Putting the Pandemic’s Death Toll Into Perspective

There are two ‘official’ death tolls on the Government’s COVID-19 dashboard. 138,852 is the number of deaths within 28 days of a positive test. 162,620 is the number of deaths with COVID-19 on the death certificate.

The main reason the latter is larger than the former is lack of testing during the first wave. In the spring of last year, about 15,000 people in whose death COVID-19 was a contributing factor died without being tested.

So is 162,620 the pandemic’s true death toll? No. And that’s because it includes a large number of deaths that probably would have happened anyway.

How do we know this? Because if we calculate the excess deaths – the number of deaths in excess of what we’d expect based on previous years – we get a much lower number.

The official death toll for England and Wales, based on death certificates, is 147,031. Yet if we add up all the deaths since the start of March 2020, and subtract the average over the last five years, we get a figure of 117,476 (about 20% lower).

What’s more, due to population ageing, the average over the last five years understates the expected number of deaths. Hence the true number of excess deaths is about 15% lower. Taking this into account, the pandemic’s total death toll in England and Wales is about 100,000.

However, when it comes to events like pandemics, estimating the total death toll isn’t the best way to gauge the impact on mortality. Consider an example.

Japan and Mexico have about the same population, but there are more deaths each year in Japan. How can this be, when everyone knows Japan is a very long-lived country? The reason is simple: there are more elderly people in Japan, so there are more people at high-risk of dying each year.

A better way of comparing the level of mortality in Japan and Mexico is to use the age-standardised mortality rate or life expectancy. Both of these measures take into account the risk of dying at different ages, as well as the age-structure of the population. (In 2019, Japan’s life expectancy was 84, whereas Mexico’s was only 76.)

Last year, the U.K.’s age-standardised mortality rate rose by 12.8%. Although this is the largest one-year change since 1940 (the first year of the Blitz), the level to which mortality rose was lower than in 2008. And even the change should be put into context: 2019 was a year of unusually low mortality.

I previously estimated that the life expectancy in England and Wales last year was 80.4 – down from 81.8 in 2019. (Other researchers have reported similar figures.) So despite tens of thousands of excess deaths, life expectancy was still around 80.

Economy Grew by Less than Expected in August and July Figures Have Been Revised Down

GDP increased by 0.4% in August, below expectations of 0.5% and still lower than pre-lockdown levels. Figures for July have also been revised downwards this morning, showing a 0.1% contraction rather than the previously reported 0.1% growth. MailOnline has the story.

Numbers published this morning by the Office for National Statistics (ONS) revealed that U.K. gross domestic product remains 0.8% lower than it was before the coronavirus crisis.

Meanwhile, the ONS said its figures for July have been revised downwards in a move likely to cause alarm in the Treasury. …

It is the first time the economy has shrunk since January this year when the winter lockdown wreaked havoc.

The ONS said growth picked up in August as the economy benefited from the full lifting of coronavirus restrictions, which boosted hospitality and events sectors.

But the August growth figure was lower than expected, showing signs of a slowdown in the U.K.’s bounce back from the pandemic as global supply chain problems take their toll.

The economy will now need to surge by 2.1% in September if it is to remain on track with the Bank of England’s forecast for overall growth of 2.1% in the third quarter of 2021.

The ONS changed its assessment of the economic picture in July because of downwardly revised data relating to the manufacture of cars, oil and gas. …

Overall consumer-facing services [also] remain 4.7% below the level recorded in February 2020.

Worth reading in full.

Is Tom Chivers Right to Say PCR False Positives are “So Rare” they can be Ignored?

Tom Chivers at UnHerd has published an article headlined “PCRs are not as reliable as you might think“, sub-headlined “Government policy on testing is worryingly misleading”. The core argument of the article is that due to high rates of false negatives, a positive lateral flow test followed by a negative ‘confirmation’ PCR should be treated as a positive. I pass no comment on this. However, the article makes a claim that itself needs to be fact checked. It’s been quite a long time since PCR accuracy last came up as a topic, but this article provides a good opportunity to revisit some (perhaps lesser known) points about what can go wrong with PCR testing.

The claim that I want to quibble with is:

False positives are so rare that we can ignore them.

