Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost

Version 1 : Received: 18 June 2020 / Approved: 21 June 2020 / Online: 21 June 2020 (15:12:43 CEST)
Version 2 : Received: 7 July 2020 / Approved: 9 July 2020 / Online: 9 July 2020 (15:57:30 CEST)

A peer-reviewed article of this Preprint also exists.

Zoe C Brooks, ART, Saswati Das, MD, COVID-19 Testing: Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost, American Journal of Clinical Pathology, aqaa141, https://doi.org/10.1093/ajcp/aqaa141 Zoe C Brooks, ART, Saswati Das, MD, COVID-19 Testing: Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost, American Journal of Clinical Pathology, aqaa141, https://doi.org/10.1093/ajcp/aqaa141

Journal reference: American Journal of Clinical Pathology 28 August, AQAA141
DOI: 10.1093/AJCP/AQAA141

Abstract

Since the beginning of the year 2020, the global healthcare system has been challenged by the threat of the SARS-COV 2 virus. Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the virus. Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serological assays detect antibodies to the Sars-CoV-2 virus in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. To evaluate the test methods, sensitivity (percent positive agreement - PPA) and specificity (percent negative agreement – PNA) are the most common metrics utilized, followed by the positive and negative predictive value (PPV and NPV), the probability that a positive or negative test result represents a true positive or negative patient. In this paper, we illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. We demonstrate the value of reporting PFP (probability of false positive results), PFN (probability of false negative results), and costs to patients and healthcare. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by one positive SARS COV-2).

Supplementary and Associated Material

Subject Areas

COVID19; risk; clinical; metrics; cost; false-positive; false-negative; prevalence; sensitivity; specificity

Comments (1)

Comment 1
Received: 9 July 2020
Commenter: Zoe Brooks
Commenter's Conflict of Interests: Author
Comment: This revision reflects changes suggested by the editor at AJCP, as submitted to them today (July 7th)
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