Covid

Heartbreaking Covid – The Conclusion

In the last post, we saw how CVD incident rates have increased since the start of the pandemic and the possible reasons for this. Today, we examine why the vaccine—and not COVID itself—has become the principal offender in the common belief.

Chemophobia

Blame it on the ‘silent spring’, the Bhopal tragedy, or Chornobyl; chemophobia, or the fear of chemicals, is real. We have seen how heuristics or mental shortcuts play a role in decision-making. Studies found that most of us, the non-experts of toxicology, tend to rely on heuristics when judging chemical safety. The public leans on three ‘rules of thumb’ when evaluating chemicals.
Natural-is-better heuristics: People associate better confidence in dealing with natural substances than synthetic ones. It may sound incredible, but people find it more comfortable trusting a herb containing 10,000 unknown molecules than a well-researched single compound drug when dealing with a medical condition. The reason? – one is natural, and the other is made. It goes to such an extent that in one study, Siegrist and Bearth found that only 18% of the people surveyed thought the chemical structures of synthetically prepared and naturally occurring NaCl were identical.
Contagion heuristics: These come from a lack of knowledge of the concept of dose. People view a chemical as either safe or toxic while missing out on the quantity. For the decision maker (the brain), this keeps the decisions simple. In the same survey, three-quarters of the people believed that a toxic substance is always dangerous irrespective of its dose.
Trust heuristics: States that people rely on their trust (or lack thereof) in key stakeholders, such as chemical industries and governmental and non-governmental organisations, to evaluate the associated risk.

For ordinary people, the leading COVID-19 vaccines—Moderna, Pfizer, and Oxford—were all human-made. Therefore, they are dangerous. On top of this, thanks to the ever-vigilant regulators in the EU and the US, the side effects of vaccines—that they could cause severe blood clots or myocarditis in a few in a million people—were public within a few months of their introduction.

Affirming the consequent

Irwin, the hypochondriac: “I’m sure I have liver disease.”
“That’s impossible”, replied the doctor. “If you have liver disease you’d never know it.”
Irwin replies: “Those are my symptoms exactly.”

Rationality by Steven Pinker

Affirming the consequent is a formal logical fallacy of the following type.
IF P, THEN Q.
Q.
Therefore, P.

In the case of the vaccine, the logical fallacy works this way:
A. Vaccines cause myocarditis and pericarditis in some.
B. The patient had a heart attack.
C. It must be the vaccine.

Not familiar with the risk-benefit trade-off

No decision is risk-free, and medication is no exception. The important thing is to evaluate the risk caused by an action compared to a situation without that action. That is the core of the risk-benefit trade-off in decision-making. And the risks due to vaccination must be viewed that way. I will end with the scheme we developed at the peak of the pandemic.

Death due to Infection (red) vs Death by Vaccine (green)

References

[1] Siegrist, M., Bearth, A. Chemophobia in Europe and reasons for biased risk perceptions. Nat. Chem. 11, 1071–1072 (2019). https://doi.org/10.1038/s41557-019-0377-8
[2] Steven Pinker, Rationality, Penguin Random House

Heartbreaking Covid – The Conclusion Read More »

Heartbreaking Covid

The World’s leading cause of death is cardiovascular diseases (CVDs) – heart attacks and strokes. Globally, the estimated number of deaths due to CVDs increased from around 12.1 million in 1990 to 18.6 million in 2019. Note that the age-standardised death rate has declined from 354.5 deaths per 100,000 people in 1990 to 239.9 deaths per 100,000 people in 2019. While pollution, unhealthy diet, alcohol and tobacco are the leading root causes, the increase in the absolute number of CVD deaths is primarily due to growth in population and life expectancy.

Against this backdrop, we examine the anomalies in death rates in the last five years. According to CDC data, heart diseases accounted for 702,880 deaths in the US in 2022. Here is the figure representing the trend from 2018 to 2022.

