We show the value of reporting on the probability of false positive outcomes, the probability of false negative outcomes, and the cost to patients and healthcare. These risk measures can be calculated from the risk factors for PPA and PDA in combination with estimates of prevalence, cost and number of reb (people infected with 1 positive CARRIER of SARS-CoV-2). The number and cost of false positive and negative test results are determined by prevalence, ASF (sensitivity) and ANP (specificity). ANP does not affect false-negative test results. Since false-negative molecular tests cost more than false-negative antigen or antibody tests, their cost shows the greatest impact. Laboratories put a lot of effort into test selection to minimize patient risks and clinical costs caused by incorrect results. Table 1 showed the different clinical interpretations of each type of test. False positive and false negative results pose risks to patients and the cost of clinical care. The authors estimated the cost to the United States in May 2020, as shown in Table 3, assuming these are rough estimates. ISO/IEC Guide 51 defines risk as “the combination of the probability of damage occurring and the severity of that damage”. 10 Examination of PPAs and PDAs alone gives no indication of the risk to the patient as the number and clinical cost of incorrect results. Risk as the likelihood and severity of false positive and false negative results can be extrapolated from manufacturers` claims and/or user data for PPPs and NAPs, as well as estimates of prevalence, reb number, and cost of your healthcare.

Reff values for each U.S. state can be found under rt.live/.20 We have roughly estimated the cost to the United States, but have not included a value for loss of life in our equations because human life is priceless. It may be advisable, although difficult, to take this into account when estimating costs at your location and in your currency. The APP and NAP are inherent in the test method. The probabilities of true and false outcomes in clinical settings change with the prevalence of the virus or antibody in the population being tested. “In a population with a prevalence of 5%, a test with a sensitivity of 90% and a specificity of 95% gives a positive predictive value of 49%. In other words, less than half of those who test positive actually have antibodies. Alternatively, in a population with an antibody prevalence of more than 52%, the same test gives a positive predictive value of more than 95%, meaning that less than one in 20 people who test positive have a false-positive test result. “11 In the latest FDA guidelines for laboratories and manufacturers, “Fda Policy for Diagnostic Tests for Coronavirus Disease-2019 during Public Health Emergency,” the FDA states that users must use a contracted clinical study to determine performance characteristics (sensitivity/ASF, specificity/NPA). Although the terms sensitivity/specificity are widely known and used, the terms PPA/NPA are not. We found it stimulating that when prevalence increases from 2% to 20%, the cost of incorrect molecular test results increases by more than $250,000 for every 1,000 molecular tests performed.

This happens because the number of true positive tests and very expensive false negative tests increases in proportion to prevalence. With a baseline PDA of 95.8%, there are few false positive results (41 at a prevalence of 2% and 33 at a prevalence of 20%), and reducing their costs makes little difference to the overall cost. CLSI EP12: User Protocol for Evaluation of Qualitative Test Performance protocol describes the terms positive percentage agreement (PPA) and negative percentage agreement (NPA). If you need to compare two binary diagnostics, you can use an agreement study to calculate these statistics. Molecular, antigen and antibody tests are the mainstays for identifying infected patients and fighting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To assess test methods, sensitivity (percentage of positive match [ASF]) and specificity (percentage of negative match [NAP]) are the most commonly used measures, followed by positive and negative predictive value – the probability that a positive or negative test result represents a truly positive or negative patient. The number, probability, and cost of incorrect results are determined by combinations of prevalence, ASF, and PDA of the individual test selected by the laboratory. Influence of positive agreement increase as a percentage (PPP) (sensitivity) on false results with baseline prevalence and negative agreement percentage (NPA). In total, 100 field truth negative patients and 100 background truth positive patients were considered.

In Panel A, there is no error in the classification of patients (i.e. the comparator perfectly matches the truth in the field). Group B assumes that a random percentage of 5% of the comparator`s classifications incorrectly deviates from the truth in the field. The difference in the distribution of test results (y-axis) between the panels in this figure leads to significant underestimates of diagnostic performance, as shown in Table 1. Reff or R0 is the actual number of people infected with a positive case of COVID-19.20,21 False-negative molecular tests in true-positive samples and false-positive antibody tests in true-negative samples can cause patients to move freely around society and infect other Reff. These people are or can be infected, bear the same costs as truly positive patients and infect other people. Other audited costs are multiplied by (1 + reff) when indicated. False positive antibody tests can cause patients to move freely in society and become infected by the prevalence rate. Other costs are multiplied by prevalence. In early December 2019, pneumonia of unknown cause was detected in Wuhan, China, and reported to the World Health Organization (WHO).1 On March 11, 2020, the WHO declared the virus pandemic.2 The new virus, previously called novel coronavirus 2019 (2019-nCoV), is currently called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).3 According to the latest WHO statistics, Coronavirus disease 2019 (COVID-19) has developed on all continents, with 6,302,318 cases diagnosed and 376,210 deaths worldwide and 1,811,277 cases and 105,147 deaths in the United States up to 2. June 2020.4 The diagnosis and laboratory management of COVID-19 has been instrumental in combating the spread of SARS-CoV-2.

Clinical decisions are based on precise molecular, antigenic and antibody tests that correctly classify patients as positive or negative for the presence of SARS-CoV-2 or for antibodies against that specific virus. Risk measures for the number and cost of false positive and negative test results are determined by risk factors for SARS-CoV-2 prevalence or for antibodies in the tested population by percentage of positive match (PPA; Sensitivity) and percentage of negative agreement (ANP; Specificity) of each test process. In this scenario, positive field truth patients and field truth negative patients are also misclassified by the comparator. (A) Comparator without misclassification, which perfectly represents the field truth for 100 negative patients and 100 positive patients. (B) Obvious performance of the diagnostic test based on the benchmark classification error rate. Error bars describe empirical 95% confidence intervals via medians, calculated over 100 simulation cycles. Actual test performance is displayed when FP and FN rates are 0% each. The terms sensitivity and specificity are appropriate if there is no misclassification in the comparator (PF rate = FN rate = 0%). The terms Positive Percentage Agreement (PFA) and Negative Percentage Agreement (MPA) should be used instead of sensitivity or specificity if it is known that the comparator contains uncertainty. Measuring risk measures as the number and cost of false positive and negative outcomes adds a lot of knowledge that is masked by the usual statistical measures of positive match percentage (PPP), negative agreement percentage (NAP), positive predictive value, and negative predictive value. The gold standard for diagnosing suspected cases of COVID-19 is currently molecular tests, such as real-time reverse transcription polymerase chain reaction (RT-PCR), a nucleic acid amplification test that detects unique sequences of sars-CoV-2.5 antigen tests that also detect the presence of SARS-CoV-2, do not amplify viral components, and are less sensitive (more likely to produce a false-negative result) than molecular tests. Negative antigen tests must be confirmed by a molecular test before a person is considered negative for COVID-19.

Molecular and antigenic tests only detect patients in the acute phase. The clinical implications and cost of false positive and negative test results can guide test selection and decisions about repeating test results for confirmation. The potential harm of false positive and false negative results14, as explained in Table 1, is applied to Figure 4, Figure 5, Figure 6 and Figure 7 to provide a rough estimate of the cost of patient and clinical care for the United States. . . .