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

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

COVID-19 Testing – Impact of Prevalence, Sensitivity, and Specificity on Patient Risk and Cost
Zoe C. Brooks * and Saswati Das

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).

Accepted for publication in AJCP, July 2020

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