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The Australian burden of invasive group A streptococcal disease: a narrative review.

Cameron M WrightKristyn LangworthyLaurens Manning
Published in: Internal medicine journal (2021)
The Australian and New Zealand governments have allocated significant funding to advance efforts towards a group A Streptococcus (Strep A) vaccine. The argument for Strep A vaccine development has to date focussed on prevention of non-invasive disease (e.g. pharyngitis) and immune-mediated complications (especially rheumatic heart disease). Because of the poorer prognosis and theoretically more precisely known burden of invasive, compared to non-invasive disease, exploration of the burden of invasive Strep A disease could lend further support to the vaccine business case. This narrative review critically assesses the Australian incidence of invasive Strep A disease. Case notification data were first assessed through government sources, expressing annual incidence as cases per 100 000 population. Published literature accessed through PubMed and MEDLINE was assessed to March 2020. Where estimates could be updated by replicating reported methods with publicly available data, this was performed. Invasive Strep A disease is currently notifiable in Queensland and the Northern Territory only. The magnitude, degree of certainty and recency of estimates vary by state/territory and between sub-populations, including higher incidence among Indigenous Australians compared to non-Indigenous Australians. According to inpatient records from 2017 to 2018, the Australian incidence of invasive Strep A disease was 8.3 per 100 000. However, this is likely to be an underestimate. Preventing invasive Strep A disease is an important use for a Strep A vaccine. This narrative review highlights deficiencies in our current understanding of the Australian disease burden. These difficulties would be overcome by nationally consistent mandatory case reporting.
Keyphrases
  • risk factors
  • systematic review
  • emergency department
  • rheumatoid arthritis
  • mental health
  • escherichia coli
  • machine learning
  • big data
  • randomized controlled trial
  • pulmonary hypertension
  • deep learning
  • drug induced