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Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

Background Optimal timing for tracheotomy for critically ill COVID‐19 patients requiring invasive mechanical ventilation (IMV) is not established. Methods Multicenter prospective cohort including all COVID‐19 patients admitted to intensive care units (ICUs) in 36 hospitals who required tracheotomy during first pandemic wave. With a target emulation trial framework, we studied the causal effects of early (7–10 days) versus late (>10 days) tracheotomy (LT) on time from tracheotomy to weaning, postoperative mortality, and tracheotomy complications. Results Of 696 patients, 20.4% received early tracheotomy (ET). ET was associated with faster weaning (hazard ratio [HR] [95% confidence interval, CI]: 1.25 [1.00–1.56]) without differences in mortality (HR [95% CI]: 0.85 [0.60–1.21]) or complications (adjusted rate ratio [95% CI]: 0.56 [0.23–1.33]). Conclusions ET had a similar or lower post‐tracheotomy weaning time than LT, potentially shortening IMV and ICU stays, without changing complication or mortality rates in COVID‐19 patients.

Background Findings from randomized controlled trials have yielded conflicting results on the association between blood pressure (BP) and dementia traits. We tested the hypothesis that a causal relationship exists between systolic BP (SBP) and/or diastolic BP (DBP) and risk of Alzheimer’s disease (AD). Methods We performed a generalized summary Mendelian randomization (GSMR) analysis using summary statistics of a genome-wide association study meta-analysis of 299,024 individuals of SBP or DBP as exposure variables against three different outcomes: 1) AD diagnosis (International Genomics of Alzheimer's Project), 2) maternal family history of AD (UK Biobank), and 3) paternal family history of AD (UK Biobank). Finally, a combined meta-analysis of 368,440 individuals that included these three summary statistics was used as final outcome. Results GSMR applied to the International Genomics of Alzheimer's Project dataset revealed a significant effect of high SBP lowering the risk of AD (βGSMR = −0.19, p = .04). GSMR applied to the maternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.12, p = .02], DBP [βGSMR = −0.10, p = .05]) and to the paternal family history of AD UK Biobank dataset (SBP [βGSMR = −0.16, p = .02], DBP [βGSMR = −0.24, p = 7.4 × 10−4]) showed the same effect. A subsequent combined meta-analysis confirmed the overall significant effect for the other SBP analyses (βGSMR = −0.14, p = .03). The DBP analysis in the combined meta-analysis also confirmed a DBP effect on AD (βGSMR = −0.14, p = .03). Conclusions A causal effect exists between high BP and a reduced late-life risk of AD. The results were obtained through careful consideration of confounding factors and the application of complementary MR methods on independent cohorts.

Background Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. Methods In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. Findings Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84–1·14) or combination use exposure (1·01, 0·90–1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68–1·21; with heterogeneity of >40%) or combination use (0·95, 0·83–1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79–0·99) and non-significant for monotherapy (0·85, 0·69–1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. Interpretation No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19.

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

Background Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients with severe systemic morbidity who are excluded from many randomised controlled trials. Objectives Stage 1 – replicate the Total or Partial Knee Arthroplasty Trial (TOPKAT), a surgical randomised controlled trial comparing unicompartmental knee replacement with total knee replacement using propensity score and instrumental variable methods. Stage 2 – compare the risk benefits and cost-effectiveness of unicompartmental knee replacement with total knee replacement surgery in patients with severe systemic morbidity who would have been ineligible for TOPKAT using the validated methods from stage 1. Design This was a cohort study. Setting Data were obtained from the National Joint Registry database and linked to hospital inpatient (Hospital Episode Statistics) and patient-reported outcome data. Participants Stage 1 – people undergoing unicompartmental knee replacement surgery or total knee replacement surgery who met the TOPKAT eligibility criteria. Stage 2 – participants with an American Society of Anesthesiologists grade of ≥ 3. Intervention The patients were exposed to either unicompartmental knee replacement surgery or total knee replacement surgery. Main outcome measures The primary outcome measure was the postoperative Oxford Knee Score. The secondary outcome measures were 90-day postoperative complications (venous thromboembolism, myocardial infarction and prosthetic joint infection) and 5-year revision risk and mortality. The main outcome measures for the health economic analysis were health-related quality of life (EuroQol-5 Dimensions) and NHS hospital costs. Results In stage 1, propensity score stratification and inverse probability weighting replicated the results of TOPKAT. Propensity score adjustment, propensity score matching and instrumental variables did not. Stage 2 included 2256 unicompartmental knee replacement patients and 57,682 total knee replacement patients who had severe comorbidities, of whom 145 and 23,344 had linked Oxford Knee Scores, respectively. A statistically significant but clinically irrelevant difference favouring unicompartmental knee replacement was observed, with a mean postoperative Oxford Knee Score difference of Limitations Although some propensity score methods successfully replicated TOPKAT, unresolved confounding may have affected stage 2. Missing Oxford Knee Scores may have led to information bias. Conclusions Propensity score stratification and inverse probability weighting successfully replicated TOPKAT, implying that some (but not all) propensity score methods can be used to evaluate surgical innovations and implantable medical devices using routine NHS data. Unicompartmental knee replacement was safer and more cost-effective than total knee replacement for patients with severe comorbidity and should be considered the first option for suitable patients. Future work Further research is required to understand the performance of propensity score methods for evaluating surgical innovations and implantable devices. Trial registration This trial is registered as EUPAS17435. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 66. See the NIHR Journals Library website for further project information.

Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.Multinational network cohort study.Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea).All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included.30-day complications during hospitalisation and death.We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%).Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases.Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions.Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.

Background Tuberculosis (TB) incidence declined in Portugal in recent decades, but trends differ between regions and population subgroups. We investigated these differences to inform prevention and control programmes. Methods We extracted TB notifications from the Portuguese National TB Surveillance System (SVIG-TB) in 2010–2017, disaggregated by region, age group, country of birth and HIV status. We calculated notification rates using denominators from the Portuguese National Institute of Statistics and the Joint United Nations Programme on HIV/AIDS and performed stratified time series analysis. We estimated interannual decline percentages and 95% confidence intervals (CI) using Poisson and binomial negative regression models. Results The overall TB notification rate decreased from 25.7 to 17.5/100,000 population from 2010 to 2017 (5.2%/year) in Portugal. Interannual decline did not differ significantly between regions, but it was smaller amongst non-Portuguese nationals (-1.57% [CI: -4.79%, 1.75%] vs -5.85% [CI: -6.98%, -4.70%] in Portuguese nationals); children under five years of age (+1.77% [CI: -4.61%, 8.58%] vs -5.38% [CI: -6.33%, -4.42%] in other age groups); and HIV-negative people (-6.47% [CI: -9.10%, -3.77%] vs -11.29% [CI; -17.51%, -4.60%] in HIV-positive). Conclusions The decline in TB notification rates in Portugal during the study period has been steady. However, the decline amongst non-Portuguese nationals, children under five years of age and non-infected-HIV patients was lower. No significant differences were observed between regions. Changes in TB epidemiology in specific risk groups and geographical areas should be closely monitored to achieve the objectives of the End TB Strategy. We recommend intensifying screening of TB in the subpopulations identified.

Background Dupuytren disease (DD) is a common fibroproliferative disease of the palmar fascia. The mainstay of DD treatment in England is surgery with either percutaneous needle fasciotomy, limited fasciectomy, or dermofasciectomy. This study aimed to investigate the temporal trends and geographical variation of primary DD surgery in England. Methods A longitudinal population-based cohort study was perfomed using the Hospital Episode Statistics database from April 1, 2007, to March 31, 2017. Directly standarized rates were estimated over time (between financial years 2007-2008 and 2016-2017) and by geographic region (by clinical commissioning groups [CCGs]; financial years 2010-2011, 2013-2014, and 2016-2017). The Office for National Statistics midyear population estimates were used as the reference population. Main findings Primary DD surgery was undertaken at a steadily increasing rate from financial year 2007 to 2008, to 2016 to 2017 in England. There was a striking 3.6-fold variation in the rates of primary DD surgery among National Health Service CCGs in England place of residence. Conclusions This significant variation in DD surgical treatment in England suggests a need for the development of standardized surgical practice across all CCGs and National Health Service hospitals, promoting equality of access to cost-effective health care.

