2020 Final Excess Deaths Evaluation – 1/5/21


This is a follow-on to two previous analyses (here and here) of excess deaths during 2020. My approach is different than most (or all) I have seen because I am looking at the impact of COVID through evaluating excess deaths in all 10-year age demographics across all US states and DC. What this does is set all demographics equal regardless of their population. I think this is a very reasonable approach because I’m primarily focused on learning what the impact was to each demographic through measuring the percentage of excess deaths (over an average from the two previous years with good data, 2017-2018) while also maintaining awareness of that demographic’s COVID impact. Note that this approach provides some interesting insight.


As I don’t want people to think that this work is conspiracy-based or whatnot, I want to make my assumptions clear here. First of all, I assume the Provisional COVID and total death data for 2020 from the CDC is correct. Or at least that it is not wildly incorrect. The CDC has made some mistakes and their analysis of their own data is often suspect, but it does seem like they’re being very careful not to mess up COVID data. Second, I want to reiterate that my approach is different from most you may have seen. This excess deaths analytic gives equal weight to every state and every 10-year demographic. Therefore, 1-4 year olds in Rhode Island have as much impact in the histogram below as 85+ year olds in New York who had very high numbers of deaths due to COVID. I think this is interesting, because COVID has impacted all of society differently. For some it has been devastating in loss of life but for others it may have been devastating for other reasons. Most analyses of excess deaths by the CDC or popular media is only focusing on the raw deaths, which overwhelmingly have come from the oldest demographics. Third, I am only using a two year set (2017 and 2018) as a baseline for the excess deaths. I could have used a 5 year average, but I chose to use the two most recent years with good death numbers to minimize the impact of growing US populations (because larger populations experience more deaths). It turns out that one of these years was a bad flu year and the other was an easy flu year, so that is serendipitous too. Data from the CDC on provisional deaths for 2020 can be found at this URL and the 2017-18 data can be easily captured on the CDC’s “Wonder” system

Histogram of Excess Deaths by Demographic and State

Histogram of excess deaths for all demographic/state combinations

Takeaway from the Histogram

This doesn’t tell us that we didn’t have excess deaths overall in 2020, so don’t be tricked. What this does tell us, however, is that the majority of state/demographic pairs didn’t have any excess deaths during 2020 over the 2017-18 average. You can also see that the numbers of demographics that experienced more than 100% excess deaths drops off very rapidly (probably at an exponential – e^n – rate) while the under 100% excess death demographics ramp up to the peak at more of a polynomial (maybe n^2 or n^3) rate. This tells us that the demographics across all states were more likely to experience less than 100% excess deaths during 2020. We’ll look at the data tables below to try to figure out both sides of the peak on our histogram.

Data Table – Highest Excess Deaths

Comparison of CDC 2020 Provisional Deaths with Average from 2017 and 2018 – sorted descending by Excess Deaths

Takeaway on Demographics with High Excess Deaths in 2020

The first thing that stands out in the above table is the large number of demographics in DC that are at the top of the excess death list. I have no idea what happened in DC this year to contribute to all these excess deaths, but only a couple of the demographics have large COVID impacts. (Edit. It struck me the reason is most likely because including DC in this analysis is like comparing deaths in Chicago with those across the whole state of Illinois. DC is more like a large city and therefore has unique death statistics)

Secondly, I also notice that there is a mix of “high-COVID” demographics and “low-COVID” demographics at the top of the table. I think most people would have expected the demographics with the most excess deaths to be over 65, but that isn’t the case. Of course, this is on a percentage basis. The demographics with the highest raw number of deaths are mostly over 65, but these are all demographics that experience higher numbers of deaths overall every year anyway. This is why I look at the percentages. Below are the demographics/states with the highest number of raw excess deaths (column on the right). Nothing much is surprising here. Elderly demographics in large states would be expected to have the most raw excess deaths because they have the highest number of deaths every year. This is a good way to parse the data if one is in search for nerve-wracking numbers, but it doesn’t give us any information we couldn’t infer on our own. It is clear, however, that in these groupings COVID deaths were significant, running from 15 to 31%.

Data sorted by “raw” excess deaths in 2020

Secondly, I also notice that there is a mix of “high-COVID” demographics and “low-COVID” demographics at the top of the table (the one sorted by percent – two above). I think most people would have expected the demographics with the most excess deaths to be over 65, but that isn’t the case. Of course, this is on a percentage basis. The demographics with the highest raw number of deaths are mostly over 65, but these are all demographics that experience higher numbers of deaths overall every year anyway. This is why I look at the percentages.

