“Alternative” COVID treatments

Image: Marcelo Guimarães Lima, One and Multiple, oil on canvas, 2020
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By JOSÉ GUILHERME CHAUI-BERLINCK*

A visual guide to understanding the problem

The purpose of this text is to present a “visual guide” that can help a person who is not in the area of ​​numbers to understand the risk they run when believing in the possibility of “alternative” treatments (so to speak) in case of contamination with the SARS-CoV-2 and developing COVID-19. Given the proposal, the reading of the text itself should be understood almost as if it were a large caption for the figures (which I therefore leave without captions). Before going, however, to the problem itself, I think that some numbers are important to be presented. Then …

some preliminary numbers

On the current date (March 12, 2021), we have 118 million cases of COVID-19 in the world, accounting for 2 million and 600 thousand deaths. Brazil accounts for 11 million and 200 thousand cases, with 270 thousand deaths. The world population is around 7 billion people, that of Brazil, 212 million. Put that way, these seem to be reporting numbers only. Figure 1 illustrates the “size” relationships between these numbers.

Figure 1

In Figure 1, we see that Brazil has a population of around 3% of the world's population, and is contributing (if this is the right term) to 9% of the total cases and deaths from COVID-19. I.e, Brazil has a participation in the current pandemic 3 times greater than what it would have in proportion to its population!

This fact alone, coming from very simple and obvious numbers, should be enough to cause embarrassment and trigger effective actions on the part of those who took over the federal government, if not out of responsibility, at least out of shame.

Our next numbers are from the State of São Paulo[I]. Figure 2 illustrates, in purple, the approximate distribution of COVID-19 cases in populations under and over 65 years of age in SP. Note that there is a slight difference in the percentage of reported cases between these two groups. The numbers indicate the total population of each age group. I make this division between over and under 65 years old just to point out to younger people that the problems we are going to discuss later are not specific to “elderly people”. As will be seen, everyone's neck is on the line. The question I'll leave you with, at the end, is who would you hand over the guillotine latch to... so let's move on to the problem.

Figure 2

The Doctor Cousin and the Wrong Question

A person, wanting to get rid of the “new abnormal” to return to the “old normal”, asks his doctor cousin: if I have COVID-19, should I take medicine “A”?

The doctor cousin replies: I had four patients who took it and improved.

The person is satisfied and returns to the “old normal”.

What I've just described is anecdotal, but it should be very similar to events you've experienced (if you're not the person or the doctor cousin).

This “A” remedy can be any of the drugs or conducts without any technical support that have been propagated for some time by groups of people without the slightest training for such statements. It doesn't matter.

The first point that interests us is the person's question. Faced with the decision she had, after the answer, to go back to the “old normal”, clearly the question that should have been asked is not the one that was. The real question was: if I have COVID-19 and take medicine “A”, will I get better (or, will I not die or have sequelae)?

Why doesn't the person ask this question? The reason people don't ask the real question they want to ask is because, from that question, they already know what the answers are and the honesty associated with the answers. So an honest answer the cousin can give is "I don't know." You don't know because there is no treatment (for any disease) that can guarantee 100% effectiveness. But “I don't know”, the person already knows, and the answer, therefore, does not bring any information to him.

On the other hand, the cousin could answer "no". This is also an honest answer to the studies for these “A” drugs that have been promoted. However, this response will not allow a return to the “old normal”.

Finally, the cousin could answer "yes". However, he would fall into the problem of guaranteeing 100% effectiveness for a treatment, which both he and the person know to be unrealistic, as we said just above. That is, the answer “yes” is the dishonest answer. But, it's what the person wants to hear and what the cousin wants to give. How to solve it?

In the same way that the person transforms the real question into a substitute, the cousin transforms the dishonest answer into a substitute: “I had four patients who took it and got better”. He didn't say "yes". This is implied, or, at the customer's discretion, decide what the answer was.

Up to this point, I must have been a bit of a no-brainer for many, as this was merely an exercise in information theory, Bayesian statistics, and cheap psychology. What I'm going to try to do now is to visually illustrate the problem posed by the person and the scope that the answer may have. In addition, I will try to ask what questions can be asked, and what is implied in the question.

