Dying "with" vs. Dying "from" Covid, pt. 3

One issue with the previous analysis is that it applies only to deaths that were purely coincidental to Covid infections.  Another set of deaths, it might be argued, that overly inflate the total death toll of Covid are those deaths in which Covid did, in fact, act as the proximate cause, but which were deaths of very weak or sickly old people who were probably going to die anyway--in a matter of days, weeks, or months.

For these deaths, the timing issue would not arise.  In fact, these deaths would technically still be attributable to Covid, since it was Covid that, so to speak, pushed these people over the edge.  So the previous analysis would not be able to separate out these deaths from other Covid deaths, whereas really the deaths *should* be more attributed to the extreme age or weakness of the people who died.

I don't think there's any question that deaths of this kind occurred and were included in the official Covid tally.  Can we get some sort of estimate, however, on the magnitude of this effect?

Average Death Rates

Here, it is good to look at the average yearly death rates.  In the United States, these vary from year to year, but not by a whole lot.  They are also relatively constant throughout the year aside from a yearly noticeable peak during flu season.

Given the relative stability of this yearly average death rate in the U.S., we should be able to eliminate at least some of these types of deaths of the extremely weak.  The hypothesis is that these people would have died soon anyway without Covid, and it just happened to be Covid that was the last straw.  If this were the case, then we should see that these deaths, though included in the official Covid death tally, would *not* have increased the total deaths that occurred in the year above the yearly average.  In other words, suppose 300,000 people are supposed to have died from Covid in 2020, but half of those people were people who were most likely going to die that year anyway.  We should then expect the total number of deaths from all causes in 2020 to have risen above the average only by 150,000, not by 300,000.

This analysis has been done (and continues to be done) by the CDC on an ongoing basis.  The report may be found here:

Excess Deaths Associated with COVID-19

Currently, the CDC is estimating that since Covid began, we have had an average of about 760,000 excess deaths above what is statistically expected given recent history.  Meanwhile, the official Covid death count is only at about 620,000 (for the time period at which the CDC data on that page was most recently updated).  This means that there seems to be an *excess* of deaths resulting from Covid, *above* what the official tally reveals--something on the order of 20% or so.

Other Analyses

The CDC is not alone in coming to this conclusion.  The Wall Street Journal has done several excellent statistical analyses of  U.S. and world data on excess deaths more than once and has come to similar conclusions (though with slightly higher estimates for the underreporting of Covid-19 deaths at 35%):

The Covid-19 Death Toll Is Even Worse Than It Looks

Covid-19 Disrupts Years of Health Progress in U.S.

Yet other statistical analyses have put the toll even higher: Estimation of excess mortality due to COVID-19 by the IHME puts the real toll of Covid in the U.S. at about 57% higher than the official tally based on excess deaths.

What Explains the Extra Deaths?

So why would there be more deaths from Covid than actually reported?  The first explanation some people might want to gravitate towards is: social measures taken to halt Covid have had bad side effects on the population.  For example, people who *should* have gone to the hospital with a heart condition were afraid to and hence died at home rather than being treated as they would otherwise have been.

The problem with this theory is that if you look at the graph of excess deaths, they very clearly track exactly with Covid infections--as Covid cases go up, the excess deaths go up, and as they go down, the excess deaths go down.  The excess deaths do *not* track with the rigor of societal restrictions, which were most strict in the very earliest phase of the pandemic, but eased off before the Summer '20 surge and were even more eased just before the disastrous Fall / Winter '20 season.  

Therefore plausible reasons to explain these excess deaths have to find a cause that's correlated with Covid infections.  I have three theories that I would like to propose:

1. In regions in which medical resources became strained due to Covid outbreaks, care of other patients suffered as well due to total lack of resources.
   
   
2. Many people who died from Covid were the elderly, in nursing homes, and with other comorbidities.  It's quite possible that many of those people died in such a way that their comorbidities were blamed rather than Covid, but that their deaths would not have happened for more than a year without Covid.  (My opinion is that this represents the bulk of the difference between official tally and actual death toll).
   
