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Vincent Lo Re III,1,2,* Noelle M Cocoros,3,4,* Rebecca A Hubbard,2 Sarah K Dutcher,5 Craig W Newcomb,2 John G Connolly,3,4 Silvia Perez-Vilar,5 Dena M Carbonari,2 Maria E Kempner,3,4 José J Hernández-Muñoz,5 Andrew B Petrone,3,4 Allyson M Pishko,6 Meighan E Rogers Driscoll,3,4 James T Brash,7 Sean Burnett,8,9 Catherine Cohet,10 Matthew Dahl,8,11 Terese A DeFor,12 Antonella Delmestri,13 Djeneba Audrey Djibo,14 Talita Duarte-Salles,15,16 Laura B Harrington,17 Melissa Kampman,18 Jennifer L Kuntz,19 Xavier Kurz,10 Núria Mercadé-Besora,15 Pamala A Pawloski,12 Peter R Rijnbeek,16 Sarah Seager,7 Claudia A Steiner,20,21 Katia Verhamme,16 Fangyun Wu,8,22 Yunping Zhou,23 Edward Burn,13 J Michael Paterson,8,22,* Daniel Prieto-Alhambra13,16,* 1Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 2Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 3Department of Population Medicine, Harvard Medical School, Boston, MA, USA; 4Harvard Pilgrim Healthcare Institute, Boston, MA, USA; 5Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA; 6Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 7IQVIA, Real World Solutions, Brighton, UK; 8Canadian Network for Observational Drug Effect Studies (CNODES), Toronto, Ontario, Canada; 9Therapeutics Initiative, University of British Columbia, Vancouver, British Columbia, Canada; 10Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Netherlands; 11Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, Manitoba, Canada; 12HealthPartners Institute, Bloomington, MN, USA; 13Pharmaco- and Device Epidemiology, Nuffield Department of Orthop

Katharina Platzbecker,1 Annemarie Voss,1 Jonas Reinold,1 Anne Elbrecht,2 Wolfgang Biewener,2 Daniel Prieto-Alhambra,3,4 Annika M Jödicke,3 Tania Schink1 1Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 2Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 3Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 4Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, NetherlandsCorrespondence: Tania Schink, Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Achterstrasse 30, Bremen, 28359, Germany, Tel +4942121856865, Email schink@leibniz-bips.dePurpose: Validation of outcomes allows measurement of and correction for potential misclassification and targeted adjustment of algorithms for case definition. The purpose of our study was to validate algorithms for identifying cases of acute myocardial infarction (AMI), stroke, and cardiovascular (CV) death using patient profiles, ie, chronological tabular summaries of relevant available information on a patient, extracted from pseudonymized German claims data.Patients and Methods: Based on the German Pharmacoepidemiological Research Database (GePaRD), 250 cases were randomly selected (50% males) for each outcome between 2016 and 2017 based on the inclusion criteria age ≥ 50 years and continuous insurance ≥ 1 year and applying the following algorithms: hospitalization with a main diagnosis of AMI (ICD-10-GM codes I21.- and I22.-) or stroke (I63, I61, I64) or death with a hospitalization in the 60 days before with a main diagnosis of CV disease. Patient profiles were built including (i) age and sex, (ii) hospitalizations incl. diagnoses, procedure

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 Epidemiolo

Antonio Naranjo,1 Daniel Prieto-Alhambra,2 Julián Sánchez-Martín,3 Alejandro Pérez-Mitru,3 Max Brosa3 1Department of Rheumatology, Hospital Universitario de Gran Canaria Dr. Negrín, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 35011, Spain; 2NIHR Musculoskeletal Biomedical Research Unit, University of Oxford, Oxford, UK; 3Market Access Area, Pharmalex Spain, Barcelona, SpainCorrespondence: Alejandro Pérez-Mitru, Market Access Area, Pharmalex Spain, C/ Comte d’Urgell, 240, 2-D, Barcelona, 08036, Spain, Tel +34 932 521 377, Fax +34 937 379 984, Email alejandro.perez@pharmalex.comPurpose: To assess the cost-effectiveness of a Fracture Liaison Service (FLS) compared with standard care for the secondary prevention of fragility fractures in Spain.Methods: Patients with osteoporosis and an initial fragility fracture who were candidates to initiate osteoporosis treatment (mean age 65 years, 90.7% female) were included in the model. Disease progression was simulated with a Markov model through seven health states (with and without osteoporosis treatment, subsequent hip, vertebral, forearm and humerus fracture, and death). A time horizon of 10 years and a 6-month duration per cycle was set. Clinical, economic, and quality of life parameters were estimated from the literature and Spanish clinical practice. Resource use and treatment patterns were validated by an expert panel. The Spanish National Health System (SNS) perspective was adopted, taking direct costs (€; 2020) into account. Effectiveness was measured in life-years gained (LYG) and quality-adjusted life years gained (QALYs). A discount rate of 3% was applied to costs and outcomes. The uncertainty of the parameters was assessed using deterministic, scenario and probabilistic sensitivity analyses (1000 iterations).Results: Setting up a FLS for the secondary prevention of fragility fractures in Spain would provide better osteoporosis treatment initiation and persistence. This would red

