Evidence map›Paper›PMID 33791732›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study.

Xintong Li, Anna Ostropolets, Rupa Makadia, Azza Shaoibi, Gowtham Rao, Anthony G Sena, Eugenia Martinez-Hernandez, Antonella Delmestri, Katia Verhamme, Peter R Rijnbeek and 5 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 55 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

  • Updated by
5 · Who and what money

Authors and funding

15 authors at 7 institutions in 4 countries.

Xintong LiCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom.
Anna OstropoletsDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
Rupa MakadiaJanssen Research and Development, Titusville, NJ, USA.
Azza ShaoibiJanssen Research and Development, Titusville, NJ, USA.
Gowtham RaoJanssen Research and Development, Titusville, NJ, USA.
Anthony G SenaJanssen Research and Development, Titusville, NJ, USA.
Eugenia Martinez-HernandezNeurology Department, Hospital Clinic de Barcelona and University of Barcelona, Barcelona, Spain.
Antonella DelmestriCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom.
Katia VerhammeDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Peter R RijnbeekDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Talita Duarte-SallesFundacio Institut Universitari per a la recerca a l'Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Marc SuchardDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA USA.
Patrick RyanDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
Daniel Prieto-AlhambraCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom.
Columbia University Irving Medical Center · USErasmus MC · NLJanssen (United States) · USOffice for National Statistics · GBHospital Clínic de Barcelona · ESInstitut Universitari d'Investigació en Atenció Primària Jordi Gol · ESUniversity of California, Los Angeles · US

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

backgroundAs large-scale immunization programs against COVID-19 proceed around the world, safety signals will emerge that need rapid evaluation. We report population-based, age- and sex-specific background incidence rates of potential adverse events of special interest (AESI) in eight countries using thirteen databases.

methodsThis multi-national network cohort study included eight electronic medical record and five administrative claims databases from Australia, France, Germany, Japan, Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model. People observed for at least 365 days before 1 January 2017, 2018, or 2019 were included. We based study outcomes on lists published by regulators: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain-Barre syndrome, hemorrhagic and non-hemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, and transverse myelitis. We calculated incidence rates stratified by age, sex, and database. We pooled rates across databases using random effects meta-analyses. We classified meta-analytic estimates into Council of International Organizations of Medical Sciences categories: very common, common, uncommon, rare, or very rare.

findingsWe analysed 126,661,070 people. Rates varied greatly between databases and by age and sex. Some AESI (e.g., myocardial infarction, Guillain-Barre syndrome) increased with age, while others (e.g., anaphylaxis, appendicitis) were more common in young people. As a result, AESI were classified differently according to age. For example, myocardial infarction was very rare in children, rare in women aged 35-54 years, uncommon in men and women aged 55-84 years, and common in those aged ≥85 years.

interpretationWe report robust baseline rates of prioritised AESI across 13 databases. Age, sex, and variation between databases should be considered if background AESI rates are compared to event rates observed with COVID-19 vaccines.

Identifiers

PMID33791732
PMCPMC8010764
OpenAlexW3146404720

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.