DARWIN EU® - Background incidence rates of selected vaccine adverse events of special interest (AESIs) in Europe

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology

  Safety study (incl. comparative)
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Additional medical condition(s):
  Vaccine adverse events of special interest

Population studied

• Short description of the study population:
  The study population will include all individuals observed in one of the participating data sources during the study period. We will require individuals to have at least 365 days of data availability before entering the cohort. ; The index date of cohort entry will be 1st January 2010 or the date that individual fulfil the data availability and outcome ‘clean window’ requirement.

Study design details

• Study design:
  This is a population-level retrospective, multi-database cohort study using electronic health record data from Europe. ; The incidence rates of AESIs will be assessed using Population Level Disease Epidemiology.
• Main study objective:
  Main objectives; 1. To estimate population level incidence rates of selected adverse events of special interest (AESIs) in the general population during 2010 and until the latest data availability, stratified by calendar year, month, sex, age groups, and data source.; 2. To estimate age and sex standardised incidence rates (to the European population) of selected adverse events of special interest (AESIs) in the general population during 2010 and until the latest data availability, stratified by calendar year.; ; Secondary objective; 3. To describe demographic and clinical characteristics of individuals with incident AESIs and comparing the characteristics with individuals of similar age and sex but without the AESI.
• Outcomes:
  AESIs of interest: ; The list was built on previously internationally recognized lists of AESIs by the Brighton Collaboration/Safety Platform for Emergency vACcines (SPEAC) and curated by experts from EMA and EMA’s Pharmacovigilance Risk Assessment Committee (PRAC), taking into account knowledge of most representative AESIs for a variety of vaccine safety signals (including for COVID-19 vaccines). Apart from AESIs included in previous studies, a broader list of conditions has been added. For example, conditions related to skin reactions are included. We excluded AESIs specific to one vaccine only and already well characterised (e.g., intussusception for rotavirus vaccines) or those which are very rare (e.g., multisystem inflammatory syndrome/MIS). The selected outcomes of interest are listed in Table 5. ; For each study outcome, a clean window was applied to define incident outcomes. This was anytime prior in the patient history for chronic events and specific (shorter) clean windows for acute and recurrent outcomes (Table 5). ; If the clean window was 90 days for a specific outcome, for example, the outcome event was considered ; incident if there was no record of the same outcome event during the preceding 90 days. An individual had ; the potential to contribute multiple outcome events if there was a gap of at least 90 days between each eligible event.
• Summary results:
  This study included a wide range of adverse events of special interest for vaccines. We estimated background rates by year, age, and sex for five European databases. We also provided detailed cohort characteristics among people with the conditions, and contextualised the results by comparing to the matched cohort from the general population. However, the background rates need to be interpreted with caution given heterogeneity across databases and underlying time trends seen for many of the outcomes. ; For any new studies aiming at using background rates for an emerging signal evaluation, it will be important to first assess if the phenotypes are fully aligned with the outcome(s) to be assessed, run diagnostics in the databases, and tailor as needed (e.g., considering information from spontaneous case reports and clinical case definitions). This work establishes a framework for future studies.