Winter deaths are usually running high at this point in January, but this year is different. According to the latest figures from the ONS, released today, in the week ending January 14th there were 6.1% fewer deaths than the five-year average in England and Wales (872 fewer deaths). Note that the five-year average the ONS uses doesn’t include 2020, but 2016-19 (which has historically low mortality) and 2021.
In the previous week there were 7.8% fewer deaths than the five-year average (1,036 fewer deaths).
A reflection of the mildness of Omicron and the level of immunity in the population, this makes 2021-22 a mild flu season, and further underlines how unjustified any measures to combat coronavirus now are. The state of emergency and all laws and guidance – including the vaccine mandates – must be removed without delay so that healthy normality can be restored.
This article has been corrected for a mistake in the information about which years are included in the ONS five-year average.
The accuracy of any data purporting to show vaccine effectiveness or safety against a disease is critically dependent on the accurate measurement of: people classified as having the disease; vaccination status; death reporting; and the population of vaccinated and unvaccinated (the so called ‘denominators’). If there are errors in any of these, claims of effectiveness or safety are unreliable.
The risk/benefit of Covid vaccines is best – and most simply – measured by all-cause mortality of vaccinated against unvaccinated, since it avoids the thorny issue of what constitutes a Covid ‘case/infection’. In principle, the data in the ONS vaccine mortality surveillance reports should provide us with the necessary information to monitor this crucial comparison over time. However, until the ONS released its November report, no age categorised data were provided, meaning that any comparisons were confounded by age (older people are both disproportionately more vaccinated than younger people and disproportionately more likely to die).
The week 44 ONS report and data release from November finally provided some relevant age categorised data. Specifically, it includes separate data for age groups 60-69, 70-79 and 80+, but there is only a single group of data for the age group 10-59. After the November data release the ONS released further data on December 20th 2021, albeit at a significant lower level of granularity that inhibits cross comparison with earlier data (different age categories; monthly rather than weekly data; age-adjusted mortality rather than raw death and population data; death counts updated; and fractional membership of vaccination category based on time spent in category) and with different categories for vaccine status than those used in November (five categories rather than four with double dose vaccinated split into less than and greater than 21 days).
At first glance the data suggest that, in each of the older age groups, all-cause mortality is lower in the vaccinated than the unvaccinated. In the 10-59 age group all-cause mortality is higher among the vaccinated, but this group is likely confounded by age since it is far too wide for the data provided to be sufficient to draw any firm conclusions.
However, despite this apparent evidence to support vaccine effectiveness for the older age groups, on closer inspection this conclusion is cast into doubt. That is because we have shown a range of fundamental inconsistencies and flaws in the data. Specifically:
New figures from the Institute and Faculty of Actuaries (IFA) Mortality Monitor released today show that cumulative mortality in the first six months of 2021 in England and Wales is running 0.4% below the 10-year average, once adjusted for the size and age of the population (see above). This means, despite the surge in winter Covid deaths in January and February that spooked the country into accepting ongoing restrictions, 2021 is officially now a low mortality year. The low mortality since March has entirely cancelled out the initial spike.
The IFA used to report the cumulative age-adjusted mortality figures compared to the 10-year average each week, but controversially changed their baseline in their weekly reports in May from the 10-year average to 2019 (the lowest mortality year on record) just as 2021 was about to go below average. This means we have had to wait for the quarterly report today for the next update in order to be able to announce this milestone.
The past 10 years are the least deadly years in history (see above), so for 2021 to be below the 10-year average (so far) means it too is one of the least deadly years in history. Even the pandemic year of 2020 was one of the least deadly years, having lower age-adjusted mortality than every year before 2009. Not all of the additional deaths are from COVID-19, of course – many are due to lockdowns and other aspects of the Government’s response and the attendant panic.
The new figures raise the obvious question: how can the Government justify continuing with any kind of restrictions or emergency measures for a moment longer when overall mortality is so low? Where is the ’emergency’ that justifies extraordinary measures?
Some will say it is only the restrictions that have prevented things being much worse. But where is the evidence of that? The U.K. has had more Covid deaths per head of population than countries such as Sweden which had fewer restrictions, while U.S. states which had few or no restrictions over the winter fared no worse (and often better) than those which had the strictest measures. Numerous studies have shown no relationship between restrictions and COVID-19 death tolls.
The latest figures, placing 2021 squarely among the least deadly years in history, should leave no one in any doubt that the emergency is well and truly over.