As the SARS-CoV-2 virus (COVID-19) continues to affect people across the globe, there is limited understanding of the long term implications for infected patients1–3. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually designed by clinicians, and not granular enough to understand the natural history or patient experiences of ‘long COVID’. In order to get a complete picture, there is a need to use patient generated data to track the long-term impact of COVID-19 on recovered patients in real time. There is a growing need to meticulously characterize these patients’ experiences, from infection to months post-infection, and with highly granular patient generated data rather than clinician narratives. In this work, we present a longitudinal characterization of post-COVID-19 symptoms using social media data from Twitter. Using a combination of machine learning, natural language processing techniques, and clinician reviews, we mined 296,154 tweets to characterize the post-acute infection course of the disease, creating detailed timelines of symptoms and conditions, and analyzing their symptomatology during a period of over 150 days.

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.

Albert Prats-Uribe,1 Junqing Xie,1 Daniel Prieto-Alhambra,1 Irene Petersen2,3 1Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine - Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK; 2Department of Primary Care and Population Health, UCL, London, NW3 2PF, UK; 3Department of Clinical Epidemiology, Aarhus University, Aarhus N, 8200, DenmarkCorrespondence: Daniel Prieto-AlhambraPharmaco- and Device Epidemiology, Centre for Statistics in Medicine - Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UKEmail daniel.prietoalhambra@ndorms.ox.ac.ukBackground: Several papers have shown contradictory evidence about the relationship between smoking and COVID-19-related deaths. There is little evidence about smoking and risk of infection. We aim to examine association between smoking and COVID-19 infection and subsequent mortality.Methods: This was a prospective study with participants from the UK Biobank cohort. Participants who lived in England were followed up from 01/02/2020 to 28/06/2020 with data linked to hospital episode statistics, Office for National Statistics and Public Health England PCR tests. We compared current-smokers, previous-smokers with never-smokers and estimated risk ratio (RR) of COVID-19 infection and subsequent mortality using Poisson regression adjusting for age, sex, ethnicity, body mass index and socio-economic status. Interactions between smoking status and age and sex were tested for using multiplicative interactions, and analyses were stratified by median age (49– 68 years, 69– 86 years) and sex.Results: In total, 402,978 participants were included in the analyses. The majority were never smokers, 226,294 (56.2%), 140,090 (34.8%) were previous smokers, and 39,974 (9.9%) current smokers. COVID-19 infection was identified in 1591 (0.39%) people, and 372/1591 (23.4%) died. Amongst the younger participants, smokers were nearly twice as likely to become infected with COVID-19 than never smokers (RR 1.88 [1.49– 2.38]) whereas there was no difference for those aged 69+ (RR 1.05 [0.82– 1.34]). In contrast, amongst the older participants, smokers were twice as likely to die from COVID-19 compared to non-smokers (RR 2.15 [1.11– 4.16]) whereas there was no difference for those under the age of 69 (RR 1.22[0.83– 1.79]). Similar patterns were observed for previous smokers. The impact of smoking was similar in men and women.Conclusion: The association between smoking and COVID-19 infection and subsequent death is modified by age. Smokers and previous smokers aged under 69 were at higher risk of COVID-19 infection, suggesting the risk is associated with increased exposure to SARS-COV-2 virus. Once infected, older smokers were twice as likely to die from COVID-19 than never smokers, possibly mediated by increased risk of chronic conditions/illnesses.Keywords: smoking, COVID-19, UK Biobank

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.

BackgroundThe COVID-19 pandemic has strained intensive care unit (ICU) resources. Tracheotomy is the most frequent surgery performed on ICU patients and can affect the duration of ICU care. We studied the association between when tracheotomy occurs and weaning from mechanical ventilation, mortality, and intraoperative and postoperative complications.MethodsMulticentre prospective cohort including all COVID-19 patients admitted to ICUs in 36 hospitals in Spain who received invasive mechanical ventilation and tracheotomy between 11 March and 20 July 2020. We used a target emulation trial framework to study the causal effects of early (7 to 10 days post-intubation) versus late (>10 days) tracheotomy on time from tracheotomy to weaning, postoperative mortality, and tracheotomy complications. Cause-specific Cox models were used for the first two outcomes and Poisson regression for the third, all adjusted for potential confounders.FindingsWe included 696 patients, of whom 142 (20·4%) received early tracheotomy. Using late tracheotomy as the reference group, multivariable cause-specific analysis showed that early tracheotomy was associated with faster post-tracheotomy weaning (fully adjusted hazard ratio (HR) [95% confidence interval (CI)]: 1·31 [1·02 to 1·81]) without differences in mortality (fully adjusted HR [95% CI]: 0·91 [0·56 to 1·47]) or intraoperative or postoperative complications (adjusted rate ratio [95% CI]: 0·21 [0·03 to 1·57] and 1·49 [0·99 to 2·24], respectively).InterpretationEarly tracheotomy reduced post-tracheotomy weaning time, resulting in fewer mechanical ventilation days and shorter ICU stays, without changing complication or mortality rates. These results support early tracheotomy for COVID-19 patients when clinically indicated.FundingSupported by the NIHR, FAME, and MRC.Research in contextEvidence before this studyThe optimal timing of tracheotomy for critically ill COVID-19 patients remains controversial. Existing guidelines and recommendations are based on limited experiences with SARS-CoV-1 and expert opinions derived from situations that differ from a pandemic outbreak. Most of the available guidance recommends late tracheotomy (>14 days), mainly due to the potential risk of infection for the surgical team and the high patient mortality rate observed early in the first wave of the COVID-19 pandemic.Recent publications have shown that surgical teams can safely perform tracheotomies for COVID-19 patients if they use adequate personal protective equipment. Early tracheotomy seems to reduce the length of invasive mechanical ventilation without increasing complications, which may release crucial intensive care unit (ICU) beds sooner.The current recommendations do not suggest an optimal time for tracheotomy for COVID-19 patients, and no study has provided conclusions based on objective clinical parameters.Added value of this studyThis is the first study aiming to establish the optimal timing for tracheotomy for critically ill COVID-19 patients requiring invasive mechanical ventilation (IMV). The study prospectively recruited a large multicentre cohort of 696 patients under IMV due to COVID-19 and collected data about the severity of respiratory failure, clinical and ventilatory parameters, and whether patients need to be laid flat during their ICU stay (proned). The analysis focused on the duration of IMV, mortality, and complication rates. We used a prospective cohort study design to compare the ‘exposures’ of early (performed at day 7 to 10 after starting IMV) versus late (performed after day 10) tracheotomy and set the treatment decision time on the 7th day after orotracheal intubation.Implications of all the available evidenceThe evidence suggests that tracheotomy within 10 days of starting COVID-19 patients on mechanical ventilation allows these patients to be removed from ventilation and discharged from ICU quicker than later tracheotomy, without added complications or increased mortality. This evidence may help to release ventilators and ICU beds more quickly during the pandemic.

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.

The natural history of coronavirus disease 2019 (COVID-19) has yet to be fully described. Here, we use patient-level data from the Information System for Research in Primary Care (SIDIAP) to summarise COVID-19 outcomes in Catalonia, Spain. We included 5,586,521 individuals from the general population. Of these, 102,002 had an outpatient diagnosis of COVID-19, 16,901 were hospitalised with COVID-19, and 5273 died after either being diagnosed or hospitalised with COVID-19 between 1st March and 6th May 2020. Older age, being male, and having comorbidities were all generally associated with worse outcomes. These findings demonstrate the continued need to protect those at high risk of poor outcomes, particularly older people, from COVID-19 and provide appropriate care for those who develop symptomatic disease. While risks of hospitalisation and death were lower for younger populations, there is a need to limit their role in community transmission., Establishing the natural history of COVID-19 requires longitudinal data from population-based cohorts. Here, the authors use linked primary care, testing, and hospital data to describe the disease in ~100,000 individuals with a COVID-19 diagnosis among a population of ~5.5 million in Catalonia, Spain.

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.

