Evidence map›Paper›PMID 35727911›Full record

ArticleBMJ (Clinical research ed.)2021

Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: multinational network cohort study.

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

Open access · hybridAbstract read
In one paragraph

Article in BMJ (Clinical research ed.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
134citing papers in PubMed, 5 pooled it
12.2field-weighted citation impact, top 1% of its field
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

134 citing papers in PubMed, 5 syntheses or guidelines pooled it, 211 citations in OpenAlex.

  1. Pooled it
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  13. Evaluation of the impact of defining observable time in real-world data on outcome incidence.Journal of the American Medical Informatics Association : JAMIA · 2025
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  20. Data Resource Profile Update: CPRD GOLD.International journal of epidemiology · 2025
    Article

74 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 7 institutions in 5 countries.

Xintong LiCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.ORCID 0000-0002-6872-5804
Anna OstropoletsDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
Rupa MakadiaJanssen Research and Development, Titusville, NJ, USA.
Azza ShoaibiJanssen Research and Development, Titusville, NJ, USA.
Gowtham RaoJanssen Research and Development, Titusville, NJ, USA.
Anthony G SenaJanssen Research and Development, Titusville, NJ, USA.ORCID 0000-0001-8630-5347
Eugenia Martinez-HernandezNeurology Department, Hospital Clinic de Barcelona and University of Barcelona, Barcelona, Spain.ORCID 0000-0002-8317-7068
Antonella DelmestriCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.
Katia VerhammeDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Peter R RijnbeekDepartment of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
Talita Duarte-SallesFundacio Institut Universitari per a la recerca a l'Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
Marc A SuchardDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
Patrick B 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, UK daniel.prietoalhambra@ndorms.ox.ac.uk.ORCID 0000-0002-3950-6346
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

objectiveTo quantify the background incidence rates of 15 prespecified adverse events of special interest (AESIs) associated with covid-19 vaccines.

designMultinational network cohort study.

settingElectronic health records and health claims data from eight countries: Australia, France, Germany, Japan, the Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model.

participants126 661 070 people observed for at least 365 days before 1 January 2017, 2018, or 2019 from 13 databases.

main outcome measuresEvents of interests were 15 prespecified AESIs (non-haemorrhagic and haemorrhagic stroke, acute myocardial infarction, deep vein thrombosis, pulmonary embolism, anaphylaxis, Bell's palsy, myocarditis or pericarditis, narcolepsy, appendicitis, immune thrombocytopenia, disseminated intravascular coagulation, encephalomyelitis (including acute disseminated encephalomyelitis), Guillain-Barré syndrome, and transverse myelitis). Incidence rates of AESIs were stratified by age, sex, and database. Rates were pooled across databases using random effects meta-analyses and classified according to the frequency categories of the Council for International Organizations of Medical Sciences.

resultsBackground rates varied greatly between databases. Deep vein thrombosis ranged from 387 (95% confidence interval 370 to 404) per 100 000 person years in UK CPRD GOLD data to 1443 (1416 to 1470) per 100 000 person years in US IBM MarketScan Multi-State Medicaid data among women aged 65 to 74 years. Some AESIs increased with age. For example, myocardial infarction rates in men increased from 28 (27 to 29) per 100 000 person years among those aged 18-34 years to 1400 (1374 to 1427) per 100 000 person years in those older than 85 years in US Optum electronic health record data. Other AESIs were more common in young people. For example, rates of anaphylaxis among boys and men were 78 (75 to 80) per 100 000 person years in those aged 6-17 years and 8 (6 to 10) per 100 000 person years in those older than 85 years in Optum electronic health record data. Meta-analytic estimates of AESI rates were classified according to age and sex.

conclusionThis study found large variations in the observed rates of AESIs by age group and sex, showing the need for stratification or standardisation before using background rates for safety surveillance. Considerable population level heterogeneity in AESI rates was found between databases.

Indexed as

AnaphylaxisCOVID-19Venous ThrombosisAdolescentCohort StudiesCOVID-19 VaccinesFemaleHumansIncidenceMaleUnited StatesCOVID-19 Vaccines

Identifiers

PMID35727911
PMCPMC8193077
OpenAlexW3166109011

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.