Category: Epidemiology

Millions of accumulated years of life will be lost to Covid-19 response

[Lockdown] policies have created the greatest global economic disruption in history, with trillions of dollars of lost economic output. These financial losses have been falsely portrayed as purely economic. To the contrary, using numerous National Institutes of Health Public Access publications, Centers for Disease Control and Prevention (CDC) and Bureau of Labor Statistics data, and various actuarial tables, we calculate that these policies will cause devastating non-economic consequences that will total millions of accumulated years of life lost in the United States, far beyond what the virus itself has caused.

https://thehill.com/opinion/healthcare/499394-the-covid-19-shutdown-will-cost-americans-millions-of-years-of-life

A request for balanced analysis and reporting

Drs Michael Jackson and Simon Thornley

A recent article in a New Zealand newspaper claims that Sweden’s approach to managing the Covid pandemic means that “56,000 more people may yet die”. We believe the article is misleading because:

  1. The author assumes an ‘infection fatality proportion’ (IFP) of 1% and states it’s the “current best estimate”. This estimate is derived from seroprevalence studies from just two countries (France and Spain – both with high per capita death rates). But, the Centre for Disease Control’s (CDC) recent best estimate is 0.26% (four times lower). A summary of studies (19 May) by Professor John Ioannidis that included studies from Asia, Europe, and North and South America derived an estimate of between 0.02% to 0.40%. This mirrors the IFR provided by the Centre for Evidence-Based Medicine at Oxford University. We believe the use of a high IFP is misleading as it produces an estimate that wasn’t based on current best estimates.
  2. The author does not include any commentary about the recent identification of cross-reactive T-cells. The paper’s findings (published May 14 and before the author’s article was published) indicate between 40-60% of a population may not even be susceptible to Covid-19 due to prior exposure to other coronaviruses that cause the common cold. This has important implications, as it lowers the number of people susceptible to infection. More recently (we acknowledge after the article was published), one of the world’s most influential neuroscientists and statisticians, Professor Karl Friston (University College London) said the figure could be as high as 80%. The inclusion of this information would have allowed for the re-calculation of an estimated fatality rate and provided the reader with further information about the uncertainty of the author’s predictions.
  3. The author assumes that 60% of a population needs to be have been infected or vaccinated to achieve herd immunity. But some are calculating it at 40% based on Sweden-specific data, not generic inputs. Also, the 60% figure is based on modelling, rather than measured seroprevalence. Given the previous data about T cell immunity and cross-reactivity of other antibodies, the true population immunity is likely to be much higher than seroprevalence surveys indicate. Again, this paints a more negative picture and doesn’t present the reader with a balanced view.
  4. The author states “After completing this article, a new study has reported that the proportion of people in Stockholm with antibodies to Covid-19 is only 7.3 per 100 people”. But an internet search will tell that the 7.3% figure “reflects the state of the epidemic earlier in April”. That’s a whole month before the article was written and when the total number of deaths in Sweden was around 1000. Sweden’s Public Health Agency estimates the figure would now be about 20% but this isn’t mentioned by the author.
  5. The author does not attempt to consider how his prediction of 56,000 extra deaths matches actual recorded data and trends for Covid-19 in Sweden (figure). With 4,874 deaths currently, and a clear downward trend (also evident when the author published his article), the author’s prediction is unrealistic.

Figure. Covid-19 daily mortality in Sweden (16/6/2020). Line indicates trend.

  1. The author claims that Sweden’s economy hasn’t fared any better than its neighbours, despite its more relaxed approach. Again, this is misleading. While this may be true for Denmark and Norway (note Norway now say they could have achieved the same results without a lockdown), Sweden’s projected downturn (1% GDP) is less than Germany (6.5%), the Netherlands (6.8%), the EU as a whole (7.4%), Belgium (8%), France (8.2%), Croatia, (9.1%), Spain (9.4%), Italy (9.5%), Greece (9.7%) and the UK (up to 14%). For comparison, the New Zealand government is predicting a downturn of around 10%. You may also be surprised to hear Sweden’s economy actually grew in the first quarter of 2020 compared to declines across Europe. The UK’s economy, for example, contracted by 2% over the same period.

We are not, here, looking to justify of Sweden’s approach. Only time will tell if Sweden took the right one. We are simply asking that commentators present their work in a balanced, evidence-based way – one that draws the reader’s attention to the complexity and uncertainty in their projections. Figures like “60,000 deaths” are headline-grabbing but are based on incomplete and overly simplistic modelling. They are not ‘reasonable best estimates” based and clearly contradict observed trends.

