does not mean that in the past we lived less

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Romanticizing the past is a very common mistake that people make, but sometimes we “go over the brakes” when trying to understand what life was like in the past. For example, imagining people in their thirties as old people awaiting near death. The truth is that although the conditions of life influence how we age, the past was not exactly like that.

Statistics lead us astray.
Statistics have a great facility for misleading us when we do not understand them well. This is a clear case of statistics leading to distorted interpretations. This is pointed out by the American bioarchaeologist and professor of anthropology Sharon DeWitte in a recent article in The Conversation.

The error occurs when erroneously using the idea of ​​life expectancy at birth. This is the main measure we use to measure longevity, but it’s simply the average age at which people in a given group die, but not necessarily the only or the best. We can detect two main problems.

The first is that it does not give us information about how we age. This can depend on many factors such as our diet, the time we spend sleeping, exercise and any disease that may affect us. The second is that infant mortality greatly alters the statistics, and for many years this has been very high.

Science has been using a key parameter for 100 years because a man did not want to pay for some tables

Life expectancy throughout our lives.
If life expectancy at birth is the average life for all people, life expectancy at 5 years is the amount of life left for 5-year-olds, on average. If we use this measure (or a higher one), we will be avoiding accounting for excess deaths in the first months of life.

Life Expectancy By Age In The Uk 1700 To 2013

Life expectancy by age in England and Wales (1700-2013). Image courtesy of Max Roser, CC-BY.

DeWitt gives an example of this. During the Middle Ages, life expectancy at birth was 31.3 years. However, the life expectancy at 25 was 25.7 years, meaning that a 25-year-old could expect to live to 50.7. This is 19.4 years apart. If we take older ages, we can also eliminate, for example, the demographic effects of war.

Lessons from bioarchaeology.
DeWitte speaks knowingly. As a bioarchaeologist, she has come across various examples, such as the study of human remains in Mexico, belonging to the last centuries before colonization (from the 10th century to the 16th century). The analysis found that among those who reached adulthood it was common to exceed half a century before dying.

Historiography also gives us numerous examples, surely the most striking is the one mentioned by DeWitte, that of Emperor Justinian, who lived to be 83. A more recent example may be that of Louis XIV of France, who lived to be 76 years old, achieving being the monarch with the most years in charge in Europe, a record that he currently holds.

Life expectancy in our environment.
Infant mortality has been greatly reduced in recent years. This implies that life expectancy at birth no longer differs so radically from life expectancy at later stages. It is possible to compare the life expectancies of many countries. Spain, for example.

According to Eurostat data, in 2019 the life expectancy at birth of Spaniards was 84 years, while at 65 it was 22 (that is, a 65-year-old person could aspire to live until 87). In France it would go from 83 to 87 years, while in Portugal it would go from 81.9 to 85.6. Obviously the numbers are not equal but the difference is not very big.

What has changed in the last centuries.
The main reason for the change is the reduction in infant mortality. Its former volume and the fact that it occurred mainly during the first year of life significantly skewed the sample. Vaccines, antibiotics and hygiene measures also have a lot to do with reducing infant and early mortality.

In addition to medical factors, socioeconomic factors, such as food stability or the lower frequency of armed conflicts, also contribute.

The conditioned hope, pending subject.
If not completely misleading us, statistics have a great facility to mislead us. Conditional expectation is the main source of confusion here. Conditional expectation tells us (to summarize a bit) that we have to reevaluate the probabilities of an uncertain event when we know the outcome of another with which it is related.

For example, our probability of suffering from a disease will be different once we have tested positive in a test designed to detect it. At least if the test really works. Bayesian statistics, which analyzes these questions, is full of paradoxes, many of which affect how we understand our health and well-being. That is why it is so important to stop to understand what is behind each of these averages, percentages and rates.

Image | Julius Jääskeläinen, Wikimedia Commons

Romanticizing the past is a very common mistake that people make, but sometimes we “go over the brakes” when trying…

Romanticizing the past is a very common mistake that people make, but sometimes we “go over the brakes” when trying…

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