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Background: Health care burden attributable to Dupuytren disease (DD) is largely unknown. The authors determined (1) the prevalence and incidence of DD, (2) the incidence of first surgical intervention, and (3) the lifetime risk of surgical intervention in the United Kingdom National Healthcare Service.Methods: In this population-based dynamic cohort analysis, data of the Clinical Practice Research Datalink was linked to Hospital Episode Statistics, to characterize the diagnosis and surgical treatment of DD. Secular trends of incidence of DD diagnosis and first surgical treatment were calculated for 2000 to 2013. A multistate Markov model was designed to estimate the lifetime risk of first surgical intervention.Results: A total of 10,553,454 subjects were included in the analyses, 5,502,879 (52%) of whom were women. Of these, 38,707 DD patients were identified. Point prevalence in 2013 was 0.67% (99% CI, 0.66 to 0.68). The incidence of DD almost doubled from 0.30 (99% CI, 0.28 to 0.33) per 1000 person-years in 2000, to 0.59 (99% CI, 0.56 to 0.62) per 1000 person-years in 2013. The incidence of first surgical intervention similarly increased from 0.29 (99% CI, 0.23 to 0.37) to 0.88 (99% CI, 0.77 to 1.00) in the same period. A man or woman newly diagnosed with DD at age 65 has a lifetime risk of surgical intervention of 23% and 13%, respectively, showing only a very subtle decrease when diagnosed later in life.Conclusions: DD is an important health condition in the older population, because prevalence and incidence rates have almost doubled in the past decade. Estimated lifetime risk of surgical treatment is relatively low, but almost twice in men compared with women. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Background: There are few data on the incidence of thrombosis among COVID-19 cases, with most research concentrated on hospitalised patients. We aimed to estimate the incidence of venous thromboembolism, arterial thromboembolism, and death among COVID-19 cases and to assess the impact of these events on the risks of hospitalisation and death. Methods: We conducted a distributed network cohort study using primary care records from the Netherlands, Italy, Spain, and the UK, and outpatient specialist records from Germany. The Spanish database was linked to hospital admissions. Participants were followed up from the date of a diagnosis of COVID-19 or positive RT-PCR test for SARS-CoV-2 (index date) for 90 days. The primary study outcomes were venous thromboembolic events, arterial thromboembolic events, and death, all over the 90 days from the index date. We estimated cumulative incidences for the study outcomes. Multistate models were used to calculate adjusted hazard ratios (HRs) for the association between venous thromboembolism or arterial thromboembolism occurrence and risks of hospitalisation or COVID-19 fatality. Findings: Overall, 909 473 COVID-19 cases and 32 329 patients hospitalised with COVID-19 on or after Sept 1, 2020, were studied. The latest index dates across the databases ranged from Jan 30, 2021, to July 31, 2021. Cumulative 90-day incidence of venous thromboembolism ranged from 0·2% to 0·8% among COVID-19 cases, and up to 4·5% for those hospitalised. For arterial thromboembolism, estimates ranged from 0·1% to 0·8% among COVID-19 cases, increasing to 3·1% among those hospitalised. Case fatality ranged from 1·1% to 2·0% among patients with COVID-19, rising to 14·6% for hospitalised patients. The occurrence of venous thromboembolism in patients with COVID-19 was associated with an increased risk of death (adjusted HRs 4·42 [3·07–6·36] for those not hospitalised and 1·63 [1·39–1·90] for those hospitalised), as was the occurrence of arterial thromboembolism (3·16 [2·65–3·75] and 1·93 [1·57–2·37]). Interpretation: Risks of venous thromboembolism and arterial thromboembolism were up to 1% among COVID-19 cases, and increased with age, among males, and in those who were hospitalised. Their occurrence was associated with excess mortality, underlying the importance of developing effective treatment strategies that reduce their frequency. Funding: European Medicines Agency.

Background:Thrombosis with thrombocytopenia syndrome (TTS) has been reported among individuals vaccinated with adenovirus-vectored COVID-19 vaccines. In this study, we describe the background incidence of non-vaccine induced TTS in six European countries. Methods:Electronic medical records from France, the Netherlands, Italy, Germany, Spain, and the United Kingdom informed the study. Incidence rates of cerebral venous sinus thrombosis (CVST), splanchnic vein thrombosis (SVT), deep vein thrombosis (DVT), pulmonary embolism (PE), and myocardial infarction or ischemic stroke, all with concurrent thrombocytopenia, were estimated among the general population of persons in a database between 2017 and 2019. A range of additional potential adverse events of special interest for COVID-19 vaccinations were also studied in a similar manner. Findings:A total of 38 611 617 individuals were included. Background rates ranged from 1.0 (95% CI: 0.7–1.4) to 8.5 (7.4–9.9) per 100 000 person-years for DVT with thrombocytopenia, from 0.5 (0.3–0.6) to 20.8 (18.9–22.8) for PE with thrombocytopenia, from 0.1 (0.0–0.1) to 2.5 (2.2–2.7) for SVT with thrombocytopenia, and from 1.0 (0.8–1.2) to 43.4 (40.7–46.3) for myocardial infarction or ischemic stroke with thrombocytopenia. CVST with thrombocytopenia was only identified in one database, with incidence rate of 0.1 (0.1–0.2) per 100 000 person-years. The incidence of non-vaccine induced TTS increased with age, and was typically greater among those with more comorbidities and greater medication use than the general population. It was also more often seen in men than women. A large proportion of those affected were seen to have been taking antithrombotic and anticoagulant therapies prior to their event. Interpretation:Although rates vary across databases, non-vaccine induced TTS has consistently been seen to be a very rare event among the general population. While still remaining very rare, rates were typically higher among older individuals, and those affected were also seen to generally be male and have more comorbidities and greater medication use than the general population.