Learning from new Covid-19 data

Simon Thornley

15/6/2020

Words: 670

In the response to Covid-19, it is easy to forget that our knowledge of the virus is provisional and still evolving. We have seen, for example, that the infection fatality rate, initially given as 3.4%, now with serology data has been dialled back considerably to between 0.02 to 0.40% which is in the range of severe influenza. This updated information brings an inevitable conflict with political decision making, in which actions are often justified at all costs.

We have now seen evidence of this, with the Medical Director of the Royal New Zealand College of General Practitioners, Dr Bryan Betty, stating that New Zealand was staring down the barrel of a “potential health system meltdown.” He continued: “We were literally one week away from that or we were going down a track of lockdown, which actually halted the spread of the coronavirus in New Zealand. You’ve got to remember that at that time we had exponential growth going on… [Our case numbers] were doubling every day.”

On the face of it, this sounds reasonable. We were looking down the barrel… Let’s pull out all the stops.

Several of Betty’s statements deserve scrutiny. The first is that numbers were doubling every day. They weren’t. In the days immediately before lockdown, numbers increased by 4 from 36 to 40 on the 24th of March, an 11% increase, the next day to 50 (25% increase), then level 4 was instituted. Only for one day did numbers at least double (23rd of March).

The statement that we were staring at a health system meltdown is exaggerated. During the so called “crisis”, hospitals had spare capacity. Hospitals were quiet, so quiet in fact, that specialists expressed concern about it. Intensive care units likewise. In fact, we now have the opposite problem with some primary care practitioners going broke owing to lack of demand and the costs of adapting to new service models. Patients with other conditions were clearly foregoing usual care.

The dire modelling, predicted, even with strong mitigation measures, never eventuated. If there is one thing this teaches us, it is that our understanding of the virus needs updating. The 80,000 predicted deaths are an overestimate of the observed mortality number by 3,400 times. In deciding policy responses, we desperately need to take account of the evolving nature of both the science and the available information rather than rely on outdated models.

A scientific approach involves learning from mistakes. The Norwegian Prime Minister, Erna Solberg admitted that she panicked into a decision to close schools and early childhood centres. Similarly, the Director General of Health in the Scandinavian country, Camilla Stoltenberg, stated that they could have achieved the same result by ‘not locking down’.

Here, we see both politicians and health officials learning from mistakes. Rather than being an admission of failure, it is a logical and healthy response to new information. This response contrasts strongly with some of New Zealand’s leaders.

We are rapidly learning that the threat posed by the virus is not as serious as we have been led to believe. New research shows that immunity is likely to be more widespread than we have previously appreciated. Immunity to this virus is also likely since other scientists have found cross-reactivity to other coronaviruses that cause the common cold. Many more of us are likely to have seen the virus than our case numbers indicate.

This new knowledge must lead to an update of policies for the country. We should continue to question whether it still makes sense for us to keep our borders firmly closed in the light of this new information. Serosurveys of New Zealanders would help us judge more accurately the degree of spread of the virus. If the virus has circulated to many more people than we think, and many more are protected than we previously believed, then we can have confidence to open our borders. Slovenia and Italy have already done this for several weeks and thus far they have not had second waves (figure).

Figure. Daily counts of Covid-19 cases for Slovenia and Italy, two European countries with open borders to European Union citizens.

Stanford epidemiologist says ‘no more lockdown’

John PA Ioannidis

Lockdowns were desperate, defendable choices when we knew little about covid-19. But, now that we know more, we should avoid exaggeration.21 We should carefully and gradually remove lockdown measures, with data driven feedback on bed capacity and prevalence/incidence indicators. Otherwise, prolonged lockdowns may become mass suicide.

 

Prolonged lockdowns fuel economic depression, creating mass unemployment. Jobless people may lose health insurance. Entire populations may witness decreased quality of life and mental health.19

 

Underprivileged populations and those in need are hit harder by crises. People at risk of starvation worldwide have already exceeded one billion.20 We are risking increased suicides, domestic violence, and child abuse. Malaise and societal disintegration may also advance, with chaotic consequences such as riots and wars.

https://www.bmj.com/content/369/bmj.m1924

 

Did the Cyprus lockdown make a difference? Yes – it made infections worse.