Claims about the false positive (FP) rate of PCR tests often turn out on close inspection to be based on circular logic or invalid assumptions of some kind. Nonetheless, there are several bits of good news here. Chivers – being a cut above most science journalists – does provide a citation for this claim. The citation is a good one: it’s the official position statement from the U.K. Office for National Statistics. The ONS doesn’t merely make an argument from authority, but directly explains why it believes this to be true using evidence – and multiple arguments for its position are presented. Finally, the arguments are of a high quality and appear convincing, at least initially. This is exactly the sort of behaviour we want from journalists and government agencies, so it’s worth explicitly praising it here, even if we may find reasons to disagree – and disagree I do.

Please note that in what follows I don’t try to re-calculate a corrected FP rate, alternative case numbers or to argue that Covid is a “casedemic”.

Let’s begin.

Government Has a Case to Answer on Children’s Vaccination, Says Judge

On Friday I reported on ONS figures which showed a worrying rise in deaths among 15-19 year-olds in summer 2021 compared to summer 2020, a period which coincided with the roll-out of vaccines to that age group. I asked whether the increase of 57%, amounting to 90 deaths, was a signal of deaths from serious vaccine side-effects such as myocarditis, or whether there was another explanation (such as increased road deaths, for example). There were only nine Covid deaths in the age group during the period, and there was no corresponding increase in deaths in 1-14 year-olds, adding to the concern.

I was not the only person to raise these questions. This data was presented, in fuller form and alongside other evidence, at a court hearing on Friday, where the Covid19 Assembly is seeking a judicial review of the Medicine and Healthcare products Regulatory Agency (MHRA)’s decision to approve the Pfizer/BioNTech vaccine for use in 12-17 year-olds.

Although the Government had asked for the case to be struck out, the judge, Mr. Justice Jay, allowed it to continue, accepting that the claimants had an arguable case. The current position is that the court has adjourned the matter to a further hearing to allow the Government further time to respond to the case. The court has given directions that the Government submit further response and evidence by October 11th, with the claimants having to October 15th to reply. After this the court will reconsider the matter “promptly”. I understand that the vaccination status of the teenagers who died during the period of the rollout is part of the evidence that the Government has been asked to provide.

The New ONS Study Claiming Masks Cut Infection Risk in Half and Vaccines are Better Than Natural Immunity is Riddled With Problems

What do you do when people have spotted that infection rates are higher in the vaccinated than the unvaccinated and are spreading this ‘misinformation‘ on the internet?

It appears that you commission the ONS to come up with a model that fixes the problem. Or rather, in this case, three models.

The ONS on Monday published a new ‘technical article‘ based on its Covid Infection Survey that provides “analysis of populations in the U.K. by risk of testing positive for COVID-19”. It covers the two-week period August 29th to September 11th, though regular updates are now promised.

It involves no fewer than three models, briefly summarised as:

Our first model, Model 1 (the core model), predicts the likelihood of an individual testing positive based on general demographic characteristics in order to help identify broad groups where infections are persisting or arising. …

We then built upon Model 1 resulting in Model 2, the screening model. This includes the core demographic characteristics from Model 1 and incorporates other characteristics individually to identify other factors associated with testing positive for COVID-19. …

Finally, Model 3 (the behaviours model) adds behaviour variables to the core demographic characteristics from Model 1 and the screened characteristics that were kept in Model 2.

I’m sure this talk of models built on models is filling you with confidence.

I’m not sure it filled the authors with very much confidence, though, as their main findings are stated without specific figures:

● People who had received one or two doses of a coronavirus vaccine were less likely to test positive for coronavirus (COVID-19) in the fortnight ending September 11th 2021.

● People living in a household of three or more occupants were more likely to test positive for COVID-19 in the fortnight ending September 11th 2021.

● Those in younger age groups were more likely to test positive for COVID-19 in the fortnight ending September 11th 2021.

● People who never wore a face covering in enclosed spaces were more likely to test positive for COVID-19 in the fortnight ending September 11th 2021.

● Those who reported socially distanced contact with 11 or more people aged 18 to 69 years outside their household were more likely to test positive for COVID-19, in the fortnight ending September 11th 2021.

The media made much of the mask finding, with the Mail declaring: “People who don’t wear face masks indoors are up to TWICE as likely to test positive for Covid.”

