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Abstract Sclerostin inhibitors protect against osteoporotic fractures, but their cardiovascular safety remains unclear. We conducted a cis-Mendelian randomisation analysis to estimate the causal effect of sclerostin levels on cardiovascular risk factors. We meta-analysed three GWAS of sclerostin levels including 49,568 Europeans and selected 2 SNPs to be used as instruments. We included heel bone mineral density and hip fracture risk as positive control outcomes. Public GWAS and UK Biobank patient-level data were used for the study outcomes, which include cardiovascular events, risk factors, and biomarkers. Lower sclerostin levels were associated with higher bone mineral density and 85% reduction in hip fracture risk. However, genetically predicted lower sclerostin levels led to 25–85% excess coronary artery disease risk, 40% to 60% increased risk of type 2 diabetes, and worse cardiovascular biomarkers values, including higher triglycerides, and decreased HDL cholesterol levels. Results also suggest a potential (but borderline) association with increased risk of myocardial infarction. Our study provides genetic evidence of a causal relationship between reduced levels of sclerostin and improved bone health and fracture protection, but increased risk of cardiovascular events and risk factors.

Abstract Understanding the genetic basis of COVID-19 vaccine seroconversion is crucial to study the role of genetics on vaccine effectiveness. In our study, we used UK Biobank data to find the genetic determinants of COVID-19 vaccine-induced seropositivity and breakthrough infections. We conducted four genome-wide association studies among vaccinated participants for COVID-19 vaccine seroconversion and breakthrough susceptibility and severity. Our findings confirmed a link between the HLA region and seroconversion after the first and second doses. Additionally, we identified 10 genomic regions associated with breakthrough infection (SLC6A20, ST6GAL1, MUC16, FUT6, MXI1, MUC4, HMGN2P18-KRTCAP2, NFKBIZ and APOC1), and one with breakthrough severity (APOE). No significant evidence of genetic colocalisation was found between those traits. Our study highlights the roles of individual genetic make-up in the varied antibody responses to COVID-19 vaccines and provides insights into the potential mechanisms behind breakthrough infections occurred even after the vaccination.

Objectives To characterise the exposure to valproate within a cohort of pregnant women using electronic health records (EHRs) from Catalonia (System for the Development of Research in Primary Care, SIDIAP).Design Drug-utilisation cohort study covering the period from January 2011 to June 2020. The study included pregnancy episodes of women from Catalonia identified by the algorithm.Setting Data were sourced from SIDIAP, a comprehensive EHR repository that includes information from various data sources: recorded prescriptions (both hospital and primary care), diagnoses and sociodemographic characteristics identified by primary care physicians, and sexual and reproductive health data from ASSIR (used by gynaecologists and midwives).Participants Women aged 12–50 with at least one pregnancy episode occurred during January 2011–June 2020 and at least a prescription of valproate during pregnancy.Primary and secondary outcomes Primary outcomes included valproate exposure, measured through prevalence and cumulative incidence in pregnancy episodes and by trimester. The impact of regulatory measures (risk mitigation measures, RMMs) was assessed, and prescriptions over time were analysed using interrupted time series analysis. Secondary outcomes included health issues, pregnancy outcomes, smoking habits and socioeconomic characteristics.Results A total of 99 605 pregnancies were identified, with at least 3.03‰ (95% CI 2.69‰ to 3.39‰) exposed to valproate at some point (302 pregnancies, 276 women). The median pregnancy duration was 38.30 weeks (IQR 12.6–40.1), and the median age at pregnancy was 32.37 years (IQR 27.20–36.56). Epilepsy was the most frequent health issue. The prevalence and cumulative incidence of valproate prescriptions decreased during pregnancy and increased postpregnancy. The RMMs implemented in 2014 led to a reduction in monthly valproate prescriptions during pregnancy in this cohort.Conclusions The study highlights the decline in valproate prescriptions during pregnancy due to RMMs and underscores the need for standardised methodologies in future studies to ensure the safety of pregnant patients and optimise scientific evidence.