A Cypriot epidemiologist tracking the data…. says around half of people infected in Cyprus may have contracted the virus while indoors.

 

Dr Elpidoforos Soteriades, who got his degree in epidemiology at Harvard, told the Sunday Mail: “In the case of population lockdown, people were forced to stay at home. However, once the virus was already spreading within the community, lockdown was literally forcing healthy individuals to stay in close contact with relatives that might have been exposed to the virus and were potentially spreading the virus at home.”

Lockdown: did it make a difference?

 

Covid-19 forecaster errors wrecked Govt decision-making

Figure. Epidemic curve of Covid-19 deaths in Sweden (1/June/2020). Line represents average trend.

By Simon Thornley, Gerhard Sundborn, Ananish Chaudhuri and Michael Jackson.

It is clear now that estimates of death from the Covid-19 pandemic were exceeded by factors of hundreds, if not thousands. This sparked public and political panic and led to our government enacting one of the most stringent lockdowns in the world.  Te Pūnaha Matatini predicted 80,000 deaths even with mitigation strategies, while the University of Otago team forecast 12,600 to 33,600 deaths.  Their best possible estimate was 5,800 deaths. The models encouraged the government to enact tight control measures. Now, we are largely over the epidemic, although some of the modelers have warned of secondary waves. New Zealand now has 22 ‘official’ Covid-19 deaths – a far cry from the forecast doom and gloom, with at least a 263 fold over estimate at this point. A recent article about Sweden followed suit, predicting a total of 60,000 deaths for that country, and decrying its decision not to lockdown.

How was it possible for these forecasts to be so erroneous? The interesting aspect, reading the modelling now, is that the number infected under each control policy scenario, including lockdown, was about the same. The Matatini group described 89% of the population being ultimately infected under even the most stringent strategy. The moment the handbrake was let off, another outbreak would occur. However, in the paper, the modellers themselves questioned the effect of lockdowns. They wrote:  “In other countries, including those that have instigating (sic.) major lockdowns such as Italy, there is as yet insufficient evidence that this has reduced [the epidemic]”. They then stated that “successful mitigation requires periods of these intensive control measures to be continued for up to 2.5 years before the population acquires a sufficient level of herd immunity.” The conclusion was that lockdowns were buying time for vaccination and learning from other countries. The modelling that justified the lockdowns was itself clearly stating that such policies were far from a panacea.

Models incorporated lockdown measures yet still predicted thousands of deaths. Critics will say that the lockdown is precisely why the models were so inaccurate. We were saved from catastrophe. Several lines of consistent statistical evidence does not, however, support this idea. US States that did not lockdown report lower Covid-19 cases and death rates on average than States that enforced heavier restrictions. Time trends in Europe show that lockdowns prolonged the recovery from the epidemic after these policies were enforced. Closer to home, it is clear that cumulative per capita cases and deaths of Covid-19 are lower for Australia than for New Zealand despite more relaxed restrictions over the Tasman.

The major factors behind these erroneous models include: (1) an overestimate of the infection fatality rate, and (2) a reciprocal underestimate of the immunity of the population.  Mathematical models of infections project the assumptions of the modellers into the future. They are mathematically elegant, but also based on many untested assumptions. Models assume a far greater degree of certainty than is true in reality.

The models used are built for infections which declare themselves, like measles. Covid-19 is different, it produces high rates of infections in people who feel well. Measles primarily affects young children who are unlikely to die from other causes. Covid-19, on the other hand, has shown to be most vicious at the other end of the age spectrum, specifically causing death most frequently in people at a mean age very similar to our life expectancy, about 82 years. This is curious, as it strongly suggests that the virus does not shorten life, since our life expectancy, or average lifespan, is similar with or without the virus on board. There is little mention of this in the Matatini document, and it is relegated to the appendix of the University of Otago report. Instead the Otago group talk of deaths of the magnitude seen in World War I. Given the age differences of deaths in World War I (mean about 27 years), compared to Covid-19, this must surely be classed as exaggeration.

Neither modelling team attempted to quantify loss of life in terms of ‘years of life lost’ (YLL), a standard epidemiological technique for comparing disease burden. Such statistics would have produced a totally different picture than headline death tallies, portrayed simplistically by the media. YLL is the sum of the differences between age at death and median life expectancy and weights death in the young higher than deaths in the old. Since Covid-19 deaths occurred at an average age in the 80s, this method of measurement would have produced a much less striking picture than the less sophisticated count that values infant and nonagenarian mortality as equivalent. Years of life lost from Covid-19 are extremely low, and pale in comparison to other risks to health, such as cardiovascular disease, diabetes and cancer.