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 (0.3

Edith M Heintjes,1 Irene D Bezemer,1 Daniel Prieto-Alhambra,2,3 Elisabeth Smits,1 Helen P Booth,4 Daniel Dedman,4 Ying He,3 Fabian Hoti,5 Minna Vehkala,5 Stefan de Vogel,6 Noah Jamie Robinson,6 Kwame Appenteng,7 Fernie JA Penning-van Beest1 1PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands; 2Idiap Jordi Gol Primary Care Research Institute and CIBERFes, Universitat Autonoma de Barcelona and Instituto de Salud Carlos III, Barcelona, Catalonia, Spain; 3Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, England, UK; 4Clinical Practice Research Datalink (CPRD), London, UK; 5Statfinn - EPID Research, Espoo, Finland; 6Astellas Pharma Europe B.V., Leiden, Netherlands; 7Astellas Pharma Global Development, Inc., Northbrook, IL, USACorrespondence: Edith M HeintjesPHARMO Institute for Drug Outcomes Research, Van Deventerlaan 30-40, Utrecht, AE 3528, the NetherlandsTel +31 30 7440 800Email pharmo@pharmo.nlBackground: Mirabegron, indicated for the treatment of overactive bladder, is contraindicated in patients with severe uncontrolled hypertension (systolic blood pressure ≥ 180 mm Hg and/or diastolic blood pressure ≥ 110 mm Hg). In September 2015, a Direct Healthcare Professional Communication (DHPC) letter was disseminated as an additional risk minimisation measure.Purpose: To assess the effectiveness of the DHPC in reducing the proportions of patients with severe or non-severe uncontrolled hypertension at mirabegron initiation.Methods: An observational multi-database cohort study was undertaken using routinely collected healthcare data (December 2012–December 2016) from the PHARMO Database Network (Netherlands), SIDIAP database (Spain), CPRD (United Kingdom, UK) and national healthcare registers and electronic medical records from Finland. DHPC effectiveness was evaluated using interrupted time series analyses comparing trends and changes in monthly proportions of severe or non-severe uncontrolled hypertensive m

Samuel Hawley,1 M Sanni Ali,1,2 Klara Berencsi,1 Andrew Judge1,3,4 Daniel Prieto-Alhambra1,5 1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 2Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK; 3MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK; 4Department of Translational Health Sciences, University of Bristol, Bristol, UK; 5GREMPAL Research Group, Idiap Jordi Gol and CIBERFes, Universitat Autònoma de Barcelona and Instituto de Salud Carlos III, Barcelona, Spain Abstract: Interrupted time series (ITS) analysis is being increasingly used in epidemiology. Despite its growing popularity, there is a scarcity of guidance on power and sample size considerations within the ITS framework. Our aim of this study was to assess the statistical power to detect an intervention effect under various real-life ITS scenarios. ITS datasets were created using Monte Carlo simulations to generate cumulative incidence (outcome) values over time. We generated 1,000 datasets per scenario, varying the number of time points, average sample size per time point, average relative reduction post intervention, location of intervention in the time series, and reduction mediated via a 1) slope change and 2) step change. Performance measures included power and percentage bias. We found that sample size per time point had a large impact on power. Even in scenarios with 12 pre-intervention and 12 post-intervention time points with moderate intervention effect sizes, most analyses were underpowered if the sample size per time point was low. We conclude that various factors need to be collectively considered to ensure adequate power for an ITS study. We demonstrate a means of providing insight into underlying sample size requirements in ordinary least squares (OLS) ITS analysis of cumulative incidence measures

