Adjusting Coronavirus Prevalence Estimates for Laboratory Test Kit Error
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Abstract
Testing representative populations to determine the prevalence or the percentage of the population with active severe acute respiratory syndrome coronavirus 2 infection and/or antibodies to infection is being recommended as essential for making public policy decisions to ease restrictions or to continue enforcing national, state, and local government rules to shelter in place. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100%—in some cases, considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence is low, possibly in the range of 1%–5%, then testing error will lead to a constant background of bias that most likely will be larger, and possibly much larger, than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. Methods are outlined in this article for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. If used, these methods also would help harmonize study results within countries and worldwide. Adjustment can lead to more accurate prevalence estimates and to better policy decisions. However, adjustment will not improve the accuracy of an individual test.
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SciScore for 10.1101/2020.05.11.20098202: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.05.11.20098202: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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