Background: European countries have included healthcare workers (HCWs) among priority groups for COVID-19 vaccination. We established a multi-country hospital network to measure the SARS-CoV-2 incidence and effectiveness of COVID-19 vaccines among HCWs against laboratory-confirmed SARS-CoV-2 infection. Methods: HCWs from 19 hospitals in 10 countries participated in a dynamic prospective cohort study, providing samples for SARS-CoV-2 testing at enrolment and during weekly/fortnightly follow-up. We measured the incidence during pre-Delta (2 May–6 September 2021), Delta (7 September–14 December 2021), and Omicron (15 December 2021–2 May 2023) waves. Using Cox regression, we measured the relative vaccine effectiveness (rVE) of the first COVID-19 booster dose versus primary course alone during Delta and Omicron waves. Results: We included a total of 3015 HCWs. Participants were mostly female (2306; 79%), with a clinical role (2047; 68%), and had a median age of 44 years. The overall incidence of SARS-CoV-2 infection was 3.01/10,000 person-days during pre-Delta, 4.21/10,000 during Delta, and 23.20/10,000 during Omicron waves. rVE was 59% (95% CI: −25; 86) during Delta and 22% (1; 39) during Omicron waves. rVE was 51% (30; 65) 7–90 days after the first booster dose during the Omicron wave. Conclusions: The incidence of SARS-CoV-2 infection among HCWs was higher during the Omicron circulation period. The first COVID-19 vaccine booster provided additional protection against SARS-CoV-2 infection compared to primary course vaccination when recently vaccinated

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. 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, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

PurposeRoutinely 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 methodsWe 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.ResultsWe 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.ConclusionWe 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.

ObjectiveTo characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.Design and settingThis is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.ParticipantsTwo non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.OutcomesDemographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported.ResultsWe identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.ConclusionsCOVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

ObjectivePatients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.MethodsA multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization.ResultsWe studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%).ConclusionCompared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.

BackgroundWe 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.MethodsWe 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.ResultsWe 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.ConclusionsPatients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.ImpactThis study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

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

Abstract Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual’s hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.

ObjectiveTo investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents.DesignMultinational network cohort study.SettingHospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea.Participants303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020.Main outcome measuresPrescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19.ResultsOf 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

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.

Context : In March 2020, public health concerns resulted in school closure throughout the United States . The prolonged sport cessation may affect knee injury risk in high school athletes. The purpose of this study was to describe and compare risk of knee injuries in high school athletes during 2019-2020 and 2020-2021 academic years, and stratify by gender, severity, mechanism of injury, injury type, and knee anatomic region . Design : Historical-prospective cohort study . Methods : This historical- prospective cohort study included 176 schools in 6 states matched by sport participation in control and COVID years from July 1, 2019 to June 30,2021. Injury rates per 1000 athletes per year were calculated with 95% confidence intervals. A negative binomial regression was performed to assess potential differences in knee injuries between academic years . Results: 94, 847 and 72,521 high school athletes participated in the 2019-2020(19-20) and 2020-2021 (20-21) seasons. Knee injury risk was higher in the 20-21 season (19-20: 28.89% [27.82-29.96-]; 20-21: 33.82% [32.50-35.14]). Risk increased for male athletes from 2019- 2020 to 2020-2021 (19-20: 29.42% [28.01-30.83]; 20-21 : 40.32% [38.89-41.75]). Female knee injury risk was similar between years (19-20: 25.78% [24.29-27.27]; 20-21: 26.03% [24.31-27.75]). Knee injuries increased by a ratio of 1.2 ([95% CI, 1.1- 1.3], P <.001) during 2020-2021. Conclusions,· Knee injury risk and relative risk increased among males in 2020-2021. Results indicate changes in knee injury risk following return from COVID shelter in place among high school athletes and implicate potential negative downstream effects of interrupted sports training and participation on high school injury risk. [ABSTRACT FROM AUTHOR]

Objectives Despite growing evidence suggesting increased COVID-19 mortality among people from ethnic minorities, little is known about milder forms of SARS-CoV-2 infection. We sought to explore the association between ethnic background and the probability of testing, testing positive, hospitalisation, COVID-19 mortality and vaccination uptake.Design A multistate cohort analysis. Participants were followed between 8 April 2020 and 30 September 2021.Setting The UK Biobank, which stores medical data on around half a million people who were recruited between 2006 and 2010.Participants 405 541 subjects were eligible for analysis, limited to UK Biobank participants living in England. 23 891 (6%) of participants were non-white.Primary and secondary outcome measures The associations between ethnic background and testing, testing positive, hospitalisation and COVID-19 mortality were studied using multistate survival analyses. The association with single and double-dose vaccination was also modelled. Multistate models adjusted for age, sex and socioeconomic deprivation were fitted to estimate adjusted HRs (aHR) for each of the multistate transitions.Results 18 172 (4.5%) individuals tested positive, 3285 (0.8%) tested negative and then positive, 1490 (6.9% of those tested positive) were hospitalised, and 129 (0.6%) tested positive at the moment of hospital admission (ie, direct hospitalisation). Finally, 662 (17.4%) died after admission. Compared with white participants, Asian participants had an increased risk of negative to positive transition (aHR 1.24 (95% CI 1.02 to 1.52)), testing positive (95% CI 1.44 (1.33 to 1.55)) and direct hospitalisation (1.61 (95% CI 1.28 to 2.03)). Black participants had an increased risk of hospitalisation following a positive test (1.71 (95% CI 1.29 to 2.27)) and direct hospitalisation (1.90 (95% CI 1.51 to 2.39)). Although not the case for Asians (aHR 1.00 (95% CI 0.98 to 1.02)), black participants had a reduced vaccination probability (0.63 (95% CI 0.62 to 0.65)). In contrast, Chinese participants had a reduced risk of testing negative (aHR 0.64 (95% CI 0.57 to 0.73)), of testing positive (0.40 (95% CI 0.28 to 0.57)) and of vaccination (0.78 (95% CI 0.74 to 0.83)).Conclusions We identified inequities in testing, vaccination and COVID-19 outcomes according to ethnicity in England. Compared with whites, Asian participants had increased risks of infection and admission, and black participants had almost double hospitalisation risk, and a 40% lower vaccine uptake.

