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Covid Deaths Are Real: Rebutting Dr. Briand
I write to rebut the claims of Dr. Genevieve Briand, a senior lecturer at Johns Hopkins who holds a PhD in Economics and recently released a study questioning the Covid death statistics. The paper was subsequently withdrawn by Johns Hopkins, quite properly in my view. Dr. Briand’s analysis is deeply flawed.
Misleading and erroneous analyses like these have serious effects. It led our friend iWe to author a post yesterday titled Covid – Just A Big Hoax?, which cited Dr. Briand’s study as supporting the assertion “that the total death rates HAVE NOT CHANGED.”
This is false information. This is not entirely iWe’s fault; though everyone should be very careful about information sources at this time.
I have decided to rebut Dr. Briand’s erroneous analysis. The article summarizing her analysis is here; the Johns Hopkins explanation of its withdrawal is here, and an hour-long video explaining her results is here. My data sources and methods will be set forth in the technical note at the bottom of this post.
I. Age Distribution Analysis
The lead graph in Dr. Briand’s analysis relies upon this chart, showing weekly deaths in the US from February to September 2020:
This graph is color-coded by age category, with reported deaths of older people at the top, showing the percentage of total deaths in each age category — for example, the light blue bar at the top shows that about 30% of all reported deaths in the US have occurred among people aged 85 and up. This graph includes all reported deaths, from all causes, not just Covid.
This does not show the absence of an increase in the number of deaths. It shows that the proportion of deaths by age category was not noticeably changed by Covid. The article setting forth Dr. Briand’s analysis claims:
Surprisingly, the deaths of older people stayed the same before and after COVID-19. Since COVID-19 mainly affects the elderly, experts expected an increase in the percentage of deaths in older age groups. However, this increase is not seen from the CDC data. In fact, the percentages of deaths among all age groups remain relatively the same.
Who are these “experts”? Why would this be at all surprising?
We know that Covid mostly kills old people. What about other causes of death? Do you think that other causes of death affect mostly young people? Of course not. Most people who die are old, thank God. The alternative is for the young to die in large numbers, which would be tragic. Something’s gotta get us all in the end.
But to rebut Dr. Briand’s claim requires empirical evidence, so I consulted two sources: (1) the CDC page for Covid deaths, which reports deaths by age category for (approximately) February-November 2020, and which lists both Covid-involved deaths and total deaths, and (2) the CDC report on final death figures for 2017, which reports all 2017 deaths by age category. This allowed me to calculate the total percentage of reported deaths, for each age bracket, for the following 3 periods:
- All deaths in 2017
- Covid deaths in 2020 (approx. February-November)
- Non-Covid deaths in 2020 (approx. February-November)
Here is the result:
It’s hard to tell the difference between the periods, isn’t it? Unsurprisingly, Covid deaths and non-Covid deaths occur mostly among the old — roughly 30% among people aged 85 and over, another 24-27% among people aged 75-84, and another 19-22% among people aged 65-74.
Thus, we would not expect Covid to significantly change the distribution of deaths by age category.
Remember the quote from the article presenting Dr. Briand’s analysis: “[E]xperts expected an increase in the percentage of deaths in older age groups.” Again, what experts? None are cited. Their hypothesis is just plain silly. We all know that deaths occurred overwhelmingly among the old, even before Covid.
I find it both surprising and disturbing that people would be misled by such nonsense. This is the sort of ridiculous claim that a thoughtful, critical reader should immediately recognize as implausible. Proving it to be incorrect takes additional work — for example, you need to find the data on the CDC website, and you need to know how to use Excel or a similar program to analyze and graph the data.
Fortunately for you, dear reader, I happen to know how to do both of these things. :)
But seriously, the real danger is the uncritical acceptance of implausible and unsubstantiated claims. This seems particularly common when the erroneous conclusion is congenial to one’s political or moral position. Be careful about this. There is very, very bad information coming from the Leftist media, and there is very, very bad information promoted by alternative conservative sites.
