Back in March of this year, I wrote blog posts providing data on GDP losses and COVID-19 deaths for 2020, both for selected countries and US states. Since we’ve now had another 6 months of GDP data and the pandemic continues to take lives, I thought it would be useful to update that data.
I will update the data for US states in a future post, but here is the most recent data for about 3 dozen countries (mostly European and North American countries, since they have the most believe COVID data).
We have known for a long time (basically since the start of the pandemic) that COVID primarily affects the elderly. Infection fatality rates are hard to calculate (since not all infections are reported), but most of the data suggest that the elderly are much more likely to die from COVID than other age groups.
For some, this has become one of the most important aspects of the pandemic. For example, Don Boudreaux emphasizes the age distribution of deaths many times in a recent episode of Econtalk, and he uses this point to argue that we addressed the pandemic incorrectly (to say the least). Boudreaux specifies that COVID is only deadly for those 70 and older. And while I won’t rehash the argument here, please also see my exchange with Bryan Caplan, where he argues that elderly lives are worth a lot less than younger lives (I disagree).
At first blush, the data seems to bear that out. The CDC reports that almost 80% of COVID-involved deaths were among those aged 65 and older (I will use the CDC’s definition of COVID-involved deaths throughout this post). In other words, of the currently reported almost 600,000 COVID deaths in the US, about 475,000 were 65 and older. Throw in the 50-64 age group, and you’ve now got 570,000 of the deaths (95% of the total).
But is this the right way to think about it? Remember, the elderly always account for a large share of deaths, around 75% in recent years. So it shouldn’t surprise us that most deaths from just about any disease are concentrated among the elderly.
Throughout 2020, I have tried to keep up with the most recent data, not only on officially coded COVID-19 deaths, but also on other measures. An important one is known as excess mortality, which is an attempt to measure the number of deaths in a year that are above the normal level. Defining “normal” is sometimes challenging, but looking at deaths for recent years, especially if nothing unusual was happening, is one way to define normal. The team at Our World in Data has a nice essay explaining the concept of excess mortality.
One thing to remember about death data is that it is often reported with a lag. The CDC does a good job of regularly posting death data as it is reported, but these numbers can be unfortunately deceptive. For example, while the CDC has some death data reported through 51 weeks of 2020, but they note that death data can be delayed for 1-8 weeks, and some states report slower than others (for reasons that are not totally clear to me, North Carolina seems to be way behind in reporting, with very little data reporting after August).
So there’s the caution. What can we do with this data? Since 2019 was a pretty “normal” year for deaths, we can compare the deaths in 2020 to the same weeks of data in 2019. In the chart at the right, I use the first 48 weeks of the year (through November), as this seems to be fairly complete data (but not 100% complete!). The red line in the chart shows excess deaths, the difference between 2019 and 2020 deaths. From this, we can see that there were over 357,000 excess deaths in 2020 in the first 11 months of the year, or about a 13.6% increase over the prior year.
Is 13.6% a large increase? In short, yes. It is very large. I’ll explain more below, but essentially this is the largest increase since the 1918 flu pandemic.