In Brief

• The Facts:

  An article written by Robert Hagen, MD for MedPage Today explains the issues with COVID testing, especially when it comes to results, false positives and symptomatic people compared to asymptomatic people.

• Reflect On:

  Should governments have the authority and power to be able to shut down the planet? Should they have this ability especially when so many people, doctors and scientists completely oppose the measures they are taking to combat COVID?

Lockdown measures and more are being enforced once again, it seems, due to the number of cases that seem to be popping up in multiple countries around the world. Despite the fact that a staggering amount of doctors and scientists, who never seem to receive any attention from mainstream media, completely oppose these measures and claim they are doing much more harm than good, government health authorities continue to force people into mandatory measures under the penalty of fines and jail time.

There are a multitude of issues that are being raised by the scientific and medical community that really call into question what’s happening with COVID and the information that we are receiving from mainstream media. One of these issues is COVID-19 case counts, and the testing being used to determine how many cases there are.

Not long ago, 22 researchers put out a paper examining why, according to them, it’s quite clear that the PCR test is not effective in identifying COVID-19 cases, and as a result we may be seeing a significant amount of false positives.  The Deputy Medical Officer of Ontario, Canada, Dr. Barbara Yaffe recently stated that COVID-19 testing may yield at least 50 percent false positives. This means that people who test positive for COVID may not actually have it.

In July, professor Carl Heneghan, director for the centre of evidence-based medicine at Oxford University and outspoken critic of the current UK response to the pandemic, wrote a piece titled “How many Covid diagnoses are false positives?” He has argued that the proportion of positive tests that are false in the UK could also be as high as 50%.

As far back as 2007, Gina Kolata published an article in the New York times about how declaring virus pandemics based on PCR tests can end in a disaster. The article was titled Faith in Quick Test Leads to Epidemic That Wasn’t.

These are a view of many examples of concerns being raised with COVID testing that I’ve written about, you can find more in this article I recently published.

In this piece, I wanted to draw your attention to an article written by Robert Hagen, MD for MedPage today. He recently retired from Lafayette Orthopaedic Clinic in Indiana, He’s an adjunct professor at Indiana University.

You can read his full post below.

There’s certainly no denying the severity of COVID-19 in the U.S., but the numbers of positive tests reported can lead to confusion – especially for those of us in university towns.

Most of us in healthcare have a fairly good understanding of math but are not nuanced in the field of statistics. Unfortunately, the lack of understanding of the statistical principle of base rate fallacy/false positive paradox has led to some confusing numbers.

A classic 1978 article in the New England Journal of Medicine reveals this problem. The researchers asked 60 Harvard physicians and medical students a seemingly simple question: If a test to detect a disease with a prevalence of 1/1,000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease?

Only 14% gave the correct answer of 2% with most answering 95%.

Base rate fallacy/false positive paradox is derived from Bayes theorem. When the incidence of a disease in a population is low, unless the test used has very high specificity, more false positives will be determined than true positives. The difference in the numbers can be quite striking and certainly not inherently understandable.

We have learned in the past from routine PSA testing and mammograms that a positive test in a screening situation needs to be taken in context. The incidence of a disease in the population that you are testing is extremely important for accuracy.

Purdue University made the decision in late spring to resume in-person classes for its fall session. Purdue is a major research university with a strong emphasis on STEM education. Many of these classes include practicums, laboratory sessions, and group projects that require some in-person attendance.

An elaborate plan was implemented, including a signed pledge from all students to behave properly, wear masks, maintain social distancing. A decision was made to perform random testing on 10% of the students and staff each week. Since staff and students combined are 50,000 at Purdue University, 5,000 tests are done every week. The purpose of the random testing was surveillance to encourage students and staff to maintain proper behavior.

The Indiana State Department of Health advised against a random testing program, as it felt overall data accuracy would be difficult. Commingling of data in our county from the people tested WITH symptoms together with the randomly tested Purdue students WITHOUT symptoms has occurred. Base rate fallacy/false positive paradox unfortunately becomes ignored when one does this.

Up to this point, Purdue has done random testing on about 1,000 students per weekday. Of those, about 35 are positive each day, according to the university’s dashboard. Students who test positive have to isolate in an old dormitory or go home. Those who choose to go home will often have another test by their personal physician. When these tests return negative, significant confusion occurs.

So far, 90% of the students who test positive do not develop symptoms. Only one has been hospitalized and none have died. Had Purdue chosen to test all 50,000 students and staff every week, 10 times the number would have reported as testing positive weekly. Had this data been commingled with testing of symptomatic individuals, there certainly would have been an outcry by the casual observer to close everything down again. Yet those numbers would be only representative of the positivity of mass testing, not the prevalence of infective patients.