II. The “Excess Deaths” Analysis
Dr. Briand’s analysis does not specifically analyze the “excess deaths” information, but the article reporting her findings claims:
Briand also noted that 50,000 to 70,000 deaths are seen both before and after COVID-19, indicating that this number of deaths was normal long before COVID-19 emerged. Therefore, according to Briand, not only has COVID-19 had no effect on the percentage of deaths of older people, but it has also not increased the total number of deaths.
This conclusion is absolute nonsense, though the first part of the first sentence is correct. The number of weekly deaths generally ranged from 50,000 to 70,000 throughout 2017, 2018, 2019, and 2020. This is immaterial. It is the sort of thing that is meant to mislead.
There is a good graph from the CDC that rebuts this claim (link here):
But perhaps you don’t trust this one. I haven’t personally verified the information, and when you look closely, it’s not actually reporting death counts. The blue bars are “Predicted number of deaths from all causes,” and if you follow the link and point to an individual data bar, it reports three numbers: “Average expected number of deaths,” “Upper bound threshold for excess deaths,” and “Predicted (weighted) number of deaths.”
I don’t think that the CDC is playing games with us. The notes explain that there is a lag in the reporting of deaths. I deduce that this particular graph uses averages to predict what the total number of reported deaths will be for each week once reporting is complete.
Also, the orange line is the “upper bound threshold for excess deaths,” which appears to be a sort of “trip-wire” for determining whether there is a serious problem. As you can see, the orange line runs quite a bit above the actual reported deaths.
I decided to so my own analysis. I was able to download data from the CDC for actual reported weekly deaths, from 2017 to 2020. The data file included both the “upper bound threshold for excess deaths” and the “average expected count.” In order to check the plausibility of the CDC’s “average expected count,” I separately calculated the weekly average deaths for 2017, 2018, and 2019, on a week-by-week basis — that is, I averaged the reported deaths for the first week in January 2017, 2018, and 2019; then for the second week in January for each such year, and so on. (One caveat — the data set did not include the first week in January 2017, for that one week, my calculated average includes only 2018 and 2019 data). Here is the result:
The red line is the actual reported deaths for each week in 2020. Note the spike starting around Week 13 — mid-March. That’s Covid. Important caveat: Recent deaths are significantly under-reported, as the data is not yet in. Do not interpret the decline at the right side of this graph as an actual decline in deaths. It is almost certainly a result of the lag in reporting.
I have two lines for the “normal death” threshold. The blue line is the CDC’s “average expected count.” The orange line is the actual weekly average for 2017, 2018, and 2019, calculated by me. As you can see, the blue line and the orange line are virtually identical, giving me high confidence in the CDC’s calculation of the “average expected count.”
The green and yellow lines are “excess deaths.” The yellow line shows the excess of actual reported deaths in 2020 compared to the CDC’s “average expected count” for each week, while the green line shows the excess of actual reported deaths for 2020 compared to the weekly average from 2017, 2018, and 2019.
This graph demonstrates that Covid is real. Its effect shows up in a sharp spike in reported deaths, starting in mid-March 2020. This is entirely in accord with Covid death figures reported elsewhere.
I performed one more calculation — my own estimate of “excess deaths,” using the green line — i.e. the increase in weekly deaths reported in 2020, compared to the average weekly death figure for the corresponding week in 2017, 2018, and 2019.
For the period from Week 12 to Week 44 — i.e. the week ending March 21, 2020 through the week ending October 31, 2020 — my calculation indicates 316,800 excess deaths. I did not include reported deaths in November because the data is evidently incomplete.
This rebuts Dr. Briand’s claim that Covid has not increased the total number of deaths in the US. That claim is demonstrably incorrect.
III. My comments
After initially taking down the article without explanation, Johns Hopkins posted an explanation, and re-posted the original article. My initial impression is to think that this response by Johns Hopkins is admirable, though on reflection, this is a sad commentary on the state of our academic and public discourse. Johns Hopkins acted properly and honorably, but this should not be surprising. This is how everyone should behave, all of the time. Still, they deserve kudos for doing the right thing.