In the next table, I’ll rank the demographics by excess deaths after COVID is subtracted out and we’ll see some interesting results.

Data Table – Sorted by Highest Excess Deaths if COVID/Flu/Pneumonia numbers removed

Data Table sorted by excess deaths after COVID/Flu/Pnemonia removed

Takeaway on Groups with Excess Deaths beyond COVID

The thing that will probably stand out in the above table is that only two over 55 demographics exist in the top excess deaths once COVID and related illnesses are removed. Both are in Washington DC (again, I need to figure out why DC has so many excess deaths across the board. Are they counting differently?). All of the groups above still have excess deaths beyond the 2017-18 average even after the COVID numbers are subtracted. What might this be counting? Arizona, Tennessee, and Colorado demographics under age 44 are all over the top of the list. We know that these states tend to have above average suicides each year. I have also seen reports that deaths due to drug overdoses are exceptionally high in 2020 in younger demographics. This is kind of hard to think about, but it would seem that these excess deaths in 2020 by such large numbers are correlated with COVID and state reactions to COVID.

Data Table – Ratio of Highest COVID+Flu+Pneumonia 2020 Deaths to 2017-18 Average Deaths

2020 to 2017-18 Average Death comparison – Sorted by Ratio of COVID+Flu+Pneumonia to 2017-18 Average

Takeaway on Demographics with High COVID/Flu/Pneumonia Impact in 2020

Note that the above table is as expected. The highest percent impact of COVID/Flu/Pneumonia are in the over 65 year communities. The numbers are pretty staggering though. In the typical year, 30% of a community’s deaths are due to heart disease and about the same percentage to Cancer. The other 40% of deaths are a mix of everything from respiratory diseases to accidents to suicide and homicide. So for a demographic to be seeing 20 to 30% of its deaths in one year to COVID is incredibly catastrophic. I also notice that the Dakotas are very high on the list for their over 65 demographics and wonder if they saw much higher excess deaths due to a lack of government COVID controls (they both seem to have been known for a more Sweden-like approach). I also see a couple of under 65 demographics on this list, both from New Jersey. That would be an interesting thing to analyze.

Data Table – Sorted by Lowest Excess Deaths over 2017-18 average

Data Table sorted ascending by Excess Deaths

Takeaway on Demographics with Low 2020 Excess Deaths

The above is a very interesting way of looking at the data. What does it tell us? 2020 was a very safe year to be under 14. Why was this? I’m not sure, but I’d guess that many causes of deaths for these groups were avoided this year due to locking down at home. Car accident deaths, other accidents, possibly flu and other viral diseases, etc., might have been in very short supply for these younger groups. Interestingly, New York State has two older demographics in this list. If one looks deeper at this, one finds that New York has really low numbers of excess deaths in general. The chart below shows the raw numbers of excess deaths for the over 85 age demographic across all 50 states. You’ll notice that NY actually experienced LESS deaths in this demographic than expected. Perhaps this has to do with the lower mobility of this group during COVID (a good number are likely in nursing facilities where they can’t come and go or receive visitors).

Excess deaths by raw count in over 85 demographic

One big takeaway, however, is that if one was to evaluate the “silver lining” of 2020 by measuring Years of Life Lost, the low incidence of deaths in the younger groups would certainly carry a lot of weight.


  1. COVID does not seem to be the overwhelming contributor to excess deaths across all demographics in 2020. This does not seem intuitive, but the data (assuming it is accurate) does make a strong case that the most impact of excess deaths in 2020 went to demographics who had lower incidence of COVID-19. This is calculated on a percentage of excess death basis, not raw numbers. Perhaps this is the right way to look at the excess deaths though, as compared to raw counts. It does capture surprise and impact to the affected group to look at the percentage of change. Plus, if one simply subtracts the COVID percentage from the Excess Death percentage, many of the younger demographics high on the list would still have well over 110% excess deaths. The older demographics do not see this same effect. What does this mean? Some demographics (younger adults and older teens) experienced significant excess deaths due to something other than COVID in 2020.
  2. COVID did have a terrible impact to a large number of the older demographics across many states. Some of these demographics saw numbers of COVID (and pneumonia and flu because they’re hard to separate out) in 2020 that ran between 20 to 30 percent of the 2017-18 average. This is in the range of heart disease and cancer, each of which contribute to 20-30% of all deaths in a normal year. These groups made up the overwhelming majority of deaths in all regions during 2020.
  3. For Americans under 14, however, 2020 was a very safe year. Much safer than the 2017-18 average. It appears that though the COVID responses may have had an adverse impact on some demographics, it had a very good impact on the folks under 14.

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