Sara Khalid,1,2 Sara Calderon-Larrañaga,3 Samuel Hawley,2 M Sanni Ali1,2,4 Andrew Judge,1,2,5 Nigel Arden,6 Tjeerd van Staa,7,8 Cyrus Cooper,2,9,10 Muhammad Kassim Javaid,2 Daniel Prieto-Alhambra2,11 1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, 2Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; 3Family and Community Medicine Teaching Unit of Granada. Cartuja University Health Centre. Andalusian Health Service (SAS), Granada, Spain; 4London School of Hygiene and Tropical Medicine, London, United Kingdom; 5Bristol NIHR Biomedical Research Centre, Musculoskeletal Research Unit, Southmead Hospital, University of Bristol, Bristol, United Kingdom; 6Arthritis Research UK Centre for Sport, Exercise, and Osteoarthritis, University of Oxford, Oxford, United Kingdom; 7Farr Institute, University of Manchester, Manchester, United Kingdom; 8Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; 9Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom; 10NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom; 11GREMPAL (Grup de Recerca en Malalties Prevalents de l’Aparell Locomotor) Research Group, Idiap Jordi Gol Primary Care Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain Purpose: This paper aims to compare the clinical effectiveness of oral anti-osteoporosis drugs based on the observed risk of fracture while on treatment in primary care actual practice.Materials and methods: We investigated two primary care records databases covering UK National Health Service (Clinical Practice Research Datalink, CPRD) and Catalan healthcare (Information System for Research in Primary Care, SIDIAP) patients during 1995–2014 and 2006

Edward Burn,1 Christopher J Edwards,2 David W Murray,1 Alan Silman,1 Cyrus Cooper,1,3 Nigel K Arden,1,3 Daniel Prieto-Alhambra,1,4 Rafael Pinedo-Villanueva1,3 1Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 2NIHR Wellcome Trust Clinical Research Facility, University Hospital Southampton, Southampton, UK; 3MRC Lifecourse Epidemiology Unit, Southampton University, Southampton, UK; 4GREMPAL Research Group, Idiap Jordi Gol and CIBERFes, Universitat Autonoma de Barcelona and Instituto de Salud Carlos III, Barcelona, Spain Purpose: To assess whether rheumatoid arthritis (RA) is associated with a greater risk of adverse events following total knee replacement (TKR) and total hip replacement (THR) than osteoarthritis (OA).Patients and methods: Individuals with a diagnosis of RA or OA were identified using primary care records. TKR and THR following diagnosis were identified using linked hospital records. Myocardial infarction (MI), prosthetic joint infection (PJI), venous thromboembolism (VTE), and death were identified within 90 days following surgery, and revision procedures over 10 years following surgery. The impact of RA compared to OA on the risk for these adverse events was assessed using Cox proportional hazard models. Univariable models, with diagnosis as the only explanatory variable, and multivariable models, with age, gender, and year of surgery first added and then a measure of other comorbidities also included, were estimated.Results: In all 20,763 individuals, with 10,260 TKR and 10,961 THR, were included in the analysis. Compared to those with OA, individuals with a diagnosis of RA had a greater incidence of MI over 90 days following TKR (OA: 0.28%, RA: 0.75%) and revision over 10 years following THR (OA: 5.55%, RA: 8.68%). Both of these differences were statistically significant with, for example, hazard ratios of 3.54 (1.44 to 8.73) for MI and 1.61 (1.06 to 2.46) for revision after controll

Elisa Martín-Merino,1 Amaia Calderón-Larrañaga,2,3 Samuel Hawley,4 Beatriz Poblador-Plou,3 Ana Llorente-García,1 Irene Petersen,5,6 Daniel Prieto-Alhambra4,7 1Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria, Division of Pharmacoepidemiology and Pharmacovigilance, Spanish Agency of Medicines and Medical Devices, Madrid, Spain; 2Aging Research Center, Karolinska Institutet, Stockholm University, Stockholm, Sweden; 3EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute, Aragon Health Research Institute, Miguel Servet University Hospital, Zaragoza, Spain; 4Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 5Department of Primary Care and Population Health, University College London, London, UK; 6Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 7GREMPAL (Grup de Recerca en Malalties Prevalents de l’Aparell Locomotor) Research Group, Idiap Jordi Gol and CIBERFes, Instituto de Salud Carlos III, Universitat Autonoma de Barcelona, Barcelona, Spain Background: Missing data are often an issue in electronic medical records (EMRs) research. However, there are many ways that people deal with missing data in drug safety studies.Aim: To compare the risk estimates resulting from different strategies for the handling of missing data in the study of venous thromboembolism (VTE) risk associated with antiosteoporotic medications (AOM). Methods: New users of AOM (alendronic acid, other bisphosphonates, strontium ranelate, selective estrogen receptor modulators, teriparatide, or denosumab) aged ≥50 years during 1998–2014 were identified in two Spanish (the Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria [BIFAP] and EpiChron co