I will anticipate an objection (which was made to my initial rebuttal, in the comments to iWe’s post). Some may claim that they do not trust the CDC figures. I see no basis for such suspicion. This is a particularly troubling response by anyone who found Dr. Briand’s flawed analysis to be convincing, as she relied on CDC data.
I am critical of one thing that Johns Hopkins stated in its explanation of its withdrawal of Dr. Briand’s study. “As assistant director for the Master’s in Applied Economics program at Hopkins, Briand is neither a medical professional nor a disease researcher.” Fair enough, but Dr. Briand (here) holds a PhD in Economics and, for years, has taught econometrics and statistics. She doubtless knows far more about statistical analysis and mathematical modeling than 99% of medical professionals. So do I, it turns out, which makes me pretty weird (even among lawyers).
A final note. There is clear empirical evidence of a significant spike in total deaths in the US, precisely corresponding to the Covid pandemic. However, it is not necessarily the case that all of those deaths were the direct result of Covid. Some may have been the result of the response to Covid, ranging from deaths of despair (such as suicide) to deaths from other causes due to failure to seek medical care. Sorting out the precise impact of Covid itself, as distinguished from its secondary effects, with require further work. Actually, I think that this is the sort of thing that Dr. Briand was trying to do, but the details got lost due to her top-line errors.
I hope that this analysis proves helpful.
IV. Technical Notes
My data source for 2020 reported deaths by age group, both Covid and total, is the CDC (here). I calculated the non-Covid total as the difference between all deaths and Covid-involved deaths. This page will be updated periodically, so here is the screenshot:
My data source for 2017 deaths by age category is the National Vital Statistics Reports, Vol. 68, No. 9, “Deaths: Final Data for 2017” (here). The relevant data is in Table 2 on page 23.
Note that in both cases, I had to combine certain age categories to match the 10-year increments reported in Dr. Briand’s analysis.
My data source for reported deaths and excess death calculations, 2017-2020, is also the CDC (here). This is the same page as the blue-bar excess-deaths graph reproduced above. To access the data, scroll down to the “Options” section, “Download Data” subsection, “CSV Format” column, and click on “National and State Estimates of Excess Deaths.” This will allow you to download a .csv file that can be opened by Microsoft Excel.
Published in Healthcare
Here is another analysis, making similar points. It’s from a very good statistician, William F Briggs.
Jerry,
I think I understand why experts would be surprised by these numbers, and seek an explanation.
I assume that they have determined that (A) the percentage of covid deaths among the elderly is higher than the pre-covid percentage of deaths in that same group.
If A is true, then B must be true, ceteris paribus, where B is the assertion “The percentage of deaths in that group is higher than before covid.”
B is determined to be false, so they are surprised. They are looking at ceteris to see why it isn’t paribus. Dr. Briand provided one hypothesis that would help explain it.
No, not at all. Here is the argument of the original article. I may have tweaked a minor error in it to give a valid argument.
[EDIT: After reading Jerry Giordano’s criticism of my summary of the study, I withdraw it and this Comment. I will leave it here for reference.]
Aaron,
I will fix a minor error in your logic.
It would be surprising because if elders are especially threatened this year compared to their threat from all causes (including infantile diseases, motorcycle accidents, gang violence, and other causes which mainly affect the non-elderly) , by
diseasecovid, then their share of all deaths should be greater than normal.Do you see why these changes are important? Without them you are making the same error of mathematical inference that I think Jerry did.
There’s another possible confounding factor.
We’ve been told for years to wash our hands, not pick our noses, etc. etc. to slow the spread of colds, flu, and so on. Mask were sometimes advocated, but “Americans will never do that.” That was then.
While most masks you see on the street mostly won’t stop spread of virus aerosols, they do a fair job of blocking outgoing droplets, though the way people use them is likely to contaminate their hands… but now we’re back to hand washing and hand sanitizer.