   
3. As I have pointed out several times, Covid has a significant second method of killing people, which is attacking the heart.  We know for sure that Covid is to blame for at least a few heart attacks that otherwise came out of the blue, even in people who were otherwise only very mildly sick from Covid or even completely asymptomatic.  Myocarditis has been found associated with Covid in otherwise healthy people who were asymptomatic at extremely high rates: up to 25% in one study of athletes with Covid.
   
   It is quite possible that a number of people over the past year have dropped dead from heart attacks directly caused by Covid, but without that cause ever having been discovered.  In fact, I believe we have some indirect evidence of this in studies which have shown that reports of heart attacks have inexplicitly risen in regions with high Covid occurrences.

What Do these Excess Deaths Mean?

That large number of deaths have indubitably occurred since Covid began and have been rising and falling largely in step with the rise and fall of Covid infections makes it exceedingly difficult for the theory that the official death tally for Covid is an overstatement.  It makes it essentially impossible to claim that most of these people who died were on death's door already.  To continue to maintain that the official death tally is a gross overstatement requires some other explanation, which I have not yet heard and which I can't even imagine currently.

This does not mean, however, that the death tally can't be put into a certain amount of perspective.  While the people who died were certainly not all on death's door, nevertheless most of them have been quite old and vulnerable in other ways.  It *is* quite possible that many and possibly the majority of them had only 3-5 years left anyway, or maybe 10 at the outside.  Given the age of the primary victims of Covid, this is something that is necessarily true.

If you wanted to be morbidly precise and weigh out everyone's life in a balance, then it is possible to take the age and comorbidity statistics that we have and figure out what the approximate toll of Covid has been in terms of total man-years of life lost.  This would certainly be a legitimate way to minimize the impact of Covid, though a rather ghoulish one, in my opinion.

If you were to do that, however, it would only then be fair to add into the balance the serious illness as well.  There are many vulnerable people who were sickened by Covid and did *not* die, but nevertheless were left with significant, long lasting recoveries and put into a permanently weakened state: weaker heart, shredded lungs.  These people are not counted yet on Covid's death toll, but have *certainly* lost total years of their life due to their battle with Covid.  This will be a harder toll to add up than just the people who died, but *if* you go down the route of counting years of life lost, then you certainly need to add those future lost years in as well.

Conclusion

It is not possible to maintain that the official death toll for Covid is vastly overstated.  By virtue of coincidental deaths, it might be as much as 10% overstated, but by virtue of excess deaths we observe, it is very likely to be something like 20% *under*-stated.  It *is* true that the majority of these deaths were elderly or otherwise vulnerable people, and you can make of that fact what you want based on how much value you place on the lives of the elderly.

Dying "with" vs. Dying "from" Covid, pt. 2

For the first method of data analysis, I note that the official Covid death tally is surmised to be composed of two series of numbers: the people each day who die of some random cause but only happen to be infected with Covid, and the people each day who actually die of Covid.  And *both* of these series of numbers will be related to another series of numbers: the number of people each day who are diagnosed with Covid.  However, the two types of people who die each day will each have  a *different* relationship to this number.

For the people who die of some other completely unrelated cause, the number of those people--who just happen to also have Covid--will be directly related to how many people currently have Covid in the population.  If a lot of people happen to have Covid at some time, a lot of people who die *at that time* will also happen to have Covid by coincidence.  If few people happen to have Covid at that time, few people will die coincidentally also having Covid.  So if you plotted the number of people who have Covid at any particular time on the same graph as the number of people who die "with" Covid at any particular time, the second graph will be a mirror of the first graph (but smaller).

The same thing is true of people who die "from" Covid--*except* for the important fact that this graph would be not only mirrored, but also time shifted.  It takes some time after you are diagnosed with Covid to actually die of Covid.  So if a lot of people at a particular time are diagnosed with Covid, then *later on* a lot of people will die from Covid--but not right away.

This time dependency represents a difference between the two types of people that we are surmising compose the total official death tally of Covid.  We should then be able to separate out roughly how many people fall into each category by doing a time-dependent analysis.

