Quantitative comparison of SARS-CoV-2 nucleic acid amplification test and antigen testing algorithms: a decision analysis simulation model
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Abstract
Background
Antigen tests for SARS-CoV-2 offer advantages over nucleic acid amplification tests (NAATs, such as RT-PCR), including lower cost and rapid return of results, but show reduced sensitivity. Public health organizations recommend different strategies for utilizing NAATs and antigen tests. We sought to create a framework for the quantitative comparison of these recommended strategies based on their expected performance.
Methods
We utilized a decision analysis approach to simulate the expected outcomes of six testing algorithms analogous to strategies recommended by public health organizations. Each algorithm was simulated 50,000 times in a population of 100,000 persons seeking testing. Primary outcomes were number of missed cases, number of false-positive diagnoses, and total test volumes. Outcome medians and 95% uncertainty ranges (URs) were reported.
Results
Algorithms that use NAATs to confirm all negative antigen results minimized missed cases but required high NAAT capacity: 92,200 (95% UR: 91,200-93,200) tests (in addition to 100,000 antigen tests) at 10% prevalence. Selective use of NAATs to confirm antigen results when discordant with symptom status (e.g., symptomatic persons with negative antigen results) resulted in the most efficient use of NAATs, with 25 NAATs (95% UR: 13-57) needed to detect one additional case compared to exclusive use of antigen tests.
Conclusions
No single SARS-CoV-2 testing algorithm is likely to be optimal across settings with different levels of prevalence and for all programmatic priorities. This analysis provides a framework for selecting setting-specific strategies to achieve acceptable balances and trade-offs between programmatic priorities and resource constraints.
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SciScore for 10.1101/2021.03.15.21253608: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Each of these algorithms may be advisable depending on the programmatic goals and resource limitations of community-based SARS-CoV-2 testing programs. Our analysis provides a quantitative framework for public health practitioners, policymakers, and stakeholders who are planning, implementing, or evaluating community-based testing …
SciScore for 10.1101/2021.03.15.21253608: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Each of these algorithms may be advisable depending on the programmatic goals and resource limitations of community-based SARS-CoV-2 testing programs. Our analysis provides a quantitative framework for public health practitioners, policymakers, and stakeholders who are planning, implementing, or evaluating community-based testing programs. A reference guide discussing and applying the results of our analyses to programmatic decisions, along with key priorities, benchmarks, and indicators, is included in Supplementary Table 2. For programs intended to minimize missed cases, algorithms (A) NAAT Only, (C) NAAT Confirmation for Sx/Ag-neg & Asx/Ag-pos, and (D) NAAT Confirmation for Ag-neg are most preferable; selecting between these algorithms depends on tolerance for missed cases and available NAAT capacity. For example, at 10% prevalence, (C) NAAT Confirmation for Sx/Ag-neg & Asx/Ag-pos is estimated to miss 1,409 cases (95% UR: 815-2,100) but save 46 NAATs (95% UR: 29-83) for each case missed. For programs intended to minimize NAAT volume, algorithms (B) Ag Only, (C) NAAT Confirmation for Sx/Ag-neg & Asx/Ag-pos, and (E) Repeat Ag for Ag-neg are most preferable; selecting between these algorithms depends on tolerance for missed cases and available NAAT and antigen test capacity. For example, at 10% prevalence, (E) Repeat Ag for Ag-neg is estimated to result in 550 fewer missed cases (95% UR: 301-854) but require 92,200 more antigen tests (95% UR: 91,200-93,200) than (B) Ag Only. ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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