PCR testen og falske positiver


Manglene ved PCR testen blir plukket i fra hverandre som jeg aldri har sett noen andre steder i boken “Covid -why most of what you know is wrong” av den svenske legen Sebastian Rushworth i kapittelet “How accurate are the covid tests?”, der han dokumenterer alle sin påstander med fakta og fagfellevurderte vitenskapelige undersøkelser.

Og det er ikke bare pcr testen Rushworth plukker ifra hverandre, han plukker ifra hverandre alle logikbristene rundt c 19, og han plukker de ifra hverandre på samme måte som han gjør med PCR testen, med rein fakta og logikk. I boka til Rushworth kan INGENTING stemples som konspirasjonsteorier, løgn eller påstander.

Når myndighetene, pressa og legestanden kaller alle som ikke er enig i covid politikken for konspirasjonsteoretikere, høyreekstreme og farlige, da MÅ man banke i bordet med fakta som viser at disse hersketeknikkene og generaliseringen av svært forskjellige individer som ikke kan slåes over en kam er feilaktig og stigmatiserende. Dvs at man må ha et våpen man kan slå tilbake med.

Boka til Sebastian Rushworth er DET våpenet, og hvis den når ut til mange nok, har den potensiale til å nøytralisere myndighetene og pressa sine feige angrep mot alle oss som ser gjennom propgandaen og indoktrineringen deres. Boka MÅ spres til så mange som mulig, og helst oversettes til norsk. Og den kraftige ammunisjonen som følger med dette våpenet er:

Covid 19 i Svergie. Hvordan man leser vitenskapelige studier? Å lese statistikk. Hvor farlig er covid 19? Hva er langtidscovid? Hvor pålitelig er testene? Fungerer lockdown? Svergie sammenlignet med sine naboer. Hvilke skader forårsaker lockdown? Fungerer munnbind? Er vaksinene sikre og effektive? 

Boka er svært lettleselig og retter seg like mye mot leg som lærd, og den er ikke stor og lang med sine 139 sider.

Under her finner du et utdrag fra kapittelet “Hvor pålitelig er testene?” (“How accurate are the covid tests?”):


“PCR is only detecting sequences of the viral genome, it is not able to detect whole viral particles, so it is not able to tell you whether what you are finding is live virus, or just non-infectious fragments of viral genome. If you get a positive PCR test and you want to be sure that what you’re finding is a true positive, then you have to perform a viral culture.


What this means is that you take the sample, add it to respiratory cells in a petri dish, and see if you can get those cells to start producing new virus particles. If they do, then you know you have a true positive result. For this reason, viral culture is considered the “gold standard” method for diagnosis of viral infections.


However, this method is rarely used in clinical practice, which means that in reality, a diagnosis is usually made based entirely on the PCR test. A systematic review looking at the ability to culture live SARS-CoV-2 after a positive PCR test (Jefferson et al.) found that the probability of a false positive result increased enormously with each additional cycle after 24 cycles.1 After 35 cycles, none of the studies included in that review was able to culture any live virus.


In most clinical settings (including the one I work in), all the doctor is provided with is a positive or negative result. No mention is made of the number of cycles used to produce the positive result. This is a problem, since it’s clear that a positive result after 40 cycles is almost certainly a false positive, while a positive result after 20 cycles is most likely a true positive.


Without information about the number of cycles, you have to assume that the patient sitting in front of you has Covid and is infe tious, with all the downstream consequences that entails in terms of self-isolation and contact tracing, and if things go badly for the patient, in terms of an incorrect cause of death being listed on the death certificate.


The other main type of test is the antibody test. Here, the sample is usually taken from the blood stream. There are five different types of antibodies, but most antibody tests only look for one type of antibody, IgG, which is the most common type. Generally it takes a week or two after a person has been infected before they start to produce IgG, and with Covid, you’re generally only infectious for about a week after you start to have symptoms, so antibody tests are not designed to find active infections. Instead the purpose is to see if you have had an infection in the past.


Apart from understanding how the tests work, we also need to understand two important terms. Those terms are sensitivity and specificity, and they are critical for all diagnostic tests used in medicine, because they tell you how good a test is.


Sensitivity is the probability that a disease will be detected if the person actually has the disease. So, for example, a test for breast cancer with a sensitivity of 90% will detect breast cancer 90% of the time. Nine out of ten patients with breast cancer will correctly be told that they have the disease. One out of ten will incorrectly be told that they don’t have the disease, even though they do.


Specificity is the opposite of sensitivity. It is the probability that a person who doesn’t have the disease will be told that they don’t have the disease. So, a specificity of 90% for our imaginary breast cancer test means that nine out of ten people who don’t have breast cancer will be correctly told that they don’t have it. One out of ten people who don’t have breast cancer will incorrectly be told that they do have it.
To put it another way, sensitivity is the ability of a test to detect true positives. Specificity is the ability of a test to avoid producing false positives.


A perfect test will have a sensitivity and specificity of 100%, which would mean that it catches everyone who has the disease, and doesn’t tell anyone they have the disease if they don’t. No such test exists. In general, sensitivity and specificity are in conflict with each other – if you push one up, the other will go down.