My Analysis

Here was my approach, using publicly available datasets and a custom Python program:

I assumed that the number of "deaths with" (the coincidental deaths) included in the official death tally was some fairly constant percentage of the total deaths (seeing as I couldn't think of any good reason for this to change over time).  I also assumed that the number of these deaths over time would be directly proportional to the number of Covid cases at the time.  I could therefore generate a time series that represented those deaths by taking the time series number of confirmed cases per day and scaling it down until the number of deaths it represented equaled a given percentage of the total official death tally.

I made this target percentage (the percentage of deaths in the official tally which are "spurious") a variable so that I could generate multiple time series of spurious (or coincidental) deaths per day corresponding to any target magnitude of this effect I wanted.

For each iteration of my run, I would generate the "spurious" deaths that would correspond to a given magnitude.  I then subtracted these deaths from the official tally.  The hypothesis of this particular run would be that the remaining deaths were the deaths caused "by" Covid, and should therefore match the Covid infection curve, but with a time delay.  I then scaled these deaths up to match the infection curve and found the best time delay which caused the death and infection curves to match.

By doing this for a target "spurious" death percentage of 0%, 10%, 25% and 50%, I figured I could see which rate of "deaths with" resulted in the best final match between time-shifted deaths and the original infections.  That is, the closer my arbitrary percent of "deaths with" ended up being to reality, the better the remaining deaths would correspond to the infections that actually caused them.

The result was as following (orange is scaled up deaths, blue is infections):

As you can clearly see, assuming that "deaths with" Covid account for either 0% or 10% of the total deaths results in a perfectly reasonable final death curve that matches the causal infection curve pretty nicely.  However, the further you increase this number above 10%, the worse the match becomes.

Periods of Rapid Infection Growth

The most telling part of these curves are the sections in which infections are increasing rapidly--primarily at the start of the Fall/Winter surge of 2020 and the current Summer surge of 2021.  The reason these diverge so strongly is that when you have infections very rapidly rising, you can start getting large differences between the infections and time-delayed deaths.  That is, you see large numbers of infections two weeks into one of these very rapid surges, whereas the deaths have not moved at all.  These time periods are extremely hard to explain using the "deaths with" hypothesis--if the infections are rising rapidly, why are coincidental deaths not also rising rapidly?  And you can see this divergence visually in my analysis by the big dips in the resulting death graph compared to infections during those periods.

You can see this problem already starting to emerge even in the 10% graph, as is clear in this blowup focusing in on the start of the Summer '21 surge:

That specific downward divergence problem only gets worse and worse as the hypothesized percentage of "spurious" deaths increase (as do other problems as well).  For this reason, I think that the 10% hypothesis has already slightly overshot the reality of how many coincidental deaths there actually are.  I would therefore put 10% as the upper cap on how much of the official death tally could be caused by purely coincidental deaths.

Another Important Factor: Amount of Time Shift

Another important thing to consider is how much the death graph had to be shifted back in time to match up with the infection graph.  Because removing spurious deaths takes deaths away from the left side of the death curve, in order to make the resulting curve match up with the infection curve, I had to increase the amount of time shift each time I increased the percentage of total deaths that I deemed spurious.

For the hypothesis that 0% of the total death tally is spurious, I had to shift the deaths back 20 days to get them to match up with the infections properly.  I had to increase this a few days for each subsequent graph, all the way up to 30 days of time shift or the graph where I assume 50% of the total death tally is spurious.

Here it is important to note that the average time-to-death from infection has been established independently based on case studies, and it's normally given at something in the range of 18 days.  This also argues against positing that the total percentage of spurious deaths goes very far above 0%--it's another way that the hypothesis results in unrealistic data the larger this percentage gets.

Some Closing Comments on this Analysis

1. Just to comment in case someone was confused: yes, there is a clear divergence between deaths and confirmed infections at the beginning of the graph.  This is a known issue caused completely by the fact that at the beginning of the pandemic we had very poor testing, meaning that the actual amount of Covid infection was far higher than what appeared by the number of confirmed Covid cases.  This hasn't been a problem since mid-last year.

