In Madrid, the lethality of the coronavirus is 13.27%; in Melilla, 2.17%: the reasons behind the differences between communities
In that 2.17% of Melilla and 13.27% of Madrid, all the autonomous communities of the country extend. That fact, the considerable differences in mortality between areas of the country, is something that one can hardly get out of your head when looking at the numbers of the epidemic.
What's going on? Why do more people die, proportionally, in Madrid or Extremadura than in the autonomous cities or in Galicia if the virus is exactly the same? The virus is the same, yes; But the epidemic changes in each community, in each province and in each hospital. The Spain of the coronavirus is so complex that not even the data that the communities give to the Ministry are comparable to each other. Why are there so many differences in the data? In this guide we summarize the main reasons.
Why do mortality figures change so much?
There are two large sets of reasons that explain the differences between autonomous communities: the first have to do with the fact that different things are happening in the different communities at the epidemiological and healthcare level. After all, it is not only that the outbreak structures have been particular in each place, but that the socioeconomic dynamics, the social stratification and the sociodemographic structure are, at times, radically different. Even healthcare capacity changes very profoundly interstate.
The other great set of reasons is methodological: each autonomous community accounts for each one of the metrics of the epidemic as it sees fit (and, moreover, they have modified the method of accounting throughout the crisis). The same Ministry has come to recognize on several occasions that the data they receive from the regional health services is not comparable. This, despite the efforts of the Health Alerts and Emergencies Coordination Center to unify them, incorporates problems into the statistics that make it difficult to understand them.
We have prepared a short summary of how the different variables affect the final data to help us read the charts in the full sociodemographic, health and epidemiological context. There are more variables and, in the future, everything indicates that we will find even more: however, we believe that the ones we collect here are fundamental to understanding the phenomenon of differences.
What is happening in the different Communities?
They are at different stages of the epidemic
In Spain, unlike other countries such as China or Italy, the measures were applied to the entire country at almost the same time. There is some variation of days in the closing of schools, but (in general terms) it was very homogeneous. However, not all communities were in the same phase of the epidemic. For practical purposes, this means that not all communities intervened at the same moment of evolution (and, therefore, in some it would be late and in others it would be early).
In recent weeks, there has been a lot of talk about what we might call the "multiplier effect of delaying action by one day." The fact that taking action sooner or later has a very important impact (more than we could intuitively believe) in the evolution of the epidemic. The differences we are encountering may be due, in part, to this: for relative purposes, there were communities that arrived at the measures much earlier than others (and the evolution of these has been much better).
The virus has affected different groups
There is a whole set of factors that have to do with what the internal dynamics (family, social and economic) are like in different societies. Many experts argue that these particular dynamics cause that, in the early stages of the pandemic, before it reaches large layers of the population, the composition of the outbreak is not identical (something that can produce differences in mortality). The best known hypothesis was the one made by Moritz Kuhn and Christian Bayer, two professors of economics from the University of Bonn, and they were trying to explain that the social networks that already existed in societies are fundamental for the disease to spread.
In this specific case, his idea told us that the number of working people who still live with their parents was a decisive factor in the high levels of mortality (because, in his opinion, intergenerational relationships were especially dangerous). However, there are many more factors of this type, ranging from the type of travelers arriving at airports (business travelers being more problematic than tourists), to the composition of the productive sectors and their seasonality.
Some are older than others
Related to the latter, the aging of the population is always a factor to take into account in this type of epidemics. At a theoretical level, as the virus is especially dangerous in these segments of the population, it would be expected that areas with a higher percentage of older people would have higher mortality rates. Of course, we know that there are elements that can moderate that impact. It should not be forgotten that the Ministry itself manages the provisional figure that there are 15% of those infected in the country: the same logic as in the previous section tells us that the outbreak should not have to focus on the elderly.
It has not been the Spanish case, of course. From the outset, Spain suffered major outbreaks in nursing homes that explain much of the dramatic mortality figures (compared to the rest of the world). And beyond that, we see that if we look at the upper graph, we can see how there is a fairly defined relationship between the density of deaths (deaths per million) and the percentage of the elderly population.
They do not have the same assistance capacity
For weeks, the idea of "flattening the curve" has become a leit motiv of the epidemic. The idea behind this concept is that health care overload is especially dangerous and triggers the number of deaths as it causes many people to not have access to the necessary health resources. However, this is a factor that is very difficult to estimate in the midst of an epidemic (at least with the data we have). However, there are other factors related to this that are easily quantifiable: care capacity.
The best example is the number of ICU beds. In an epidemic like this, intensive care units are a crucial element in the treatment of the most serious cases. This could lead us to think that a greater endowment of these facilities (per million inhabitants) should translate into lower mortality rates or, at least, in deaths more delayed in time. Although some of these phenomena cannot be ruled out with the available data, what the statistics show is in fact a completely different relationship.
There are various explanations for this. The first one we will see later in detail and focuses on the fact that healthcare capacity has a very close relationship with the number of cases, hospitalized and deceased. The rest indicate that there are probably confounding factors. That is, the reason that there are more healthcare facilities is the same as the one that drives the virulence of the outbreaks. However, these hypotheses are only suggestive. More detailed analysis will be required to clarify the matter.