Despite much research on the topic, little work has been done comparing the use of methods to control for confounding in the estimation of COVID-19 vaccine effectiveness in routinely collected medical record data. We conducted a trial emulation study to replicate the ChAdOx1 (Oxford/AstraZeneca) and BNT162b2 (BioNTech/Pfizer) COVID-19 phase 3 efficacy studies. We conducted a cohort study including individuals aged 75+ from UK CPRD AURUM (N = 916,128) in early 2021. Three different methods were assessed: Overlap weighting, inverse probability treatment weighting, and propensity score matching. All three methods successfully replicated the findings from both phase 3 trials, and overlap weighting performed best in terms of confounding, systematic error, and precision. Despite lack of trial data beyond 3 weeks, we found that even 1 dose of BNT162b2 was effective against SARS-CoV-2 infection for up to 12 weeks before a second dose was administered. These results support the UK Joint Committee on Vaccination and Immunisation modelling and related UK vaccination strategies implemented in early 2021.Key messagesReal world evidence generated using weighting (overlapping weights and inverse probability of treatment weights) and propensity score matching: all methods successfully replicate the findings of Phase 3 trials for COVID-19 vaccine effectiveness.Overlap weighting provides the least biased estimates in our study and should be considered amongst the most suitable methods for future COVID-19 vaccine effectiveness research.Despite a lack of trial data, our findings suggest that first-dose BNT162b2 provides effective protection against SARS-COV-2 infection for up to 12 weeks, in line with UK’s Joint Committee on Vaccination and Immunisation modelling and subsequent vaccination strategies.

Background:Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. Methods:Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum®De-identified Clinformatics®Data Mart Database using L1- regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. Findings:Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC Interpretation:We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. Funding:This activity under the European Health Data&Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Under- taking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.

Objective:Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma. Methods:We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients (‘diagnosed’ and ‘hospitalized’) based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results:The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6–8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7–6.8) to 18.5% (95% CI 18.2–18.8) in the diagnosed cohort and 5.2% (95% CI 4.0–6.8) to 20.5% (95% CI 18.6–22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8–2.4) to 16.9% (95% CI 13.8–20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15–30% of hospitalized COVID-19 asthma patients. Conclusion:The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.

Objectives:To estimate threshold prices for computer- and robot-assisted knee and hip replacement. Methods:A lifetime cohort Markov model provided the framework for analysis. Linked primary care and inpatient hospital records informed estimates of outcomes under current practice. Outcomes were estimated under a range of hypothetical relative improvements in quality of life if unrevised and in revision risk after computer or robot-assisted surgery. Threshold prices, a price at which the net health benefit from funding the intervention would be zero, for these improvements were estimated for a cost-effectiveness threshold of £20 000 per additional quality-adjusted life-year (QALY) gained. Results:For average patient profiles under current knee and hip replacement practice, lifetime QALYs were 10.3 (9.9 to 10.7) and 11.0 (10.6 to 11.4), with costs of £6060 (£5947 to £6203) and £6506 (£6335 to £6710) for knee and hip replacement, respectively. A combined 50% relative reduction in risk of revision and 5% improvement in postoperative quality of life if unrevised would, for example, result in QALYs increasing to 10.9 (10.4 to 11.3) and 11.6 (11.2 to 12.0), and costs falling to £5880 (£5816 to £5956) and £6258 (£6149 to £6376) after knee and hip replacement, respectively. These particular improvements would have an associated threshold price of £11 182 (£10 691 to £11 721) for knee replacement and £12 134 (£11 616 to £12 701) for hip replacement. The 50% reduction in revision rate alone would have associated threshold prices of £1094 (£788 to £1488) and £1347 (£961 to £1842), and the 5% improvement in quality of life alone would have associated threshold prices of £9911 (£9476 to £10 296) and £10 578 (£10 171 to £10 982). Conclusions:At current prices, computer- and robot-assisted knee and hip replacement will likely need to lead to improvements in patient-reported outcomes in addition to any reduction in the risk revision.

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primà ria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

ObjectiveTo investigate how trends in incidence of anxiety and depressive disorders have been affected by the COVID-19 pandemic.DesignPopulation-based cohort study.SettingRetrospective cohort study from 2018 to 2021 using the Information System for Research in Primary Care (SIDIAP) database in Catalonia, Spain.Participants3 640 204 individuals aged 18 or older in SIDIAP on 1 March 2018 with no history of anxiety and depressive disorders.Primary and secondary outcomes measuresThe incidence of anxiety and depressive disorders during the prelockdown period (March 2018–February 2020), lockdown period (March–June 2020) and postlockdown period (July 2020–March 2021) was calculated. Forecasted rates over the COVID-19 periods were estimated using negative binomial regression models based on prelockdown data. The percentage of reduction was estimated by comparing forecasted versus observed events, overall and by sex, age and socioeconomic status.ResultsThe incidence rates per 100 000 person-months of anxiety and depressive disorders were 151.1 (95% CI 150.3 to 152.0) and 32.3 (31.9 to 32.6), respectively, during the prelockdown period. We observed an increase of 37.1% (95% prediction interval 25.5 to 50.2) in incident anxiety diagnoses compared with the expected in March 2020, followed by a reduction of 15.8% (7.3 to 23.5) during the postlockdown period. A reduction in incident depressive disorders occurred during the lockdown and postlockdown periods (45.6% (39.2 to 51.0) and 22.0% (12.6 to 30.1), respectively). Reductions were higher among women during the lockdown period, adults aged 18–34 years and individuals living in the most deprived areas.ConclusionsThe COVID-19 pandemic in Catalonia was associated with an initial increase in anxiety disorders diagnosed in primary care but a reduction in cases as the pandemic continued. Diagnoses of depressive disorders were lower than expected throughout the pandemic.