Contrary to trends in the last few decades, the death rates jumped from 200 to 211 from 2019 to 2020. Notably, 2020 also marked the start of the global pandemic, COVID-19. The story was no different for the rate of mortality from Coronary Heart Disease (CHD) in England.  

Hypothesis on test

Let’s examine the two hypotheses to explain this rise in deaths due to the pandemic. 1) Covid-19 played a role, and 2) Covid vaccine played a role. We will start with the easier one – the vaccine.

The authorisation of leading vaccines – Moderna, Pfizer and AstraZeneca – for first use happened in December 2020, and the active vaccination program only started months later. Note that the ‘jump’ occurred from 2019 to 2020, a year earlier than the start of vaccination.

Now, the impact of COVID-19 on heart disease. Again, there are two possibilities: the virus directly causes heart disease, or the virus is part of the causal chain (VIRUS—MEDIATOR—CVD). Data suggest that there is evidence for the first possibility. While COVID-19 is a risk modifier—something that worsens pre-existing CVD risk factors such as hypertension—heart attacks are only the fourth or fifth cause of death in COVID-19 patients, respiratory failure being the leading cause.

The elephant in the room

The British Heart Foundation published a report in 2022 that summarises their investigation of the excess deaths due to CVD after the pandemic breakout. They found that COVID-19 infection alone was not sufficient to explain the 14% increase in ischaemic heart disease (IHD) compared to the pre-pandemic period. Instead, the breakdown of the healthcare system was the likely cause. The team surveyed and found

  • 43% of patients who needed medical treatment for their heart condition have put off seeking NHS help due to ongoing fears of catching Covid or burdening NHS services.
  • 20% of heart patients reported having had an appointment for their heart condition cancelled over the last year.
  • The proportion of patients with diagnosed hypertension who had their BP checked fell from 89% in March 2020 to 64% by March 2021.
  • Two million fewer people were recorded as having controlled hypertension in 2021 compared to the previous year.
  • Modelling from NHSE shows that this reduction in blood pressure control could lead to an estimated 11,190 additional heart attacks and 16,702 additional strokes over three years.

Here is a trend of the number of patients waiting for treatment (source: NHS England)

The picture is no different for heart procedures. (Source: NHS England (2022) Consultant-led Referral to Treatment Waiting Times (number of incomplete pathways)):

Another study published in Nature Medicine used monthly counts of prevalent and incident medications dispensed and found a systematic trend of decline, especially during the lockdown periods.

In summary

Managing cardiovascular diseases requires constant action via public health agencies. These include detection, consultations, medications, and procedures. The COVID pandemic has temporarily affected the flow of this machinery, and the result was an increase in CVD mortality. Yet, the public perception focused on vaccines. Why did that happen? We’ll see that next.

References

[1] Top 10 causes of death: WHO

[2] Cardiovascular diseases: WHO

[3] Elezkurtaj, S., Greuel, S., Ihlow, J.hospitalisedes of death and comorbidities in hospitalised patients with COVID-19. Sci Rep 11, 4263 (2021). https://doihospitalised/s41598-021-82862-5

[4]Dale, C.E., Takhar, R., Carragher, R. et al. The impact of the COVID-19 pandemic on cardiovascular disease prevention and management. Nat Med 29, 219–225 (2023). https://doi.org/10.1038/s41591-022-02158-7

[5] Vosko, I., Zirlik, A., Bugger, H., Impact of COVID-19 on Cardiovascular Disease, Viruses, 15(2), 508 (2023).

[6] Top 10 Causes of Death in the U.S.: WebMD

[7] The NHS waiting list: The Health Foundation

[8] World Heart Report 2023: World Heart Federation

[8] Tipping Point: British Heart Foundation

[9] Underlying Cause of Death: CDC

[10] Cardiovascular disease and diabetes profiles: statistical commentary: Office for Health Improvement & Disparities, UK

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Bland-Altman Plot

Bland-Altman analysis is used to study the agreement between two measurements. Here is how it is created.