Amid warnings of a third wave of Covid infections in the U.K., fuelled largely by the fear of the Indian Delta variant and, of course, the prospect of new variants, such as the one recently discovered in Russia, Westminster City Council has advertised a new contract opportunity for the construction of “temporary body storage facilities” in the event of an “excess deaths situation”. Here is some of the information provided on the Gov.uk website.
The Authority seeks to procure a framework agreement for temporary body storage in the event of an excess deaths situation for the 32 London boroughs and the City of London, led by Westminster City Council. The framework agreement will appoint a single provider and will be for a period of four years. This will be a contingency contract, only called upon in the event that an excess deaths situation arises in the future and existing local body storage capacity needs to be augmented.
The over-arching aim of this tender is to provide a single framework supplier that will be able to provide temporary body storage facilities to house deceased in the event of an excess deaths situation. The deceased will be stored with dignity and respect, at locations to be determined based on local London needs at the time and will require some design elements to accommodate local site conditions and constraints, while being capable of rapid deployment, construction and commissioning to an agreed standard. This framework will be procured by the Authority as the pan-London lead, but all London local authorities may call off against the framework.
This will be a contingency cover framework and as such, there is no minimum guarantee of any level of spend or call-off under the framework agreement.
The Council estimates that the total value of this contract (excluding VAT) will be around £6 million and it is not set for renewal. But how likely is it that this is just another local government overreaction?
One of the most reliable and informative sources of mortality data over the past year has been the Institute and Faculty of Actuaries’ weekly mortality monitor report. It shows weekly and cumulative mortality for the year, and unlike the ONS, adjusts for population size and age so we get a truer reflection of how the current trends compare with the past.
Last week the report showed that the trend of deaths in 2021 has been so low since mid-March that all the excess deaths in January and February had been almost cancelled out and cumulative standardised mortality stood at just 1.1% above the 10-year average (see graph below).
At Lockdown Sceptics we were waiting for the moment when, at some point in the next few weeks, this figure would hit 0% so we could announce that, despite the winter Covid surge, 2021 was now officially a low mortality year with below average age-standardised mortality.
However, it appears that moment now may never come, as unexpectedly this week the Institute changed the baseline on its key chart. The 10-year baseline is gone, and in its place is a straight comparison to 2019.
The important thing to know about 2019 is it is the year with the lowest age-standardised mortality ever (see below).
The ONS announced today that there were 45,567 deaths registered in England in March, which is 18% less than in February, though still 1.5% more than the five-year average. (Note that deaths decreased throughout the month, so that by the week ending March 26th, the number of deaths was in fact below the five-year average.)
However, the best overall measure of mortality isn’t the number of deaths, or even the death rate (i.e., deaths divided by total population), but rather the age-standardised mortality rate. This takes into account the ages of those who died, as well as the age-structure of the overall population.
In March, the age-standardised mortality rate was 26% lower than in February, and 5.5% lower than the five-year average. This chart from the ONS shows the age-standardised mortality rate for the first three months of the year, each year, going back to 2001:
It indicates that 2021 has seen the highest level of mortality in the first three months of the year in England since 2006. However, it’s worth noting that the figure for 2021 is only 5% higher than the figure for 2018. And in Wales, the level of mortality in the first three months of the year was actually lower than in 2018.
January saw a much lower peak than April of last year, and today’s figures confirm that the mortality rate has fallen substantially further since then.
Last spring, imposing lockdowns was sold as the only way to prevent a deadly virus from spreading unchecked in the population, and taking hundreds of thousands or even millions of lives. Since then, lockdowns have come under increasing scrutiny. Opponents claim they have upended the economy, undermined children’s education, and violated our basic civil liberties – all without having much impact on the COVID-19 death rate.
When lockdowns were first imposed in the UK and other Western countries, no attempt was made to carry out a cost-benefit analysis. It was simply taken for granted that lockdowns were the correct policy choice. This was because, so proponents argued, they would only be imposed for a limited time, in order to “flatten the curve” and prevent healthcare systems from being overwhelmed. A second justification for lockdowns, which proved influential in some jurisdictions, was that they could be used to suppress the virus completely, thereby preventing any further outbreaks until such time as a vaccine or treatment became available.
Since March 22nd 2020, the UK has spent more than five months under some form of lockdown. And in recent weeks, the country’s lockdown measures have been among the most stringent in the world. Yet its death rate – whether measured as the number of COVID-19 deaths per million people or in terms of excess mortality – is above the European average. Has it all been worth it? I will argue that no, it has not; the costs of the UK’s lockdowns have probably outweighed their benefits.