BackgroundFew datasets have been established that capture the full breadth of COVID-19 patient interactions with a health system. Our first objective was to create a COVID-19 dataset that linked primary care data to COVID-19 testing, hospitalisation, and mortality data at a patient level. Our second objective was to provide a descriptive analysis of COVID-19 outcomes among the general population and describe the characteristics of the affected individuals.MethodsWe mapped patient-level data from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). More than 3,000 data quality checks were performed to assess the readiness of the database for research. Subsequently, to summarise the COVID-19 population captured, we established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or positive test results for SARS-CoV-2, hospitalisations with COVID-19, and COVID-19 deaths during follow-up, which went up until 30th June 2021.FindingsMapping data to the OMOP CDM was performed and high data quality was observed. The mapped database was used to identify a total of 5,870,274 individuals, who were included in the general population cohort as of 1st March 2020. Over follow up, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation with COVID-19, 5,642 had an ICU admission with COVID-19, and 11,233 had a COVID-19 death. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised in general and those who died.InterpretationWe have established a comprehensive dataset that captures COVID-19 diagnoses, test results, hospitalisations, and deaths in Catalonia, Spain. Extensive data checks have shown the data to be fit for use. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19 outcomes over time were described.FundingGeneralitat de Catalunya and European Health Data and Evidence Network (EHDEN).

OBJECTIVEWe aimed to study the association between COVID-19 vaccines, SARS-CoV-2 infection, and the risk of immune-mediated neurological events.METHODSDesignPopulation-based historical rate comparison study and self-controlled case series (SCCS) analysis.SettingPrimary care records from the United Kingdom.ParticipantsIndividuals who received the first dose of ChAdOx1 or BNT162b2 between 8 December 2020 and 6 March 2021. A cohort with a first positive RT-PCR test for SARS-CoV-2 between 1 September 2020 and 28 February 2021 was used for comparison.Main outcome measuresOutcomes included Guillain-Barre syndrome (GBS), Bell’s palsy, encephalomyelitis, and transverse myelitis.Incidence rates were estimated in the 28 days post first-dose vaccine, 90 days post-COVID-19, and between 2017 to 2019 for the general population cohort for background rates. Indirectly standardised incidence ratios (SIRs) were estimated. Adjusted incidence rate ratios (IRR) were estimated from the SCCS when sufficient statistical power was reached.ResultsWe included 1,868,767 ChAdOx1 and 1,661,139 BNT162b2 vaccinees; 299,311 people infected with COVID-19; and 2,290,537 from the general population. SIRs for GBS were 1.91 [95% CI: 0.86 to 4.26] after ChAdOx1, 1.29 [0.49 to 3.45] after BNT162b2, and 5.20 [1.95 to 13.85] after COVID-19. In the same cohorts, SIRs for Bell’s palsy were 1.34 [1.05 to 1.72], 1.15 [0.88 to 1.50], and 1.23 [0.80 to 1.89], and for encephalomyelitis 1.62 [0.61 to 4.31], 0.86 [0.22 to 3.46], and 11.05 [5.27 to 23.17], respectively. Transverse myelitis was too rare to analyse (nConclusionsWe found no consistent association between either vaccine and any of the studied neuroimmune adverse events studied. Conversely, we found a 5-fold increase in risk of GBS and an 11-fold of encephalomyelitis following COVID-19.

The relationship between cancer and coronavirus disease 2019 (COVID‐19) infection and severity remains poorly understood. We conducted a population‐based cohort study between 1 March and 6 May 2020 describing the associations between cancer and risk of COVID‐19 diagnosis, hospitalisation and COVID‐19‐related death. Data were obtained from the Information System for Research in Primary Care (SIDIAP) database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non‐melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID‐19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID‐19 diagnosis) and COVID‐19‐related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (, What's new? Studies addressing associations between cancer and severity of coronavirus disease 2019 (COVID‐19) have focused primarily on hospitalized patients. Findings have been inconsistent, however, owing to varying cancer criteria, lack of representative samples, and other factors. Here, the natural history of COVID‐19 in cancer patients during the first wave of the pandemic in 2020 in Spain was investigated in a large, representative cohort with a heterogenous cancer population. Patients with cancer were at increased risk of severe COVID‐19. Risk was notably high among those over age 70 and those with recent cancer diagnosis, hematological cancer, or lung and bladder cancer.

Population-based studies can provide important evidence on the safety of COVID-19 vaccines. Using data from the United Kingdom, here we compare observed rates of thrombosis and thrombocytopenia following vaccination against SARS-CoV-2 and infection with SARS-CoV-2 with background (expected) rates in the general population. First and second dose cohorts for ChAdOx1 or BNT162b2 between 8 December 2020 and 2 May 2021 in the United Kingdom were identified. A further cohort consisted of people with no prior COVID-19 vaccination who were infected with SARS-Cov-2 identified by a first positive PCR test between 1 September 2020 and 2 May 2021. The fourth general population cohort for background rates included those people in the database as of 1 January 2017. In total, we included 3,768,517 ChAdOx1 and 1,832,841 BNT162b2 vaccinees, 401,691 people infected with SARS-CoV-2, and 9,414,403 people from the general population. An increased risk of venous thromboembolism was seen after first dose of ChAdOx1 (standardized incidence ratio: 1.12 [95% CI: 1.05 to 1.20]), BNT162b2 (1.12 [1.03 to 1.21]), and positive PCR test (7.27 [6.86 to 7.72]). Rates of cerebral venous sinus thrombosis were higher than otherwise expected after first dose of ChAdOx1 (4.14 [2.54 to 6.76]) and a SARS-CoV-2 PCR positive test (3.74 [1.56 to 8.98]). Rates of arterial thromboembolism after vaccination were no higher than expected but were increased after a SARS-CoV-2 PCR positive test (1.39 [1.21 to 1.61]). Rates of venous thromboembolism with thrombocytopenia were higher than expected after a SARS-CoV-2 PCR positive test (5.76 [3.19 to 10.40]).