2. One objection might be made, suppose there were other causes of overreporting aside from purely coincidental deaths?  This analysis doesn't rule those out per se, however given how well the time-shifted deaths matches the infections (when scaled), those causes of overreporting would have to be somehow time-matched to actual Covid deaths.  That is, the overreporting would get worse when *actual deaths from Covid* go up (not just Covid infections) and get better when these deaths go down.  I have not yet been able to think of a cause of overreporting that would be proportional to correct reporting in such a way.

3. Finally, I should note that this analysis will only catch overreporting of deaths due to coincidental Covid infections.  It would not catch any *underreporting* of Covid deaths.  Most causes of underreporting that you might think of would actually be time-matched with the actual deaths: for example, suppose elderly people with severe comorbidities who died of heart attacks due to stress on their system caused by Covid were sometimes thought to have died just from the heart attack, because it was known that their hearts were weak already.  In this case, a certain percentage of deaths actually caused by Covid could be put down as "just heart attacks" by whomever recorded their death. 

This could happen on a regular basis a certain percentage of time and it would not show up as an anomaly on this kind of a comparison graph, since the deaths are just scaled up to match with the infections anyway.  That would be one time-matched factor causing deaths to be *underreported*, and others could also be easily thought of.

This means that this particular analysis does not offer any sort of cap on how much the official death tally might be under-representing the actual death toll of Covid.  More on this point in Part 3.

Conclusion

The hypothesis that a significant portion of the official tally of Covid deaths are actually coincidental and result from some other cause is consistent with the timing of those deaths, but only if the total proportion of coincidental deaths is held at about 10% or below.  Meanwhile, the possibility that there might be signficant *undercounting* of Covid deaths for other reasons is still, at this stage, a possibility.

Dying "with" vs. Dying "from" Covid, pt. 1

You can still find people claiming, nowadays, that the official death toll from Covid is an overstatement of the actual deaths caused by Covid.  The rules for reporting deaths as "from" Covid, they say, are far too broad, and many people who are dying of other causes but simply happen to have Covid as well are being counted in Covid's official death toll.

There have been several serious flaws in this argument from the beginning, in my opinion.  First, proponents of this theory have frequently misread official guidelines for diagnosis or misapplied guidelines made at one level of government to local hospitals.  It's been a confusing set of changing guidelines, and unfortunately the tendency has been to jump on any rule change or guideline that supports this theory and publicize it widely, while ignoring rules or guidelines that don't.

Second, and more importantly, proponents of this theory have typically overemphasized the rules and down-played the common sense that actual humans writing death certificates bring to the table.  In my experience, there is quite a lot of interpretation according to common sense when it comes to the medical field.  Neither doctors nor nurses typically spend a lot of time robotically applying the exact written rules without regard for what they think is likely the right thing to do.  So I think it likely that common sense and good judgment is going to eliminate a lot of obviously wrong diagnosis.

Note that I am consistent in applying this principle.  At some point, the official rules for reporting adverse effects of a vaccination were updated to include guidelines specifically for Covid, and these rules are ridiculously strict--you are supposed to report any serious side effect after a Covid vaccination, whether you think it is linked to the vaccination or not ("regardless of causality", see text here: Reporting Adverse Events Following Vaccination).  This is explicitly different from the normal vaccination side effect reporting rules.  And yet I've never assumed that this rule has been followed completely, which--if it were--would imply that 100% of adverse side effects from the vaccines were being reported.  I believe the percentage is pretty high--but I also believe that despite the official rules, you will still get a lot of doctors applying common sense and saying, "no, I don't think that adverse effect is related" and not reporting something.  This, in my experience, is how the medical field operates most of the time.

However, I admit that these reasons are not super convincing.  Basically, how well you trust the numbers boils down to how much you trust the average hospital reporter to apply common sense.  And it's reasonable to have greater or less trust in these people, depending on your experiences and knowledge of the field.

So the question is, do we have any better, objective way of determining how often people's deaths are attributed to Covid purely because of coincidence?  And at this late stage in the pandemic, we do.  We now have a lot of data to work with and we can do some "forensic analysis" to get an idea of how often this happens.  We probably can't get a precise percentage, but we will be able to put some bounds of plausibility.

In Part 2 and Part 3, I will demonstrate two different ways of quantifying how often this kind of coincidental death occurs.