Do lockdowns work?
The case against the UK’s lockdowns begins by noting that, except in a few cases – which I shall get to – lockdowns have not been associated with substantially fewer deaths from COVID-19. This point has been made at length by the researcher Philippe Lemoine in a report for the Center for the Study of Partisanship and Ideology. As he notes, there are many places where case numbers rose in the presence of a lockdown, as well as several places where they fell in the absence of one. Although case numbers often start falling around the time a lockdown is imposed, it is frequently just before that event, rather than just after. Lemoine suggests this is because people start changing their behaviour voluntarily when they see deaths and hospitalisations rising. The Government, meanwhile, feels an increasing need to “do something”, and the subsequent imposition of a lockdown happens to coincide with the peak of the curve.
For example, the statistician Simon Wood has presented evidence that each of the three English lockdowns was only introduced after the corresponding peak of fatal infections. And in fact, the Chief Medical Officer Chris Whitty told MPs that the epidemic was probably already in retreat when the first full lockdown was imposed. Wood’s conclusions are supported by the findings of economist David Paton, who notes that seven separate indicators all appear to show infections declining before the start of January’s lockdown.
Perhaps the clearest example illustrating the argument that numbers can fall in the absence of a lockdown is South Dakota. The state’s Republican governor Kristi Noem has been stalwart in her opposition to lockdowns, arguing that “the people themselves are primarily responsible for their safety”. As a consequence, there were practically no restrictions in place when the state’s epidemic burgeoned at the end of August. Over the next three months, cases increased – slowly at first, and then rapidly – up to a peak in mid-November. However, despite no shift in policy, they then fell rapidly, and have remained low for the past three months.
What’s even more remarkable is that, according to Google mobility data, there was no major change in people’s movement around the time of the peak in South Dakota. Retail mobility decreased gradually during October and November, and residential mobility was mostly flat. Crucially, there was no sharp change that could explain the sudden decline in cases. One explanation for this anomaly (aside from the Google mobility index being a poor measure of the behaviours that drive transmission) is that the level of prior immunity has been underestimated. Another possibility is that most infections are caused by a small number of “super-spreaders”, and once these individuals have been infected the epidemic swiftly retreats.
When it comes to health outcomes, South Dakota has not fared particularly well during the pandemic – it currently has the eighth highest death rate among US states. But it has not done catastrophically either. Despite imposing almost zero restrictions on the economy, the state ended up with only a slightly higher death rate than Britain. This and other better-matched comparisons cast seriousdoubt on the epidemiological models that served as the basis for lockdowns. (It should be noted that South Dakota has probably benefited, at least to some extent, from its low population density.)
Although lockdowns have not generally been associated with fewer deaths from COVID-19, there are several Western countries where they appear to have worked: Australia, New Zealand, Finland, Norway and Cyprus. So far, these countries have kept the number of COVID-19 deaths below 300 per million; and in fact, they had negligible excess mortality in 2020. Yet, as I noted in a previous article, all five are geographically peripheral countries that imposed strict border controls at the start of the pandemic.
Since none of the five contains an international hub comparable to London, Paris or New York, each had a head start in responding to the pandemic. As a consequence, case numbers were still low at the time lockdowns were imposed, meaning that sporadic outbreaks never cohered into a full-blown epidemic. Meanwhile, the imposition of strict border controls stopped new cases being brought in from outside. It was therefore the combination of early lockdowns and early border controls, under geographically favourable conditions, that allowed countries like Australia and New Zealand to contain the virus.
While one might argue that Britain should have followed the same strategy, it is unclear whether this was ever a viable option, given the country’s size, density and connectedness. And in any case, even if it might have been possible to contain the epidemic in late January, the opportunity had almost certainly come and gone by late February. Having said this, it is somewhat concerning that the strategy was nevergiven serious consideration by the Government’s scientific advisers. For example, the minutes of a meeting on January 22nd record that “NERVTAG does not advise port of entry screening” and “NERVTAG does not advise use of screening questionnaires”.