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.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems’ response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

OBJECTIVESTo describe comorbidities, symptoms at presentation, medication use, and 30-day outcomes after a diagnosis of COVID-19 in pregnant women, in comparison to pregnant women with influenza.DESIGNMultinational network cohortSETTINGA total of 6 databases consisting of electronic medical records and claims data from France, Spain, and the United States.PARTICIPANTSPregnant women with ≥ 1 year in contributing databases, diagnosed and/or tested positive, or hospitalized with COVID-19. The influenza cohort was derived from the 2017-2018 influenza season.OUTCOMESBaseline patient characteristics, comorbidities and presenting symptoms; 30-day inpatient drug utilization, maternal complications and pregnancy-related outcomes following diagnosis/hospitalization.RESULTS8,598 women diagnosed (2,031 hospitalized) with COVID-19 were included. Hospitalized women had, compared to those diagnosed, a higher prevalence sof pre-existing comorbidities including renal impairment (2.2% diagnosed vs 5.1% hospitalized) and anemia (15.5% diagnosed vs 21.3% hospitalized).The ten most common inpatient treatments were systemic corticosteroids (29.6%), enoxaparin (24.0%), immunoglobulins (21.4%), famotidine (20.9%), azithromycin (18.1%), heparin (15.8%), ceftriaxone (7.9%), aspirin (7.0%), hydroxychloroquine (5.4%) and amoxicillin (3.5%).Compared to 27,510 women with influenza, dyspnea and anosmia were more prevalent in those with COVID-19. Women with COVID-19 had higher frequency of cesarean-section (4.4% vs 3.1%), preterm delivery (0.9% vs 0.5%), and poorer maternal outcomes: pneumonia (12.0% vs 2.7%), ARDS (4.0% vs 0.3%) and sepsis (2.1% vs 0.7%). COVID-19 fatality was negligible (NCONCLUSIONSComorbidities that were more prevalent with COVID-19 hospitalization (compared to COVID-19 diagnosed) in pregnancy included renal impairment and anemia. Multiple medications were used to treat pregnant women hospitalized with COVID-19, some with little evidence of benefit. Anosmia and dyspnea were indicative symptoms of COVID-19 in pregnancy compared to influenza, and may aid differential diagnosis. Despite low fatality, pregnancy and maternal outcomes were worse in COVID-19 than influenza.WHAT IS ALREADY KNOWN ON THIS TOPICCompared to non-pregnant women of reproductive age, pregnant women are less likely to experience typical COVID-19 symptoms, such as fever and myalgia.Obesity, high maternal age, and comorbid hypertension and diabetes are risk factors for severe COVID-19 among pregnant women.Despite relatively high rates of pneumonia and need for oxygen supplementation, fatality rates in pregnant women with COVID-19 are generally low (WHAT THIS STUDY ADDSAlthough not often recorded, dyspnea and anosmia were more often seen in pregnant women with COVID-19 than in women with seasonal influenza, in 6 databases from 3 countries (US, France, Spain).Renal impairment and anemia were more common among hospitalized than diagnosed women with COVID-19 during pregnancy.Despite limited data on benefit-risk in pregnancy, a large number of medications were used for inpatient management of COVID-19 in pregnant women: approximately 1 in 3 received corticosteroids (some may have been given for a pregnancy-related indication rather than for COVID-19 treatment), 1 in 4 enoxaparin, and 1 in 5 immunoglobulin, famotidine and azithromycin.Compared to influenza, there was a higher frequency of pregnancy-related complications (cesarean section and preterm deliveries), as well as poorer maternal outcomes (pneumonia, acute respiratory distress syndrome, sepsis, acute kidney injury, and cardiovascular and thromboembolic events) seen in pregnant women diagnosed with COVID-19.

Background To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza. Methods We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014–19. Results 6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use. Conclusions Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

Dupuytren’s disease (DD) is a common fibro-proliferative disorder of the palm. We estimated the risk of serious local and systemic complications and re-operation after DD surgery. We queried England’s Hospital Episode Statistics database and included all adult DD patients who were surgically treated. A longitudinal cohort study and self-controlled case series were conducted. Between 1 April 2007 and 31 March 2017, 121,488 adults underwent 158,119 operations for DD. The cumulative incidence of 90-day serious local complications was low at 1.2% (95% CI 1.1–1.2). However, the amputation rate for re-operation by limited fasciectomy following dermofasciectomy was 8%. 90-day systemic complications were also uncommon at 0.78% (95% CI 0.74–0.83), however operations routinely performed under general or regional anaesthesia carried an increased risk of serious systemic complications such as myocardial infarction. Re-operation was lower than previous reports (33.7% for percutaneous needle fasciotomy, 19.5% for limited fasciectomy, and 18.2% for dermofasciectomy). Overall, DD surgery performed in England was safe; however, re-operation by after dermofasciectomy carries a high risk of amputation. Furthermore, whilst serious systemic complications were unusual, the data suggest that high-risk patients should undergo treatment under local anaesthesia. These data will inform better shared decision-making regarding this common condition.

Background Currently, there is a missing link in the natural history of COVID-19, from first (usually milder) symptoms to hospitalization and/or death. To fill in this gap, we characterized COVID-19 patients at the time at which they were diagnosed in outpatient settings and estimated 30-day hospital admission and fatality rates. Methods This was a population-based cohort study. Data were obtained from Information System for Research in Primary Care (SIDIAP)—a primary-care records database covering >6 million people (>80% of the population of Catalonia), linked to COVID-19 reverse transcriptase polymerase chain reaction (RT-PCR) tests and hospital emergency, inpatient and mortality registers. We included all patients in the database who were ≥15 years old and diagnosed with COVID-19 in outpatient settings between 15 March and 24 April 2020 (10 April for outcome studies). Baseline characteristics included socio-demographics, co-morbidity and previous drug use at the time of diagnosis, and polymerase chain reaction (PCR) testing and results. Study outcomes included 30-day hospitalization for COVID-19 and all-cause fatality. Results We identified 118 150 and 95 467 COVID-19 patients for characterization and outcome studies, respectively. Most were women (58.7%) and young-to-middle-aged (e.g. 21.1% were 45–54 years old). Of the 44 575 who were tested with PCR, 32 723 (73.4%) tested positive. In the month after diagnosis, 14.8% (14.6–15.0) were hospitalized, with a greater proportion of men and older people, peaking at age 75–84 years. Thirty-day fatality was 3.5% (95% confidence interval: 3.4% to 3.6%), higher in men, increasing with age and highest in those residing in nursing homes [24.5% (23.4% to 25.6%)]. Conclusion COVID-19 infections were widespread in the community, including all age–sex strata. However, severe forms of the disease clustered in older men and nursing-home residents. Although initially managed in outpatient settings, 15% of cases required hospitalization and 4% died within a month of first symptoms. These data are instrumental for designing deconfinement strategies and will inform healthcare planning and hospital-bed allocation in current and future COVID-19 outbreaks.

Background: Cognitive reserve (CR) could attenuate the impact of the brain burden on the cognition in people with MS (PwMS). Objective: To explore the relationship between CR and structural brain connectivity, and investigate their role on cognition in PwMS cognitively impaired (PwMS-CI) and cognitively preserved (PwMS-CP). Methods: In this study, 181 PwMS (71% female; 42.9±10.0 years) were evaluated using the Cognitive Reserve Questionnaire (CRQ), Brief Repeatable Battery of Neuropsychological tests and MRI. Brain lesion and gray matter volumes were quantified, as was the structural network connectivity. Patients were classified as PwMS-CI (z-scores -1.5 SD in at least two tests) or PwMS-CP. Linear and multiple regression analyses were run to evaluate the association of CRQ and structural connectivity with cognition in each group. Hedges’s effect size was used to compute the strength of associations. Results: We found a very low association between CRQ scores and connectivity metrics in PwMS-CP while in PwMS-CI this relation was low-to-moderate. The multiple regression model, adjusted for age, gender, mood, lesion volume and graphs metrics (local and global efficiency, and transitivity), indicated that the CRQ [β=0.26, 95% CI (0.17-0.35)] was associated with cognition (adjR2=0.34) in PwMS-CP (55%). In PwMS-CI, CRQ [β=0.18, 95% CI (0.07-0.29)], age, and network global efficiency were independently associated with cognition (adjR2=0.55). The age and gender adjusted association between CRQ score and global efficiency on having an impaired cognitive status was -0.338 (OR: 0.71, p = 0.036) and -0.531 (OR: 0.59, p = 0.002), respectively. Conclusions: CR seems to have a marginally significant effect on brain structural connectivity, observed in patients with more severe clinical impairment. It protects PwMS from cognitive decline regardless of their cognitive status, yet once cognitive impairment has set in, brain damage and aging are also influencing cognitive performance.

