Waiting for Zero

For all the people carefully counting COVID deaths and cases there may be an expectation that the number we are aiming for is zero. This is a mistake.  There will always be a proportion of cases tested who will be given a false positive test result when they do not have COVID.

What does zero look like?

Thankfully for COVID testing the false positive rate is less than 1%.  But it is not zero. It will be impossible for us to ever reach zero.  No more COVID will mean we only have cases and deaths that can be attributed to false positive test results.

I do not think we have reached zero COVID.  The outbreaks in Leicester and Oldham were genuine.  The prediction by the government SAGE advisors of a best-case scenario of zero deaths this winter is as absurd as their worst-case scenario of 80,000 deaths.  However, understanding what the data will look like when COVID is gone is critical to prevent it causing prolonged unnecessary damage and to allow us seasonal freedoms.

What is the false positive rate?

There are two ways to find out what the false positive rate of a test is.  One is to run the test on cases where there is certainty about the diagnosis and figure out how many results are wrong.  This can give a false impression of the rate of false-positives because the experimenters usually remove all ambiguous or complicated scenarios to enable that certainty, but the ambiguous and complicated cases exist in real life.  The other method is to use it in the real world and compare testing in different laboratories and over time.

Our real-world testing has resulted in a summer where the hospital positive rate has flatlined at 0.4%.  For two full months there have been day to day fluctuations but that has been the figure it keeps returning to.  Incidentally this is the figure the government is using to estimate false positive results (1). Having 99.6% of your results not being false positives is a phenomenal result.  Testing rarely gets better than this.

Figure 1: Percentage of cases as a proportion of tests processed minus the 0.4% false positive tests

When COVID is zero, how many false positive cases should we be expecting?

On an average month there are 1,400,000 admissions to hospital.  Tragically that figure was only  900,000 in June.  Data has not yet been published for July and August.  Although the trend was upwards for the purposes of this prediction a conservative 900,000 will be assumed (2).

For that period, all admissions to hospital were tested for COVID.  That is 30,000 tests a day of which 120 a day will give a false positive.

Some of these patients will die with this false positive COVID result (they die because whatever brought them into hospital kills them, not COVID, which they don’t have). In a normal year, across all patients admitted to hospital, the average risk of dying is 1.7% (3). So, I will assume that 1.7% of our false-positive die each week.  This would account for 14 deaths a week falsely labelled as COVID deaths.  With admission levels back to normal at 1,400 a month then we can expect 22 deaths a week.  It will be impossible for deaths to fall below that level.

That is the level that deaths in hospitals have reached at the end of August.

How can we tell they are not genuine COVID deaths?

In April, the chance of someone dying having been admitted to hospital with COVID was 6%.  By June that had fallen to 1.5%.  This was attributed to changes in treatment and some of that drop may well be due to improved treatment.  However, as soon as the rate of death reached the background death rate for general hospital admissions we have to be suspicious about how many of these patients actually had COVID.

If these cases were false positives then there would be other signals in the data.  For example, cases would be randomly dispersed through the population with only one per household rather than clustering.  Since the beginning of June, the ONS Infection Survey pilot (the pillar 4 testing of randomly selected households), has found 95% of the positive cases have been the only case in their household (4). This compares with the ONS estimate of an average of 1.6 cases per household in May (4). (This is worth emphasising – either the large majority of these positive cases in the survey are false-positive, as I am arguing – or Coronavirus is so hard to catch from people in your household that only 5% of the time does this happen.)

In addition, the age distribution would shift.  Cases during the epidemic were disproportionately seen among older people, in fact 60% were over 60 yrs old.  Of the cases we are seeing now only 11% are in the over 60s.  This means they are slightly under represented but this may be a reflection of their willingness to be tested compared with younger people and it would help to know the age of those tested for comparison.  In contrast, only 2% of cases were seen in the under 20s during the epidemic and this is now up to 19%, much closer to the 24% that would be seen if positive tests were distributed entirely randomly through the population (5). The age distribution of COVID deaths has been the same as the usual mortality data throughout.

During the pandemic men accounted for 60% of deaths.  An increasing proportion of false positive results would push that figure nearer to 50%.  Throughout June and July that figure has been 50% (6).

Until the beginning of June, asymptomatic cases accounted for less than 1% of reported cases.  By August that figure had reached 72% (7).  This may be in part due to the criteria for testing at the beginning but false positives are likely to play a significant role here.

Finally, a tight relationship of the time from diagnosis to death would become broader as cases moved from true positives to false positives.  I have not been able to find data on this. However, there is evidence of the diagnosis and deaths becoming uncoupled.  Public Health England were counting all deaths after a diagnosis regardless of the time frame.  When they moved to only counting those that had occurred within 28 days of diagnosis, over 5000 deaths were no longer counted and these deaths were almost all from patients who had died in the summer (8).

Testing the hypothesis

The trends in the data above may not be enough to convince everyone that the explanation is testing of false positives rather than a change in the biology of the virus.  The following work could test the hypothesis:

1. Using information of the age distribution of people tested to give a probability of a positive test for each age group over time.  If the probability tends to the same number for every age group then that would be indicative of reaching a false positive rate.  (Incidentally, this may show an age below which there have been no true positive cases).
2. For laboratories that have reached a positivity rate of below 2% use viral culture of those that are PCR positive to give an indication of the true positive rate.  No positive cultures will be possible if they are false positive cases.
3. A cohort of patients with a positive PCR test should have CT lung scans to demonstrate ground glass or other radiological changes in those that are true positives.  It has been shown that, even in pre-symptomatic patients, ground glass changes are seen in 77-93% on CT scan (9-11). Ground glass opacities are a very specific finding which would not be seen in false positive cases.
4. Post Mortem examination of a cohort of patients who have died with a label of COVID. True positive cases will show hyaline membranes in the lungs and/or interstitial lymphocytic infiltrate (94%) and positive PCR can be demonstrated up to 28 days after death (12).

Predictions for this winter

Coronaviruses are seasonal and that season begins in December running through to May or June.  Between now and then we will see occasional small outbreaks like Leicester and Oldham but the numbers (largely false positives) will remain stable elsewhere.  This means the R value will remain at 1.  As the number of people being tested continues to increase to reach the 250,000 a day target, the number of cases will increase in line with this. This will cause a slight increase in the R value.  As the numbers  attending hospital returns to normal, there will be a rise in false positive deaths causing unwarranted concern. However, the numbers will not breach 1.7% of admissions. The R value will suddenly pick up in December simply because this is a seasonal infection and real cases will start to appear.  Admissions will increase in December and before Christmas there will be speculation as to why the death rate from COVID has rocketed back up to somewhere near 4% or higher.  An early family Christmas in November might be worth planning ahead for.