TO THE EDITOR
I read with interest the letter by Klaus Rose et al. regarding the article about delayed diagnosis of severe medical conditions during the coronavirus disease 2019 (COVID-19) pandemic.1 The author stressed the importance of recognizing that delayed presentation of patients, during the COVID-19 pandemic, was not limited to the pediatric population. I agree with this important point, and the article does not claim otherwise. The presented cases are pediatric, given that they took place in a pediatric department, but it is reasonable to assume that adults have faced the same challenges.
Objective: Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence.
Methods: Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate’s error is acceptable.
Results: Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV.
Conclusions: The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions.
At the time of writing, in July 2020, the COVID-19 pandemic has already inflicted dramatic international restrictions, including airports closing and limiting international travel. It has been suggested that re-opening of airports should involve and even rely on testing travelers for COVID-19. This paper discusses the methodology of estimating the detection and diagnostic accuracy of COVID-19 tests. It explains the clear distinction between the technical characteristics of the tests, the detection measures, and the diagnostic measures that have clinical and public health implications. It demonstrates the importance of the prevalence of COVID-19 in terms of determining the ability of a test to yield a diagnosis. We explain the methodology of evaluating diagnostic tests, using the predictive summary index (PSI), and the minimum number of tests that need to be performed in order to correctly diagnose one person, which is estimated by 1/PSI. In a population with low prevalence, even a high-sensitivity test may lead to a high percentage of false positive diagnoses, resulting in the need for multiple high-cost tests to achieve a correct diagnosis. Thus, basing a policy for opening airports on diagnostic testing, even with the best test for COVID-19, has some limits.
The outbreak of coronavirus disease 2019 (COVID-19) in Italy, the first Western country hit by the pandemic, seriously impacted the Italian healthcare system and social and economic environment. This perspective piece focuses on the main challenges faced by Italian hospital managements: hospital overcrowding; the need for urgent reorganization of the country’s healthcare systems; the lack of data regarding COVID-19 diagnostics, clinical course, and effective treatment; individual and collective consequences of the crisis; and the importance of disease containment measures and early treatment strategies.
The recently published paper “US Medical Schools’ 2024 Commencements and Antisemitism: Addressing Unprofessional Behavior” discusses antisemitism expressions and unprofessional behavior in US medical schools’ 2024 commencement ceremony. While we share the authors’ concerns regarding rising antisemitic, anti-Palestinian and anti-Muslim bias, alongside hateful behavior toward minorities and immigrants in the US in general and in medical schools in particular, we are also concerned about the significant bias informing this paper. The authors mistakenly conflate antisemitism with harsh criticism of Israeli government and the actions of its military, and legitimate acts of solidarity with people under oppression. This fallacy is further aggravated by serious concerns (mentioned by the authors themselves) involving the paper’s methodological and statistical shortcomings. Ultimately, the paper lacks scientific rigor and appears to be ideologically motivated rather than a contribution to objective research. Scholars worldwide, Jews and Israelis amongst them, have demonstrated that these are legitimate protests, and the interpretation of their messaging as antisemitic is just another way to silence Palestinian voices calling for freedom and liberation, and delegitimizing critique on the Israeli government. This paper aims to provide the reader with currently published evidence and scientific controversy regarding this issue, that the discussed paper failed to mention.