My SWAG is:
The fear driven measures inspired by COVID-19 made for somewhat cleaner hands, cleaner public surfaces like grocery carts and PIN pads, and of course masks.
All of these things would decrease transmission of flu and other respiratory diseases as well as COVID, and while touching the outside of your mask and then a PIN pad may be worse for others than touching an unmasked face and then the pad, the mask does pose a bit of a barrier to contagion from pad to you.
So perhaps the imperfect effect of social isolation, encouraging sick people to stay home, wearing masks, and attention to hygiene have decreased the overall transmission of flu and other non-COVID respiratory illnesses enough to reduce respiratory illness mortality in general.
I find the Johns Hopkins article and the discussion to be a distraction from important questions still glossed over in pursuit of spectacularly bad policies.
1. There is no question that wherever COVID peaks, lots of excess deaths follow, on a similar curve about a week behind. It may be that lockdown-caused deaths will increase, persist and replace some of the expected deaths in which COVID supplanted other causes making interpretation of excess deaths an adventure in months to come.
2. The attribution to COVID as the COD is probably overstated but given the overall death total, such overstatement can’t be a big factor.
3. Contrary to breezy assertions by medical professionals about having a handle on the science of transmission and the positive effect of closings and/or mask and distance mandates, we have no clue about the nature and scope of transmission. If we did, we would have answers for these questions:
a) Why is there identical behavior of COVID within climatically similar regions despite the presence, absence and timing of various intervention strategies.
b) Why does COVID incidence rise, peak and fall at a near fixed rate within an almost fixed time span?
c) Why does the infection incidence fall at all unless there is pre-existing resistance and a great deal of heterogeneity among potentially susceptible & effective carriers? It is not herd immunity but something like that is at work. Why/what is it?
d) Why are so many people so clearly resistant to SARS-COVID-19?
e) Why are fatality rates so much vastly lower now in every jurisdiction that had a prior deadly wave? (Deaths in NY, NJ, MA have flatlined despite large “case” increases.) If the “dry tinder” theory is not the explanation, then what?
As for the belief that we have not significantly reduced the number of potential fatality victims and therefore the fatality rate can again soon be in NY-NJ-MA levels, I have serious doubts. Here is a state government page reporting most LTCs COVID record in the state of Michigan.
There are 3,123 deaths among the 13,332 reported cases, roughly half the state death totals as of the 10/31 report date.
On the one hand, the great majority of Michigan LTCs have remained untouched which is a lot of “dry tinder”. On the other hand, Michigan’ second COVID case peak is now occurring which strongly suggests that the spread will decline just as rapidly following it’s indelible, inevitable pattern and thus run out of good carriers and thus steadily drop the odds of invading more LTCs. And we can hope that despite the asinine focus on futile, costly efforts to stop the general spread that health authorities and LTC operators have stepped up serious facility-level protections.
I think the most impressive thing with the @arizonapatriot analysis here is that it’s presented practically. I’d like to see more thoughtful analysis like this in general.
My only objection is that Jerry started out by stating the fact of the rebuttal, not the nature of the flaw. I think if this post had started with a simple statement of the rebuttal hypothesis, it would have been a stronger frame. “Essentially, Briand’s analysis was narrowly focused on the question of whether the COVID-19 pandemic has increased the proportion of mortality in aged populations, and initial interpretations didn’t catch that nuance.”
I agree with the Johns Hopkins decision to post a statement clarifying the thesis, because this may be an artifact of academic writing that’s lost on a general population. When we democratized information gathering and analysis, we expanded the likelihood of unqualified people doing that information gathering. When information flashes around the world at the speed of light, but corrections move like ripples in a pond, it’s reasonable to yank the information so that it has the correct context.
Initial impressions should always be treated with great caution. There’s no such thing as love at first sight: love comes from trust and experience, so love at first sight is more about the senses than the soul. There’s no such thing as a lightbulb of inspiration: inspiration comes from years of study, analysis, and knowledge.
