>> In our commentary we would like to point out errors in reporting that can be found in almost all media and can lead to incorrect scientific and political conclusions. There are two ways to describe the frequency of events: prevalence and incidence.

Prevalence describes the proportion of people who fulfill a defined condition at a certain point in time within a defined population, e.g. B. suffer from a certain illness. Prevalence is reported to provide a snapshot of a condition in a defined population for comparison with other populations.

The incidence describes the number of new events that have occurred within a defined period of time, usually based on 100,000 people in a defined population. The spread of the incidence value increases the smaller the population and/or the observation period is chosen. Depending on the question being answered, it may make sense to calculate changes at daily and yearly intervals.

Both results, the prevalence and the incidence, are based on test results that can be positive or negative and also right or wrong.

When reporting the prevalence or incidence of deaths, false-positive results may be neglected because false-positive deaths rarely occur. This statement does not apply to determining the cause of death or confirming an existing illness or infection. Causes of death, illnesses and infections can only be confirmed with detailed additional information because tests carried out are by no means free of false results and a medical diagnosis - with a few exceptions - cannot be proven by a single sign, symptom or a single test. Neither a positive PCR test nor a positive mammogram can prove the suspected disease without additional information because false-positive results can only be distinguished from true-positive results by comparing them with a gold standard. In medicine, the gold standard is a condition that comes closest to the currently valid truth. Others refer to “a proven, useful reference condition” as the so-called gold standard.

Two frequently cited statistical analyzes confirm that incorrect
positive results of mammography (1) and ferritin measurement as part of the diagnosis of iron deficiency anemia at around 10%
the tests (2) occur. The problem with regard to the informative value of a test is that in almost all tests “false positive” results are found significantly more often (5 to 274 times) than “false negative” results. It is also problematic that the “false-positive” test results pose a higher risk of misinterpretation
test results as “false negatives”.

The result of a test is referred to as “false negative” if a test subject has a condition (e.g. breast cancer or SARS-CoV-2) even though the test did not detect this condition. In practice, “false negative” results can be easily explained because the further clinical course allows you to quickly identify what the test did not detect. Here's an example: If a patient has a new sign two weeks after a normal mammogram - e.g. B. If you feel a new lump in the breast, which can then be recognized when the mammography is repeated and can also be detected by sonography and confirmed as a malignant tumor by histological examination - the result of the first (negative) mammogram is considered a “false negative”. “classified.

It is important to emphasize that in many cases, by comparing the two mammograms, the initially unrecognized tumor can be identified in both images. However, one cannot necessarily conclude from this that the tumor was overlooked in the first diagnosis because additional prior information was already available before the second diagnosis of the first X-ray image. In technical jargon, this difference is referred to as different “pre-test probabilities” for the first and second findings. This argument of different “pre-test probabilities” becomes clear when you apply the scenarios of “false-negative” and “false-positive” test results to the example of the PCR test.

Basically, a “false positive” result occurs when a test indicates the presence of a condition (e.g. breast cancer or SARS-Cov-2) even though there is a very high probability that this condition is not present, i.e. H. cannot yet be confirmed with all other available methods. Therefore, although expected “false positive” test results can be easily recognized (because a condition cannot be recognized), they are not easily explained. An example: If a mammography test shows an area suspicious for tumor that cannot be confirmed by the ultrasound examination and possibly also by a histopathological examination (gold standard), the positive result of the mammography is ultimately classified as a “false positive”. The following applies: The result of a test is called “false positive” if there is a positive test result that is not confirmed by the gold standard.

This comparison confirms that in the case of a “false-negative” result, the initially undetected finding can be corrected with additional information – in our example the newly appeared nodule – in the further clinical course. From a scientific perspective, the likelihood of a “false positive” result from a mammogram can be reduced by another non-invasive procedure, e.g. B. an ultrasound examination can be increased and, if there is still suspicion of a malignant disease, an additional negative histopathological examination can be classified with sufficient probability as a “false-positive” result without evidence of a substrate.

In the case of a PCR test, however, differentiating a “true-positive” from a “false-positive” test is more difficult than with mammography because other methods (ultrasound, histology) are available for mammography in addition to X-ray technology. to refute the suspicion of a “true positive” result. In the case of a “false-positive” PCR test, however, there is currently no other method that can be used to substantiate the distinction between “true-positive” and “false-positive” PCR tests.

The “ignorance of the unknown” that becomes apparent here describes a lack of evidence that doctors constantly have to deal with by using additional examination methods. Experienced clinicians can solve this problem with little effort, while novice clinicians incur significant diagnostic costs. Whether PCR tests are suitable for diagnostics due to this lack of evidence is discussed differently by different scientists. Legal clarification will be necessary due to the imposition of significant restrictions through regulations and laws.

The time until the decision could be used to document the documentation already suggested by various scientists
to catch up with supply requirements. For this purpose, the need for care must be documented in addition to the result of the PCR test for all tested people in one or more defined cohorts. The test results are available to the test centers and the need for care can be determined by those affected (no care or inpatient or intensive care care within three months of carrying out the PCR test). This data must be collected for ethical, medical, epidemiological and economic reasons.

However, responsible scientists could forestall the formation of political will by collecting all (positive and negative) test results from defined samples of a cohort to be determined within a defined test period together with the actual inpatient care needs (general or intensive care) within the first three months since the implementation of the test tests are reported. With this simple correlation, a not insignificant contribution to the evaluation of the suitability of the PCR test as a diagnostic tool could be verified. The additional effort is definitely justified. <<

1. Breast Cancer Surveillance Consortium tool. https://tools.bcsc-scc.org/BC5yearRisk/calculator.htm Last download January 8, 2021
2. Sackett DL et al. Evidence-based medicine. Churchill Linvingstone 1997. Page 120