ObjectivesTo investigate how incidence trends of anxiety and depressive disorders have been affected by the COVID-19 pandemic.DesignPopulation-based cohort study.SettingObservational cohort study from 2018 to 2021 using the Information System for Research in Primary Care (SIDIAP) database in Catalonia, Spain.Participants4,255,847 individuals aged 18 or older in SIDIAP on 1 March, 2018 with no prior history of anxiety and depressive disorders.Primary and secondary outcomes measuresIncidence of anxiety and depressive disorders prior to COVID-19 (March, 2018 to February, 2020), during the COVID-19 lockdown (March to June, 2020) and post-lockdown periods (from July, 2020 to March, 2021) were calculated. Forecasted rates over COVID-19 periods were estimated using negative binomial regression models based on previous data. The percentage reduction was estimated by comparing forecasted versus observed events, overall and by age, sex and socioeconomic status.ResultsThe incidence rates per 100,000 person-months of anxiety and depressive disorders were 171.0 (95%CI: 170.2-171.8) and 46.6 (46.2-47.0), respectively, during the pre-lockdown period. We observed an increase of 39.7% (95%PI: 26.5 to 53.3) in incident anxiety diagnoses compared to the expected in March, 2020, followed by a reduction of 16.9% (8.6 to 24.5) during the post-lockdown periods. A reduction of incident depressive disorders occurred during the lockdown and post-lockdown periods (46.6% [38.9 to 53.1] and 23.2% [12.0 to 32.7], respectively). Reductions were higher among adults aged 18 to 34 and individuals living in most deprived areas.ConclusionsThe COVID-19 pandemic in Catalonia was associated with an initial increase in anxiety disorders diagnosed in primary care, but a reduction in cases as the pandemic continued. Diagnoses of depressive disorders were lower than expected throughout the pandemic.Summary boxWhat is already known on this topic-While previous self-reported studies have provided evidence of increased mental health burden during the initial phase of the COVID-19 pandemic, a number of studies observed that fewer diagnoses were made in primary care settings than would have been expected during the initial stages of the pandemic.-Population data that examine the impact of COVID-19 on temporal trends of incident cases of common mental health disorders are lacking in Catalonia, Spain.What this study adds-This study has quantified the impact of the COVID-19 pandemic on trends of incidence of anxiety and depressive disorders among adults living in Catalonia.-Reductions in incident cases of anxiety and depressive disorders were higher for young adults and people living in most deprived areas.-Incident diagnoses of anxiety and depressive disorders have not been fully recovered to what would have been expected.

ObjectivesTo calculate the observed rates of thrombosis and thrombocytopenia following vaccination against SARS-CoV-2, infection with SARS-CoV-2, and to compare them to background (expected) rates in the general population.DesignCohort study using routinely collected primary care records.SettingRoutine practice in the United Kingdom.ParticipantsTwo mutually exclusive vaccinated cohorts included people vaccinated with either ChAdOx1 or BNT162b2 between 8 December 2020 and 6 March 2021. A third cohort consisted of people newly infected with SARS-Cov-2 identified by a first positive RT-PCR test between 1 September 2020 and 28 February 2021. The fourth general population cohort for background rates included those people with a visit between 1 January 2017 and 31 December 2019. In total, we included 1,868,767 ChAdOx1 and 1,661,139 BNT162b2 vaccinees, 299,311 people infected with SARS-CoV-2, and 2,290,537 people from the general population.InterventionsFirst-dose of either ChAdOx1 or BNT162b2Main outcome measuresOutcomes included venous thrombosis, arterial thrombosis, thrombocytopenia, and thrombosis with thrombocytopenia. Outcome rates were estimated for recipients of the ChAdOx1 or BNT162b2 vaccines, for people infected with SARS-CoV-2, and background rates in the general population. Indirectly standardized incidence ratios (SIR) were estimated.ResultsWe included 1,868,767 ChAdOx1 and 1,661,139 BNT162b2 vaccinees, 299,311 people infected with SARS-CoV-2, and 2,290,537 people from the general population for background rates. The SIRs for pulmonary embolism were 1.23 [95% CI, 1.09-1.39] after vaccination with ChAdOx1, 1.21 [1.07-1.36] after vaccination with BNT162b2, and 15.31 [14.08 to 16.65] for infection with SARS-CoV-2. The SIRs for thrombocytopenia after vaccination were 1.25 [1.19 to 1.31] for ChAdOx1 and 0.99 (0.94 to 1.04) for BNT162b2. Rates of deep vein thrombosis and arterial thrombosis were similar among those vaccinated and the general population.ConclusionsChAdOx1 and BNT162b2 had broadly similar safety profiles. Thrombosis rates after either vaccine were mostly similar to those of the general population. Rates of pulmonary embolism increased 1.2-fold after either vaccine and 15-fold with SARS-CoV-2 infection. Thrombocytopenia was more common among recipients of ChAdOx1 but not of BNT162b2.Summary boxWhat is already known on this topicSpontaneous reports of unusual and severe thrombosis with thrombocytopenia syndrome (TTS) raised concerns regarding the safety of adenovirus-based vaccines against SARS-CoV-2In a cohort study including over 280,000 people aged 18-65 years vaccinated with ChAdOx1 in Denmark and Norway, Pottegård et al reported increased rates of venous thromboembolic events as well as thrombocytopenia among vaccine recipients.What this study addsIn this cohort study, ChAdOx1 and BNT162b2 were seen to have broadly similar safety profiles.Rates of thrombosis after either vaccine were generally similar to those of the general population. Rates of pulmonary embolism were though 1.2-fold higher than background rates after either vaccine, which compared to 15-fold higher after SARS-CoV-2 infection.Thrombocytopenia was more common among recipients of ChAdOx1 but not of BNT162b2.