Let's assume, now, that the question "if I have COVID-19, should I take medicine "A"?" is being used honestly, that is, without replacing another one for which the person already knows the set of honest answers that could be given (and that he does not want to hear ). What does this question mean, or what does it refer to?

The question can be understood in three different ways:

  1. What is the probability of treatment TA be the cause of improvement M? or, given that treatment T has been carried outA, what is the probability of improvement M?
  2. Given that there was improvement M, what is the probability that you received treatment TA?
  3. Since no treatment was performedA, what is the probability of improvement M?

Most people might think that the question only alludes to case 1 above, but all three interpretations are equally valid. In fact, the cousin's answer leads us to formulation 1.

But, he could have said "I met two patients who did not take “A” and died”. Note that now it is formulation 3 that is being accessed.

Formulation 2 is the most complicated and the cousin will hardly look for an answer that alludes to it. The numbers that were given in the cousin's other answers (“four patients” and “two patients”) are easy to be transposed, by the interlocutor, into some tangible “probability” to be understood. However, formulation 2 requires a reversal of reasoning and any given number is not self-evident.

However, if the three formulations are valid as equivalent to the non-formal question that was asked, it means that for this question to be properly answered we have to know the answer to the 3 formulations that can be equivalent to such a question. I will speak then...

… of any disease …

Let's imagine a disease that has arisen and there is no treatment for it. Figure 3 illustrates the distribution of cases and spontaneous improvement (since, for now, there is only improvement without treatment). The area delimited by the green line indicates the cases that have spontaneous improvement within the total number of cases observed, delimited by the red line. Then, some treatments appear, and we move on to the chart illustrated in Figure 4. We assume that the treatments do not interfere with the rate of spontaneous cure and, thus, the region delimited by the blue line appears, expanding the number of improvements.

Let's see "who" inhabits each region of the graphical representation of this disease (Figure 5):

a: the region delimited by the green line is already known from Figure 3, inhabited by those who will improve with or without treatment;

β: between the region delimited by the green line and the one delimited by the blue line, inhabit those who would previously die, but now, thanks to possible treatments, improve;

γ: between the blue and red boundaries are those who will die, with or without treatment (note that I am not saying that the treatment was the cause of death, I am saying that any treatment, if given, is ineffective to overcome the disease in these individuals)

 

Figure 3
Figure 4
Figure 5

Note that the answer “I had four patients who took it and got better” leads you to consider that the treatment with “A” is what creates region b, but this region is inhabited by all individuals who received some treatment, whether “A” or not.

On the other hand, the answer “I met two patients who did not take “A” and they died” leads you to think that the g region is inhabited only by those who did not take “A”, when, in fact, this region is inhabited by individuals who did not take “A”. “A”, individuals who took “A” and individuals who had no treatment and did not belong to group a.

If we ignore the inductions made by the responses, we see that there are two questions that have not been answered:

  • How many of the four patients who took “A” belonged to group a? Those belonging to this group would improve although of any treatment. See how the following answer completely changes the induction made, despite containing the same previous statement: “I had four patients who took it and got better and I had four to whom I didn't give anything and they got better”.
  • How many patients received “A” and are in group g? These patients are those for whom treatment is ineffective. See how the following answer completely changes the induction made, despite containing the same previous statement: “I met two patients who did not take “A” and died and two who took “A” and died”.

In this simplified picture, but general enough not to lose the validity of the analysis we are doing, the scientific problem of validating a certain treatment is that of differentiating treatments that expand the range b from those that do nothing. Remember that we are ignoring, precisely for simplicity, the possibility of a certain treatment moving an individual from group a to g (that is, the treatment becoming the cause of death), and also the concomitance of treatments (that is, the treatment “A” being given together with “B”, or “C”, etc.).

Of the 3 formulations that I said were equivalent to the non-formal question “if I have COVID-19, should I take medicine “A”?”, we can see, in Figure 5, that only if only treatment “A” exists can direct answers be obtained for formulations 1, 2 and 3: formulation 3 is the a region; and formulations 1 and 2 would be the same formulation[ii] and the answer would be region b.

