The world is filled with data. It is meaningless until you know it is true when it can become information and then you can investigate to make it into knowledge.
A tweet from Dr Aaron Kheriaty @akheriaty provides an example. The image is from a CDC report. The link is shown but does not link as it is part of the picture.
This is data although it is possible it is true. CDC is not so reliable as it once was for purveying truth, but for now, let’s accept it.
Meaning is the beginning of knowledge. Assigning meaning to information is a necessary skill and one often overlooked in the rush to judge.
Begin with an assessment of the information provided. Is the comparison to excess deaths at age 85 meaningful? Probably not very. The coronavirus seems relatively harmless in the absence of other problems with one’s health. The under 25 group was nearly immune in the early months and the deaths that appeared were generally connected with significant preexisting conditions. That there is a 4% excess death rate may not have any significant meaning as there are very few deaths in the group’s baseline. If normal is 1,000 per million, a 4% excess is 40 deaths in a million. It is very hard to attribute a cause to that.
The older groups may have some meaningful information included.
What are the sizes of the population of each age group? What is the expected death rate within each? That will help us to understand the relationships we need when looking further. At first glance, a 55% excess death rate seems formidable but as we saw with children if the numbers are small, the percentage doesn’t help with an answer. Never pay much attention to average increase when the baseline number is low. If something happens twice in a million and increases to three in a million, the change is +50%. Notice there is now way to get an increase lower than that.
Percentage change usually show us where to look first though.
Could I get a series of these particular graphics so I could assess the change over time?
Are there non-Covid causes of death that have changed dramatically? Suicide, overdoses, etc. As you refine data you find subgroups that have not changed, as well as several that have. Overall averaged results around causation mislead and are usually intended to mislead.
Are the results in the United States similar to the results in other countries? Especially the non-virus parts.
I have no ability to get the data and only limited ability to analyze it. Data scientists would eventually come up with correlations, maybe leading to causes between several variables connected to but not the virus itself.
It is not smart to look for answers to what you think are the causes, but it is human nature. The best data scientists let the information lead them to an answer, not make their mind up and then look for proof of their thesis. We non-experts usually follow the weaker course.
Always pay attention to two factors.
First of all, let’s choose to ignore two potential causes.
Why ignore those for now? Because they will appear more vividly if they are causes and we have found little else. Their clarity will enhance as other things are discarded or assessed as contributing but of little effect.
Things to examine include contributing factors to death. We know that for people over 85, at least half have four or more co-morbidities. We should examine which they are. If heart disease or diabetes always are in the grouping we might like to keep track of that. Maybe those with a poor diet are always affected worse. Immobility is related? How about Vitamin D deficiency? Maybe some potentially contributing factors are caused by others. Do you suppose immobility increases the chances of low Vitamin D levels?
In the 25-44 age group There are likely fewer co-morbidities so there may be an easier analysis. Does anyone know the effect of inadequate sleep, stress from the changing work environment, and the change in the conflict level with both children and spouse?
The three groups between 45 and 85 are confusing. A more refined assessment might provide help. I think the averaging effects are the source of the confusion but more information would help.
Comparing the ends might offer the most help 25 to 45 and over 85 are vulnerable to different degrees but likely have some useful comparisons. Under 25 likely has too few clear indicators.
Be healthier. It cannot hurt you and it has benefits beyond disease prevention.
There are too many people who are invested in the Covid response. Expect little transparency, and that will be reluctantly provided.
Question everything and avoid cognitive bias as best you can.
I build strategic, fact-based estate and income plans. The plans identify alternate ways to achieve spending and estate distribution goals. In the past, I have been a planner with a large insurance, employee benefits, and investment agency, a partner in a large international public accounting firm, CEO of a software start-up, a partner in an energy management system importer, and briefly in the restaurant business. I have appeared on more than 100 television shows on financial planning. I have presented to organizations as varied as the Canadian Bar Association, The Ontario Institute of Chartered Accountants, The Ontario Ministry of Agriculture and Food, and Banks – from CIBC to the Business Development Bank.
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