PropertyValue
?:abstract
  • Background: Testing plays a critical role in treatment and prevention responses to the COVID-19 pandemic. Compared to nucleic acid tests (NATs), antigen-detection rapid diagnostic tests (Ag-RDTs) can be more accessible, but typically have lower sensitivity and specificity. By quantifying these trade-offs, we aimed to inform decisions about when an Ag-RDT would offer greater public health value than reliance on NAT. Methods: Following an expert consultation, we selected two use cases for analysis: rapid identification of people with COVID-19 amongst patients admitted with respiratory symptoms in a hospital setting; and early identification and isolation of people with mildly symptomatic COVID-19 in a community setting. Using decision analysis, we evaluated the cost and impact (deaths averted and infectious days isolated) of an Ag-RDT-led strategy, compared to a strategy based on NAT and clinical judgment. We performed a multivariate sensitivity analysis to identify key parameters. Results: In a hospital setting, an Ag-RDT-led strategy would avert more deaths than a NAT-based strategy, and at lower cost per death averted, when the sensitivity of clinical judgement is less than 85%, and when NAT results are available in time to inform clinical decision-making for less than 90% of patients. The use of an Ag-RDT is robustly supported in community settings, where it would avert more transmission at lower cost than relying on NAT alone, under a wide range of assumptions. Conclusions: Despite their imperfect sensitivity and specificity, Ag-RDTs have the potential to be simultaneously more impactful, and cost-effective, than current approaches to COVID-19 diagnostic testing.
is ?:annotates of
?:creator
?:doi
  • 10.1101/2020.11.20.20235317
?:doi
?:license
  • medrxiv
?:pdf_json_files
  • document_parses/pdf_json/4944a578d4603582fdc67904cc4b6e7fec23a4e1.json
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • MedRxiv; WHO
?:title
  • Quantifying the potential value of antigen-detection rapid diagnostic tests for COVID-19: a modelling analysis
?:type
?:year
  • 2020-11-23

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