Step 1: Collect the two measurements

Sample_Data <- data.frame(A=c(6, 5, 3, 5, 6, 6, 5, 4, 7, 8, 9,
10, 11, 13, 10, 4, 15, 8, 22, 5), B=c(5, 4, 3, 5, 5, 6, 8, 6, 4, 7, 7, 11, 13, 5, 10, 11, 14, 8, 9, 4))

Step 2: Calculate the means of the measurement 1 and measurement 2

Sample_Data$average <- rowMeans(Sample_Data)

Step 3: Calculate the difference between measurement 1 and measurement 2

Sample_Data$difference <- Sample_Data$A - Sample_Data$B

Step 4: Calculate the limits of the agreement based on the chosen confidence interval

mean_difference <- mean(Sample_Data$difference)
lower_limit <- mean_difference - 1.96*sd( Sample_Data$difference )
upper_limit <- mean_difference + 1.96*sd( Sample_Data$difference )

Step 5: Create a scatter plot with the mean on the X-axis and the difference on the Y-axis. Mark the limits and the mean of difference.

ggplot(Sample_Data, aes(x = average, y = difference)) +
  geom_point(size=3) +
  geom_hline(yintercept = mean_difference, color= "red", lwd=1.5) +
  geom_hline(yintercept = lower_limit, color = "green", lwd=1.5) +
  geom_hline(yintercept = upper_limit, color = "green", lwd=1.5) +
  ggtitle("")+ 
       ylab("Difference")+
       xlab("Average")

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Accuracy and Asymmetry

Let’s develop a simple prediction technique to identify the sex of a person based on height. Here is data from 1050 participants and has the following form.

The first step is to plot them and check their distributions.

A naive way to set up the prediction is to assign everyone with height > 64 inches as male.

y_hat <- ifelse(heights$height > 64, "Male", "Female") 
mean(heights$sex == y_hat)

The answer is an impressive 83%

But how well did it predict individually?

mean(yy[heights$sex == "Male"] == y_hat[heights$sex == "Male"])
mean(yy[heights$sex == "Female"] == y_hat[heights$sex == "Female"])

For males, the accuracy is about 94% and for females, it’s only 44%. The discrepancy prompts us to look at the respective number of samples in the set.

length(heights$sex[heights$sex == "Female"])
length(heights$sex[heights$sex == "Male"])
Females are 238, and males are 812.

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News From Huanan Market

After a brief interval, here is some Covid news. A new peer-reviewed article is now available in Nature for preview. The study summarises the RNA sequence results from several samples from Huanan Seafood Market in Wuhan. The market was linked to several of the early cases of the illness. Since the market’s closure (1st of January 2020), 923 environmental and 457 animal samples were collected from 1-Jan to 2-Mar 2020. Here is the high-level summary:

# Samples# +ve by
RT-PCR
Huanan Seafood Market71840
Warehouses145
Other markets301
Drainage11024
Sewerage wells513
Total92373
Summary of environmental sample results

Notably, 35 samples from February showed positive, suggesting a pretty long persistence of the viral material in the environment.

Of the 457 samples collected from animals belonging to 18 species, none of them tested positive for the virus.

While several samples had genetic material belonging to mammals of genera such as homo (e.g. human), ovis (e.g. sheep), bos (e.g. cow), canis (e.g. dog) etc., it is not, however, proof that these animals were infected but may only mean that there was an increased focus (for sample collection) on those shops and locations, where animals were sold. The same goes with the case of racoon dogs as carriers: the study found genetic material from those; it could only mean that two things (virus-carrying entity and racoon dogs) co-existed, and nothing further.

Reference

Surveillance of SARS-CoV-2 at the Huanan Seafood Market: Nature

News From Huanan Market Read More »

The behavioural immune system

It is a term introduced by the psychological scientist Mark Schaller, describing mechanisms devised by animals, including humans, to counter microbes that cause infection. A simple example is the repulsion towards rotten food.

The behaviour immune system may be considered complementary to the body’s immunological defence. The latter consumes energy and is reactive; the pathogens first enter, and then the body produces compounds (e.g. antibodies) to counter. But a repulsive smell or taste prevents some from consuming it in the first place.