Background A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. Methods We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. Results We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. Conclusion We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

ObjectivesConcern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.MethodsWe performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2

Objectives To compare outcomes of total knee replacement (TKR) and total hip replacement (THR) for individuals with RA and OA. Methods We performed a cohort study using routinely collected data. Oxford Knee Score, Oxford Hip Score, and EuroQol 5-dimension 3-level (EQ-5D-3L) questionnaires were collected before and 6 months after surgery. Multivariable regressions were used to estimate the association between diagnosis and post-operative scores after controlling for pre-operative scores and patient characteristics. Results Study cohorts included 2070 OA and 142 RA patients for TKR and 2030 OA and 98 RA patients for THR. Following TKR, the median Oxford Knee Score was 37 [interquartile range (IQR) 29–43] for OA and 36 (27–42) for RA while the median EQ-5D-3L was 0.76 (0.69–1.00) and 0.69 (0.52–0.85), respectively. After THR, the Oxford Hip Score was 42 (IQR 36–46) for OA and 39 (30–44) for RA while the EQ-5D-3L was 0.85 (0.69–1.00) and 0.69 (0.52–1.00), respectively. The estimated effect of RA, relative to OA, on post-operative scores was −0.05 (95% CI −1.57, 1.48) for the Oxford Knee Score, −0.09 (−0.13, −0.06) for the EQ-5D-3L following TKR, −1.35 (−2.93, −0.22) for the Oxford Hip Score, and −0.08 (−0.12, −0.03) for the EQ-5D-3L following THR. Conclusion TKR and THR led to substantial improvements in joint-specific scores and overall quality of life. While diagnosis had no clinically meaningful effect on joint-specific outcomes, improvements in general quality of life were somewhat less for those with RA, which is likely due to the systemic and multijoint nature of rheumatoid disease.

Background Thrombosis with thrombocytopenia syndrome (TTS) has been reported among individuals vaccinated with adenovirus-vectored COVID-19 vaccines. In this study we describe the background incidence of TTS in 6 European countries. Methods Electronic medical records from France, Netherlands, Italy, Germany, Spain, and the United Kingdom informed the study. Incidence rates of cerebral venous sinus thrombosis (CVST), splanchnic vein thrombosis (SVT), deep vein thrombosis (DVT), pulmonary embolism (PE), and stroke, all with concurrent thrombocytopenia, were estimated among the general population between 2017 to 2019. A range of additional adverse events of special interest for COVID-19 vaccinations were also studied in a similar manner. Findings A total of 20,599,134 individuals were included. Background rates ranged from 1.0 (0.7 to 1.4) to 1.5 (1.0 to 2.0) per 100,000 person-years for DVT with thrombocytopenia, from 0.5 (0.3 to 0.6) to 1.4 (1.1 to 1.8) for PE with thrombocytopenia, from 0.1 (0.0 to 0.1) to 0.7 (0.5 to 0.9) for SVT with thrombocytopenia, and from 0.2 (0.0 to 0.4) to 4.4 (3.9 to 5.0) for stroke with thrombocytopenia. CVST with thrombocytopenia was only identified in one database, with incidence rate of 0.1 (0.0 to 0.2) per 100,000 person-years. The incidence of TTS increased with age, with those affected typically having more comorbidities and greater medication use than the general population. TTS was also more often seen in men than women. A sizeable proportion of those affected were seen to have been taking antithrombotic and anticoagulant therapies prior to their TTS event. Interpretation Although rates vary across databases, TTS has consistently been seen to be a very rare event among the general population. While still very rare, rates of TTS are typically higher among older individuals, and those affected were also seen to generally be male and have more comorbidities and greater medication use than the general population. Funding This study was funded by the European Medicines Agency (EMA/2017/09/PE Lot 3). Research in context Evidence before this study We searched PubMed to identify studies that have previously described the background incidence of the study outcomes. Estimates of the incidence of cerebral venous sinus thrombosis (CVST) among the general population have ranged from 0.3 to 2 per 100,000 person-years. The background incidence of splanchnic vein thrombosis (SVT) is not well-known, although the incidence of portal vein thrombosis, the most commonly involved vein, has been estimated at around 3 per 100,000 person-years. Deep vein thrombosis (DVT) and pulmonary embolism (PE) are far more common with estimates of their incidence among the general population typically around 100 and 60 per 100,000 person-years respectively, while the incidence of stroke is typically estimated to be above 100 per 100,000 person-years. The background incidence of these events with concurrent thrombocytopenia has not, however, previously been described in detail. Added value of this study Based on spontaneous reports from the US, the United Kingdom, and the European Union, thrombosis with thrombocytopenia syndrome (TTS) has been raised as a serious, albeit rare, adverse event that may be caused by adenovirus-vectored COVID-19 vaccines. Assessing this safety signal requires a consideration of whether the rates of TTS being reported after vaccinations against SARS-CoV-2 differ from those normally seen among the general population in the absence of any such vaccinations. This study brings together data from six European countries to estimate the background incidence of TTS. We used six large databases of routinely-collected data allowing for sufficient sample size to study TTS across multiple study populations and in different age and sex strata. Additionally, we characterised patients affected by TTS in historical data, describing their comorbidities and prior medication use. Implications of all the available evidence TTS is very rare and based on the highest estimates among the databases included in this study, one would normally expect approximately 1 case of CVST with thrombocytopenia, 5 of SVT with thrombocytopenia, 11 of DVT with thrombocytopenia, 11 of PE with thrombocytopenia, and 34 of stroke with thrombocytopenia among a general population of 10 million individuals per 28 days. Expected events would however be higher for an older cohort, as patients with TTS are typically older than the general population. Patients with TTS are more often men and appear to typically have more comorbidities and greater medication use, including prior use of antithrombotic and anticoagulant therapies, than the general population. Not only do the rates of TTS being reported among those who had received an adenovirus-based vaccine against SARS-CoV-2 raise a concern, but early evidence from spontaneous reports suggests that TTS among those vaccinated has been more commonly been seen among women and younger persons. While this might be in part explained by potential biases in spontaneous reporting, it also underlines the importance of further evaluation of the safety signal for TTS. As with other adverse events of special interest for COVID-19 vaccines, estimates of TTS vary depending on the data source used. Comparison of observed versus expected TTS rates during the monitoring of COVID-19 vaccines should ideally be done using the same databases for the estimation of post-vaccine (observed) and background (expected) rates, with adjustment for patient characteristics (such as age and sex) done as part of the analysis.

