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Schultze A, Martin I, Messina D, Bots S, Belitser S, José Carreras-Martínez J, Correcher-Martinez E, Urchueguía-Fornes A, Martín-Pérez M, García-Poza P, Villalobos F, Pallejà-Millán M, Alberto Bissacco C, Segundo E, Souverein P, Riefolo F, Durán CE, Gini R, Sturkenboom M, Klungel O, Douglas I. A comparison of four self-controlled study designs in an analysis of COVID-19 vaccines and myocarditis using five European databases. Vaccine 2024; 42:3039-3048. [PMID: 38580517 DOI: 10.1016/j.vaccine.2024.03.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/07/2024]
Abstract
INTRODUCTION The aim of this study was to assess the possible extent of bias due to violation of a core assumption (event-dependent exposures) when using self-controlled designs to analyse the association between COVID-19 vaccines and myocarditis. METHODS We used data from five European databases (Spain: BIFAP, FISABIO VID, and SIDIAP; Italy: ARS-Tuscany; England: CPRD Aurum) converted to the ConcePTION Common Data Model. Individuals who experienced both myocarditis and were vaccinated against COVID-19 between 1 September 2020 and the end of data availability in each country were included. We compared a self-controlled risk interval study (SCRI) using a pre-vaccination control window, an SCRI using a post-vaccination control window, a standard SCCS and an extension of the SCCS designed to handle violations of the assumption of event-dependent exposures. RESULTS We included 1,757 cases of myocarditis. For analyses of the first dose of the Pfizer vaccine, to which all databases contributed information, we found results consistent with a null effect in both of the SCRI and extended SCCS, but some indication of a harmful effect in a standard SCCS. For the second dose, we found evidence of a harmful association for all study designs, with relatively similar effect sizes (SCRI pre = 1.99, 1.40 - 2.82; SCRI post 2.13, 95 %CI - 1.43, 3.18; standard SCCS 1.79, 95 %CI 1.31 - 2.44, extended SCCS 1.52, 95 %CI = 1.08 - 2.15). Adjustment for calendar time did not change these conclusions. Findings using all designs were also consistent with a harmful effect following a second dose of the Moderna vaccine. CONCLUSIONS In the context of the known association between COVID-19 vaccines and myocarditis, we have demonstrated that two forms of SCRI and two forms of SCCS led to largely comparable results, possibly because of limited violation of the assumption of event-dependent exposures.
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Affiliation(s)
- Anna Schultze
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
| | - Ivonne Martin
- Department of Data Science and Biostatistics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Davide Messina
- Agenzia Regionale di Sanità (ARS), Florence, Toscana, Italy
| | - Sophie Bots
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Svetlana Belitser
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Juan José Carreras-Martínez
- Vaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), Valencia, Spain; CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
| | - Elisa Correcher-Martinez
- Vaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), Valencia, Spain; CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
| | - Arantxa Urchueguía-Fornes
- Vaccine Research Department, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO - Public Health), Valencia, Spain; CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
| | - Mar Martín-Pérez
- Spanish Agency for Medicines and Medical Devices (AEMPS), Madrid, Spain
| | | | - Felipe Villalobos
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Meritxell Pallejà-Millán
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Carlo Alberto Bissacco
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Elena Segundo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Patrick Souverein
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Fabio Riefolo
- Teamit Institute, Partnerships, Barcelona Health Hub, Barcelona, Spain
| | - Carlos E Durán
- Department of Data Science and Biostatistics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Rosa Gini
- Agenzia Regionale di Sanità (ARS), Florence, Toscana, Italy
| | - Miriam Sturkenboom
- Department of Data Science and Biostatistics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Olaf Klungel
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Ian Douglas
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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SCHUEMIE M, REPS J, BLACK A, DeFALCO F, EVANS L, FRIDGEIRSSON E, GILBERT JP, KNOLL C, LAVALLEE M, RAO GA, RIJNBEEK P, SADOWSKI K, SENA A, SWERDEL J, WILLIAMS RD, SUCHARD M. Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research. Stud Health Technol Inform 2024; 310:966-970. [PMID: 38269952 PMCID: PMC10868467 DOI: 10.3233/shti231108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.
