Hi Mark - we have covered this extensively on the forum so at risk of repeating myself to some of the avid followers of the thread here goes.Originally Posted by Mark G
If I get a bit of terminology incorrect I apologise, but I firmly believe the thrust of what I am posting here is correct.
All tests have a False Negative and False Positive rate and they are usually declared. They refer to the clinical, laboratory errors that are associated with all tests.
Normally these FNs and FPs are declared so that analyse can take the range of error in to account.
With the PCR test, these figures have not been declared.
In addition to the technical shortcomings of tests, there can be additional shortcomings and this is particularly relevant where Guinea Pigs are mostly using home testing kits, or turning up at outdoor testing stations where mostly non-clinically trained staff are handling the testing process and batches of samples are being picked up at times by self-employed couriers and taxis, more often handling Just Eat deliveries or Hermes orders from Next.
As Governments haven't declared the FNs and FPs for the PCT tests, there has been analysis of various findings that have led to a consensus on FNs in the range of 20-30% and of FPs in the range of 0.8% to 4%.
I'm going to set out some illustrations now using 20% and 0.8% to give you an idea of outcome.
Scenario 1.
In hospital testing where likelyhood of CV infection is high and assuming a 50% rate of infection with CV.
Test 1000 people. Expect 500 positives.
400 Positives will test positive.
4 Negatives will test positive.
596 test negative.
The consequences are that 400 will be treated correctly, 4 people might be treated incorrectly as having CV but they clearly have a severe respiratory illness as they are in hospital.
96 people will not be treated as having covid who should, but repeat testing will end up catching most.
Most importantly, the testing results whether right or wrong are backed up by a clinical diagnosis.
Scenario 2.
General Public Testing where most are not showing any symptoms and community infection is thought to be 1%
Test 1000 people. Expect 10 positives.
8 positives will test positive.
8 negatives will test positive.
984 will test negative.
The consequences are that 2 of the infected cases will go about their business as usual. 8 will be quarantined, kept off work without genuine cause.
The figures are inflated by 60%. Policy decisions are based figures that are out by a significant amount.
Scenario 3.
Covid has disappeared from the general population.
Test 1000 people. Expect 0 positives.
There are no positives to test negative as a result of the FN error.
8 negatives will test positive.
The consequences are that Covid can not be eliminated. That this rate of FPs gives us a community rate of 800 per 100,000 population.
Here is the table for the Top 30 Middle Layer Super Output areas (statistical areas of around 7500 population).
https://twitter.com/RP131/status/133...407747/photo/1
Our Scenario 3 would fall in to 4th place at the moment nationally, be placed in Tier 3 and yet the reality is that there is no community infection.
I hope that sets out in a straightforward way the problems that have been highlighted around use of the PCR test for mass public surveys. It's an issue that I first came across in July, but experts at Porton Down flagged it up to SAGE in May, asking for SAGE to deal with this.