# Background Upper quarter injuries are a serious problem in high school sports. The distinctive differences in males and females and within sports concerning specific upper quarter body parts necessitates the need to evaluate these injuries across these groups. The COVID-19 pandemic has created an opportunity to evaluate the potential added burden abrupt and prolonged sport stoppage had on upper quarter injury risk. # Hypothesis/Purpose To 1) describe and compare upper quarter injury rates and risk in high school athletes in the 2019-2020 and 2020-2021 academic school years; 2) examine injuries by gender, sport, injury type, and location of injury. # Methods An ecological study of the athletes from 176 high schools over six states, matching high schools between 2019-2020 (19-20) and 2020-2021 (20-21) years was performed. Injuries were reported by at least one high school athletic trainer assigned to each school into a centralized database and data collected from July 1, 2019, to June 30, 2021. Injury rates were calculated per 1,000 athletes per academic year. Interrupted time series models assessed the incidence ratio between academic years. # Results A total of 98,487 athletes from all sports participated in 19-20 and 72,521 in 20-21. Upper quarter injury rates increased in from 19-20 \[41.9 (40.6, 43.1)\] to 20-21 \[50.7 (48.1, 51.3)\]. Upper quarter injury risk \[1.5 (1.1, 2.2)\] was greater in 20-21 compared to 19-20. Females did not demonstrate increased injury rates between 19-20 \[31.1 (29.4, 32.7)\] to 20-21 \[28.1 (26.4, 30.0)\]. Males reported increased injury rates from 19-20 \[50.3 (48.5, 52.2)\] to 20-21 \[67.7 (65.2, 70.2)\]. Increased injury for the shoulder, elbow, and hand were reported in 20-21. Collision, field, and court upper quarter injury rates were increased in 20-21. # Discussion Upper quarter injury rates and injury risk were greater during the 2020-2021 school year than in the prior year. Males demonstrated increased upper quarter injury rates, while females did not. Return to play protocols for high school athletes should be considered following abrupt sport stoppage. # Level of Evidence 2

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 Primària 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

Several vaccines have been approved for Covid-19 and effectiveness data could guide public health decisions on vaccine usage. Here, the authors compare effectiveness of BNT162b2 and ChAdOx1 vaccines in people over 50 in the UK and find that BNT162b2 confers lower risk of infection and hospitalization.

Abstract Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. 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, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data. We studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression.Methods: Monte Carlo simulations were used to generate variable cluster-level confounding intensity [odds ratio (OR) = 1.01–2.5] and cluster size (20–1,000 patients per cluster). The following PS estimation strategies were compared: i) logistic regression omitting cluster-level confounders; ii) logistic regression including cluster-level confounders; iii) the same as ii) but including cross-level interactions; iv), v), and vi), similar to i), ii), and iii), respectively, but using REM instead of logistic regression. The same strategies were tested in a trial emulation of partial versus total knee replacement (TKR) surgery, where observational versus trial-based estimates were compared as a proxy for bias. Performance metrics included bias and mean square error (MSE).Results: In most simulated scenarios, logistic regression, including cluster-level confounders, led to the lowest bias and MSE, for example, with 50 clusters × 200 individuals and confounding intensity OR = 1.5, a relative bias of 10%, and MSE of 0.003 for (i) compared to 32% and 0.010 for (iv). The results from the trial emulation also gave similar trends.Conclusion: Logistic regression, including patient and surgeon-/hospital-level confounders, appears to be the preferred strategy for PS estimation.

