COVID-19 Re-categorisation of deaths

Summary

Forgive me omitting the usual ‘population prevalence’ update, but roughly speaking, nothing has changed.

However the government have introduced new ways to count the dead, and that is really important.

Surprisingly – to me at least – I have concluded that is a not a self-serving manipulation of the data to reduce headline rates of death. It actually helps us to understand what is going on with the pandemic.

New ways to count the dead

Last week I wrote:

I feel the best thing I can do in the face of this tidal wave of uncertainty is to try to focus on the simple statistics that require only minimal theoretical interpretation.

I was away from home last week and so missed the announcement about new ways to ‘count the dead’. New ways to count the dead?! What?

The government announced it would divide daily deaths into three categories:

1. Deaths of people who have died within 28 days of a positive COVID-19 test
   • Irrespective of any ‘underlying conditions’ we can reasonably say these people ‘died from COVID-19‘
2. Deaths of people who have died within 60 days of a positive COVID-19 test
   • Similarly, despite any ‘underlying conditions’ we can reasonably say these people also ‘died from COVID-19‘
3. Deaths of people who have died anytime after a positive COVID-19 test
   • Depending on the length of time to death, it could be that the COVID-19 test might possibly be less relevant to these deaths than other pre-existing difficulties.

At first I found it hard not to think that the government was doing this in order to generate lower numbers.

But after writing this article, I have concluded that this categorisation is actually helpful.

Let’s look at the data.

The government now produce three curves as shown in the two figures below. The first graph shows the daily death statistic throughout the pandemic. The three curves only differ in the ‘tail’ of the curve.

Click for larger figure. The Red Curve shows the total number of deaths per day. The Cyan Curve shows the number of deaths within 28 days of a test, and the Blue Curve shows the number of deaths within 60 days of a test. All curves are 7-day retrospective rolling averages.

Let’s look at the recent data in more detail.

Click for larger figure. The Red Curve shows the total number of deaths per day. The Cyan Curve shows the number of deaths within 28 days of a test, and the Blue Curve shows the number of deaths within 60 days of a test. All curves are 7-day retrospective rolling averages.

Notice that the ‘All deaths’ curve includes all the deaths counted in the ’60-day’ curve and the ’60-day’ curve includes all deaths on the ’28-day’ curve.

In order to understand these data  we need to re-categorise them by subtracting the datasets from each other to yield:

• Deaths of people who have died within 28 days of a positive COVID-19 test
• Deaths of people who have died between 28 and 60 days of a positive COVID-19 test
• Deaths of people who have died more than 60 days after a positive COVID-19 test

These data are summarised in the figures below. Now the data in each of three categories are independent of each other and add up to give the total deaths.

Click for larger figure. The Red Curve shows the total number of deaths per day. The Cyan Curve shows the number of deaths within 28 days of a test, and the Black Curve shows the number of deaths between 28 and 60 days after a test. The Blue Curve shows the number of deaths occurring at least 60 days after a test. All curves are 7-day retrospective rolling averages.

So for example on day 229 (16th August 2020), the average number of people dying per day in the previous seven days was 61.7 deaths per day:

• On average, 10.9 of those people were diagnosed less than 28 days previously
• A further 10.4 were diagnosed between 28 and 60 days previously.
• But 40.4 of those people were diagnosed more than 60 days previously.

It is this last datum which is most significant: most people dying after a recent infection with COVID-19 acquired the infection more than 60 days earlier. Further more, deaths in this category are rising! THis is the real insight arising from this re-categorisation.

We can also plot these categories as fractions of the total deaths: we see that roughly two thirds of daily deaths occur more than 60 days after a positive COVID test – and that fraction is rising!

Click for larger figure. The Cyan Curve shows the percentage of deaths within 28 days of a test, and the Black Curve shows the percentage of deaths between 28 and 60 days after a test. The Blue Curve shows the percentage of deaths occurring at least 60 days after a test. All curves are 7-day retrospective rolling averages.

What does this tell us?

Here is my current understanding. And it is broadly good news!

• The fraction of people dying from COVID-19 who die within 28 days has been falling since the peak of the pandemic. Currently, one sixth of people who eventually die survive for less than 28 days from their diagnosis.
  • The most likely reason for this is that our doctors have got better at treating people. Only a few people die quickly.
• The fraction of people dying from COVID-19 who died between 28 and 60 days rose as doctors kept people alive beyond 28 days. But this too has now started to fall and only a further one sixth of people who will die survive between 28 and 60 days from their diagnosis.
  • The most likely reason for this is once again that people are being kept alive longer, but doctors are unable to cure them.
• The fraction of people dying from COVID-19 who die and were diagnosed more than 60 days previously is still rising and now constitutes two thirds of all ongoing deaths. I find this surprising. And now we need to consider two possible causes:
  • Firstly, doctors are keeping people alive longer but are unable to cure them. If this were so then people would be dying after what must be an appalling 60 days in hospital. I was not aware that there many patients in this condition.
  • Secondly, people might have fully or partially recovered from COVID-19, but then die of another cause.

But how large is this second category? We can estimate it thus:

• About 1% of the UK population die each year (roughly 600,000 people). So on average we would would expect around 1%/365 = 0.027% of the population to die each day, or roughly 1640 deaths per day, irrespective of COVID-19.
• Thus current daily deaths from COVID-19 constitute only a small percentage of normally-expected deaths. If the disease did not have the capability to re-infect the entire population and kill literally millions then we would not be so worried about deaths at this rate.
• So far around 320,000 people have tested positive for COVID-19 and roughly 260,000 have survived. What is the chance that this cohort of 260,000 might have recovered from COVID infection and then died of something else? Well a first guess would be roughly 1% chance per year or 0.027% per day- the same chance as applies to the general population. Thus we might expect 0.027% of the 260,000 recovered people to die each day i.e. around 70 deaths per day.
• Even allowing for several biasing factors, this is a significant fraction of the daily deaths data – much larger than I would have estimated.

So my understanding of the data is this.

• Deaths within 28 days of a positive test can be understood as being deaths arising from COVID-19. There are currently around 10 deaths per day in this category.
• Deaths beyond 60 days of a positive test are primarily due to deaths from other causes. We should expect deaths in this category to rise to about 70 deaths per day (or some other similar number) and then stabilise.
• Deaths after 28 but before 60 days can probably not be categorised as being clearly in one category or the other. The fact that deaths in this category first rose and then fell probably indicates deaths in this category initially arose directly from COVID infection.

Overall, this is good news. It means that there are fewer deaths arising from COVID-19 than we previously thought.

And by looking at the ‘prompt’ deaths, policy makers can get better feedback on how well their policies are working on the ground.