. 2023 Apr:58:101932.

doi: 10.1016/j.eclinm.2023.101932. Epub 2023 Apr 4.

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Erica A Voss^{1

2

3}, Azza Shoaibi^{1

3}, Lana Yin Hui Lai^{1

4}, Clair Blacketer^{1

2

3}, Thamir Alshammari^{1

5}, Rupa Makadia^{1

3}, Kevin Haynes³, Anthony G Sena^{1

2

3}, Gowtham Rao^{1

3}, Sebastiaan van Sandijk^{1

6}, Clement Fraboulet⁷, Laurent Boyer⁷, Tanguy Le Carrour⁸, Scott Horban⁹, Daniel R Morales^{10

11}, Jordi Martínez Roldán¹², Juan Manuel Ramírez-Anguita^{13

14}, Miguel A Mayer^{13

14}, Marcel de Wilde^{1

2}, Luis H John^{1

2}, Talita Duarte-Salles^{1

15}, Elena Roel¹⁵, Andrea Pistillo¹⁵, Raivo Kolde¹⁶, Filip Maljković¹⁷, Spiros Denaxas^{18

19

20}, Vaclav Papez^{18

19}, Michael G Kahn^{1

21}, Karthik Natarajan^{1

22

23}, Christian Reich^{1

24}, Alex Secora²⁴, Evan P Minty^{1

25}, Nigam H Shah^{1

26}, Jose D Posada^{1

27}, Maria Teresa Garcia Morales²⁸, Diego Bosca²⁹, Honorio Cadenas Juanino³⁰, Antonio Diaz Holgado³⁰, Miguel Pedrera Jiménez³¹, Pablo Serrano Balazote³¹, Noelia García Barrio³¹, Selçuk Şen³², Ali Yağız Üresin³², Baris Erdogan³³, Luc Belmans³⁴, Geert Byttebier³⁴, Manu L N G Malbrain^{34

35}, Daniel J Dedman³⁶, Zara Cuccu³⁶, Rohit Vashisht^{1

37}, Atul J Butte^{1

37

38}, Ayan Patel^{1

37}, Lisa Dahm^{1

38}, Cora Han^{1

38}, Fan Bu³⁹, Faaizah Arshad^{1

39}, Anna Ostropolets^{1

22}, Fredrik Nyberg⁴⁰, George Hripcsak^{1

22

23}, Marc A Suchard^{1

39

41}, Dani Prieto-Alhambra^{1

2

42}, Peter R Rijnbeek^{1

2}, Martijn J Schuemie^{1

3

39}, Patrick B Ryan^{1

3

22}

Affiliations

¹ OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA.
² Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
³ Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA.
⁴ Department of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
⁵ College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia.
⁶ Odysseus Data Services, Prague, Czechia.
⁷ Department of Medical Information, Assistance Publique - Hopitaux de Marseille, Marseille, France.
⁸ Easter Eggs, Paris, France.
⁹ Health Informatics Centre, University of Dundee, Dundee, UK.
¹⁰ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
¹¹ Department of Public Health, University of Southern Denmark, Odense, Denmark.
¹² Hospital del Mar, Department of Innovation and Digital Transformation, Barcelona, Spain.
¹³ Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.
¹⁴ Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
¹⁵ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain.
¹⁶ University of Tartu, Tartu, Estonia.
¹⁷ Heliant, Belgrade, Serbia.
¹⁸ University College London, Institute of Health Informatics, London, UK.
¹⁹ Health Data Research UK, London, UK.
²⁰ British Heart Foundation Data Science Centre, London, UK.
²¹ University of Colorado Anschutz Medical Campus, Denver, CO, USA.
²² Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²³ New York-Presbyterian Hospital, New York, NY, USA.
²⁴ IQVIA, Falls Church, VA, USA.
²⁵ O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Alberta, CA, USA.
²⁶ Stanford School of Medicine, Stanford University, Palo Alto, CA, USA.
²⁷ Department of Systems Engineering, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Instituto de Investigación, Hospital 12 de Octubre, Madrid, Spain.
²⁹ VeraTech for Health, Valencia, Spain.
³⁰ Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria (FIIBAP), Madrid, Spain.
³¹ Hospital Universitario 12 de Octubre, Madrid, Spain.
³² Division of Clinical Pharmacology, Department of Medical Pharmacology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye.
³³ Clinerion, Basel, Switzerland.
³⁴ Medaman BV, Geel, Flanders, Belgium.
³⁵ 1st Department Anaesthesiology and Intensive Therapy, Professor Critical Care Research, Medical University Lublin, Lublin, Poland.
³⁶ Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
³⁷ Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
³⁸ Center for Data-driven Insights and Innovation, University of California Health, Oakland, CA, USA.
³⁹ Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
⁴⁰ School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁴¹ VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA.
⁴² Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.