Objective To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents. Design Multinational network cohort study. Setting Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea. Participants 303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020. Main outcome measures Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19. Results Of the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from Conclusions Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.

Context A comprehensive understanding of the association between body mass index (BMI) and coronavirus disease 2019 (COVID-19) is still lacking. Objective To investigate associations between BMI and risk of COVID-19 diagnosis, hospitalization with COVID-19, and death after a COVID-19 diagnosis or hospitalization (subsequent death), accounting for potential effect modification by age and sex. Design Population-based cohort study. Setting Primary care records covering >80% of the Catalan population, linked to regionwide testing, hospital, and mortality records from March to May 2020. Participants Adults (≥18 years) with at least 1 measurement of weight and height. Main outcome measures Hazard ratios (HR) for each outcome. Results We included 2 524 926 participants. After 67 days of follow-up, 57 443 individuals were diagnosed with COVID-19, 10 862 were hospitalized with COVID-19, and 2467 had a subsequent death. BMI was positively associated with being diagnosed and hospitalized with COVID-19. Compared to a BMI of 22 kg/m2, the HR (95% CI) of a BMI of 31 kg/m2 was 1.22 (1.19-1.24) for diagnosis and 1.88 (1.75-2.03) and 2.01 (1.86-2.18) for hospitalization without and with a prior outpatient diagnosis, respectively. The association between BMI and subsequent death was J-shaped, with a modestly higher risk of death among individuals with BMIs ≤ 19 kg/m2 and a more pronounced increasing risk for BMIs ≥ 40 kg/m2. The increase in risk for COVID-19 outcomes was particularly pronounced among younger patients. Conclusions There is a monotonic association between BMI and COVID-19 diagnosis and hospitalization risks but a J-shaped relationship with mortality. More research is needed to unravel the mechanisms underlying these relationships.

OBJECTIVES To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017–2018. METHODS International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age RESULTS A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications ( CONCLUSIONS Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.

Background: Thromboembolism and thrombocytopenia have emerged as potential adverse events associated with vaccines against SARS-CoV-2. We compared rates of thromboembolism and thrombocytopenia following vaccination with BNT162b2 and ChAdOx1 with expected rates. Rates for people with COVID-19 were estimated to provide context. Methods: Primary care data from Catalonia, Spain, informed the analysis. Study participants were vaccinated with BNT162b2 or ChAdOx1 (27/12/2020-19/05/2021), diagnosed with COVID-19 (1/09/2020-1/03/2021) or present as of 1/01/2017. Outcomes included venous thromboembolism (VTE), arterial thromboembolism (ATE), thrombocytopenia, and thrombosis with thrombocytopenia syndrome (TTS). Incidence rates were estimated in the 21 and 90 days after vaccination and COVID-19 diagnosis, respectively, and up to 31/03/2019 for background rates. Age indirectly standardised incidence ratios (SIR) were estimated. Findings: We included 945,941 BNT162b2 (778,534 with 2 doses), 426,272 ChAdOx1, 222,710 COVID-19, and 4,570,149 background participants. SIRs for VTE were 1.29 [95% CI 1.13-1.48] and 0.90 [0.76-1.07] after first- and second-dose BNT162b2, and 1.15 [0.83-1.58] after first-dose ChAdOx1. The SIR for VTE in COVID-19 was 8.04 [7.37-8.78]. SIRs for thrombocytopenia were 1.35 (1.30-1.41) and 1.19 (1.14-1.25) after first- and second-dose BNT162b2, 1.03 (0.93-1.14) after first-dose ChAdOx1 and 3.52 (3.39 to 3.67) for COVID-19. Rates of ATE were similar to expected rates for BNT162b2 and ChAdOx1, as were rates of TTS for BNT162b2, while fewer than 5 such events were seen for ChAdOx1. Interpretation: Safety profiles of BNT162b2 and ChAdOx1 were similar. A safety signal was seen for VTE after first-dose of BNT162b2. Although confidence intervals were wider, a similar estimate was seen for first-dose of ChAdOx1. The 1.3 fold increase in the rate of VTE after first-dose of BNT162b2 compared with an 8 fold increase after diagnosis of COVID-19. No safety signals were seen for ATE or TTS. Further research is needed to investigate the causality in the observed associations. Funding Information: This study was funded by the European Medicines Agency in the form of a competitive tender (Lot ROC No EMA/2017/09/PE). Declaration of Interests: DPA’s research group has received research grants from the European Medicines Agency, from the Innovative Medicines Initiative, from Amgen, Chiesi, and from UCB Biopharma; and consultancy or speaker fees from Astellas, Amgen and UCB Biopharma. Peter Rijnbeek works for a research institute who receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, GSK, Amgen, UCB, Novartis, Astra-Zeneca, Chiesi, Janssen Research and Development, none of which relate to the content of this work. Katia Verhamme works for a research institute who receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, GSK, Amgen, UCB, Novartis, Astra-Zeneca, Chiesi, none of which relate to the content of this work .All other authors have no conflicts of interest to declare. Ethics Approval Statement: This study was approved by the Clinical Research Ethics Committee of the IDIAPJGol (project code: 21/054-PCV).

