, UCB, Janssen; honoraria from Galapagos, AbbVie, BMS; support for attending meetings from AbbVie; and has participated in a data/advisory board for GSK/Galvini. KB has received grant funding from NIHR; honoraria from Galapagos, UCB and Viforpharma; and educational support from UCB. EA has received support for attending meetings from UCB. BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. No other authors reported relationships or activities that could appear to have influenced the submitted work. ### Funding Statement The OpenSAFELY platform is principally funded by grants from NHS England [2023-2025]; the Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC\_PC\_20030, MC\_PC\_20059) [2020-2022] and the Data and Connectivity strand (MC\_PC\_20058) [2021-2022]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC\_PC\_20051) [2021-2024]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. MDR is funded by an NIHR Clinical Lectureship. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), the Department of Health and Social Care, or other funders. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval to undertake this study under the remit of service evaluation was obtained from Kings College Hospital NHS Foundation Trust, London. No further ethical approval was required as per UK Health Research Authority guidance. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data were linked, stored and analysed securely using the OpenSAFELY platform, [https://www.opensafely.org/,][1] as part of the NHS England OpenSAFELY COVID-19 service. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included. No GP data from patients who have registered a Type-1 opt-out with their GP surgery were included in this study. All code is shared openly for review and re-use under MIT open license . Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in the NHS England OpenSAFELY COVID-19 service is drawn from General Practice data across England where TPP is the data processor. TPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. University of Oxford, Bennett Institute for Applied Data Science developers and PIs, who hold contracts with NHS England, have access to the OpenSAFELY pseudonymised data tables to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline — from raw data to completed results for this analysis — and for the OpenSAFELY platform as a whole is available for review at github.com/OpenSAFELY. The data management and analysis code for this paper was led by MDR and contributed to by JBG. [1]: https://www.opensafely.org/

BACKGROUND:We assessed the safety and effectiveness of the first- and second-dose BNT162b2 COVID-19 vaccination, offered as part of the national COVID-19 vaccine roll-out from September 2021, in children and adolescents in England. METHODS:Our observational study using OpenSAFELY-TPP, included adolescents aged 12-15 years and children aged 5-11 years. It compared individuals receiving (1) the first vaccination to unvaccinated controls and (2) the second vaccination to single-vaccinated controls. We matched vaccinated individuals with controls on age, sex, and other important characteristics. Outcomes were positive SARS-CoV-2 test (adolescents only), COVID-19 accident and emergency (A&E) attendance, COVID-19 hospitalization, COVID-19 critical care admission, and COVID-19 death; with safety outcomes, A&E attendance, unplanned hospitalization, pericarditis, and myocarditis. RESULTS:Among 820,926 previously unvaccinated adolescents, 20-week incidence rate ratios (IRRs) comparing vaccination with no vaccination were 0.74 for positive SARS-CoV-2 test, 0.60 for COVID-19 A&E attendance, and 0.58 for COVID-19 hospitalization. Among 441,858 adolescents who had received the first vaccination, IRRs comparing second dose with single-vaccination were 0.67 for positive SARS-CoV-2 test, 1.00 for COVID-19 A&E attendance, and 0.60 for COVID-19 hospitalization. In both children groups, COVID-19-related outcomes were too rare to allow IRRs to be estimated precisely. Across all analyses, there were no COVID-19-related deaths, and fewer than seven COVID-19-related critical care admissions. Myocarditis and pericarditis were documented only in the vaccinated groups, with rates of 27 and 10 cases/million after the first and second doses, respectively. CONCLUSIONS:BNT162b2 vaccination in adolescents reduced COVID-19 A&E attendance and hospitalization, although these outcomes were rare. Protection against positive SARS-CoV-2 tests was transient.

BACKGROUND:UK COVID-19 lockdowns significantly affected primary care access and delivery. Little is known about whether lockdowns disproportionally impacted vulnerable groups, including people who misuse substances, people who have experienced domestic violence or abuse, those with intellectual disability, and children with safeguarding concerns. AIM:To evaluate the impact of UK COVID-19 lockdowns on primary care contact rates among vulnerable groups. DESIGN & SETTING:Natural experimental design using all registered patients in the OpenSAFELY platform. METHOD:With approval from NHS England, we conducted controlled interrupted time-series analyses on records from 24 million patients in England between September 2019 and September 2021. RESULTS:Pre-pandemic, primary care contact rates were 110.1 per 1000 patients per week. Following the initiation of the first lockdown (23 March 2020), there was a large reduction of 29-61 contacts per 1000 patients per week among vulnerable and general population groups. For patients with alcohol misuse, those aged ≥14 years with intellectual disability, and children with safeguarding concerns, this reduction was significantly more extreme than corresponding general populations (relative rate difference -23.8 [95% confidence interval {CI} = -39.8 to -7.7, P = 0.003], -24.6 [95% CI = -38.8 to -10.5, P<0.001], and -15.4 [95% CI = -26.9 to -3.8, P = 0.009], respectively). Following the final lockdown (29 March 2021), all groups had contact rates exceeding pre-pandemic rates (with increases more marked in vulnerable populations), except those only including children. CONCLUSION:Our results suggested a larger short-term impact of the first COVID-19 lockdown on primary care contact for some vulnerable groups, compared with the general population, and differential impacts persisted through subsequent lockdowns and beyond for some vulnerable groups. There is a need to examine drivers of these differences to enable more equitable primary care access and provision.

Background Healthcare services in England experience increased pressure during winter months due to seasonal infectious diseases, increased multimorbidity, and fluctuating demand. Understanding how characteristics of general practices, and their registered patient case-mix contribute to secondary care use—particularly for Ambulatory Care Sensitive Conditions (ACSCs)—is essential for planning and resource allocation. Primary and secondary care activity also significantly changed during the COVID-19 pandemic, and not all activity-types have returned to pre-pandemic levels in the years since, making it critical to examine trends across both pre-and post-pandemic periods. Methods OpenSAFELY-TPP was used to access linked electronic health record data, covering approximately 2,600 general practices (about 40% of all practices in England) and 26 million registered patients in England using TPP SystmOne software (2018-2025). Our analysis focused on weekly and aggregated rates of A&E attendances and hospital admissions during the flu and winter months (October to February), comparing patterns before and after the COVID-19 pandemic. Practice-level exposures included consultation rate per capita, practice size, region, and patient case-mix variables (e.g. age, sex, ethnicity, deprivation, multimorbidity). Outcomes included weekly rates of A&E attendances, total hospital admissions, and admissions for ACSCs. Analyses We will summarise variation in practice characteristics, registered patient sociodemographics, case-mix, and service use across time periods. Associations between exposures and outcomes will be examined using generalised linear models, with additional subgroup analyses by age distribution. Sensitivity analyses will assess alternative flu season definitions and account for holiday and extreme weather. Discussion This high-level descriptive study will provide valuable insights into variation in secondary care use across general practices and identify practice-level and case-mix factors that may contribute to winter pressures. The inclusion of both pre- and post-pandemic data will provide essential benchmarking data for future health system planning and further understanding of how the general practice context and patient case-mix affects hospital demand.