PMID: 37034358
PMCID: PMC10072853
DOI: 10.1016/j.eclinm.2023.101932

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Erica A Voss et al. EClinicalMedicine. 2023 Apr.

. 2023 Apr:58:101932.

doi: 10.1016/j.eclinm.2023.101932. Epub 2023 Apr 4.

Authors

Affiliations

¹ OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA.
² Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
³ Janssen Pharmaceutical Research and Development LLC, Titusville, NJ, USA.
⁴ Department of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK.
⁵ College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia.
⁶ Odysseus Data Services, Prague, Czechia.
⁷ Department of Medical Information, Assistance Publique - Hopitaux de Marseille, Marseille, France.
⁸ Easter Eggs, Paris, France.
⁹ Health Informatics Centre, University of Dundee, Dundee, UK.
¹⁰ Division of Population Health and Genomics, University of Dundee, Dundee, UK.
¹¹ Department of Public Health, University of Southern Denmark, Odense, Denmark.
¹² Hospital del Mar, Department of Innovation and Digital Transformation, Barcelona, Spain.
¹³ Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.
¹⁴ Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
¹⁵ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain.
¹⁶ University of Tartu, Tartu, Estonia.
¹⁷ Heliant, Belgrade, Serbia.
¹⁸ University College London, Institute of Health Informatics, London, UK.
¹⁹ Health Data Research UK, London, UK.
²⁰ British Heart Foundation Data Science Centre, London, UK.
²¹ University of Colorado Anschutz Medical Campus, Denver, CO, USA.
²² Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²³ New York-Presbyterian Hospital, New York, NY, USA.
²⁴ IQVIA, Falls Church, VA, USA.
²⁵ O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Alberta, CA, USA.
²⁶ Stanford School of Medicine, Stanford University, Palo Alto, CA, USA.
²⁷ Department of Systems Engineering, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Instituto de Investigación, Hospital 12 de Octubre, Madrid, Spain.
²⁹ VeraTech for Health, Valencia, Spain.
³⁰ Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria (FIIBAP), Madrid, Spain.
³¹ Hospital Universitario 12 de Octubre, Madrid, Spain.
³² Division of Clinical Pharmacology, Department of Medical Pharmacology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Türkiye.
³³ Clinerion, Basel, Switzerland.
³⁴ Medaman BV, Geel, Flanders, Belgium.
³⁵ 1st Department Anaesthesiology and Intensive Therapy, Professor Critical Care Research, Medical University Lublin, Lublin, Poland.
³⁶ Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
³⁷ Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
³⁸ Center for Data-driven Insights and Innovation, University of California Health, Oakland, CA, USA.
³⁹ Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
⁴⁰ School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
⁴¹ VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, UT, USA.
⁴² Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.

PMID: 37034358
PMCID: PMC10072853
DOI: 10.1016/j.eclinm.2023.101932

Abstract

Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population.

Methods: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases.

Findings: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism.

Interpretation: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term.

Funding: None.

Keywords: Adverse events of special interest; COVID-19; OMOP CDM; Observational research.