Mark, I don’t follow your argument. These so-called, unidentified “experts” were completely wrong. One would think that “experts” in this area would know the proportions of deaths in the various age groups, which I didn’t know, so I calculated it. But my calculation was in accord with my common sense — which is that most people who die in this country are very old.
The highlighted portion is factually incorrect. This is the problem in Dr. Briand’s analysis. It is based on a premise that is false. In this case, it should have been obviously false.
Again, the highlighted portion is factually incorrect.
Jerry, thanks for the correction. I was assuming, incorrectly, that Briand’s logic must have been sloppily written in the summary.
Mark, Jerry, forgive me for being a bit dense here, but I want to understand just what you’re saying. And, obviously, I don’t.
In its simplest terms, I took the argument to be this:
Have I got it wrong already and, if so, how?
Thanks for your patience,
H.
I think the paper looked at deaths by age group in which elderly deaths are always expected to be higher, found no significant change/difference in any of these age bands and concluded that COVID was not causing excess deaths. The problem is that March and April deaths and in July and August death totals (all overwhelmingly concentrated in the higher age brackets) were clearly in excess of expectation so that does not jibe with the analysis in the Hopkins paper.
Bat, okay, I understand that excess deaths occurred. What I don’t understand is why we didn’t see the elderly making up a greater proportion of total deaths (my point 2, above) IF in fact they have a higher CFR for COVID-19 (point 1 above). I think Jerry was trying to answer that question in his original post, but I’m not sure.
That same CDC page has a dashboard option to change data displays. The excess deaths were higher for the elderly at the spring peak:
That is why the Briand paper does not seem to jibe with the data sets the paper was based on.
One weird thing is that there will be people whose death is directly attributable to the lockdowns but who tested positive at time of death so they will be counted as COVID victims whose deaths will be used to justify more closings and lockdowns.
The other weird thing is that deaths in some categories will start to show up at less than normal rates (that is who COVID kills) and mental health patients are already starting to die at greater rates even as COVID deaths subside after the latest wave. The debate about whether any lives were saved (spoiler alert: few to none) and how many deaths were actually caused by the interventions (a lot) has not begun in earnest. Fauci knows it’s coming and his ilk are acting to fight to the death to protect the narrative (with help from the usual media suspects).
For what it’s worth, I took this data on total weekly deaths by age group from the CDC site and made this graph, because I can’t follow stacked bar charts. A couple of things to note, the top line “Total Deaths” in measured on the right hand scale, each age group is on the left scale. I did this to keep some perspective between age groups. Dr. Briand’s point, that there are or were 200,000 – 250,000 COVID-related deaths and this is much more than the difference between the peaks in 2018 and early 2020 (the difference between just over 70,000 per week in 2018 and just under 80,000 in 2020 (right scale). But she didn’t account for a secondary peak in August 2020 or the failure of 2020 deaths to fall below 50,000 per week during the summers of the previous years.
From summing the data between weeks 16 and 41 (probably the last complete data reported to CDC, mid-October deaths) you get a total of roughly 1,611,000 for 2020, and the highest death count between 2015 and 2019 for that 26 week period is approximately 1,367,000 or about 244,000, which is more than the 216,000 COVID deaths reported on October 15. She tries to make the point that we seem to be seeing fewer heart-related deaths in 2020. But this is something we will have to wait for the final data to see. It does make sense that over the next few years we will, be seeing fewer deaths from heart disease, as she says (I think she assumed too much of this will happen in 2020). This has to be the case, if the total population of people with heart-disease die at a predictable rate (actuarial tables say they do), an early death due to COVID reduces the population of heart-disease patients in the future. A number of those patients would have died in 2020, 2021, … , without COVID, so COVID is moving numbers of death from future years into this year. The good news, as she says, is that this appears to be mostly among people who do not have a lot of remaining expected lifetime. As this is calculated, we will start to see the true cost of the shutdown. And when the final data arrives, we will see what the true impact of COVID was.