BackgroundMandatory COVID-19 certification, showing proof of vaccination, negative test, or recent infection to access to public venues, was introduced at different times in the four countries of the UK. We aim to study its effects on the incidence of cases and hospital admissions.MethodsWe performed Negative binomial segmented regression and ARIMA analyses for four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences models to compare the latter three to England, as a negative control group, since it was the last country where COVID-19 certification was introduced. The main outcome was the weekly averaged incidence of COVID-19 cases and hospital admissions.ResultsCOVID-19 certification led to a decrease in the incidence of cases and hospital admissions in Northern Ireland, as well as in Wales during the second half of November. The same was seen for hospital admissions in Wales and Scotland during October. In Wales the incidence rate of cases in October already had a decreasing tendency, as well as in England, hence a particular impact of COVID-19 certification was less obvious. Method assumptions for the Difference-in-Differences analysis did not hold for Scotland. Additional NBSR and ARIMA models suggest similar results, while also accounting for correlation in the latter. The assessment of the effect in England itself leads one to believe that this intervention might not be strong enough for the Omicron variant, which was prevalent at the time of introduction of COVID-19 certification in the country.ConclusionsMandatory COVID-19 certification reduced COVID-19 transmission and hospitalizations when Delta predominated in the UK, but lost efficacy when Omicron became the most common variant.

# Background It is presently unclear how the cessation of high school sport has affected injury incidence at different socioeconomic levels. The COVID-19 pandemic may have disproportionately affected athletes of lower socioeconomic status, potentially increasing injury risk in this population. # Purpose To 1) Describe athlete injury incidence prior to and during the 2019-2020 and 2020-2021 school years in high school athletes by socioeconomic status; 2) Investigate the association between socioeconomic status and injury incidence in high school athletes. # Study Design Ecological Study # Methods High schools were matched between the 2019-2020 and 2020-2021 school years. All athletes from all sports were included. High school socioeconomic status was determined by the school district median household income. Socioeconomic strata were defined as $100,000. Injury incidence proportion with 95% confidence interval (95% CI) was calculated for each academic year. Mixed effects negative binomial models with robust errors were performed to assess the association between the incidence proportion ratio and high school median household income. Six states and 176 high schools were included (2019-2020: 98,487 athletes; 2020-2021: 72,521 athletes). # Results Injury incidence increased in three of four socioeconomic strata during the 2020-2021 year ($100,000: 2019-2020: 18.4 (18.1-18.8), 2020-2021: 17.3 (16.8-17.7)). An association was observed between injury incidence ratio and log median high school household income in 2019-2020 \[1.6 (1.1-2.5)\] but not 2020-2021 \[1.1 (0.8-1.6)\] school years. # Conclusions Athletes from lower socioeconomic high schools reported increased injury incidence compared to higher socioeconomic high schools during the 2020-2021 academic school year. These results highlight the increased COVID-19 pandemic vulnerability in athletes from lower socioeconomic high schools. High school sport stakeholders should consider how abrupt sport stoppage can affect lower socioeconomic athletes. # Level of Evidence 2

Abstract Background Before the COVID-19 pandemic, Sexually transmitted infections (STIs) were increasing in Europe, and Spain and Catalonia were not an exception. Catalonia has been one of the regions with the highest number of COVID-19 confirmed cases in Spain. The objective of this study was to estimate the magnitude of the decline, due to the COVID-19 pandemic, in the number of STI confirmed cases in Catalonia during the lockdown and de-escalation phases. Methods Interrupted time series analysis was performed to estimate the magnitude of decline in the number of STI reported confirmed cases - chlamydia, gonorrhoea, syphilis, and lymphogranuloma venereum- in Catalonia since lockdown with historical data, from March 13th to August 1st 2020, comparing the observed with the expected values. Results We found that since the start of COVID-19 pandemic the number of STI reported cases was 51% less than expected, reaching an average of 56% during lockdown (50% and 45% during de-escalation and new normality) with a maximum decrease of 72% for chlamydia and minimum of 22% for syphilis. Our results indicate that fewer STIs were reported in females, people living in more deprived areas, people with no previous STI episodes during the last three years, and in the HIV negative. Conclusions The STI notification sharp decline was maintained almost five months after lockdown started, well into the new normality. This fact can hardly be explained without significant underdiagnosis and underreporting. There is an urgent need to strengthen STI/HIV diagnostic programs and services, as well as surveillance, as the pandemic could be concealing the real size of the already described re-emergence of STIs in most of the European countries.