Objectives A plethora of medicines have been repurposed or used as adjunctive therapies for COVID-19. We characterized the utilization of medicines as prescribed in routine practice amongst patients hospitalized for COVID-19 in South Korea, China, Spain, and the USA. Design International network cohort Setting Hospital electronic health records from Columbia University Irving Medical Centre (NYC, USA), Stanford (CA, USA), Tufts (MA, USA), Premier (USA), Optum EHR (USA), department of veterans affairs (USA), NFHCRD (Honghu, China) and HM Hospitals (Spain); and nationwide claims from HIRA (South Korea) Participants patients hospitalized for COVID-19 from January to June 2020 Main outcome measures Prescription/dispensation of any medicine on or 30 days after hospital admission date Analyses Number and percentage of users overall and over time Results 71,921 people were included: 304 from China, 2,089 from Spain, 7,599 from South Korea, and 61,929 from the USA. A total of 3,455 medicines were identified. Common repurposed medicines included hydroxychloroquine ( Conclusions Multiple medicines were used in the first months of COVID-19 pandemic, with substantial geographic and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed medicines. 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. What is already known in this topic Drug repurposing is a common approach in the clinical management of novel diseases and conditions for which there are no available pharmacotherapies Hydroxychloroquine was widely used in the management of COVID-19 patients during the early phases of the pandemic Recent NIH (and other) guidelines recommend the use of concomitant therapies including immune-based, antithrombotic, antibiotic and other treatments What this study adds This study demonstrates great variability and extensive drug repurposing and utilization in the management of COVID-19 patients. A wide range of adjunctive treatments has been used, including antithrombotics, antibiotics, H2 receptor antagonists, and systemic corticosteroids. Emerging clinical data on the safety and efficacy of hydroxychloroquine and azithromycin impacted their rise and rapid decline in use internationally Conversely, the use of corticosteroids grew only in more recent months, with little use in the early stages of the pandemic (January to April)

Background: To assess whether genetically determined quantitative and qualitative HDL characteristics were independently associated with coronary artery disease (CAD). Methods: We designed a two-sample multivariate Mendelian randomization study with available genome-wide association summary data. We identified genetic variants associated with HDL cholesterol and apolipoprotein A-I levels, HDL size, particle levels, and lipid content to define our genetic instrumental variables in one sample (Kettunen et al. study, n = 24,925) and analyzed their association with CAD risk in a different study (CARDIoGRAMplusC4D, n = 184,305). We validated these results by defining our genetic variables in another database (METSIM, n = 8372) and studied their relationship with CAD in the CARDIoGRAMplusC4D dataset. To estimate the effect size of the associations of interest adjusted for other lipoprotein traits and minimize potential pleiotropy, we used the Multi-trait-based Conditional & Joint analysis. Results: Genetically determined HDL cholesterol and apolipoprotein A-I levels were not associated with CAD. HDL mean diameter (β = 0.27 [95%CI = 0.19; 0.35]), cholesterol levels in very large HDLs (β = 0.29 [95%CI = 0.17; 0.40]), and triglyceride content in very large HDLs (β = 0.14 [95%CI = 0.040; 0.25]) were directly associated with CAD risk, whereas the cholesterol content in medium-sized HDLs (β = -0.076 [95%CI = -0.10; -0.052]) was inversely related to this risk. These results were validated in the METSIM-CARDIoGRAMplusC4D data. Conclusions: Some qualitative HDL characteristics (related to size, particle distribution, and cholesterol and triglyceride content) are related to CAD risk while HDL cholesterol levels are not. This work was supported by the Instituto de Salud Carlos III– European Regional Development Fund [grant numbers CD17/00122, IFI14/00007, PI18/00017], the Medical Research Council [grant numbers MR/K501256/1, MR/N013468/1], the Spanish Ministry of Economy and Competitiveness [grant numbers BES-2014-069718, SAF2015-71653- R], the European Union's Horizon 2020 Research and Innovation Program [grant number 796216], and the Government of Catalonia through the Agency for Management of University and Research Grants [2017 SGR 222]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CIBER Enfermedades Cardiovasculares (CIBERCV), Epidemiología y Salud Pública (CIBERESP), and Fisiopatología de la Obesidad y Nutrición (CIBEROBN) are initiatives of the Instituto de Salud Carlos III, Madrid, Spain, and financed by the European Regional Development Fund.

Introduction: Smoking is one of the few modifiable risk factors associated with the development of rheumatoid arthritis (RA). Most published data are over 10 years old, and none included Mediterranean populations. We therefore took advantage of primary care routinely collected data to study the association between smoking and the development of RA in the general population of Catalonia, Spain. Methods: We conducted a case–control study including all patients with a new diagnosis of RA registered in the SIDIAP database between 01/01/2008 and 31/12/2018; and matched them to up to 1:5 controls by age, gender and general practitioner. Smoking was classified by primary care staff into never, ex- or current smoking. Odds Ratios and 95% confidence intervals for the association between current and ex-smoking (compared to never smoking) and RA were estimated using conditional logistic regression adjusted for potential confounders. Results: A total of 13,920 RA cases and 69,535 controls were included. Compared with never smokers, current and ex-smokers were at increased risk of RA, with adjusted OR of 1.28 [95% CI 1.20–1.37] and OR 1.19 [1.12–1.26] respectively. Conclusion: Our findings confirm an association between smoking and the risk of developing RA. The effect seems to prevail in the long-term and even in ex-smokers for 2 or more years after smoking cessation. More research is needed on the effects of smoking discontinuation on RA prevention and related outcomes.

BackgroundCOVID-19 may differentially impact people with obesity. We aimed to describe and compare the demographics, comorbidities, and outcomes of obese patients with COVID-19 to those of non-obese patients with COVID-19, or obese patients with seasonal influenza.MethodsWe conducted a cohort study based on outpatient/inpatient care, and claims data from January to June 2020 from the US, Spain, and the UK. We used six databases standardized to the OMOP common data model. We defined two cohorts of patientsdiagnosedand/orhospitalizedwith COVID-19. We created corresponding cohorts for patients with influenza in 2017-2018. We followed patients from index date to 30 days or death. We report the frequency of socio-demographics, prior comorbidities, and 30-days outcomes (hospitalization, events, and death) by obesity status.FindingsWe included 627 044 COVID-19 (US: 502 650, Spain: 122 058, UK: 2336) and 4 549 568 influenza (US: 4 431 801, Spain: 115 224, UK: 2543) patients. The prevalence of obesity was higher amonghospitalizedCOVID-19 (range: 38% to 54%) thandiagnosedCOVID-19 (30% to 47%), ordiagnosed(15% to 47%) orhospitalized(27% to 48%) influenza patients. ObesehospitalizedCOVID-19 patients were more often female and younger than non-obese COVID-19 patients or obese influenza patients. Obese COVID-19 patients were more likely to have prior comorbidities, present with cardiovascular and respiratory events during hospitalization, require intensive services, or die compared to non-obese COVID-19 patients. Obese COVID-19 patients were more likely to require intensive services or die compared to obese influenza patients, despite presenting with fewer comorbidities.InterpretationWe show that obesity is more common amongst COVID-19 than influenza patients, and that obese patients present with more severe forms of COVID-19 with higher hospitalization, intensive services, and fatality than non-obese patients. These data are instrumental for guiding preventive strategies of COVID-19 infection and complications.FundingThe European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, and IQVIA. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016201 and INV-019257). APU has received funding from the Medical Research Council (MRC) [MR/K501256/1, MR/N013468/1] and Fundación Alfonso Martín Escudero (FAME) (APU). VINCI [VA HSR RES 13-457] (SLD, MEM, KEL). JCEL has received funding from the Medical Research Council (MR/K501256/1) and Versus Arthritis (21605). No funders had a direct role in this study. The views and opinions expressed are those of the authors and do not necessarily reflect those of the Clinician Scientist Award programme, NIHR, Department of Veterans Affairs or the United States Government, NHS, or the Department of Health, England.Research in contextEvidence before this studyPrevious evidence suggests that obese individuals are a high risk population for COVID-19 infection and complications. We searched PubMed for articles published from December 2019 until June 2020, using terms referring to SARS-CoV-2 or COVID-19 combined with terms for obesity. Few studies reported obesity and most of them were limited by small sample sizes and restricted to hospitalized patients. Further, they used different definitions for obesity (i.e. some reported together overweight and obesity, others only reported obesity with BMI>40kg/m2). To date, no study has provided detailed information on the characteristics of obese COVID-19 patients, such as the prevalence of comorbidities or COVID-19 related outcomes. In addition, despite the fact that COVID-19 has been often compared to seasonal influenza, there are no studies assessing whether obese patients with COVID-19 differ from obese patients with seasonal influenza.Added value of this studyWe report the largest cohort of obese patients with COVID-19 and provide information on more than 29 000 aggregate characteristics publicly available. Our findings were consistent across the participating databases and countries. We found that the prevalence of obesity is higher among COVID-19 compared to seasonal influenza patients. Obese patients with COVID-19 are more commonly female and have worse outcomes than non-obese patients. Further, they have worse outcomes than obese patients with influenza, despite presenting with fewer comorbidities.Implications of all the available evidenceOur results show that individuals with obesity present more comorbidities and worse outcomes for COVID-19 than non-obese patients. These findings may be useful in guiding clinical practice and future preventative strategies for obese individuals, as well as provide useful data to support subsequent association studies focussed on obesity and COVID-19.