One other point, the CDC reports several sets of death data. The “excess death” data (bar chart labeled “Weekly number of deaths (from all causes)”) are based on estimates and “provisional” data. This data was developed by the CDC to be an “early warning” system to help public health workers identify public health issues before a new disease is widely identified. The cause of death data for 2019 and 2020 is still provisional, and will be revised when all of the data is available and the cause of death determined.
I may be missing something as well, but I think the point is that the elderly are more susceptible to most causes of death, especially the heavy hitters – the ones with big numbers – like cancer, heart disease, pneumonia, etc… The elderly just more fragile all around – so diseases, accidents, excessively rough amusement park rides, teenagers in gruesome Halloween costumes jumping out from behind bushes – all these things kill them at higher rates. Covid is just another cause that follows that same pattern. Maybe a little more so, but not enough to move the averages. That is why it should not have been surprising that elderly deaths, as an overall percentage of the total deaths, didn’t change much when Covid hit.
Jerry Giordano is a lawyer of uncommon intelligence and commitment. He is a huge credit to the Ricochet community.
D.A., you are correct. I’m sorry if I didn’t make this point clearly enough in the OP. I was setting forth a lot of information in the OP. The portion relevant to this discussion is:
Here are the precise figures for the percentages of all deaths by age category, for the older groups:
While the Covid percentages are slightly higher, the difference is small. About 10% of reported 2020 deaths have been from Covid. So, unsurprisingly, the change in the percentages of death for each age category is quite small, and not noticeable in a chart.
To get back to what I was trying to say before (without getting myself muddled this time in trying to make sense of a muddled article Yanni Gu, A Closer Look at U.S. Deaths due to COVID-19 (the Johns Hopkins News-Letter, November 22, 2020):
The bolded inference is not correct.
If I understand Briandi’s thesis, it is, in part.
In April, we expect disease from each disease category individually and in sum (not counting covid19) to increase sharply.
Instead we saw a sharp decrease, accompanied by very closely matching increase (virtually identical for W/E 4/18 and 4/25) in covid19 deaths.
This can be accounted for by a normal seasonal increase in all non-covid19 deaths, with many of them being categorized as covid19-related, in obedience to CDC guidelines.
The average weekly number of deaths by age group in 2020 and 2015-2019 are approximately the same in each age group, and the hypothesis of equal mean average deaths in each group of years appears to be statistically significant in the older age groups (age 75 and up) and for all deaths. But need to recheck my work.
I did this exercise twice, once for all weeks, and again with average deaths for weeks 16 through 41 (roughly mid-March through mid-October) the only age group where the average number of deaths were not statistically equal was the 25-44 age group, which saw average deaths through the COVID period higher in 2020 (2,782 per week 2015-2019, vs. 3,503 per week in 2020). Perhaps not surprisingly the weekly variances are very large in 2020 for the oldest age group, and very different from the early group. This is probably trying to tell another story, maybe that when COVID runs through a nursing home it causes a lot of deaths in a short time, which is not seen when vulnerable populations are adequately protected? Again, I need to check my work. It will be interesting to see what the causes of death are in the 25-44 group when the final data is released.
It could be correct if COVID deaths are a small proportion of deaths in each age category, even if most COVID deaths are in the higher age categories, because that is where most of the deaths are, normally. But the CDC still throws a lot of caveats around provisional cause-of-death data. Some causes are reported earlier than others, some require more scrutiny before the data is finalized, etc.
The newsletter is a mess. The lecture is less so, but still not clear.
Mark, please explain. I think that the inference is precisely correct. One of us is getting this wrong. I don’t understand your position.
As an example, consider the 85 and over age category. Pre-Covid, in 2017, 31.5% of deaths occurred in this age category. Per the CDC data, 31.7% of deaths involving Covid occurred in this age category. How could such a small differential cause a significant change in the overall percentage of deaths in the 85 and over age category?
I don’t know where you are getting your data for this.
