BLOG – 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

Key Findings

To our knowledge this is the largest COVID-19 study to date with more than twenty-four million COVID-19 patients and twenty-six databases across three continents.

This study provides essential context on the complications in unvaccinated subjects and shows a striking increase in risk of outcomes after COVID-19, like pulmonary embolism (x12), disseminated intravascular coagulation (x9), and myocarditis/pericarditis (x8).


Study Detail


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.

This study provides essential context on the counterfactual effects of SARS-CoV-2 infection in unvaccinated subjects.


Incidence rates were obtained from 26 databases, which included eight administrative claims databases, twelve EHRs, one EHR with a registry, and five general practitioner (GP) databases. These databases represented eleven countries: Belgium, Estonia, France, Germany, Japan, the Netherlands, Serbia, Spain, Turkey, the United Kingdom (UK), and the United States (US). The sixteen pre-specified prevalent AESIs were: acute myocardial infarction (AMI), anaphylaxis, appendicitis, Bell’s palsy, deep vein thrombosis, disseminated intravascular coagulation (DIC), encephalomyelitis, Guillain-Barré syndrome (GBS), haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia (ITP), myocarditis/pericarditis, narcolepsy, pulmonary embolism (PE), transverse myelitis, and thrombosis with thrombocytopenia (TWT). Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases.


Across all the databases there were 945 million patients and there were twenty-four million patients with COVID-19.

Incidence rates for the ‘Patients with COVID-19’ population observed similar age trends across databases. Some AESIs had a clear increase in incidence rate with age: AMI, non-haemorrhagic stroke, DVT, PE, haemorrhagic stroke, Bell’s Palsy, TWT, ITP, DIC, and GBS. In contrast, some AESIs had a clear decrease in incidence rate with age: appendicitis and anaphylaxis. Finally, some age trends were less clear: myocarditis and pericarditis, narcolepsy, encephalomyelitis, and transverse myelitis. Few also observed substantial database heterogeneity within individual AESIs. For example, for DIC in the 90 days after COVID-19 diagnosis, males aged 35–54 in JMDC experienced the event 2417 per 100,000 person-years as compared to CPRD which experienced the event 5 per 100,000 person-years.

Figure 1 reports the meta-analytic estimates of SIRs comparing the ‘Patients with COVID-19’ to the ‘Pre-Pandemic Background Population’. Twelve of sixteen AESIs had ratios above two and seven of sixteen AESIs had ratios above five. Pulmonary embolism had the highest SIRs (11.7 [95% confidence interval 10.1–13.7]), suggesting that the observed incidence of pulmonary embolism in the 90 days after COVID-19 diagnosis was over 11 times higher than expected in a 90-day period for the background population in the pre-pandemic period.


To our knowledge this is the largest study to date on the descriptive epidemiology of AESIs among the COVID-19 population.

These results found large variations in the rates of AESIs in ‘Patients with COVID-19’ across age groups and sex, showing the need for stratification. Considerable database heterogeneity was found across the AESIs, suggesting individual study estimates should be interpreted with caution. Comparing the ‘Patients with COVID-19’ to the ‘Pre-Pandemic Background Population’ showed a fairly consistent elevated rate in experiencing an AESI within 90-days after index. This elevated risk we see both consistently across the database stratified results as well as the meta-analysis.


Erica Voss

Sr. Director, Observational Health Data Analytics, Janssen R&D LLC

Health Data Science Erasmus Medical Center