ObjectivesConcern has been raised in the rheumatological community regarding recent regulatory warnings that hydroxychloroquine used in the COVID-19 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation, or psychosis associated with hydroxychloroquine as used for rheumatoid arthritis (RA).MethodsNew user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and US). RA patients aged 18+ and initiating hydroxychloroquine were compared to those initiating sulfasalazine (active comparator) and followed up in the short (30-day) 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 (HR), with estimates pooled where I2Results918,144 and 290,383 users of hydroxychloroquine and sulfasalazine, respectively, were included. No consistent risk of psychiatric events was observed with short-term hydroxychloroquine (compared to sulfasalazine) use, with meta-analytic HRs of 0.96 [0.79-1.16] for depression, 0.94 [0.49-1.77] for suicide/suicidal ideation, and 1.03 [0.66-1.60] for psychosis. No consistent long-term risk was seen, with meta-analytic HRs 0.94 [0.71-1.26] for depression, 0.77 [0.56-1.07] for suicide/suicidal ideation, and 0.99 [0.72-1.35] for psychosis.ConclusionsHydroxychloroquine as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation, or psychosis compared to sulfasalazine. No effects were seen in the short or long term. Use at higher dose or for different indications needs further investigation.TRIAL REGISTRATIONRegistered with EU PAS; Reference number 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/Covid19EstimationHydroxychloroquine.WHAT IS ALREADY KNOWN ON THIS TOPICRecent regulatory warnings have raised concerns of potential psychiatric side effects of hydroxychloroquine at the doses used to treat COVID-19, generating concern in the rheumatological communitySerious psychiatric adverse events such as suicide, acute psychosis, and depressive episodes have been identified by the US Food and Drug Administration (FDA) adverse events reporting system and at case report levelWHAT THIS STUDY ADDSThis is the largest study on the neuro-psychiatric safety of hydroxychloroquine to date, including >900,000 users treated for their RA in country-level or private health care systems in Germany, the UK, and the USWe find no association between the use of hydroxychloroquine and the risk of depression, suicide/suicidal ideation, or severe psychosis compared to sulfasalazineHOW MIGHT THIS IMPACT ON CLINICAL PRACTICEOur data shows no association between hydroxychloroquine treatment for RA and risk of depression, suicide or psychosis compared to sulfasalazine. These findings do not support stopping or switching hydroxychloroquine treatment as used for RA due to recent concerns based on COVID-19 treated patients.

ObjectivesThere is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.DesignTime-series study of influenza and COVID-19 cases, 2010–2020.SettingPrimary care, Catalonia, Spain.ParticipantsPeople registered in primary-care practices, covering >6 million people and >85% of the population.Main outcome measuresWeekly new cases of influenza and COVID-19 clinically diagnosed in primary care.AnalysesDaily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010–2011 to 2019–2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019–2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.ResultsFour influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) were used to estimate the number of expected cases of influenza in 2019–2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15–64 age group.ConclusionsCOVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.

BackgroundThe natural history of Coronavirus Disease 2019 (COVID-19) has yet to be fully described, with most previous reports focusing on hospitalised patients. Using linked patient-level data, we set out to describe the associations between age, gender, and comorbidities and the risk of outpatient COVID-19 diagnosis, hospitalisation, and/or related mortality.MethodsA population-based cohort study including all individuals registered in Information System for Research in Primary Care (SIDIAP). SIDIAP includes primary care records covering > 80% of the population of Catalonia, Spain, and was linked to region-wide testing, hospital and mortality records. Outpatient diagnoses of COVID-19, hospitalisations with COVID-19, and deaths with COVID-19 were identified between 1st March and 6th May 2020. A multi-state model was used, with cause-specific Cox survival models estimated for each transition.FindingsA total of 5,627,520 individuals were included. Of these, 109,367 had an outpatient diagnosis of COVID-19, 18,019 were hospitalised with COVID-19, and 5,585 died after either being diagnosed or hospitalised with COVID-19. Half of those who died were not admitted to hospital prior to their death. Risk of a diagnosis with COVID-19 peaked first in middle-age and then again for oldest ages, risk for hospitalisation after diagnosis peaked around 70 years old, with all other risks highest at oldest ages. Male gender was associated with an increased risk for all outcomes other than outpatient diagnosis. The comorbidities studied (autoimmune condition, chronic kidney disease, chronic obstructive pulmonary disease, dementia, heart disease, hyperlipidemia, hypertension, malignant neoplasm, obesity, and type 2 diabetes) were all associated with worse outcomes.InterpretationThere is a continued need to protect those at high risk of poor outcomes, particularly the elderly, from COVID-19 and provide appropriate care for those who develop symptomatic disease. While risks of hospitalisation and death are lower for younger populations, there is a need to limit their role in community transmission. These findings should inform public health strategies, including future vaccination campaigns.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19., Detailed knowledge of the characteristics of COVID-19 patients helps with public health planning. Here, the authors use routinely-collected data from seven databases in three countries to describe the characteristics of >30,000 patients admitted with COVID-19 and compare them with those admitted for influenza in previous years.

Importance COVID-19 is causing high mortality worldwide. Developing models to quantify the risk of poor outcomes in infected patients could help develop strategies to shield the most vulnerable during de-confinement. Objective To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient9s risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. Design Multinational, distributed network cohorts. Setting We analyzed a federated network of electronic medical records and administrative claims data from 13 data sources and 6 countries, mapped to a common data model. Participants Model development used a patient population consisting of >2 million patients with a general practice (GP), emergency room (ER), or outpatient (OP) visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The model was validated on patients with a GP, ER, or OP visit in 2020 with a confirmed or suspected COVID-19 diagnosis across four databases from South Korea, Spain and the United States. Outcomes Age, sex, historical conditions, and drug use prior to index date were considered as candidate predictors. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 43,061 COVID-19 patients were included for model validation, after initial model development and validation using 6,869,127 patients with influenza or flu-like symptoms. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, and kidney disease) which combined with age and sex could discriminate which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.73-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.82-0.90. Calibration was overall acceptable, with overestimated risk in the most elderly and highest risk strata. Conclusions and relevance A 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and death. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus9 impact on morbidity and mortality.

ObjectivesRecent data suggest higher COVID-19 rates and severity in Black, Asian, and minority ethnic (BAME) communities. The mechanisms underlying such associations remain unclear. We aimed to study the association between ethnicity and risk of COVID-19 infection and disentangle any correlation with socioeconomic deprivation or previous comorbidity.DesignProspective cohort.SettingUK Biobank linked to Hospital Episode Statistics (HES) and COVID-19 tests until 14 April 2020.ParticipantsUK Biobank participants from England, excluding drop-outs and deaths.Main measuresCOVID-19 infection based on a positive PCR test. Ethnicity was self-reported and classified using Office of National Statistics groups. Socioeconomic status was based on index of multiple deprivation quintiles. Comorbidities were self-reported and completed from HES.AnalysesMultivariable Poisson analysis to estimate incidence rate ratios of COVID-19 infection according to ethnicity, adjusted for socioeconomic status, alcohol drinking, smoking, body mass index, age, sex, and comorbidity.Results415,582 participants were included, with 1,416 tested and 651 positive for COVID-19. The incidence of COVID-19 was 0.61% (95% CI: 0.46%-0.82%) in Black/Black British participants, 0.32% (0.19%-0.56%) in ‘other’ ethnicities, 0.32% (0.23%-0.47%) in Asian/Asian British, 0.30% (0.11%-0.80%) in Chinese, 0.16% (0.06%-0.41%) in mixed, and 0.14% (0.13%-0.15%) in White. Compared with White participants, Black/Black British participants had an adjusted relative risk (RR) of 3.30 (2.39-4.55), Chinese participants 3.00 (1.11-8.06), Asian/Asian British participants 2.04 (1.36-3.07), ‘other’ ethnicities 1.93 (1.08-3.45), and mixed ethnicities 1.07 (0.40-2.86). Socioeconomic status (adjusted RR 1.93 (1.51-2.46) for the most deprived), obesity (adjusted RR 1.04 (1.02-1.05) per kg/m2) and comorbid hypertension, chronic obstructive pulmonary disease, asthma, and specific renal diseases were also associated with increased risk of COVID-19.ConclusionsCOVID-19 rates in the UK are higher in BAME communities, those living in deprived areas, obese patients, and patients with previous comorbidity. Public health strategies are needed to reduce COVID-19 infections among the most susceptible groups.