Pre-existing differences in mortality
The second point against the UK’s lockdowns is that the increases in mortality associated with COVID-19 – even in the worst hit Western countries – have been small relative to pre-existing differences within Europe. For example, the UK’s Office for National Statistics recently calculated age-standardised mortality rates from the first week of 2015 to the last week of 2020 for most countries in Europe. The largest rise from 2019 to 2020 was seen in Bulgaria, where the mortality rate went from 28 to 31.6 – an increase of 3.6 deaths per 100,000. Yet in the year before the pandemic hit, the range of mortality rates (the difference between the highest and lowest values) was 13.8. In other words, the range of mortality rates in 2019 was larger than the largest increase seen by any European country during 2020.
One might counter that the increases in mortality associated with COVID-19 would have been much larger in the absence of lockdowns, but this seems doubtful given the availableevidence. To take one example, Sweden – the only major country in Europe that didn’t lock down – saw age-adjusted excess mortality of just 1.7% in 2020. (Incidentally, a model published last April overestimated Swedish deaths by a factor of 17.) This is not to say that lockdowns had no impact on mortality over and above that of basic restrictions (e.g. bans on large gatherings, self-isolation of symptomatic people) but any impact they did have appears to be quite limited.
The observation that COVID-19’s impact on mortality has been small relative to pre-existing differences can also be made of the UK itself. As Simon Wood noted in an article last October, “the gap in life expectancy between the richer and poorer segments of British society amounted to some 200 million life years lost for the current UK population, which is somewhere around 70 times what Covid might have caused”. He added: “Even the firmest believer in laissez-faire would have to concede that some percentage of that loss is preventable.” The fact that the Government never locked down society (or imposed costs of equivalent magnitude) to reduce much larger differences in mortality within Britain calls its coronavirus strategy into serious question.
Lockdown proponents might say this logic doesn’t apply to COVID-19, since lockdowns prevent individuals from harming others, whereas pre-existing differences in mortality are not due to such “externalities”. But I don’t find this argument very convincing. First, it’s not clear that lockdowns do have much impact on mortality over and above that of basic restrictions. Second, some of the pre-existing differences in mortality are caused by other people’s behaviour (e.g. air pollution, road accidents, flu deaths). And third, blanket lockdowns impose costs on people regardless of whether they contribute to the “externalities” of viral transmission (e.g. people who live away from major population centres, those who have already been infected).
The costs of lockdown
The third key point against the UK’s lockdowns is that their costs have been enormous: not only to the economy, but also to health, education and civil liberties. Take the economy. Britain has suffered its largest economic contraction in 300 years, with GDP falling by almost 10% in 2020. (Note that in the “Great Recession” of 2009, it only fell by 4.2%.) Of course, not all the drop in economic output can be blamed on lockdowns; some – perhaps more than half – would have happened anyway, as a result of voluntary social distancing, cancellation of large events, and reductions in international trade. But a contraction of three or four percentage points on top of that is still very significant.
However, some commentators insist that locking down the economy does not involve any trade-offs. For these “trade-off deniers” (who can count both Rishi Sunak and Chris Whitty among their number) lockdowns are a win-win – or at the very least, a win-draw. However, this argument only works if locking down allows you to completely suppress the virus, since only once complete suppression has been achieved can economic activity resume. The idea is that if you completely suppress the virus after a short, sharp lockdown, you can then re-open the economy as normal, and you end up suffering less economic damage overall than if you’d let the virus spread through the population. But as I’ve already argued, it’s unlikely that suppression was ever a realistic option for the UK. (It almost certainly wasn’t by late February 2020.) Locking down for several months as a way to “flatten the curve” might reduce death rates slightly, but it’s certainly not good for the economy.
The claim that there’s no trade-off between health and the economy appears to be based on one specific observation: virtually all Western countries and US states – regardless of their policies – saw a sharp drop in economic activity during the early weeks of the pandemic. Yet as the historian Phil Magness points out, over the following weeks and months, large differences emerged between the most and least-open US states. And recent data from OECD countries shows a clear inverse relationship between the stringency of government measures and the level of economic growth (with the UK having the most stringent measures and the lowest level of growth). Unsurprisingly, the majority of economists in a survey last November said the UK’s March lockdown did at least some damage to the economy.
I began this essay by noting that no real attempt was made to quantify the costs of lockdowns when the pandemic began. (Instead, projections from computer models weretaken as proof that, without lockdowns, healthcare systems would be inexorably overwhelmed.) Since then, several cost-benefit analyses have been attempted, andeachonehas concluded that the costs almost certainly outweighed the benefits. Of course, accurately gauging all the relevant quantities is no easy task, and these analyses are not without their limitations. But the onus is now on lockdown proponents to show that their preferred measures did pass a cost-benefit test.