ObjectivesTo investigate the associations between cancer and risk of outpatient COVID-19 diagnosis, hospitalisation, and COVID-19-related death, overall and by years since cancer diagnosis (5-years), sex, age, and cancer type.DesignPopulation-based cohort studySettingPrimary care electronic health records including ∼80% of the population in Catalonia, Spain, linked to hospital and mortality records between 1 March and 6 May 2020.ParticipantsIndividuals aged ≥18 years with at least one year of prior medical history available from the general population. Cancer was defined as any prior diagnosis of a primary invasive malignancy excluding non-melanoma skin cancer.Main outcome measuresCause-specific hazard ratios (aHR) with 95% confidence intervals for each outcome. Estimates were adjusted by age, sex, deprivation, smoking status, and comorbidities.ResultsWe included 4,618,377 adults, of which 260,667 (5.6%) had a history of cancer. Patients with cancer were older and had more comorbidities than cancer-free patients. A total of 98,951 individuals (5.5% with cancer) were diagnosed and 6,355 (16.4% with cancer) were directly hospitalised (no prior diagnosis) with COVID-19. Of those diagnosed, 6,851 were subsequently hospitalised (10.7% with cancer) and 3,227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1,963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID-19 diagnosis (aHR: 1.08; 95% confidence interval [1.05-1.11]); direct COVID-19 hospitalisation (1.33 [1.24-1.43]); and death following a COVID-19 hospitalisation (1.12 [1.01-1.25]). These associations were stronger for patients recently diagnosed with cancer, aged ConclusionsPatients recently diagnosed with cancer, aged What is already known on this subjectPrior studies addressing the relationship between cancer and COVID-19 infection and adverse outcomes have found conflicting resultsThe majority of these studies had small sample sizes, were not population-based (i.e. restricted to hospitalised patients), thus increasing the risks of selection and collider bias.In addition, they used different definitions for cancer (i.e. some included only patients with active cancer, while others focused on specific cancer types, etc.), which limits the comparability of their findings, and only a few analysed the effect of cancer across different patient subgroups.What this study addsWe conducted a population-based cohort study to analyse the associations between having a prior diagnosis of cancer and the risks of COVID-19 diagnosis, hospitalisation and COVID-19-related deaths from 1 March to 6 May 2020.In a population of 4,618,377 adults, we found that cancer was associated with an increased risk of COVID-19 diagnosis (aHR: 1.08; 95% confidence interval [1.05-1.11]); direct COVID-19 hospitalisation (1.33 [1.24-1.43]); and death following a COVID-19 hospitalisation (1.12 [1.01-1.25]).These risks were higher for patients recently diagnosed with cancer (within the last year), younger than 70 years, or with haematological cancers. We also found a particularly high risk of COVID-19 hospitalisation and death among patients with lung and bladder cancer.

Background Elective total knee replacement (TKR) is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify those patients who are at particularly high risk who could then be targeted with preventative interventions. We aimed to develop a simple model to help inform treatment choices. Methods We trained and externally validated adverse event prediction models for patients with TKR using electronic health records (EHR) and claims data from the US (OPTUM, CCAE, MDCR, and MDCD) and general practice data in the UK (IQVIA Medical Research Database ([IMRD], incorporating data from The Health Improvement Network [THIN], a Cegedim database). The target population consisted of patients undergoing a primary TKR, aged ≥40 years and registered in any of the contributing data sources for ≥1 year before surgery. LASSO logistic regression models were developed for four adverse outcomes: post-operative (90-day) mortality, venous thromboembolism (VTE), readmission, and long-term (5-year) revision surgery. A second model was developed with a reduced feature set to increase interpretability and usability. Findings A total of 508,082 patients were included, with sample size per data source ranging from 1,853 to 158,549 patients. Overall, 90-day mortality, VTE, and readmission prevalence occurred in a range of 0.20%-0.32%, 1.7%-3.0% and 2.2%-4.8%, respectively. Five-year revision surgery was observed in 1.5%-3.1% of patients. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and yielded an AUROC of 0.70 when externally validated on THIN. We then developed a 12 variable model which achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. The discriminative performances of the models predicting 90-day VTE, readmission, and 5-year revision were consistently poor across the datasets (AUROC Interpretation We developed and externally validated a simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR. Our model had a greater discriminative ability than the Charlson Comorbidity Index in predicting 90-day mortality. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and for appropriate precautions to be taken for those at high risk. The other outcomes examined had low performance. Funding This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). TDS is funded by the Department of Health of the Generalitat de Catalunya under the Strategic Plan for Research and Innovation in Health (PERIS; SLT002/16/00308). The views expressed in this publication are those of the authors and not those of the NHS, the National Institute for Health Research or the Department of Health. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Key Points Question Is it possible to predict adverse events following total knee replacement? Findings Mortality was the only adverse event studied that we were able to predict with adequate performance. We produced a 12 variable prediction model for 90-day post-operative mortality that achieved an AUROC of 0.77 on internal test validation (Optum) and 0.71 when externally validated in THIN. The model also showed adequate calibration. Meaning Patients can now be presented with an accurate risk assessment for short term mortality such that they are well-informed before the decision for surgery is taken. Importance Total Knee Replacement is generally a safe, effective procedure that is performed on thousands of patients each year. However, a small number of those patients will experience adverse events. Due to the surgery’s elective nature, a well calibrated, high performing risk model could pre-emptively inform the patient and clinician decision making process and help to guide preventative treatment.