BackgroundTo date, characterisation studies of COVID-19 have focussed on hospitalised or intensive care patients. We report for the first time on the natural history of COVID-19 disease from first diagnosis, including both outpatient and hospital care.MethodsData was obtained from SIDIAP, a primary care records database covering >6 million people (>80% of the population of Catalonia), linked to COVID-19 RT-PCR tests, hospital emergency and inpatient, and mortality registers. All participants >=15 years, diagnosed with COVID-19 in outpatient between 15th March and 24th April 2020 (10thApril for outcome studies) were included. Baseline characteristics, testing, and 30-day outcomes (hospitalisation for COVID-19 and all-cause fatality) were analysed.ResultsA total of 121,263 and 95,467 COVID-19 patients were identified for characterisation and outcome studies, respectively. Women (57.8%) and age 45-54 (20.2%) were predominant. 44,709 were tested, with 32,976 (73.8%) PCR+. From 95,467 cases, a 14.6% [14.4–14.9] were hospitalised in the month after diagnosis, with male predominance (19.2% vs 11.3%), peaking at age 75-84. Overall 30-day fatality was 4.0% [95%CI 3.9%-4.2%], higher in men (4.8%) than women (3.4%), increasing with age, and highest in those residing in nursing homes (25.3% [24.2% to 26.4%]).ConclusionsCOVID-19 is seen in all age-sex strata, but severe forms of disease cluster in older men and nursing home residents. Although initially managed in primary care, 15% of cases require hospitalization within a month, with overall fatality of 4%.

BackgroundHydroxychloroquine has recently received Emergency Use Authorization by the FDA and is currently prescribed in combination with azithromycin for COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin.MethodsNew user cohort studies were conducted including 16 severe adverse events (SAEs). Rheumatoid arthritis patients aged 18+ and initiating hydroxychloroquine were compared to those initiating sulfasalazine and followed up over 30 days. Self-controlled case series (SCCS) were conducted to further establish safety in wider populations. Separately, SAEs associated with hydroxychloroquine- azithromycin (compared to hydroxychloroquine-amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, Netherlands, Spain, UK, and USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (CalHRs) according to drug use. Estimates were pooled where I2ResultsOverall, 956,374 and 310,350 users of hydroxychloroquine and sulfasalazine, and 323,122 and 351,956 users of hydroxychloroquine-azithromycin and hydroxychloroquine-amoxicillin were included. No excess risk of SAEs was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. SCCS confirmed these findings. However, when azithromycin was added to hydroxychloroquine, we observed an increased risk of 30-day cardiovascular mortality (CalHR2.19 [1.22- 3.94]), chest pain/angina (CalHR 1.15 [95% CI 1.05-1.26]), and heart failure (CalHR 1.22 [95% CI 1.02- 1.45])ConclusionsShort-term hydroxychloroquine treatment is safe, but addition of azithromycin may induce heart failure and cardiovascular mortality, potentially due to synergistic effects on QT length. We call for caution if such combination is to be used in the management of Covid-19.Trial registration numberRegistered with EU PAS; Reference number 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/Covid19EstimationHydroxychloroquine.Funding sourcesThis research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and Senior Research Fellowship (DPA), US National Institutes of Health, Janssen Research & Development, IQVIA, and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea [grant number: HI16C0992]. Personal funding included Versus Arthritis [21605] (JL), MRC-DTP [MR/K501256/1] (JL), MRC and FAME (APU). The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. No funders had a direct role in this study. The views and opinions expressed are those of the authors and do not necessarily reflect those of the Clinician Scientist Award programme, NIHR, NHS or the Department of Health, England.

Background:Recent studies show possible mechanisms of microbiota alterations that lead to the development of Ankylosing spondylitis (AS). These disturbances in the microbiota can be caused by long-term and high dose intake of antibioticsObjectives:To analyze the association between antibiotic/s use and the risk of developing AS.Methods:Population based case-control study using electronic medical records data from SIDIAP, covering >80% of the population in Catalonia, Spain. AS diagnoses with 2+ years data available were matched to up to 5:1 controls of same age, sex, and GP practice, and similar follow-up. Tracking of antibiotic use in the previous two years was done through pharmacy dispensation data standardized with ATC codes, and categorized in terms of recency of use and quartiles of cumulative dose. Adjusted odds ratios were estimated using conditional logistic regression analyzing antibiotic use (yes/no), recency of intake (current, recent, past, no use) and cumulative dose (quartiles of daily defined doses in the previous two years). All analyses were adjusted for age, body mass index, smoking, co-morbidity, socio-economic deprivation and number of GP visits as a proxy for healthcare resource use.Results:The study included 4,493 cases diagnosed with AS and 22,016 controls. 46.3% of cases and 28.2% of controls had taken antibiotics. An association between taking beta-lactams (OR 1.18 [95% CI: 1.09-1.28]) and taking macrolides (OR 1.34 [95% CI: 1.18-1.52]) and getting diagnosed with AS was found. This association was stronger with current/recent use (Figure 1), but no dose-response pattern was seen (Figure 2).Figure 1.Recency of use and AS diagnosis.Figure 2.Cumulative use and AS diagnosisConclusion:There is an association between use of certain types of antibiotics and the diagnosis of AS, but no dose-response gradient. These data do not support a causal effect of cumulative antibiotic use on the development of AS. More research is needed on the impact of microbiome disturbance on the risk of developing AS.Acknowledgments:Partial support received from the Oxford NIHR Biomedical Research Centre (BRC)Disclosure of Interests:Ana Pascual-Dapena: None declared, Albert Prats-Uribe: None declared, Daniel Prieto-Alhambra Grant/research support from: Professor Prieto-Alhambra has received research Grants from AMGEN, UCB Biopharma and Les Laboratoires Servier, Consultant of: DPA’s department has received fees for consultancy services from UCB Biopharma, Speakers bureau: DPA’s department has received fees for speaker and advisory board membership services from Amgen

Background Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis. Methods In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I2 value was less than 0·4. Findings The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12–2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22–3·95]), chest pain or angina (1·15 [1·05–1·26]), and heart failure (1·22 [1·02–1·45]). Interpretation Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit–risk trade-off when counselling those on hydroxychloroquine treatment. Funding National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.

In 2012, the Spanish government enforced a healthcare exclusion policy against undocumented immigrants. The newly elected government has recently derogated this policy. To analyze how this decree could have affected population health, we looked at primary health patients who would have been excluded and compared with a matched sample of non-excluded patients. Potentially excluded patients had decreased odds of: depression, chronic obstructive pulmonary disease, dyslipidaemia, heart failure and hypertension while diabetes mellitus rates were similar to non-excluded. Infectious diseases were more frequent in potentially excluded population (HIV, tuberculosis and syphilis). The exclusion of patients impedes the control of infectious diseases at a community level.