Some are richer than others
And, indeed, one of the most striking variables at the international level is the relationship between the incidence of coronavirus and income per capita. SARS-Cov-2 outbreaks have a rather striking statistical tendency to occur in wealthier areas. This occurs worldwide and, as you can see in the graph above, it also occurs in Spain. The mechanisms behind this phenomenon are unclear.
However, it seems like a good proxy to understand some of the differences between communities. It is true that, as the epidemic evolves, we are seeing this trend fade. It is expected that the virus will have a similar mortality per million in all territories. However, we can still see how these socioeconomic differences play an important role.
Why is the data not comparable?
When we face the data collected during a pandemic like the current one, we must bear in mind that its function is not only to be reliable, valid and precise, but also (and above all) they must be useful. Health systems are so stressed that they cannot normally afford to invest resources in generating statistics that are not of clear clinical utility, and often postpone the generation of data for informational purposes only for later.
This means that many basic definitions (infected, hospitalized, critically ill or deceased) change from one place to another to adapt to the healthcare, demographic and epidemiological situation. Part of the lack of homogeneity is explained, as we will see, by this. This would be less of a problem if there were transparency in explaining definitions and quantification procedures. However, it is something that in Spain is not happening more than sporadically.
This has reached such an extreme that the MUNQU research group of the Universitat Politècnica de València, one of the leading groups in epidemiology, has stopped publishing reports because the lack of information makes it impossible to know what the data actually measures. Therefore, what interests us in this part of the guide is to try to understand why the data of the different communities are different. Obviously, there are more factors to consider than we review here (some of a purely strategic nature), but going into them at this time would be too speculative.
What is a deceased COVID?
As the Ministry itself has acknowledged in its press conferences, except in some isolated cases, in Spain no autopsy is being carried out to find out whether or not the deceased had the disease. This means that only those who were diagnosed prior to death are counted as deceased by COVID. This is something we have seen in Italy (where in some localities the records indicate that, taking as a reference the average of other years, there are four times more deaths than those indicated by the coronavirus record) or the United Kingdom, where in the last week of the month ppsado there were 1,000 more deaths than expected and only 539 have been attributed to COVID.
In this way, the diagnostic procedures have a very close relationship with the number of deaths. In other words, in the regions where the fewest tests have been carried out per million inhabitants, it is reasonable to find fewer deaths from the initial registration. At the end of the epidemic, when we can count everything in detail, we will see that the current official figures are only a small part of the real impact.
What is an inpatient?
On several occasions, the Ministry has explained that, in its opinion, as most Spanish cases have been hospitalized, this phenomenon would be corrected. Without going into debate on the huge composition effect of the sample (if you test mostly hospitalized people, the result is that most positives will have been hospitalized), this introduces yet another criterion: we cannot assume that 'hospitalization' it is governed by stable criteria.
The truth is that the different autonomies have followed different criteria when it comes to hospitalizing patients. In fact, as we will see what happens in ICUs, these criteria change from hospital to hospital. This is because there is no "target set of symptoms" that automatically makes patients hospitalized, but instead those symptoms (and their severity) change depending on the level of saturation in the system.
In the first few weeks, we saw people with mild or non-existent symptoms go through quarantine in the hospital. Now, in the most saturated areas, we see how much more serious patients have to wait at home or in the emergency room to be admitted. At some points in the epidemic, in the Madrid emergency department, there were more than 3,000 people waiting for bed. In this sense, in the most saturated communities (with uncontrolled community contagion), it is reasonable to expect that many cases did not go to hospital due to lack of capacity.
How does this translate? In a huge variety of ways to measure hospitalized patients. For example, while Castilla y León or Valencia separate patients who are in the hospital and those who are in the ICU, Andalusia counts the patients who are in the ICU as part of the total number of admissions. To make matters worse, the Community of Madrid also count patients who are recovering at home after being discharged, something that other communities do not do.
What is a critical patient?
Something similar happens with patients who need intensive care. There can be no common criteria even between hospitals. The number of ICU beds is limited and, despite attempts to expand, this means that in a context of saturation, doctors have to make decisions about who occupies them. Both Pablo de Lora, on the one hand, and Javier Padilla and Borja Barragué on the other, have written at length about the criteria for making these decisions. But, be that as it may, the result is the same: the more saturated the intensive care units, the greater the number of patients (who would normally be in them) would be left out.
In fact, beyond this, which would be 'healthcare' differences, we know that the different communities count ICU patients differently. Some report to the Ministry active cases during the previous 24 hours, while others report the total accumulated cases. On April 2, for example, the ministry clarified that "the Communities of Galicia, Castilla-La Mancha, Castilla y León, Comunidad Valenciana and the Comunidad de Madrid present prevalence data (people admitted as of today)" and " they do not reflect the total number of people who have been hospitalized or admitted to the ICU throughout the reporting period. " Making dozens of epidemiological models based on these data unsustainable.