As in the case of swine flu, antibody tests of the virus, are dialling down the infection fatality rate, to a range similar to influenza (0.03% to 0.5%). This contrasts from the genetic test evidence used by some commentators. This cuts down the dire predictions for Sweden by a large ratio. Since even people without antibodies have evidence of seeing the virus, the true infection fatality ratios are likely to be even lower than those adjusted for antibody tests alone. It is now clear that the dire prediction is very unlikely to be correct, since Sweden is now well into the downward slide of its epidemic curve for Covid-19 deaths (figure). The value of observed data over modelled predictions is demonstrated here.

Figure1 (above). Epidemic curve of Covid-19 deaths in Sweden (1/June/2020). Line represents average trend.

Related to the immunity tests, a strong, and very questionable assumption of the modelling is that we are all, as a population, susceptible to the ‘novel’ virus. Since from early on in the epidemic, it was clear that infection was more likely in the elderly, this was unlikely to be so. Recent evidence from immunologists strongly indicate cross-reactivity between “common cold” coronaviruses and SARS-CoV-2, which was present in at least 30% of people that don’t show other evidence of having seen the disease before. This theory is supported by a study that showed that 34% of a sample of healthy blood donors who did not have antibodies, instead had other evidence of immunity, with reactive T cells to the virus. Also, the finding of test-positive samples in France well before the epidemic ‘officially’ occurred, dents the ‘we are all sitting ducks’ theory.

In trying to make sense of these erroneous predictions we have to ask how this happened? We believe two basic features of the human psyche have been at work. The first of these is loss aversion: the desire to avoid losses that are right in front of us even if it means larger losses elsewhere or further down the road. The second is confirmation bias: that is the tendency to look for evidence that confirms one’s pre-supposition and discounts evidence that calls those beliefs into question. Of course, the 24-hour news-cycle, the cacophony of social media, the need for eyeballs, clicks, likes, tweets and retweets exacerbates these matters, since apocalyptic predictions are more likely to draw attention.

Several lines of evidence give us hope, to counter pessimistic modelling. One thing the inaccuracy of the models teach us is that our understanding of the behaviour of the virus is incomplete. Better understanding should translate to more accurate prediction. Epicurves by country in Europe and many parts of Asia, along with Australia and New Zealand are showing waning epidemics with insignificant secondary peaks. These patterns strongly suggest growing immunity in these countries, despite measured low antibody prevalence in some areas. The high rates of cellular and cross immunity explains this phenomenon. China, a very densely populated country, has now widely opened up after a lockdown and had few secondary waves. Japan is the same, although they had lighter restrictions. The sustained low number of cases when the curve falls strongly indicates that we can safely return to normality much more rapidly than was thought possible.

 

Norway officially concludes that its lockdown was not necessary

the Norwegian public health authority has published a report with a striking conclusion: the virus was never spreading as fast as had been feared and was already on the way out when lockdown was ordered.

“It looks as if the effective reproduction rate had already dropped to around 1.1 when the most comprehensive measures were implemented on 12 March…”

https://www.spectator.co.uk/article/norway-health-chief-lockdown-was-not-needed-to-tame-covid

Are the coronavirus epidemiological models any good?

Health issues India discuss coronavirus models with Dr Simon Thornley.

 

Why the prejudice against tests for Covid-19 immunity?

Simon Thornley

27/5/2020

Words: 1090

A curious phenomenon has developed in the race to beat Covid-19. Advisors to the government have recently become anti anti-bodies. Before I explain what that means, let me provide some context. While we’ve weathered the initial Covid-19 storm, we now have a more challenging set of questions ahead of us as we decide how far and fast to ease social restrictions and open our borders back up to the world.

One of the most critical is: just how widespread is this virus? If, as the Government’s advisors believe, it’s a case of ‘what you see is what you get’, then our options are limited. But if, as we are seeing around the world, the virus has spread through far more of our population than we are aware, then that changes everything. All of a sudden, we need to radically re-think whether our control measures make sense. The genetic test that we are relying on can tell us if the virus is active in the here and now. That is the focus of the daily case counts. These tests are accurate, and the best for diagnosing cases, but they don’t give us a complete picture.