Two of the cost-benefit analyses mentioned above estimated the benefits of lockdowns by multiplying the total quality-adjusted life years (QALYs) they might have saved by £30,000 – which is the amount the NHS attaches to a QALY when deciding whether to pay for new treatments that extend patients’ lives by a certain number of years. Although by no means perfect, this seems to me like a reasonable approach. I therefore attempted my own cost-benefit analysis, making what I consider generous assumptions about the public health benefits of lockdowns. I still found that the costs (which I gave as one third of last year’s decline in output) outweighed the benefits by a large margin.
Overall, the UK’s lockdowns were probably a mistake. Looking at the Western world, lockdowns have not been associated with substantially fewer deaths from COVID-19, except in geographically peripheral countries that imposed strict border controls at the start. What’s more, the increases in mortality associated with COVID-19 – even in the worst-hit Western countries – have been small relative to pre-existing differences. Finally, the societal costs of lockdowns have been substantial, and preliminary analyses suggest they almost certainly outweighed the benefits.
Of course, none of this implies that the optimal approach to COVID-19 was “do nothing”. COVID-19 is a deadly disease, and the pandemic clearly warranted government action. In a follow-up essay, I will outline what I believe (with the benefit of hindsight) could have been a more effective approach.
Noah Carl writes about COVID-19 and other topics in his Substack newsletter (where this article was originally published). You can follow him on Twitter @NoahCarl90.
A new report from the Office for National Statistics (ONS) was all over the papers on Monday afternoon making the striking claim that COVID-19 caused more deaths last year in England and Wales than other infectious diseases have caused in any year for more than a century.
The ONS report, entitled “Coronavirus: A Year Like No Other”, was released to mark the one year anniversary of people in the UK first being told to limit their non-essential contact with others and to stop all unnecessary travel.
The report confirmed that COVID-19 caused more deaths last year than other infectious diseases caused in any year for more than 100 years.
More than 140,000 people have died in the UK with coronavirus either described as the underlying cause or as a contributory cause on their death certificates.
Some 73,500 people in England and Wales who died in 2020 had COVID-19 registered as the underlying cause of death.
The ONS said coronavirus is “likely to be classed as an infectious and parasitic disease”, allowing a comparison with previous deadly outbreaks.
The statistics body said: “This means COVID-19 was the underlying cause of more deaths in 2020 than any other infectious and parasitic diseases had caused in any year since 1918; that year there were just over 89,900 deaths from various infectious and parasitic diseases registered in England and Wales.”
The crew at the Oxford Centre for Evidence-Based Medicine (CEBM) have done an analysis of excess mortality for 2020 across 32 countries to get a clearer picture of the impact of the pandemic and lockdowns. They used excess mortality instead of “Covid deaths”, they explain, to avoid problems with recording and classification of deaths and include any impact of anti-Covid measures. They used age-adjusted mortality to take into account differences in the average age of populations. They compared 2020’s figures to the average of the previous five years to give a percentage increase or excess during the pandemic year (they have made the tool they used to analyse the data publicly available).
The results are plotted in the graph below. Perhaps the most telling result is that Sweden, which did not impose strict lockdown measures throughout the year (it kept all retail and hospitality and most schools open and imposed no restrictions on private gatherings) saw only a 1.5% increase in age-adjusted mortality. Surely no one can argue that such a small increase in mortality (and almost entirely among the elderly and already unwell) can justify the severe and harmful suspensions of civil liberties we have endured over the past year?
Studies which show social restrictions do not lead to lower Covid mortality and infection rates are numerous (see this collection of 31 from AIER, which is kept up to date).
We now have another paper to add to the collection. Published last week in Scientific Reports in Nature, it looks at whether the extent to which people stayed at home (measured using Google mobility data) is associated with Covid mortality in different countries. Doesn’t look like it, the researchers conclude. Here’s an excerpt from the abstract:
Countries with over 100 deaths and with a Healthcare Access and Quality Index of at least 67 were included. Data were pre-processed and analysed using the difference between number of deaths per million between two regions and the difference between the percentage of staying at home. … After pre-processing the data, 87 regions around the world were included, yielding 3,741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in around 98% of the comparisons after epidemiological weeks 9 to 34.
The authors add:
We were not able to explain the variation of deaths per million in different regions in the world by social isolation, herein analysed as differences in staying at home, compared to baseline. In the restrictive and global comparisons, only 3% and 1.6% of the comparisons were significantly different, respectively.