Normally, in April, there is a seasonal decrease in deaths from all causes. The average weekly deaths for the 12th week of the year (late March), in 2017-2019, was 56,769. The average weekly deaths for the 16th week of the year (late April), in 2017-2019, was 53,968. That is a decline of 2,801 deaths per week, as part of what appears to be the normal seasonal variation.
If we focus on the weeks that you picked (which I think are the same weeks that Dr. Briand picked), these are the 16th week and 17th week (the weeks ending 4-18-2020 and 4-25-2020). Reported deaths for these weeks, in 2020, were 76,653 and 73,704, respectively.
Average reported deaths for these same weeks in 2017-2019 were 53,968 and 53,675, respectively. This indicates excess deaths of about 20,000-23,000/week in this particular two-week time period.
The precise figures are 22,685 excess deaths in the week ending 4-18-2020 and 20,132 excess deaths in the week ending 4-25-2020 (defining “excess” to mean “exceeding the 2017-2019 average for the corresponding week’).
The data source for this is the Excel-compatible .csv file that you can download by following the directions in the last paragraph of the OP.
If you go to this CDC page for weekly provisional Covid death counts, you can scroll down to a graph titled “Provisional Death Counts for Coronavirus Disease 2019 (COVID-19) By Week of Death).” You can point to the graph and read off the weekly figures. The weekly figures for the weeks ending 4-18-2020 and 4-25-2020 are 17,088 and 15,457, respectively.
Compare those to my calculation of excess deaths:
This data does not support the hypothesis that large numbers of non-Covid deaths are being erroneously coded as Covid deaths. If anything, this data suggests the opposite.
However, it is possible that the indirect effects of Covid caused a larger number of deaths in this week — for example, by people not seeking medical treatment due to fear of Covid.
My wife’s lab is running a combined test, it runs Covid, influenza A and influenza B. Getting lots of Covid very little flu.
MtWW,
I didn’t watch the lecture*. but I agree with you that the newsletter is a mess.
*I need to read the paper, which seems to be unavailable, perhaps deliberately! I can spend five hours spread out over three weeks watching these damned Internet videos and
It would take me ten minutes of reading if they would just give me the non-video files.
I disagree. The inference I refer to is
Depending on other circumstances the consequent will be true or false, but the inference is always incorrect. Covid and election fraud have shown that to try to apply statistical analysis one must pass through a minefield; we have to read, think, and write precisely.
It is hard to reconcile in the mind the idea that percentage of death by age is small and news items like 77% of all Covid deaths in Canada come from long-term care facilities.
I agree with those doubts. It seems to be the case that COVID probably did steal from other COD categories people with not much time left rather than killing many people who otherwise had a potentially long run left. That means that the net excess death rate in the long run would be far less than the total number of deaths attributed to COVID. But those elderly COVID deaths still represent an acceleration of date of death.
I recall a comment by a British MD a few months ago who estimated that the average COVID death merely shortened lives that had a year or less left in projected life span.
So even if there were no excess deaths from COVID (which is not credible) on the theory that it only killed people who were about to die anyway, the shortening should still show up as an excess because the deaths accrued months ahead of expectations. I don’t understand how there would be no change in excess deaths in that age bracket.
This is how CDC breaks it out by age:
Further, I would speculate that, because this cohort is both the most vulnerable and the most intensively medically served, a wholesale interruption of normal medical care would tend to impact it more dramatically than it does the general population. I would expect to see disproportionate excess mortality in this demographic both because of COVID (directly) and because of disrupted medical care.
Dr. Briand mentioned in her lecture that estimating future deaths this situation is somewhat like estimating the future demand for automobiles in the “Cash for Clunkers” program. The problem was to estimate the change in the expected life of the fleet due to clunkers exiting the market before the end of their normal service life. The program resulted more cars purchased in the near term, and fewer old cars being replaced in the near future.
Regarding what Dr. Briandi said, my source is Yanni Gu, “A Closer Look at U.S. Deaths Due to COVID-19” (the Johns Hopkins News-Letter, November 27, 2020)
Please re-read it, and if I have misquoted it, please let me know.