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Affiliation(s)
- Martijn SCHUEMIE
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Biostatistics, UCLA, Los Angeles, CA, USA
| | - Jenna REPS
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Adam BLACK
- Observational Health Data Science and Informatics, New York, NY, USA
- Odysseus Data Services Inc., Cambridge, MA, USA
| | - Frank DeFALCO
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Lee EVANS
- Observational Health Data Science and Informatics, New York, NY, USA
- LTS Computing LLC, West Chester, PA, USA
| | - Egill FRIDGEIRSSON
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - James P. GILBERT
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Chris KNOLL
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Martin LAVALLEE
- Observational Health Data Science and Informatics, New York, NY, USA
- Virginia Commonwealth University, Richmond, VA, USA
| | - Gowtham A. RAO
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Peter RIJNBEEK
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Katy SADOWSKI
- Observational Health Data Science and Informatics, New York, NY, USA
- TrialSpark Inc., New York, NY, USA
| | - Anthony SENA
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joel SWERDEL
- Observational Health Data Science and Informatics, New York, NY, USA
- Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA
| | - Ross D. WILLIAMS
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marc SUCHARD
- Observational Health Data Science and Informatics, New York, NY, USA
- Department of Biostatistics, UCLA, Los Angeles, CA, USA
- VA Informatics and Computing Infrastructure, Department of Veterans Affairs, Salt Lake City, UT, USA
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Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e49185. [PMID: 38241067 PMCID: PMC10837764 DOI: 10.2196/49185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/06/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.
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Affiliation(s)
- Emily C Clark
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Sophie Neumann
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Stephanie Hopkins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Alyssa Kostopoulos
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Leah Hagerman
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
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Arshad F, Schuemie MJ, Bu F, Minty EP, Alshammari TM, Lai LYH, Duarte-Salles T, Fortin S, Nyberg F, Ryan PB, Hripcsak G, Prieto-Alhambra D, Suchard MA. Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance. Drug Saf 2023; 46:797-807. [PMID: 37328600 PMCID: PMC10345011 DOI: 10.1007/s40264-023-01324-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Vaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown. METHODS We assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes. RESULTS The historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined. CONCLUSION While serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.
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Affiliation(s)
- Faaizah Arshad
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
- Observational Health Data Sciences and Informatics, New York, NY, USA
| | - Martijn J Schuemie
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
- Observational Health Data Sciences and Informatics, New York, NY, USA
- Observational Health Data Analytics, Janssen R&D, Titusville, NJ, USA
| | - Fan Bu
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA
- Observational Health Data Sciences and Informatics, New York, NY, USA
| | - Evan P Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Lana Y H Lai
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen R&D, Titusville, NJ, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
| | - Patrick B Ryan
- Observational Health Data Sciences and Informatics, New York, NY, USA
- Observational Health Data Analytics, Janssen R&D, Titusville, NJ, USA
| | - George Hripcsak
- Observational Health Data Sciences and Informatics, New York, NY, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Medical Informatics Services, New York-Presbyterian Hospital, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
- Health Data Sciences, Medical Informatics, Erasmus Medical Center University, Rotterdam, The Netherlands
| | - Marc A Suchard
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
- Observational Health Data Sciences and Informatics, New York, NY, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA.
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Mbinta JF, Wang AX, Nguyen BP, Paynter J, Awuni PMA, Pine R, Sporle AA, Bowe S, Simpson CR. Herpes zoster vaccine safety in the Aotearoa New Zealand population: a self-controlled case series study. Nat Commun 2023; 14:4330. [PMID: 37468475 DOI: 10.1038/s41467-023-39595-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
In Aotearoa New Zealand, zoster vaccine live is used for the prevention of zoster and associated complications in adults. This study assessed the risk of pre-specified serious adverse events following zoster vaccine live immunisation among adults in routine clinical practice. We conducted a self-controlled case series study using routinely collected national data. We compared the incidence of serious adverse events during the at-risk period with the control period. Rate ratios were estimated using Conditional Poisson regression models. Falsification outcomes analyses were used to evaluate biases in our study population. From April 2018 to July 2021, 278,375 received the vaccine. The rate ratio of serious adverse events following immunisation was 0·43 (95% confidence interval [CI]: 0·37-0·50). There was no significant increase in the risk of cerebrovascular accidents, acute myocardial infarction, acute pericarditis, acute myocarditis, and Ramsay-Hunt Syndrome. The herpes zoster vaccine is safe in adults in Aotearoa New Zealand.