References

Mark Schaller, Phil. Trans. R. Soc. B (2011) 366, 3418–3426

Behavioural immune system: Wiki

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Why Most Published Results are Wrong

It is the title of a famous analysis paper published by Ioannidis in 2005. While the article goes a bit deeper in its commentary, we check the basic understanding behind the claim – through Bayesian thinking.

Positive predictive value, the ability of analysis to predict the positive outcome correctly, is the posterior probability of an event based on prior knowledge and the likelihood. The definition of PPV in the language of Bayes’ theorem is,

P(T|C_T) = \frac{P(C_T|T) P(T) }{P(C_T|T) P(T) + P(C_T|nT) P(nT)}

P(T|CT) – The probability that the hypothesis is true given it is claimed to be true (in a publication)
P(CT|T) – The probability that the claim is true given it is true (true hypothesis proven correct)
P(T) – The prior probability of a true hypothesis
P(CT|nT) – The probability that the claim is true given it is not true (false hypothesis not rejected = 1 – false hypothesis rejected)
P(nT) – The prior probability of an incorrect hypothesis (1 – P(T))

Deluge of data

The last few years have seen an exponential growth of correlations due to a flurry of information and technology breakthroughs. For example, the US government issues data of ca. 45000 economic statistics and an imaginative economist can find out several millions of correlations among those, most of which are just wrong. In other words, the proportion of causal relationships in these millions of correlations is declining with more data. In the language of our equation, the prior (P(T)) drops.

Suppose the researcher can rightly identify a true hypothesis 80% of the time (which is quite impressive) and rightly reject an incorrect one at 90% accuracy. Yet, the overall success, PPV, is only 47% if the prior probability of a true relationship is only 1 in 10.

P(T|C_T) = \frac{0.8 * 0.1}{0.8 * 0.1 + 0.1 * 0.9} = 0.47

References

Why Most Published Research Findings Are False: John P. A. Ioannidis; PLoS Medicine, 2005, 2(8)

The Signal and the Noise: Nate Silver

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Life Expectancy in Covid Times

The story of Covid-19 is getting three years old, and we are still getting the magnitude of the calamity it caused. One way of studying the impact is by mapping the change in life expectancy during the pandemic. Nature human behaviour has just published a paper on this topic, summarising data collected from 29 countries.

Understanding life expectancy

The calculations, known as the period life expectancies, are not a prediction but an estimate of how long a newborn will live if today’s death rate persists for her entire life. So these numbers will vary between 2020, when it was severe cases of Covid-19 with no vaccinations available, to 2021, where there were some mitigations available, to 2022, where the deadliness was relatively lower.

LE deficit

The researchers covered the change in LE rates of countries, that included Europe, the USA and Chile, since 2019, using data on all-cause mortality. They also define a term, LE deficit, which is the difference between the observed LE, and expected LE based on pre-pandemic estimates. Consider this: a country estimates an LE of 80 years in the last quarter of 2021. Imagine it was 79 years in 2015 and was slowly progressing upward (based on the trends from the past few years), and the expectation by Q4 2021 was 82. Then the LE deficit is 80 – 80 = 2 years.

There are a bunch of findings worth mentioning here:
Of the countries under investigation, only Finland, Norway and Denmark did not see a decline in LE (in comparison with 2019) in 2020.
Many Western European countries bounced back in 2021, i.e. positive change LE from 2020 to 2021, whereas most of Eastern European, the USA and Chile continued the fall.

One impressive trend was the correlation between vaccination coverage and life expectancy deficit.

Life expectancy changes since COVID-19: Nature Human Behaviour

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Covid and Smoking

A paper was published in April 2020 on the open science platform, Qeios. The topic was the potential benefit of tobacco smoking to protect against Covid-19.

The conclusions in the article were based on data from observational studies and not randomised clinical trials. We have already discovered issues which arise from observational studies, collider bias being one of them.