Covid Cases Fall After Schools Reopen In Spite of Widespread Predictions to the Contrary

Covid infections have plummeted despite fears that the new school term would fuel an autumn surge, according to the latest ONS data. The Daily Mail has more.

One in 90 people in England had the virus last week, with around 620,100 infected in total, testing by the Office for National Statistics revealed.

This is down 18% from a fortnight earlier, when one in 70 tested positive and estimated total infections stood at 754,000.

The weekly ONS survey, based on random swab testing of 150,000 people, is seen by the Government as the most reliable measure of the epidemic.

In a further boost for hopes that the pandemic may be over, Government scientists said the R rate – the average number infected by someone with the virus – may have dropped below one for the first time since March. R is between 0.8 and 1 in England, meaning the epidemic is shrinking.

ONS study leader Kara Steel said: ‘Infection levels have decreased in England for the first time in several weeks, though rates remain generally high across the UK.

‘It’s encouraging that infection rates have continued to decrease among young adults, possibly reflecting the impact of the vaccination programme.’

Infections are highest in secondary schoolchildren, with around one in 35 testing positive, reflecting the fact that many in this age group are yet to be jabbed. But the ONS report shows cases have decreased or remained flat in every other age group.

Worth reading in full.

August’s Age-Standardised Mortality Rate Was 2.5% Higher Than the Five-Year Average

The ONS announced on Tuesday that there were 40,460 deaths registered in England in August, which is approximately the same number as in July, and 9.9% more than the five-year average.

As you can see on this chart, weekly deaths remained above the five-year average for most of the month. Then in week 35, the August bank holiday artificially lowered death registrations:

Deaths being roughly 10% higher than the five-year average sounds like quite a lot. And in fact, the number of deaths registered in August of 2020 was 5.6% less than the five-year average.

Of course, infections were at a local minimum last August, and some of the deaths that would have occurred then had been brought forward by the pandemic. By contrast, August of 2021 coincided with the tail end of the Delta wave, and infections remained elevated throughout the month.

Consistent with this interpretation, COVID-19 was the third leading cause of death in August (a month when mortality is usually low) and deaths from the other nine leading causes were all below their five-year averages.

But as I always note in these updates, age-adjusted measures provide a much better guide to changes in mortality than the absolute number of deaths. In August, the age-standardised mortality rate was about the same as in July, and was only 2.5% higher than the five-year average.

This chart from the ONS shows the age-standardised mortality rate for the first eight months of the year, each year, going back to 2001:

As in the preceding two months, cumulative mortality to date was lower than the corresponding figures for both 2015 and 2018. In other words, the first eight months of 2018 – a year with no pandemic – were more deadly than the first eight months of 2021.

Overall then, 2021 is still a fairly normal year for mortality in England. As a matter of fact, it’s the sixth least deadly year on record! This could change, however, if the winter brings a particularly large wave of COVID-19 or seasonal flu.

Long Covid Is Even Less Common Than Previously Thought

In a post on long Covid back in July, I said that “estimates of the chance of reporting symptoms after 12 weeks range from less than 1% to almost 12%”. That 12% figure came from the ONS, who found that individuals who tested positive were 12 percentage points more likely than controls to report at least one symptom 12 weeks after infection.

In my post, I argued that 12% is probably an overestimate on the grounds that some people who tested positive might have been inclined to exaggerated their symptoms – to report things they normally wouldn’t have done (thanks to all the media attention on long Covid).

And I noted that a study published in Nature Medicine had observed a much smaller percentage of people still reporting symptoms 12 weeks after infection, namely 2.3%.

A new analysis by the ONS has obtained a figure almost identical to that observed in the Nature Medicine study, namely 2.5% (the difference between the blue and green lines in the chart below). This is clearly much lower than its previous estimate.

Interestingly, the reason for the discrepancy with the earlier figure isn’t the one I suggested (i.e., that some people who tested positive were inclined to exaggerate their symptoms). Rather, it’s a statistical issue.

In both their original and updated analyses, the ONS defined symptom discontinuation as two consecutive visits without reporting any symptoms. (Participants in the ONS’s survey were visited at regular intervals for the purpose of data collection.)

This means that someone would be classified as ‘having symptoms’ if they’d gone one, but not two, visits without reporting any symptoms. However, in their original analysis, participants were only followed for a median of 80 days (less than 11 weeks).

