This is now perhaps old news (May 2022) but I was not a writer on this blog at the time this paper was released. With my co-authors, we decided to analyse serology and mortality data from 62 studies of 25 developing countries. In this paper published in BMJ Global Health we find that age-stratified IFRs are about two times higher than the benchmark metaregression for high-income countries.
Because vaccination and “newer treatments” muddy the waters, we used studies from before those were commonplace in developing countries, and compared against our benchmark for high-income nations. We also corrected for death under-ascertainment which was a very heterogeneous problem across the studies/locations
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One of the issues in developing countries was the fact that seroprevalence was rather uniform across ages which is a symptom that these countries could not protect their elderly. In high-income countries, seroprevalence tends to be higher amongst the less-vulnerable and young population.
Since the publication, other studies have found similar worrying results, highlighting the importance for future pandemics to assist and distribute vaccines to all without keeping them first for high-income places.
A commenter who goes by “Anoneuoid” has pointed out that ‘excess deaths’ in the U.S. have been about as high in the past year as they were in the year before that. If vaccines work, shouldn’t excess deaths decrease?
Well, maybe not. Anoneuoid seems to think vaccines offer protection against COVID but increase the risk of deaths from other causes. Or something. I don’t much care about Anon’s belief system, but I do think it’s interesting to take a look at excess deaths. So let’s do that.
I went to https://stats.oecd.org and searched for ‘excess’ in the search field, which led me to a downloadable table of ‘excess deaths by week’ for OECD countries. “Excess deaths” means the number of deaths above a baseline (which I believe is the average over the previous ten years or something, perhaps adjusted for population; I don’t know the exact definition used for these data). “Excess deaths” over the past couple of years have been dominated by COVID deaths but that’s not the only effect: at least in the first year of the pandemic people were avoiding doctors and hospitals and thus missing out on being diagnosed or treated for cancer and heart disease and so on, suicides and car accident numbers have changed, and so on.
Below is a plot of excess deaths, by week since the beginning of 2020, in nine OECD countries that I selected somewhat haphazardly. You can download the data yourself and make more plots if you like.
“Excess Deaths” by week, as a percent of baseline deaths, in nine OECD countries, including the US.
If you had asked me a year or so ago, “what do you think will happen with US COVID deaths now that we have vaccines” I probably would have guessed something like what has happened in Italy or the UK or Belgium or France: there would be some ups and downs, but at substantially decreased magnitude. Instead, the US really stands out as being the only country that had high excess mortality prior to the vaccines and also has high mortality now.
But then, I also expected that just about everyone in the US would get vaccinated, which isn’t even close to being the case (about 20% of US residents haven’t gotten any COVID vaccination, and about 30% are not ‘fully vaccinated’…a term that is a bit misleading, perhaps, as the effects of the vaccines wears off for those of us who got our booster several months ago).
Also, there are competing factors — competing in the sense that some tend to make excess deaths increase while some make it decrease. Vaccines provide substantial protection, and doctors have gotten better at treating COVID, so those tend to lead to lower COVID deaths. But most people seem to have resumed normal life without many COVID precautions, presumably leading to higher infection rates than there would otherwise be. And of course there are still traffic accidents and suicides and drug overdoses and so on that could either increase or decrease compared to baseline.
I find the figure above really interesting. Here are a few things that stand out to me, in no particular order:
Denmark had no excess mortality through early 2021! That’s remarkable, they saved a lot of lives compared to the other countries.
Canada looks like the US in temporal pattern, which kinda makes sense, but with mortality at about half the US level.
I knew Italy got hit very hard early on, northern Italy especially, but hadn’t realized Belgium had it so bad. Jeez they had a terrible first year.
The time series in the U.S. is much smoother than in the other countries. Belgium, France, Sweden, the UK, Italy…they all had a big initial spike and then dropped all the way back to 0 excess deaths for a few weeks before the next spike. The U.S. went up and never came all the way back down, even briefly, until a few weeks ago. The U.S. has a much larger population and much larger geographic area than any single European country; perhaps the data on some small part of the U.S., like just New England or just Florida, it would look more like one of these other countries.
If the U.S. had matched the average excess mortality of the rest of the OECD countries, hundreds of thousands of Americans would be alive who are now dead.
I guess I’ll leave it to commenters to provide insights on all of this. Go to it!
Jordana’s a staff writer for Quanta, a popular science magazine funded by the Simons Foundation, which also funds the Flatiron Institute, where I now work. She’s a science reporter, not a statistician or machine learning specialist. A lot of Simons Foundation funding goes to community outreach and math and science education. Quanta aims to be more like the old Scientific American than the new Scientific American; but it also has the more personal angle of a New Yorker article on science (my favorite source of articles on science because the writing is so darn good).
There’s also a film that goes along with Jordana’s article:
I found the comments on YouTube fascinating. Not as off the wall as replies to newspaper articles, but not the informed stats readership of this (Andrew’s) blog, either.
Users input characteristics of the test taken, the event size, the time the tests are taken before the event, the duration of the event itself, etc. The tool then produces plots of expected number of infectious people at your event and even the projected viral load at your event with uncertainties.
This obviously isn’t a tool for amateurs. I don’t even understand the units Ct for the very first input slider; the authors describe it as “the RT-qPCR cycle threshold”. They said they’d welcome feedback in making this more usable.
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