If I just told everyone I meet that they have breast cancer, my sensitivity for detecting breast cancer would be 100%, because I wouldn’t miss a single case, but my specificity would be 0%, because every single person who doesn’t have breast cancer would be told that they do. So, when designing a test, you have to decide if you’re going to maximize sensitivity or specificity. If you design a Covid PCR test with a cycle threshold of 40, then you are going for maximal sensitivity – the probability of missing a case is minimized, but you’re going to get a lot more false positives than if you set the threshold at 30.


Ok, now we know how the PCR test works and how the antibody test works, and we understand sensitivity and specificity. That means we’re ready to determine how good the Covid tests are. Let’s look at a systematic review that was published in Evidence Based Medicine in October 2020.2 The review sought to determine the accuracy of the Covid tests. The review included 25 studies of antibody tests and 38 studies of PCR tests (and LAMP tests, an alternative technique that is similar to PCR).


Only ten of the 25 studies of antibody tests (with a total of 757 patients) provided enough data to allow sensitivity to be calculated. The sensitivity of the different antibody tests varied from 18% to 96%. 12 studies provided enough information for specificity to be determined, and in these it varied from 89% to 96%.


The overall sensitivity for PCR/LAMP was between 75% and 100% in the different studies, while the overall specificity was between 88% and 100%. 16 studies, with a total of 3,818 patients, were able to be pooled together to get a more accurate estimate of sensitivity. In the pooled analysis, sensitivity was determined to be 88%. It wasn’t possible to determine a pooled specificity value, since the studies included in the pooled analysis were all of people who were already known with complete certainty to be infected with Covid. A separate systematic review found an average specificity for the PCR tests of 96%.


However, during the summer of 2020, in Sweden, PCR test positivity was at 3% when it was at its lowest, and the number of false positives cannot be higher than the total number of positives, so that would suggest a specificity of at least 97%. In some other countries however, the number of positives has been even lower, suggesting that the specificity might be much better, maybe well over 99%.


This is of course a problem. No-one knows how good the tests are, and even small differences in specificity can make a very big difference to the probability that someone who is told they have Covid actually has the disease. This might be a little hard to understand spontaneously, so we’re going to play around with the numbers a bit in order to clarify it.


Let’s say the disease is spreading rampantly through the population, and one in ten people are infected at the same time. If we test 1,000 people at random, that will mean 100 of those people actually have Covid, while 900 don’t. Let’s further assume that the test has a sensitivity of 88% (what the review says) and a specificity of 97% (what the real world Swedish data suggests it must be at minimum). Of the 100 who have Covid, the test will successfully pick up 88.


Of the 900 who don’t have Covid, the test will correctly tell 873 that they don’t have it, but it will also tell 27 healthy people that they do have Covid. So, in total 115 people out of 1,000 are told that they have Covid. Of those 115 people, 77% actually have the disease, and 23% don’t.


That’s not great. Two in ten people getting a positive test result don’t actually have Covid, even in a situation where the disease is common and 10% of people being tested really do have the disease.


Unfortunately, it gets worse. Let’s assume the disease is starting to wane, and now only one in a hundred people being tested actually has Covid. If we test 1,000 people, that will mean ten will really have Covid, while 990 won’t. Of the ten who have Covid, nine will be correctly told that they have it.


Of the 990 who don’t have it, 960 will be correctly told that they don’t have it, while 30 will be incorrectly told that they do have the disease. So, in total, 39 people will be told that they have Covid. But only 9 out of 39 will actually have the disease. To put it another way, in a situation where only 1% of the population being tested has the disease, 77% of positive results will be false positives.


There is another thing about this that I think is worth paying attention to. When one in ten people being tested has the disease, you get 115 positive results per 1000 people tested. But when one in a hundred has the disease, you get 39 positive results. So, even though the actual prevalence of the disease has decreased by a factor of ten, the prevalence of PCR positive results has only decreased by a little over half.


So if you’re only looking at PCR results, and consider that to be an accurate reflection of how prevalent the disease is in the population, thenyou will be fooled as the disease starts to decline, because it will continue to seem much more prevalent than it is.


Let’s do one final thought experiment to illustrate this. Say the disease is now very rare, and only one in a thousand tested people actually has Covid. If you test 1,000 people, you will get back 31 positive results. Of those, one will be a true positive, and 30 will be false positives. So, even though the prevalence of true disease has again decreased by a factor of ten, the number of positive results has only decreased slightly, from 39 to 31 (of which 30 are false positives!).


The rarer the disease becomes in reality, the less likely you are to notice any difference in the number of tests returning positive results. In fact, the disease could vanish from the face of the Earth, and you would still be getting 30 positive results for every 1,000 tests carried out!


So, if politicians continue to base decisions on the number of positive tests (so called “cases”) rather than on hospitalizations, ICU admissions, and deaths, they might well be able to continue to claim that the pandemic is still ongoing years from now, when it is in reality long gone.


The same trend is seen even if the PCR tests were to have a much better specificity than we are estimating here, of say 99.5%. Here’s a quick illustration, since I don’t want to tire you with too many more numbers. If one in ten has the disease and you test 1,000 people, you will get back 92 positive results, of which 88 will be true positives and 4 will be false positives.


