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Quantitative comparison of SARS-CoV-2 nucleic acid amplification test and antigen testing algorithms: a decision
Phillip P Salvatore1,2, Melisa M Shah3,4, Laura Ford3,4
1COVID-19 Response Team, Centers for Disease Control and Prevention (CDC), 1600 Clifton Road NE, Atlanta, USA. pgx5@cdc.gov.
BMC Public Health
|January 14, 2022
Summary
Comparing SARS-CoV-2 testing strategies, this study found that selectively using nucleic acid amplification tests (NAATs) to confirm antigen test results in symptomatic individuals offers the most efficient use of NAATs. This approach balances case detection with resource limitations.
Area of Science:
- Infectious Diseases
- Public Health
- Diagnostic Testing
Background:
- Antigen tests for SARS-CoV-2 are faster and cheaper than nucleic acid amplification tests (NAATs) but less sensitive.
- Public health organizations propose varied strategies for using antigen and NAATs.
- A quantitative framework is needed to compare these recommended testing strategies.
Purpose of the Study:
- To develop a framework for quantitatively comparing SARS-CoV-2 testing strategies.
- To evaluate the performance of different antigen and NAAT testing algorithms.
Main Methods:
- Decision analysis was used to simulate six SARS-CoV-2 testing algorithms.
- Each algorithm was simulated 50,000 times in a population of 100,000.
- Key outcomes included missed cases, false-positive diagnoses, and total test volumes.
Main Results:
- Confirming all negative antigen tests with NAATs minimized missed cases but demanded high NAAT capacity (92,200 tests at 10% prevalence).
- Selective NAAT confirmation for discordant antigen results (e.g., symptomatic with negative antigen test) proved most efficient.
- This selective strategy required only 25 NAATs to detect one additional case compared to using antigen tests alone.
Conclusions:
- No single SARS-CoV-2 testing strategy is universally optimal.
- The choice of strategy depends on prevalence and programmatic priorities.
- This analysis offers a framework for selecting setting-specific testing strategies to balance performance and resource constraints.