Background: To date, characterisation studies of COVID-19 have focused on hospitalised or intensive-care patients We report for the first time on the natural h

Background There is uncertainty around whether to use unicompartmental knee replacement (UKR) or total knee replacement (TKR) for individuals with osteoarthritis confined to a single compartment of the knee. We aimed to emulate the design of the Total or Partial Knee Arthroplasty Trial (TOPKAT) using routinely collected data to assess whether the efficacy results reported in the trial translate into effectiveness in routine practice, and to assess comparative safety. Methods We did a population-based network study using data from four US and one UK health-care database, part of the Observational Health Data Sciences and Informatics network. The inclusion criteria were the same as those for TOPKAT; briefly, we identified patients aged at least 40 years with osteoarthritis who had undergone UKR or TKR and who had available data for at least one year prior to surgery. Patients were excluded if they had evidence of previous knee arthroplasty, knee fracture, knee surgery (except diagnostic), rheumatoid arthritis, infammatory arthropathies, or septic arthritis. Opioid use from 91–365 days after surgery, as a proxy for persistent pain, was assessed for all participants in all databases. Postoperative complications (ie, venous thromboembolism, infection, readmission, and mortality) were assessed over the 60 days after surgery and implant survival (as measured by revision procedures) was assessed over the 5 years after surgery. Outcomes were assessed in all databases, except for readmission, which was assessed in three of the databases, and mortality, which was assessed in two of the databases. Propensity score matched Cox proportional hazards models were fitted for each outcome. Calibrated hazard ratios (cHRs) were generated for each database to account for observed differences in control outcomes, and cHRs were then combined using meta-analysis. Findings 33 867 individuals who received UKR and 557 831 individuals who received TKR between Jan 1, 2005, and April 30, 2018, were eligible for matching. 32 379 with UKR and 250 377 with TKR were propensity score matched and informed the analyses. UKR was associated with a reduced risk of postoperative opioid use (cHR from meta-analysis 0·81, 95% CI 0·73–0·90) and a reduced risk of venous thromboembolism (0·62, 0·36–0·95), whereas no difference was seen for infection (0·85, 0·51–1·37) and readmission (0·79, 0·47–1·25). Evidence was insufficient to conclude whether there was a reduction in risk of mortality. UKR was also associated with an increased risk of revision (1·64, 1·40–1·94). Interpretation UKR was associated with a reduced risk of postoperative opioid use compared with TKR, which might indicate a reduced risk of persistent pain after surgery. UKR was associated with a lower risk of venous thromboembolism but an increased risk of revision compared with TKR. These findings can help to inform shared decision making for individuals eligible for knee replacement surgery.

BackgroundThe causal role of high-density lipoproteins (HDL) in coronary artery disease (CAD) has been questioned. Our aim was to analyze whether genetically determined quantitative and qualitative HDL characteristics were independently associated with CAD.MethodsWe designed a two-sample multivariate Mendelian randomization study with available genome-wide association summary data. We identified genetic variants associated with HDL cholesterol and apolipoprotein A-I quantity, HDL size, particle levels, and lipid content to define our genetic instrumental variables in one sample (Kettunen et al study,N=24,925) and analyzed their association with CAD risk in a different study (CARDIoGRAMplusC4D,N=184,305). We validated these results by defining our genetic variables in another database (METSIM,N=8,372) and studied their relationship with CAD in the CARDIoGRAMplusC4D dataset. To estimate the effect size of the associations of interest adjusted for other lipoprotein traits (potential pleiotropy) we used the Multi-trait-based Conditional & Joint analysis.ResultsGenetically determined HDL cholesterol and apolipoprotein A-I levels were not associated with CAD. HDL mean diameter (β=0.27 [95%CI=0.19; 0.35]), cholesterol levels in very large HDLs (β=0.29 [95%CI=0.17; 0.40]), and triglyceride content in very large HDLs (β=0.14 [95%CI=0.040; 0.25]) were directly associated with CAD risk, whereas the cholesterol content in medium-sized HDLs (β=-0.076 [95%CI=-0.10; −0.052]) was inversely related to this risk. These results were validated in the METSIM-CARDIoGRAMplusC4D data. Genetic variants linked to both HDL qualitative traits and CAD risk were located withinLIPC, theAPOE/C1/C4/C2cluster,APOB, PCIF1-PLTP, andTTC39B.ConclusionsSome HDL characteristics related to size, particle distribution, and triglyceride content are related to CAD risk whilst HDL cholesterol levels are not. This relationship could be mediated by the hepatic lipase; the apolipoproteins E, C-I, and B; the phospholipid transfer protein; and the tetratricopeptide repeat domain protein 39B, which arise as potential therapeutic targets in cardiovascular disease.

Background:RA is associated with an increased cardiovascular (CV) risk. csDMARDs are first-line treatments for RA and can mitigate this risk, but limited data exist on their CV effects. Previous trials have reported protective effects for methotrexate (MTX) and hydroxychloroquine (HCQ), but no similar data exist on sulfasalazine (SSZ) or leflunomide (LEF).Objectives:To assess the comparative effect of csDMARDs on the risk of myocardial infarction (MI) and stroke in RA patientsMethods:Data from 6 claims/electronic health records databases across Germany, US, and UK, all mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. A cohort study was conducted including patients ≥18 years old, with first RA diagnosis in 2005-2019, initiating csDMARD monotherapy with MTX, HCQ, SSZ, or LEF. Those with a prior diagnosis of other inflammatory arthritis or 2for heterogeneity Results:145,248 patients were included (MTX: 73,996, HCQ: 49,752, SSZ: 12,256, LEF: 9,244). Pooled rates of MI and stroke for MTX were 7.64 and 10.26 per 1,000 person years respectively. Detailed estimate cHRs are shown in Figure 1 for the intention to treat analysis. MI risk with SSZ and LEF was comparable to MTX. Risk of stroke was similar between LEF and MTX, but reduced for HCQ and SSZ compared to MTX, with pooled cHR (95% CI) 0.86 (0.78 to 0.95) and 0.71 (0.52 to 0.98) for HCQ and SSZ respectively. Similar results were found for “on treatment” analyses.Figure 1.Calibrated hazard ratios (cHRs) for MI and strokeConclusion:Overall, all four csDMARDs had similar effects on MI risk. HCQ and SSZ use were associated with a decreased risk of stroke compared to MTX. The observed differences may be attributable to differential effects on the atherosclerotic process, differential disease control, or both.Database estimates not reported where adequate covariate balance not attained. Meta-analysis results not reported where I2>0.4. MEDICARE did not pass diagnostics for SSZ and LEF analyses. cHR: calibrated Hazard Ratio; CI: Confidence Interval; MTX: Methotrexate; HCQ: Hydroxychloroquine; SSZ: sulphasalazine; LEF: Leflunomide; THIN: The Health Improvement Network (UK); Optum: Optum de-identified Clinformatics Datamart (US); MDCR: Medicare (US); GERMANY: IQVIA Disease Analyzer EMR (Germany); CCAE: IBM MarketScan Commercial Claims and Encounters (US); AMBEMR: IQVIA Ambulatory EMR (US)Database estimates not reported where adequate covariate balance not attained. Meta-analysis results not reported where I2>0.4. MEDICARE did not pass diagnostics for SSZ and LEF analyses. cHR: calibrated Hazard Ratio; CI: Confidence Interval; MTX: Methotrexate; HCQ: Hydroxychloroquine; SSZ: sulphasalazine; LEF: Leflunomide; THIN: The Health Improvement Network (UK); Optum: Optum de-identified Clinformatics Datamart (US); MDCR: Medicare (US); GERMANY: IQVIA Disease Analyzer EMR (Germany); CCAE: IBM MarketScan Commercial Claims and Encounters (US); AMBEMR: IQVIA Ambulatory EMR (US)Disclosure of Interests: :Albert Prats-Uribe: None declared, Ben Illingens: None declared, David Vizcaya Employee of: Bayer, James Weaver Shareholder of: J&J Shares, Grant/research support from: Full-time employment salary from Janssen, Consultant of: Janssen employee, Employee of: Janssen, Paid instructor for: Janssen employee, have instructed at conferences, Speakers bureau: Janssen employee, have spoken at conferences, Edward Burn: None declared, Ruta Sawant Shareholder of: AbbVie, Employee of: AbbVie, Karine Marinier Employee of: Servier, Patrick Ryan: None declared, Daniel Prieto-Alhambra Grant/research support from: Professor Prieto-Alhambra has received research Grants from AMGEN, UCB Biopharma and Les Laboratoires Servier, Consultant of: DPA’s department has received fees for consultancy services from UCB Biopharma, Speakers bureau: DPA’s department has received fees for speaker and advisory board membership services from Amgen