If one in 100 has the disease, you will get back 14 positive results, of which 9 will be true positives and five will be false positives. If one in 1,000 has the disease, you will get back 6 positive results, of which 5 will be false positives.


So, even if the test has a very high specificity of 99.5%, when the virus stops being present at pandemic levels in the population and starts to decrease to more endemic levels, you quickly get to a point where a large proportion of all positive tests are false positives, and where the disease seems to be much more prevalent than it really is.


This may not be a problem when the virus is common or when you are only testing people who are showing symptoms, and where there is a high likelihood of disease, but it becomes a problem when the virus is rare or when the test is being used for mass screening.


As mentioned, no one knows what the specificity of the PCR test is, and as I’ve now shown the number of false positives varies a lot depending on how prevalent the virus is. There is another aspect to this that I’ve avoided so far. Some people have pointed out that there have been periods in Australia where fewer than 0.1% of tests have been positive.


According to the principle that the number of false positive tests can’t be higher than the total number of positive tests, that would mean the specificity is over 99.9%. If that were the case then the proportion of false positives would remain low even at quite a low prevalence of the virus in the population.


There are two problems with this reasoning. The first is that different countries are using different PCR tests, and have different CT-limits at which they consider a result to be positive, so just because you see one specificity in one country doesn’t mean you will see the same specificity in another country. Secondly, and perhaps more importantly, the PCRtest can’t magically produce a positive result from nothing.


So, say we have an island far away from other countries, and we’ve closed our borders and managed to keep Covid out, our specificity is going to appear to be very good, better than in a situation where the virus is widespread in the country. How can this be possible?


Because the test needs to be contaminated in some way in order to produce a false positive result, and if contamination isn’t a possibility, because there is practically no virus in the country, then that’s not going to happen.


Paradoxically, the number of false positives is therefore going to increase as the prevalence of the virus increases, because the risk of contamination in the lab increases, and because the probability increases that a healthy uninfected person will have small bits of the virus in their respiratory tract. The probability also increases that a person has had the virus a month or two ago, and is still producing some viral remnants in their airway.


So we can have the paradoxical situation where the share of false positives is at its highest when the virus is spread in the population but not very common, and at its lowest in a situation where the virus is barely present in the population at all.


The point I’m trying to make is that it’s impossible to know what the specificity of the test is, and that it’s not possible to compare countries with each other. Just because you see one specificity in one country that is at one stage of the pandemic, that doesn’t mean you will see the same specificity in a different country that is at another stage. The specificity changes to a large extent over time.


There is another problem with the tests, which I have already touched on earlier in the book. And that is the reporting of testing without clarifying what the denominator is. In Sweden, we went from testing 26,000 people per week in April to testing 260,000 people per week in November. In the media, there is little mention of this. Instead we keep hearing that the number of cases keeps rising to ever higher numbers. Of course the absolute number of cases keeps rising, because we test ever increasing numbers of people.


A more nuanced and accurate picture is gained by looking at the share of tests that are positive. This shows much less dramatic fluctuations than is seen when just looking at the numberof cases, but this is rarely done by mass media, whose apparent primary function is to terrify people.


At the same time that the PCR tests are reported on a daily basis by the mass media, and interpreted in the most frightening light possible, the antibody testing has been largely ignored. This is interesting, because the antibody testing shows a gradual and continuous increase in the proportion of people with antibodies week by week, which clearly signals a strong buildup of immunity in the population.”


Utdrag fra boken «Covid: Why most of what you know is wrong», av den svenske legen Sebastian Rushworth, utgitt februar 2021. ANBEFALES PÅ DET STERKESTE!

Boken kan kjøpes i papirutgave eller som e-bok her https://www.adlibris.com/no/e-bok/varfor-det-mesta-du-vet-om-covid-19-ar-fel-en-evidensbaserad-utvardering-9789188729811

Boken er gitt ut på engelsk og svensk.

Linker som viser hvor lite pålitelig PCR testen er, linkene kommer i fra Rushworth sin bok:







Relatert lesing:


Ny studie slakter hurtigtestene Norge har betalt 280 millioner for https://www.nrk.no/norge/ny-studie-slakter-hurtigtestene-norge-har-betalt-280-millioner-for-1.15241363


18 særdeles gode grunner til IKKE å ta c 19 vaksine https://www.deconstructingconventional.com/post/18-reason-i-won-t-be-getting-a-covid-vaccine


Masse testing av barn i skolene https://olehartattordet.blogg.no/massetesting-av-barn-i-skolene.html


10 av 9 blir lurt av statistikk https://olehartattordet.blogg.no/1432076802_10_av_9_blir_lurt_av_.html


Hvor ofte får vi disse opplysningene med på kjøpet når vi får servert en meningsmåling? https://olehartattordet.blogg.no/1504590430_hvor_ofte_fr_vi_disse_opplysningene_med_p_kjpet_nr_vi_fr_servert_en_meningsmling_.html


Korona kavalkade – hvor mye av dette har du fått med deg? https://olehartattordet.blogg.no/korona-kavalkade.html

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