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Affiliation(s)
- James F Mbinta
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand.
| | - Alex X Wang
- School of Mathematics and Statistics, Wellington Faculty of Engineering, Victoria University of Wellington, Wellington, New Zealand
| | - Binh P Nguyen
- School of Mathematics and Statistics, Wellington Faculty of Engineering, Victoria University of Wellington, Wellington, New Zealand
| | - Janine Paynter
- Department of General Practice & Primary Healthcare, University of Auckland, Auckland, New Zealand
| | | | - Russell Pine
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Andrew A Sporle
- iNZight Analytics Ltd., Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand
| | - Steve Bowe
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand
| | - Colin R Simpson
- School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand.
- Usher Institute, The University of Edinburgh, Edinburgh, UK.
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Li X, Burn E, Duarte-Salles T, Yin C, Reich C, Delmestri A, Verhamme K, Rijnbeek P, Suchard MA, Li K, Mosseveld M, John LH, Mayer MA, Ramirez-Anguita JM, Cohet C, Strauss V, Prieto-Alhambra D. Comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with different covid-19 vaccines: international network cohort study from five European countries and the US. BMJ 2022; 379:e071594. [PMID: 36288813 PMCID: PMC9597610 DOI: 10.1136/bmj-2022-071594] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVE To quantify the comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with use of adenovirus based covid-19 vaccines versus mRNA based covid-19 vaccines. DESIGN International network cohort study. SETTING Routinely collected health data from contributing datasets in France, Germany, the Netherlands, Spain, the UK, and the US. PARTICIPANTS Adults (age ≥18 years) registered at any contributing database and who received at least one dose of a covid-19 vaccine (ChAdOx1-S (Oxford-AstraZeneca), BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), or Ad26.COV2.S (Janssen/Johnson & Johnson)), from December 2020 to mid-2021. MAIN OUTCOME MEASURES Thrombosis with thrombocytopenia syndrome or venous or arterial thromboembolic events within the 28 days after covid-19 vaccination. Incidence rate ratios were estimated after propensity scores matching and were calibrated using negative control outcomes. Estimates specific to the database were pooled by use of random effects meta-analyses. RESULTS Overall, 1 332 719 of 3 829 822 first dose ChAdOx1-S recipients were matched to 2 124 339 of 2 149 679 BNT162b2 recipients from Germany and the UK. Additionally, 762 517 of 772 678 people receiving Ad26.COV2.S were matched to 2 851 976 of 7 606 693 receiving BNT162b2 in Germany, Spain, and the US. All 628 164 Ad26.COV2.S recipients from the US were matched to 2 230 157 of 3 923 371 mRNA-1273 recipients. A total of 862 thrombocytopenia events were observed in the matched first dose ChAdOx1-S recipients from Germany and the UK, and 520 events after a first dose of BNT162b2. Comparing ChAdOx1-S with a first dose of BNT162b2 revealed an increased risk of thrombocytopenia (pooled calibrated incidence rate ratio 1.33 (95% confidence interval 1.18 to 1.50) and calibrated incidence rate difference of 1.18 (0.57 to 1.8) per 1000 person years). Additionally, a pooled calibrated incidence rate ratio of 2.26 (0.93 to 5.52) for venous thrombosis with thrombocytopenia syndrome was seen with Ad26.COV2.S compared with BNT162b2. CONCLUSIONS In this multinational study, a pooled 30% increased risk of thrombocytopenia after a first dose of the ChAdOx1-S vaccine was observed, as was a trend towards an increased risk of venous thrombosis with thrombocytopenia syndrome after Ad26.COV2.S compared with BNT162b2. Although rare, the observed risks after adenovirus based vaccines should be considered when planning further immunisation campaigns and future vaccine development.
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Affiliation(s)
- Xintong Li
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Can Yin
- Real World Solutions, IQVIA, Durham, NC, USA
| | | | - Antonella Delmestri
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Katia Verhamme
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mees Mosseveld
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Luis H John
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Miguel-Angel Mayer
- Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Faculty of Health and Life Sciences, University of Pompeu Fabra, Barcelona, Spain
| | - Juan-Manuel Ramirez-Anguita
- Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Faculty of Health and Life Sciences, University of Pompeu Fabra, Barcelona, Spain
| | - Catherine Cohet
- Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Netherlands
| | - Victoria Strauss
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
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