The Impossibility of 100% Specificity in Rapid Diagnostic Test Reporting
DOI:
https://doi.org/10.52609/jmlph.v6i4.318Keywords:
Bayes’ Theorem, Confidence Intervals, Point-Of-Care Testing, Predictive Value of Tests, Sensitivity and Specificity, Statistics as TopicAbstract
Background: In commercial materials for rapid diagnostic tests, it is still common to find claims of “100% specificity”. From a statistical and epistemological point of view, this kind of statement is deeply problematic.
Aims: This paper examines how justified those claims really are and what they mean in concrete clinical and public health settings.
Methods: We analyze the statistical foundations of specificity estimation, apply the Mayo-Spanos severity framework, and review three commercial rapid antigen test listings to illustrate reporting practices.
Results: Claims of 100% specificity are either logically falsified by observed false positives or unsupported by finite validation samples. The rule of three shows that zero false positives in n negatives only rule out a false-positive rate above 3/n. Severity analysis reveals that even with 210 negative samples, the claim that specificity >99.5% is supported with only 65% severity. In low-prevalence settings, assuming 100% specificity inflates positive predictive value (PPV) from 66% to 100%.
Conclusion: Reporting 100% specificity is scientifically untenable. Regulatory and editorial standards could realistically require confidence intervals and, at the very least, discourage unqualified point estimates.
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