In almost all infectious diseases, antibody tests play a crucial role in determining who is protected from the germ and who is not. They tell us that a virus or germ has been and gone. They are the fingerprints that the virus leaves behind, and allow us to be better prepared for the next encounter. For Covid-19, we may not otherwise know we have met and dispatched the virus, since not all of us develop symptoms. In Iceland, of the few areas of the world a survey was carried out, rather than only testing sick people, 1% of the population tested positive, but half all these positives were perfectly well. It is now clear that just because we don’t have a fever, runny nose or cough, it doesn’t mean we haven’t seen the virus. For this reason, we simply cannot rely on genetic tests from people with symptoms to tell us how far the virus has spread. To really get a handle on how many of us have seen a virus, we need to not only count active cases, but start measuring people who have seen the virus before with antibodies.

New Zealand is now at a cross-roads. We have two explanations for our results. Professor Michael Baker, one of the main experts advising the government, has expressed that antibody tests “would be a waste of time and resources” since a “vanishingly small” proportion of the population have been exposed. Through Baker’s eyes, the lockdown was astonishingly effective, quashing the virus, while leaving all except the one and a half thousand or so cases sitting ducks waiting for infection to strike. We had better live in fear and shut down the borders hard. This narrative goes with the elimination story. So much for our travel and tourist industry. Sorry Rotorua and Queenstown, we have laid you on the altar as a casualty on the path to vanquishing the virus.

Another explanation for the rise and fall of cases in New Zealand is from growing immunity, rather than from the lockdown. The cases of infection rise as the virus encounters more susceptible people. This is great for the virus until it encounters people who have seen the virus before. Their bodies have wised up, thanks to our miracle antibody factories, and the virus sees the door is shut. Some may not even need antibodies. The innate and cellular immune system, like a razor wire fence, may keep the virus out before the soldier-like antibodies need to be enlisted.

Immunity from other viruses is also likely to play a part. A recent study estimated that half of people who haven’t seen the novel virus before, have T cells that react against it which are primarily directed against ‘common cold’ coronaviruses. The virus looks elsewhere, but the door is shut with the next person, and the next, and it soon has nowhere to go. This has been the way we have defeated almost every other lung virus of equivalent severity to Covid-19 in the past.

Now critics will say there are holes in this immunity theory. If that had really happened, we should have seen chocka intensive care units like in Italy. Well, we may have, or we may not. It is clear that New Zealand is not Milan, London and New York, as we would like to believe. We are simply nowhere near as population-dense as these metropolises.

Surely we would have noticed excess deaths? Or excess people coming to hospital with influenza-like symptoms? Since the deaths from Covid-19 are about the same average age as our life expectancy, we may not have noticed. If we hadn’t tested for it, we would have probably not batted an eyelid. We would have put the death down to the growing list of diseases that were likely to have afflicted the deceased. And it is not as if Covid-19 gives a unique clinical presentation. As a former hospital doctor, I know only too well that patients who present with flu-like illness are extremely common. A recent positive test in a French patient well before the ‘official’ epidemic occurred support this theory of widespread infection.

Teasing out which of these two beliefs to follow is now critical. History may help. In recent memory, a story played out according to the widespread immunity theory. We strongly believed that H1N1 was a killer virus, rapidly spreading out of Mexico. The death rate was astonishingly high initially. The clamour to ‘stamp out’ the virus in New Zealand was long and loud. It was, at least, until needles were put in veins, and antibodies were present in 47% percent of some age groups. These tests established that many New Zealanders had seen the virus and the chorus to defeat the virus lost its stuffing.

Evidence from other countries supports the idea of widespread immunity. The very small secondary overseas outbreaks, such as in China and the Australian state of Victoria are further evidence that widespread immunity is growing. If, instead, immunity were sparse, we should expect many further large outbreaks. Other commentators have condemned the low accuracy of Covid-19 tests, however, Roche now has produced a test that has sensitivity and specificity values approaching perfection (100%) that has now got widespread acceptance in Europe. Not even many of our established antibody tests have achieved this.

The philosopher George Santayana reasoned, “those who cannot remember the past are condemned to repeat it.” At this crucial juncture, history indicates that the value of antibody tests and the idea of growing immunity cannot be so easily dismissed. If the virus is more widespread than the genetic tests indicate, we need to urgently reconsider whether or not border closures and social restrictions are really worthwhile.

 

Video: epidemiologist’s take on Covid-19

Dr. Simon Thornley

  • Deaths due to coronavirus have been exaggerated
  • Mean age of death – 80 years old