Author(s): Lane, Jennifer CE; Weaver, James; Kostka, Kristin; Duarte-Salles, Talita; Abrahao, Maria Tereza F; Alghoul, Heba; Alser, Osaid; Alshammari, Thamir M; Areia, Carlos; Biedermann, Patricia; Banda, Juan M; Burn, Edward; Casajust, Paula; Fister, Kristina; Hardin, Jill; Hester, Laura; Hripcsak, George; Kaas-Hansen, Benjamin Skov; Khosla, Sajan; Kolovos, Spyros; Lynch, Kristine E; Makadia, Rupa; Mehta, Paras P; Morales, Daniel R; Morgan-Stewart, Henry; Mosseveld, Mees; Newby, Danielle; Nyberg, Fredrik; Ostropolets, Anna; Woong Park, Rae; Prats-Uribe, Albert; Rao, Gowtham A; Reich, Christian; Rijnbeek, Peter; Sena, Anthony G; Shoaibi, Azza; Spotnitz, Matthew; Subbian, Vignesh; Suchard, Marc A; Vizcaya, David; Wen, Haini; Wilde, Marcel de; Xie, Junqing; You, Seng Chan; Zhang, Lin; Lovestone, Simon; Ryan, Patrick; Prieto-Alhambra, Daniel; OHDSI-COVID-19 consortium | Abstract: ObjectivesConcern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA.MethodsWe performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 l40%.ResultsA total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis.ConclusionHCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation.Trial registrationRegistered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.

Objective To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). Design A network cohort study. Setting Six databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. Patients Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. Interventions Dialysis, tracheostomy, and ECMO. Measurements and Main Results 240,392 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 139,971 from IQVIA Open Claims, 29,061 from Optum EHR, 4,336 from OPTUM SES, 36,019 from Premier, and 8,118 from VA-OMOP). Across the six databases, 9,703 (4.04% [95% CI: 3.96% to 4.11%]) patients received dialysis, 1,681 (0.70% [0.67% to 0.73%]) had a tracheostomy, and 398 (0.17% [95% CI: 0.15% to 0.18%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was generally concentrated among patients who were younger, male, and with fewer comorbidities except for obesity. Tracheostomy was used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. Conclusion Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial and can be expected to continue grow given the continuing spread of the COVID-19.

ObjectivesTo characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children/adolescents diagnosed or hospitalized with COVID-19. Secondly, to describe health outcomes amongst children/adolescents diagnosed with previous seasonal influenza.DesignInternational network cohort.SettingReal-world data from European primary care records (France/Germany/Spain), South Korean claims and US claims and hospital databases.ParticipantsDiagnosed and/or hospitalized children/adolescents with COVID-19 at age Main outcome measuresBaseline demographics and comorbidities, symptoms, 30-day in-hospital treatments and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome (ARDS), multi-system inflammatory syndrome (MIS-C), and death.ResultsA total of 55,270 children/adolescents diagnosed and 3,693 hospitalized with COVID-19 and 1,952,693 diagnosed with influenza were studied.Comorbidities including neurodevelopmental disorders, heart disease, and cancer were all more common among those hospitalized vs diagnosed with COVID-19. The most common COVID-19 symptom was fever. Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more common in COVID-19 than influenza.In-hospital treatments for COVID-19 included repurposed medications (Hospitalization was observed in 0.3% to 1.3% of the COVID-19 diagnosed cohort, with undetectable (NConclusionsDespite negligible fatality, complications including pneumonia, ARDS and MIS-C were more frequent in children/adolescents with COVID-19 than with influenza. Dyspnea, anosmia and gastrointestinal symptoms could help differential diagnosis. A wide range of medications were used for the inpatient management of pediatric COVID-19.What is already known on this topic?Most of the early COVID-19 studies were targeted at adult patients, and data concerning children and adolescents are limited.Clinical manifestations of COVID-19 are generally milder in the pediatric population compared with adults.Hospitalization for COVID-19 affects mostly infants, toddlers, and children with pre-existing comorbidities.What this study adds⍰This study comprehensively characterizes a large international cohort of pediatric COVID-19 patients, and almost 2 million with previous seasonal influenza across 5 countries.⍰Although uncommon, pneumonia, acute respiratory distress syndrome (ARDS) and multi-system inflammatory syndrome (MIS-C) were more frequent in children and adolescents diagnosed with COVID-19 than in those with seasonal influenza.⍰Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more frequent in COVID-19, and could help to differentiate pediatric COVID-19 from influenza.⍰A plethora of medications were used during the management of COVID-19 in children and adolescents, with great heterogeneity in the use of antiviral therapies as well as of adjunctive therapies.