PubMed Disclaimer

Conflict of interest statement

EAV, AS, CB, RM, KH, AGS, GR, MJS, and PBR are employees of Janssen Research and Development LLC and shareholders of Johnson & Johnson (J&J) stock. Many data partners (including JMR, JMRA, MAM, MdW, LHJ, GB, PRR) on this project have received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The funding source had no role in writing the manuscript or the decision to submit it for publication. Odysseus Data Services, where SvS is employed, is a beneficiary of the mentioned IMI 2 JU project. DPA has received support from the EMA and Innovative Medicines Initiative. DPA's research group has received grant support from Amgen, Chesi-Taylor, Novartis, GSK, UCB Biopharma and J&J. DPA's department has received advisory/consultancy fees from AstraZeneca, Amgen, Astellas, J&J, and UCB Biopharma. DPA's department has received fees for speaker services from Amgen and UCB Biopharma. DPA's has other financial or non-financial interests in Janssen, on behalf of the IMI-funded EHDEN and European Medical Information Framework (EMIF) consortiums, and Synapse Management Partners have supported training programmes organised by DPA's department that was open for external participants. TDS received financial support from the Institute de Salud Carlos Ill (ISCIII; Miguel Servet 2021: CP21/00023) which is co-funded by the European Union. The funding source had no role in writing the manuscript or the decision to submit it for publication. PRR, MdW, LHJ work for a research group who received/receives unconditional research grants from Chiesi, Amgen, UCB, J&J, European Medicines Agency (EMA), and IMI. GH provided funding for use of the CUIMC database and the OHDSI infrastructure. GH received support through a grant to Columbia University (NIH R01 LM006910). MAS receives grants from the US Department of Veterans Affairs within the scope of this work, and grants and contracts from the US National Institutes of Health, US Food & Drug Administration, and Janssen Research & Development outside the scope of this work. EPM receives salary support from the Department of Medicine at University of Calgary. EPM receives consulting fees from Janssen Research & Development for work that is outside the scope of the current study. FM's main employment is for Heliant d.o.o, an EHR provider in Serbia. JDP received salary support through the Universidad de Norte. DJD and ZC are full-time employees of CPRD, which provides contract research services for government and related healthcare authorities, and the pharmaceutical industry, including Janssen. FN holds some AstraZeneca shares and was employed there until 2019. RV, AJB, AP2, LD, and CH received partial funding for this work by the University of California Health System and its Center for Data-Driven Insights and Innovation. MGK received support for the manuscript via institutional (internal funding) only. AJB's research has been funded by NIH, Peraton (as the prime on an NIH contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research Foundation, California Governor’s Office of Planning and Research, California Institute for Regenerative Medicine, L’Oréal, and Progenity. AJB receives royalty payments through Stanford University, for several patents and other disclosures licensed to NuMedii and Personalis. AJB is a co-founder and consultant to Personalis and NuMedii; consultant to Mango Tree Corporation, and in the recent past, Samsung, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, Merck, and Roche. AJB has received honoraria and travel reimbursement for invited talks from J&J, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat, many academic institutions, medical or disease specific foundations and associations, and health systems. AJB is a shareholder in Personalis and NuMedii; is a minor shareholder in Apple, Meta (Facebook), Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics, Illumina, Regeneron, Sanofi, Pfizer, Royalty Pharma, Moderna, Sutro, Doximity, BioNtech, Invitae, Pacific Biosciences, Editas Medicine, Nuna Health, Assay Depot, Vet24seven, and several other non-health related companies and mutual funds. DRM has grants or contracts from Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). FB has a leadership or fiduciary role as the program chair of j-ISBA.

Figures

**Fig. 1**
**For ‘patients with COVID-19’, age group and sex stratified incidence rates of the 16 adverse events of special interest for 24∗ databases**.∗ FIIBAP and IU did not contribute to the ‘Pre-Pandemic Background Population’ and thus and IR could not be calculated. APHM = Health Data Warehouse of Assistance Publique - Hopitaux de Marseille, CPRD_AURUM = Clinical Practice Research Datalink AURUM, CU_AMC = University of Colorado Anschutz Medical Campus- Health Data Compass, CUIMC = Columbia University Irving Medical Center, F = Female, FIIBAP = Fundación para la Investigación e Innovación Biosanitaria en Atención Primaria COVID19, HIC = Health Informatics Centre, IBM_CCAE = IBM® MarketScan® Commercial Claims and Encounters Database, IBM_MDCD = IBM® MarketScan® Multi-State Medicaid Database, IBM_MDCR = IBM® MarketScan® Medicare Supplemental and Coordination of Benefits Database, IMASIS = Parc de Salut Mar Barcelona Information System, IPCI = Integrated Primary Care Information, IQVIA_FRANCE_DA = IQVIA Disease Analyzer France, IQVIA_GERMAN_DA = IQVIA Disease Analyzer Germany, IQVIA_OPENCLAIMS = IQVIA LRxDX Open Claims, IQVIA_PHARMETRICS = IQVIA Pharmetrics, IU = Istanbul Faculty of Medicine, Istanbul University, M = Male, MHD = Medaman Hospital Data, OPTUM_EHR = Optum® de-identified Electronic Health Record Dataset, OPTUM_SES = Optum De-Identified Clinformatics® Data Mart Database–Socio-Economic Status, SIDIAP = The Information System for Research on Primary Care, STARR = STAnford medicine Research data Repository-OMOP, U_OF_TARTU = University of Tartu (U_OF_TARTU), UCCS = University Clinical Center of Serbia, UCHDW = University of California Health Data Warehouse, UK_BIOBANK = UK Biobank.