As a result, some participants who would have been classified as ‘not having symptoms’ if they’d been followed a little bit longer were still classified as ‘having symptoms’ at the end of their observation period. (In the jargon, their follow-up time was ‘right-censored’.) This is shown in the diagram below, taken from the ONS:

In the ONS’s updated analysis, which followed participants for a median of 204 days, individuals in the situation of Participant D above were correctly classified as ‘not having symptoms’ before the end of their observation period.

Using this revised method, the ONS found that less than 1% of children aged 2-11 continue to report symptoms 12 weeks after infection, with the figure rising to just 1.2% for those aged 12-16. Hence long Covid is particularly rare in children, further undermining the case for vaccinating that age-group.

While the ONS deserves credit for being completely transparent about the limitations of their original analysis, their updated analysis is still open to the criticism I mentioned above. This means that 2.5% should probably be considered an upper bound on the chances of getting long Covid, the true figure being somewhat lower.

More than Half of People ‘With’ Long Covid Might Not Have… Long Covid, According to New Research

New research by the Office for National Statistics (ONS) suggests that more than half of those who are suffering ‘from’ long Covid might not actually have it and could simply be suffering from normal bouts of ill health. The Telegraph has the story.

The ONS surveyed nearly 27,000 people, who tested positive for Covid, in the U.K. Coronavirus Infection Survey and used three different methods to estimate the prevalence of long Covid.

In one analysis, they found that 5% reported at least one symptom 12 to 16 weeks after their infection.

However, the study also found that 3.4% of people who had not been diagnosed with Covid also reported the same long Covid symptoms.

Kevin McConway, Emeritus Professor of Applied Statistics at the Open University, said: “That’s not all that much less than the 5.0% for the infected people, which does show that having one or more of these symptoms isn’t uncommon regardless of Covid.”

Long Covid symptoms are fever, headache, muscle ache, weakness/tiredness, nausea/vomiting, abdominal pain, diarrhoea, sore throat, cough, shortness of breath, loss of taste and loss of smell.

However, the ONS said that such conditions were experienced regularly within the general population.

A second analysis found that just 3% of people reported continuous symptoms for at least 12 weeks after an infection, compared to 0.5% of the control population.

However, in a third analysis, when the group was asked to self-identify as suffering from long Covid, 11.7% said that they believed they had the condition, with 7.5% saying the condition limited their day-to-day activities.

When confined to only people who had suffered symptomatic Covid, the number saying they suffered from the condition rose to 17.7%.

Previous studies have suggested up to a fifth of people catching Covid will suffer from long-term after-effects.

The ONS said that depending on which measure was used, the data showed between three and 11.7% of Covid cases still had symptoms 12 weeks after an infection.

Worth reading in full.

Why is the ONS Claiming Just 1% of Covid Deaths Are in the Vaccinated When PHE Data Shows the True Figure For August was 70%?

The ONS has published a new study on Covid deaths which purports to show how few vaccinated people die of Covid. Here’s how the Telegraph reported the headline claim: “Only 59 fully vaccinated people without serious health conditions died from COVID-19 out of more than 50,000 deaths in England this year, new figures from the Office for National Statistics (ONS) show.”

The Telegraph report continues:

In the first study of deaths by vaccination status, the ONS found that around 99% of COVID-19 deaths between January 2nd and July 2nd 2021 were in people who had not had two doses.

Overall 640 (1.2%) of deaths were in those who had received both vaccine doses, but the ONS said many of those could have been infections picked up before the second dose. 

Just 256 deaths (0.5%) were considered true “breakthrough” infections where the second dose had long enough to work, but still did not offer protection. 

However, the average age of those “breakthrough” infections was 84 and the majority (76%) were classed as “extremely clinically vulnerable”. Just 59 did not have serious medical conditions.

These statistics appear remarkable – until you realise what they’ve done. Although the data is presented as “this year” in fact the cut-off date is July 2nd. That is significant because it is just before the Delta surge got going. This means the data all comes from the Alpha surge, when almost no-one was vaccinated and tens of thousands of Covid deaths were reported, and from the quiet spring and early summer when many were vaccinated but almost no-one died (see chart below).