This study aims to estimate the prevalence of autism spectrum disorders (ASD) in 2017 and the ASD diagnosis incidence between 2009 and 2017 in children living in Catalonia region in Spain, and their temporal and geographical variability. We used administrative data for all children aged 2–17 years who were insured in the public Catalan Health System between 2009 and 2017. We identified all ASD cases diagnosed between 2009 and 2017 (ICD-9 codes 299.0, 299.1, 299.8, and 299.9). We estimated the ASD prevalence in 2017 and the overall annual incidence between 2009 and 2017, then stratified by sex, age group, and healthcare area. We used Poisson regression models to assess temporal trends in the incidence and mixed-effects Poisson regression models to assess geographical variability. We observed an ASD prevalence of 1.23% (95% confidence interval [CI] 1.21–1.25) in 2017, with 1.95% (95% CI 1.92–1.99) for boys and 0.46% (95% CI 0.44–0.48) for girls, the highest prevalence being in 11- to 17-year-olds (1.80%, 95% CI 1.76–1.83). The ASD diagnosis incidence increased from 0.07% (95% CI 0.06–0.09) in 2009 to 0.23% (95% CI 0.21–0.24) in 2017, with a higher increase in girls, and in children aged 2–5 years at the time of diagnosis. We only observed geographical differences in prevalence in the 2017 data. We also detected a threefold increase in the diagnosis incidence overall, which was even more pronounced in girls and at early ages. In conclusion, the ASD prevalence observed in this study was 1.23% in 2017, with a sex ratio of 4.5 in favor of boys, which is consistent with previous studies. Autism Res2019. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: Autism spectrum disorders (ASD) are currently well known in our society as one of the most common neurodevelopmental disorders during childhood. The results of our study showed that, in 2017 in Catalonia, slightly more than one in a 100 children had an ASD diagnosis, it was more common in boys than in girls, and also in older children. In addition, between 2009 and 2017, we observed an increase in the number of new cases diagnosed each year. The data presented in this study will assist in planning and evaluating the needs of health services in this geographical region. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc., The research described in this article was conducted under contract with the Health Effects Institute (HEI), an organization funded jointly by the United States Environmental Protection Agency (EPA; Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. M.G. is funded by a Miguel Servet fellowship (MS13/00054, CP18/00018) from the Spanish Institute of Health Carlos III. A.P. is supported by the Medical Research Council (grant numbers MR/K501256/1, MR/N013468/1). The authors would like to express their gratitude to Joan Albert d'Escofet, from the Agency for Health Quality and Assessment of the Catalan Health Department, for providing essential data and support for this study. The research materials supporting this publication can be accessed by contacting the corresponding author on reasonable request.

Background To determine the risk of hospital readmission and associated factors in patients with a positive sample for multidrug-resistant microorganisms (MRM) and to analyze whether there is a higher risk of hospital readmission with some of the more common MRM. Methods Retrospective cohort study (2012–16) performed in a tertiary-care teaching hospital in Barcelona. Patients were divided into two groups, depending on the presence or absence of an MRM-positive sample during hospital admission. Logistic regression models were used to estimate the risk of hospital readmission in the first 30 and 90 days, and the first year for patients with an MRM-positive sample compared with those without. The models were stratified by the presence or absence of an MRM-positive sample and by grouped Charlson comorbidity index. Results We included 983 patients with an MRM-positive sample and 39 323 patients without. The risk of hospital readmission in the first 30 days was 41% higher in admitted patients with an MRM-positive sample (95%CI=1.17 to 1.69) than in those without. Stratified models showed similar results to the overall results for all Charlson comorbidity index groups. When the models were stratified by the presence of an MRM-positive sample, methicillin-resistant Staphylococcus aureus showed the highest risk of readmissions within the more common MRM [103% (95%CI=1.10 to 3.75)]. Conclusion MRMs seem to be an important risk factor for hospital readmissions both among patients with and without comorbidities. Specific types of MRM may represent a higher risk for hospital readmissions than other MRMs, depending on the particular environment or hospital.

Background Childhood obesity is a strong predictor of adult obesity. Body mass index (BMI) has been associated with a dose-response increase in risk of osteoarthritis. However, studying causality in this association is difficult due to confounding factors Objectives To study the causal association of childhood and adult BMI on the risk of developing osteoarthritis using Mendelian Randomization (MR) methods Methods A two-step MR analysis was conducted. Exposures were determined by reviewing data from two genome-wide association study (GWAS) from >47,000 children describing 15 SNPs associated with childhood BMI, and another GWAS describing 97 SNPs related to BMI within 3 39 224 adults. For the outcomes, data gathered from a GWAS analysing 3 37 000 unrelated individuals in the UK BioBank was used to assess the association between the SNPs of interest and self-reported osteoarthritis and ICD10 linked hospital data: knee, hip and first carpo-metacarpal joint osteoarthritis. Traumatic eye injury from the same cohort was used as a negative control. An inverse variance weighted meta-analysis estimator was used to test for association. In presence of heterogeneity, a weighted median approach was used and, when necessary, pleiotropy was controlled for using the MR Egger method. All analyses were performed using the TwoSampleMR package in R Results 14 and 91 SNPs associated with BMI in the childhood and adulthood were identified in the UK BioBank GWAS respectively. Association between childhood BMI and adult OA was seen for self-reported and hip and knee OA. Adult BMI also appeared associated with them [figure 1]. Conversely, no associations were found between BMI (either childhood or adult) and Hand OA risk. Finally, no relationship was seen between BMI and traumatic eye injury (negative control) Conclusions Higher BMI in both childhood and adulthood overweight/obesity cause an excess in risk of knee and hip osteoarthritis. Whilst the effect of adult BMI seems stronger on knees, childhood BMI might impact both knee and hip osteoarthritis risk similarly. Age-specific associations should be treated with caution given significant overlap between loci associated with BMI across age ranges. In contrast, our findings contradict previous cohorts that found an association between overweight/obesity and hand osteoarthritis, which could be explained by confounding factors such as manual work or related socio-economic factors. The association between increased BMI and knee or hip osteoarthritis adds further impetus to strategies for the prevention of childhood and adult obesity in order to reduce subsequent disability. Disclosure of Interest None declared

Este artículo contiene 13 páginas, 6 figuras, 1 tabla., Background: On February 1st 2016 the WHO declared the Zika Virus (ZIKV) infection a worldwide public health emergency because of its rapid expansion and severe complications, such as Guillain-Barré Syndrome or microcephaly in newborn. The huge amount of people traveling to endemic areas and the presence of Aedes albopictus in Barcelona increase the risk of autochtonous transmission. The objective of this study was to describe the first ZIKV cases diagnosed in our city and to analyze the surveillance, prevention, and control measures implemented to avoid autochthonous transmission. Methods: An observational cross-sectional population-based study in Barcelona, Spain was performed.An analysis of the socio-demographic, epidemiological, clinical characteristics, and mosquito control activities of the ZIKV cases detected between January 1st and December 2016 was carried out using a specific ZIKV epidemiological survey of the Barcelona Public Health Agency. Results: A total of 118 notifications of possible ZIKV infections were received, and 44 corresponded to confirmed cases in Barcelona residents.Amongst these, the median age was 35 years and 57% were women. All cases were imported, 48% were Spanish-born and 52% foreign-born. Dominican Republic was the most visited country amongst foreign-born patients and Nicaragua amongst Spanish-born. The most frequent symptoms were exanthema, fever, and arthralgia. Among the 24 diagnosed women, 6 (25%) were pregnant. There was one case of microcephaly outside Barcelona city. Entomological inspections were done at the homes of 19 cases (43.2% of the total) and in 34 (77.3%) public spaces. Vector activity was found in one case of the 44 confirmed cases, and 134 surveillance and vector control were carried out associated to imported ZIKV cases. In all cases prevention measures were recommended to avoid mosquito bites on infected cases.Conclusion: Epidemiological and entomological surveillance are essential for the prevention of autochthonous transmission of arbovirosis that may have a great impact on Public Health.The good coordination between epidemiologists, entomologists, microbiologists, and clinicians is a priority in a touristic city with an intense relationship with endemic countries to minimize the risk of local transmission by competent vectors., MJ, JG, NR, and JM receives funds from 2014 SGR 26 grant from the Department d’Universitats, Recerca i Societat de la Informacio de la Generalitat de Catalunya (AGAUR) and from RD12/0018/0010 grant, ISCIII RETIC (MICINN, Spain). PROSICS authors are supported by the 6th National Plan (PN) of Research + Development + Innovation (I + D + I) 2008–2011, ISCIII-General Division Networks and Cooperative Research Centres + FEDER funds + Collaborative Research Network on Tropical Diseases (RICET): RD12/0018/0020.

ObjectivePatients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.DesignMultinational network cohort studySettingElectronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea).ParticipantsAll patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included.Main outcome measures30-day complications during hospitalisation and deathResultsWe studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%).ConclusionsPatients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases.What is already known about this topicPatients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions.What this study addsMost people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.