Sclerostin inhibitors protect against osteoporotic fractures, but their cardiovascular safety remains unclear. We conducted a cis-Mendelian randomisation analysis to estimate the causal effect of sclerostin levels on cardiovascular risk factors. We meta-analysed three GWAS of sclerostin levels including 49,568 Europeans and selected 2 SNPs to be used as instruments. We included heel bone mineral density and hip fracture risk as positive control outcomes. Public GWAS and UK Biobank patient-level data were used for the study outcomes, which include cardiovascular events, risk factors, and biomarkers. Lower sclerostin levels were associated with higher bone mineral density and 85% reduction in hip fracture risk. However, genetically predicted lower sclerostin levels led to 25–85% excess coronary artery disease risk, 40% to 60% increased risk of type 2 diabetes, and worse cardiovascular biomarkers values, including higher triglycerides, and decreased HDL cholesterol levels. Results also suggest a potential (but borderline) association with increased risk of myocardial infarction. Our study provides genetic evidence of a causal relationship between reduced levels of sclerostin and improved bone health and fracture protection, but increased risk of cardiovascular events and risk factors. Sclerostin inhibitors enhance bone health and reduce fracture risk but may increase cardiovascular risk. Here, authors show that lower sclerostin levels are associated with a higher risk of coronary artery disease, type 2 diabetes, and adverse cardiovascular biomarkers across the lifespan. [ABSTRACT FROM AUTHOR]

Background: European countries have included healthcare workers (HCWs) among priority groups for COVID-19 vaccination. We established a multi-country hospital network to measure the SARS-CoV-2 incidence and effectiveness of COVID-19 vaccines among HCWs against laboratory-confirmed SARS-CoV-2 infection. Methods: HCWs from 19 hospitals in 10 countries participated in a dynamic prospective cohort study, providing samples for SARS-CoV-2 testing at enrolment and during weekly/fortnightly follow-up. We measured the incidence during pre-Delta (2 May–6 September 2021), Delta (7 September–14 December 2021), and Omicron (15 December 2021–2 May 2023) waves. Using Cox regression, we measured the relative vaccine effectiveness (rVE) of the first COVID-19 booster dose versus primary course alone during Delta and Omicron waves. Results: We included a total of 3015 HCWs. Participants were mostly female (2306; 79%), with a clinical role (2047; 68%), and had a median age of 44 years. The overall incidence of SARS-CoV-2 infection was 3.01/10,000 person-days during pre-Delta, 4.21/10,000 during Delta, and 23.20/10,000 during Omicron waves. rVE was 59% (95% CI: −25; 86) during Delta and 22% (1; 39) during Omicron waves. rVE was 51% (30; 65) 7–90 days after the first booster dose during the Omicron wave. Conclusions: The incidence of SARS-CoV-2 infection among HCWs was higher during the Omicron circulation period. The first COVID-19 vaccine booster provided additional protection against SARS-CoV-2 infection compared to primary course vaccination when recently vaccinated <90 days. This multi-country HCW cohort study addressing infection as the main outcome is crucial for informing public health interventions for HCWs. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of COVID-19 vaccine seroconversion is crucial to study the role of genetics on vaccine effectiveness. In our study, we used UK Biobank data to find the genetic determinants of COVID-19 vaccine-induced seropositivity and breakthrough infections. We conducted four genome-wide association studies among vaccinated participants for COVID-19 vaccine seroconversion and breakthrough susceptibility and severity. Our findings confirmed a link between the HLA region and seroconversion after the first and second doses. Additionally, we identified 10 genomic regions associated with breakthrough infection (SLC6A20, ST6GAL1, MUC16, FUT6, MXI1, MUC4, HMGN2P18-KRTCAP2, NFKBIZ and APOC1), and one with breakthrough severity (APOE). No significant evidence of genetic colocalisation was found between those traits. Our study highlights the roles of individual genetic make-up in the varied antibody responses to COVID-19 vaccines and provides insights into the potential mechanisms behind breakthrough infections occurred even after the vaccination. Here, the authors use a large cohort to corroborate a link between genetic variants in the HLA region and COVID−19 vaccine responses, and locate 10 independent genetic loci significantly associated with breakthrough infection phenotypes after the vaccination. These findings support the notion that genetics play important roles in COVID−19 response and effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. Methods: Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19–59 and ≥ 60 years old). Seasonality‐adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. Results: Post‐RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19–59) and 19.29% (≥$$ \ge $$ 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19–59) and 41.47% (≥$$ \ge $$ 60) decreases. Slope analysis indicated increases for East Scotland (19–59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's ≥$$ \ge $$ 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19–59 group. Both models showed no post‐modelling autocorrelations across databases, yet Barts Health's residuals were non‐normally distributed with non‐constant variance. Conclusions: Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non‐normal white noise using both methods. [ABSTRACT FROM AUTHOR]

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

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.

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.