However, this is where a large part of the problem remains: since there are a multiplicity of treatments that are given concomitantly (even for ethical reasons), how to differ the participation of each one in the final outcome? That is, which treatments enlarge the region b and which not.

This scientific problem is not solved with 2, 4, 6, or 20 patients a doctor has seen. This is the experience of the professional, but it is not the experience of validating a treatment.

The populations contained in the areas represented in the figures are huge, in the thousands, and in the case of COVID-19, we are in the hundreds of millions around the world.

Figure 6 illustrates, without further pretensions, which specialists deal with which COVID-19 patients. The white lines indicate what types of patients these specialists see. The important point to be realized is that "general clinics"and "other specialties”, whose professionals are not part of the front line, will basically see COVID-19 patients from our group a. That is, they are professionals who are only in contact with the group that will improve although of the treatment. It is important to note that the vast majority of doctors and health professionals find themselves in this condition: peripheral contact with COVID-19 patients.

In addition, we see that region b, the one that contains individuals who need adequate treatment in order not to go to region g (death), makes up approximately 20% of the area[iii]. This means that 1 in 5 people who contract COVID-19 will be in this region b (of course without knowing, as there is nothing to indicate, in advance, who will be in which of the regions).

Figure 6

Appropriate treatments

As I said above, it is not 2 or 20 patients seen by a professional that make up a clinical test group. As I also said, this type of circumstance is the mere experience of the professional. Not only does a study need to have lots and lots of cases to form adequate statistics, it also needs to be well designed in terms of groups and treatments. So, what is the current situation (March 2021) in terms of treatment recommendations for COVID-19? Or, using the vocabulary we created above, what are the drug treatments considered adequate to place individuals from the g region into the b region?

The table below is based on the COVID-19 treatment guidelines made by the American Infectious Disease Society (IDSA[iv]), US National Institutes of Health (NIH) and World Health Organization (WHO). In it I listed only the drugs most publicized by the groups I referred to earlier, and corticosteroids. A simple reading of the table already allows us to perceive which ones can and which will not make the individual leave region g and go to region b.

CT: clinical trials

What if it was you?

Finally, we come to the question I announced at the beginning.

Figure 7 corresponds to the purple squares in Figure 2, using the color pattern of the previous figures to discriminate between groups a, b and g (see Figures 5 and 6). Imagine that you are a dart being thrown at random (since you do not know, a priori, to which group it belongs), in Figure 7.

If you land in the green region, you are saved despite any treatment.[v]. If it falls in the red region, I'm sorry, there isn't or wasn't anything I could do for you.

The dart may also end up in the blue region, corresponding to group b. We saw that the chance of this happening is around 20% (1 in 5).

And here comes the big question: if your dart lands in the blue region, the one that depends on correct handling, would you put your life on the reckless dart thrown by the cousin?

In other words, if you are in the blue region, would you use the treatment suggested by the inconsequential ones who only see patients who are in the green region?

Figure 7

 

*José Guilherme Chaui-Berlinck is a physician and professor at the Department of Physiology at the Institute of Biosciences at USP.

Notes


[I] I took the State of São Paulo as an example just to make it easier to obtain the data. The picture, in general terms, is similar in Brazil and the analysis that matters to us is not exactly the accuracy of these numbers.

[ii] By way of formalization, formulations 1 and 2 are, respectively, P( M | T ) and P( T | M ). There is only treatment TA (that is, T = TA) the probabilities become the same.

[iii] The numbers were obtained from current data for the State of São Paulo. The discrimination of group b between the cases with improvement was obtained from the fraction of patients who need medical care beyond the outpatient level (appointment on 12/Mar/21: https://www.uptodate.com/contents/coronavirus-disease-2019-covid-19-clinical-features?topicRef=126981&source=related_link)

[iv] https://www.idsociety.org/practice-guideline/covid-19-guideline-treatment-and-management/

[v] What we know is not true, as there are treatments, especially those of type “A”, with serious possibilities of serious side effects.

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