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On Covid-19, Multi-morbidity, Causality and Complexity Profiling

Multi-morbidity is a common condition in older age, and can substantially influence individuals’ health and quality of life, making management more difficult. The COVID-19, like other types of influenza, is known to kill mainly old and debilitated individuals, suffering from at least 2 to 3 serious chronic diseases.

In the times of COVID-19, it has become popular to say the following:

If an elderly individual has disease A, and also diseases B and C, and is infected by the COVID-19 virus and dies, the cause of death is COVID-19. Without COVID-19 the individual would have survived hence it is COVID-19 that is the ultimate cause of death. However, if, for example, the individual did not suffer from disease A, he would have survived even though he may have contracted COVID-19. But, because diseases A, B and C are already there, then COVID-19, being the last addition to the list, is the cause of death. This is the prevailing logic.

The sequence of disease contractions and its temporal connotation makes people think in the above terms. And to the delight of the authorities.

However, there are other dimensions in a complex context such as that of multi-morbidity. Many diseases and disorders are correlated or inter-dependent. The human body and the human immune system are immensely complex. Adding things up as if one were doing arithmetic is not always correct. In fact, in nonlinear contexts the order of operations is important and changes the outcome dramatically. What this means is that

A + B is not equal to B + A

There are numerous examples. One is that of finite rotations – changing the order of rotations changes the final outcome. This is of course a very simple example.

The uneducated public – mainly journalists – has the urge to determine the cause of events, facts, accidents or misfortunes. However, when numerous factors come into play in a highly complex multi-dimensional context, things can get very involved. In such contexts a single cause rarely exists and one must speak of a number of concurrent causes, even though there may be factors that are evidently dominant.

Let us suppose that medicine disposes of a rational means of measuring the gravity of a disease (the NYHA class or Glasgow scales come to mind). Tracking the clinical trajectory and evolution of a patient with diverse diseases and disorders, and using QCM technology, it is possible to compute the Complexity Profile of a patient, in which the complexity contribution of each disease is ranked (in % terms). An example is shown below.

In this case, Hypertension and Chronic Kidney disease alone are responsible for 37% of the patient’s complexity (gravity). Hypertension is what we could call the hub of the patient’s clinical situation. What makes a particular factor a hub is the fact that is is correlated with a multitude of other factors. When various correlated diseases are present it is not easy to devise a treatment strategy as one is often forced to face conflicting requirements and constraints. In any event, a Complexity Profile of a patient is a precious piece of information in that it not only reflects the structure of a given patient’s clinical situation, it also indicates where to concentrate therapies.

In the context of the above, let us focus on the attribution of deaths to COVID-19. Because COVID-19 is an immensely profitable business – it is said that hospitals are over-reporting their COVID patients because they have an economic advantage in doing so – it is convenient to focus the public’s attention on COVID-19 at the expense of many other, far more deadly diseases, turning it into the first killer of the twenty first century. This is why, when a patient dies with COVID-19 he is reported as having died because of COVID-19. It is just a matter of (not so creative!) accounting.

From a Complexity Profiling perspective, the difference between “with” and “because of” is illustrated in two (hypothetical) Complexity Profiles in which the footprint of COVID-19 is evidently different. In the case on the right hand side, COVID-19 is the hub of the patient’s Complexity Profile, therefore, in the case of death, it would be legitimate to declare COVID-19 as the cause. In the other case, Hypertension would be the candidate cause of death.

However, it is important to stress that even in a highly skewed Complexity Profile, the top dominant entries are not necessarily the cause of death – it is the entire clinical landscape of the patient that determines the gravity of a given situation. And what would one say in a case as the one depicted below?

There is no dominant disease. Where does one start to devise a strategy for treatment? If such a patient dies, what does he die of? Evidently, we all die from the same cause – our heart stops beating – but that is another matter!

The conclusion is that Complexity Profiling is a useful technique when it comes to determining dominant factors, or the key players, in a complex multi-dimensional scenario. However, each case is unique, each case is different. This is probably true more in medicine that in any other discipline.

PS. A report by the Italian ISS –

indicates that “Overall, 2.9% of the sample (7910 patients) presented with no comorbidities, 11.4% with a single comorbidity, 18.0% with 2, and 67.7% with 3 or more”.

This means that out of 130.468 official reported deaths, only 3.783 are due to covid-19 alone.

In other words, in 2.9% of the cases, covid-19 would be at the top of our Patient’s Complexity Profile.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

2 comments on “On Covid-19, Multi-morbidity, Causality and Complexity Profiling

  1. Pingback: How Many Covid-19 Waves Are There? | Quantitative Complexity Management

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