**Fig. 2**
**Pooled estimated age and sex stratified incidence rates per 100,000 person years (with 95% prediction intervals), calculated from meta-analyses for ‘Patients with COVID-19’**.

**Fig. 3**
**Standardised incidence ratios forest plot with meta analysis comparing the ‘patients with COVID-19' to the ‘pre-pandemic background population’**. CI = confidence interval.

See this image and copyright information in PMC

Cited by

Advancing Real-World Evidence Through a Federated Health Data Network (EHDEN): Descriptive Study.
Blacketer C, Schuemie MJ, Moinat M, Voss EA, Camprubi M, Rijnbeek PR, Ryan PB. Blacketer C, et al. J Med Internet Res. 2025 Aug 7;27:e74119. doi: 10.2196/74119. J Med Internet Res. 2025. PMID: 40774333 Free PMC article.
Background rates of medical events of interest before and during the COVID-19 pandemic: a longitudinal cohort study using claims data.
Popoola VO, Keebler D, Skornicki M, Sobel RE, Lucia J, Breskin A, Askin G, Asubonteng J, Patrone C, Shao P, Szendrey A, Kong A. Popoola VO, et al. BMJ Open. 2025 Jun 26;15(6):e098311. doi: 10.1136/bmjopen-2024-098311. BMJ Open. 2025. PMID: 40578892 Free PMC article.
Background incidence rates of health outcomes of interest for COVID-19 vaccine safety monitoring in a US population: a claims database analysis.
Banga S, Khromava A, Serradell L, Chabanon AL, Pan C, Estevez I, Schilsky S, Kreisberg H. Banga S, et al. BMJ Open. 2024 Jul 11;14(7):e083947. doi: 10.1136/bmjopen-2024-083947. BMJ Open. 2024. PMID: 38991681 Free PMC article.
Long-term follow-up of symptom and quality of life changes in patients with narcolepsy during and after the COVID-19 pandemic.
Chin WC, Huang YS, Tang I, Wang CH. Chin WC, et al. Sleep Biol Rhythms. 2024 Mar 20;22(3):373-384. doi: 10.1007/s41105-024-00521-4. eCollection 2024 Jul. Sleep Biol Rhythms. 2024. PMID: 38962790 Free PMC article.
Signal Monitoring for Adverse Events Following Immunisation with COVID-19 Vaccines During the SARS-CoV-2 Pandemic: An Evaluation of the South African Surveillance System.
Sankar C, Evans S, Meyer JC, Gunter HM, Sekiti V, McCarthy K. Sankar C, et al. Drug Saf. 2025 Aug;48(8):909-922. doi: 10.1007/s40264-025-01547-4. Epub 2025 Apr 16. Drug Saf. 2025. PMID: 40238055 Free PMC article.

See all "Cited by" articles

References

1. WHO coronavirus (COVID-19) dashboard. 2022. https://covid19.who.int/
1. Dagan N., Barda N., Kepten E., et al. BNT162b2 mRNA Covid-19 vaccine in a nationwide mass vaccination setting. N Engl J Med. 2021;384(15):1412–1423. - PMC - PubMed
1. Vasileiou E., Simpson C.R., Shi T., et al. Interim findings from first-dose mass COVID-19 vaccination roll-out and COVID-19 hospital admissions in Scotland: a national prospective cohort study. Lancet. 2021;397(10285):1646–1657. - PMC - PubMed
1. Holder J. Tracking coronavirus vaccinations around the world. New York Times. 2022
1. Li X., Ostropolets A., Makadia R., et al. Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study. BMJ. 2021;373:n1435. - PMC - PubMed

Grants and funding

LinkOut - more resources

Full Text Sources
Miscellaneous
- NCI CPTAC Assay Portal

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Affiliations

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Grants and funding

LinkOut - more resources

Full Text Sources

Miscellaneous

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Miscellaneous