GVDN Background

Data Dashboard

Background rates
  • Health conditions occur normally in the population with people becoming ill everyday and seeking treatment. In order to understand if a vaccine (or any other medicine for that matter) increases the rates of people developing a medical problem we must first understand what is normal, when there is no vaccine being used (background rates).

  • Next we need to observe if we are seeing more cases than we might normally expect (observed over expected rates). If we are then we might have a safety signal that needs investigating more thoroughly. Seeing more cases of a condition than we might normally expect could me due to many reasons, such as circulating viruses, weather, social changes.

  • To determine if a vaccine is causing some of the cases we need studies that compare the risk of a condition in vaccinated people compared with unvaccinated people (association studies).

Background rates of AESIs in New Zealand 2008–2019 project

As with other countries, New Zealand did not have the baseline data required to form the basis of robust vaccine safety monitoring prior to the COVID-19 pandemic and introduction of COVID-19 vaccines. The outcomes of the Background rates of AESIs in New Zealand 2008–2019 project, the data for which are available in the data dashboard below, helped prepare New Zealand for local assessment of COVID-19 vaccine safety by establishing baseline rates for 20 adverse events of special interest (AESIs) extracted from the Brighton Collaboration SPEAC (Safety Platform for Emergency Vaccines) project prioritised list, and myocarditis, pericarditis, multisystem inflammatory syndrome, a range of haematological conditions potentially associated with the newly identified thrombosis with thrombocytopenia syndrome (TTS), herpes zoster (shingles), narcolepsy, and sudden death for the New Zealand population overall and key subgroups from 2008–2019. These background rates may be used as a first step to contextualise data from prospective monitoring studies, spontaneous reports from the Centre for Adverse Reaction Monitoring (CARM) and other databases, and case reports, and thereby form a basis for identifying potential COVID-19 vaccine safety signals. With the baseline established, signals may be verified through the conduction of observed over expected analysis with the same population cohort.