Collider bias happens when two variables, e.g., risk factor and outcome, influence a third, namely, the likelihood of being sampled. In our case, the sampling occurred on or before April 2020, in the earlier part of the pandemic. As you may recall, testing was in the developing stages, and the focus was on front-line health workers and patients with severe symptoms. In technical terms, the sample was not random or representative.

Therefore, the data space has narrowed down to health workers, and within those, there are smokers and non-smokers. As a consequence of the testing strategy, the survey censored out the smokers who had no symptoms. And this exaggerated proportion of non-smokers who had symptoms in the sample.

References

Low incidence of daily active tobacco smoking in patients with symptomatic COVID-19: Qeios, CC-BY 4.0 · Article, April 21, 2020

Collider bias undermines our understanding of COVID-19 disease risk and severity: Nature Communications, 2020, 11:5749

Randomised Controlled Trials: BMJ

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Based on a Lancet Study …

In this post, we discuss an article that otherwise requires no special mention in this space. Yet, we discuss it today, perhaps as an illustration of 1) the diverse objectives that scientific researchers set for their work and 2) how the ever-imaginative media, and subsequently the public, could interpret the messages. Before we examine the motivation or the results, we need to understand something about the study’s publication status.

Preprints with The Lancet 

It is a non-peer-reviewed work or preprint and, therefore, is not a published article in the Lancet, at least for now. The SSRN page, the repository at which it appeared, further states that it was not even necessarily under review with a Lancet journal. So, a preprint with The Lancet is not equivalent to a publication by the Lancet.

The motivation

You may read it from the title: Randomised clinical trials of COVID-19 vaccines: do adenovirus-vector vaccines have beneficial non-specific effects? It is a review paper, and the investigators specifically wanted to understand the impact of COVID-19 vaccines on non-COVID diseases, which, I think, is a valid reason for the research. By the way, you have every right to ask why COVID-19 vaccines should impact accidents and suicides!

Motivated YouTubers

The following line from the abstract turned out to be the key attraction for the YouTuber scientist. It reads: “For overall mortality, with 74,193 participants and 61 deaths (mRNA:31; placebo:30), the relative risk (RR) for the two mRNA vaccines compared with placebo was 1.03“. Now, ignore the first three words, “For overall mortality”, add The Lancet, and you get a good title and guaranteed clicks! 

The results

First, the results from mRNA vaccines (Pfizer and Moderna):

Cause of
death		Death/total
Vaccine group		Death/total
Placebo group		Relative
Risk (RR)
Overall mortality31/3711030/370831.03
Covid-19 mortality2/371105/370830.4
CVD mortality16/3711011/370831.45
Other non-Covid-19
mortality		11/3711012/370830.92
Accidents2/371102/370831.00
Non-accidents,
Non-Covid-19		27/3711023/370831.17

In my opinion, the key messages from the table are:
1) The number of deaths due to Covid-19 is too small to make any meaningful inference
2) The deaths due to other causes show no clear trends upon vaccination

Results from adenovirus-vector vaccines (several studies combined):

Cause of
death		Death/total
Vaccine group		Death/total
Placebo group		Relative
Risk (RR)
Overall mortality16/7213830/500261.03
Covid-19 mortality2/721388/500260.4
CVD mortality0/721385/500261.45
Other non-Covid-19
mortality		8/7213811/500260.92
Accidents6/721386/500261.00
Non-accidents,
Non-Covid-19		8/7213816/500261.17

My messages are:
Accidental accumulation of non-Covid-19-related deaths (five of them coming from cardiovascular) gives an edge to the vaccine group and, therefore, “saves” people immunised with Adenovirus-vector vaccines from dying from other causes, including accidents, in some countries! The statistical significance of the number of cases is dubious.

Lessons learned

1) Be extremely careful before accepting commentaries about scientific work (including this post)
2) As much as possible, find out and read the original paper after being enlightened by YouTube teachers.

Randomised clinical trials of COVID-19 vaccines: do adenovirus-vector vaccines have beneficial non-specific effects?: Benn et al.

Based on a Lancet Study … Read More »