covid-19

Excess Mortality and Vaccination Rates in Europe

Jeremy HorpedahlLeave a comment

Much ink has been spilled making cross-country comparisons since the start of the COVID-19 pandemic. I have made a few of these, such as a comparison of GDP declines and COVID death rates among about three dozen countries in late 2021. I also made a similar comparison of G-7 countries in early 2022. But all such comparisons are tricky to interpret if we want to know why these differences exist between countries, which surely ultimately we would like to know. I tried to stress in those blog posts that I was just trying to visualize the effects, not make any claims about causation.

Here’s one more chart which I think is a very useful visualization, and it may give us some hint at causation. The following scatterplot shows COVID vaccination rates and excess mortality for a selection of European countries (more detail below on these measures and the countries selected):

The selection of countries is based on data availability. For vaccination rates, I chose to use the rate for ages 60-69 at the end of 2021. Ages 60-69 is somewhat arbitrary, but I wanted a rate for an elderly age group that was somewhat widely available. There is no standard source for an international organization that published these age-specific vaccine rates (that I’m aware of), but Our World in Data has done an excellent job of compiling comparable data that is available.

Note: I’m using the data on at least one dose of the vaccine. OWID also has it available by full vaccine series, and by booster, but first dose seemed like a reasonable approach to me. Also, I could have used different age groups, such as 70-79 or 80+, but once you get to those age groups the data gets weird because you have a lot of countries over 100%, probably due to both challenging denominator calculations and just general challenges with collecting data on vaccination rates. By using 60-69, only one country in my sample (Portugal) is over 100%, and I just code them as 100%. Using the end of 2021, rather than the most current data, is a bit arbitrary too, but I wanted to capture how well early vaccination efforts went, though ultimately it probably wouldn’t have mattered much.

Also: dropping the outliers of Bulgaria and Romania doesn’t change things much. The second-degree best fit polynomial still has an R2 over 0.60 (for those unfamiliar with these statistics, that means about 60% of the variation is “explained” in a correlational sense).

The excess mortality measure I use comes from the following chart. In fact, this entire post is inspired by the fact that this chart and others similar to it have been shared frequently on social media.

The chart comes from a Tweet thread by Paul Collyer. The whole thread is worth reading, but this chart is the key and summary of the thread. What he has done is shown the average and range of a variety of ways of calculating excess mortality. Read his thread for all the details, but the basic issues are what baseline to use (2015-2019 or 2017-2019? A case can be made for both), how to do the age-standardized mortality, and other issues. I won’t make a claim as to which method is best, but averaging across them seems like a fine approach to me.

For the y-axis in my chart, I just used the average for each country from Collyer’s chart. There are 34 countries in his chart, but in the OWID age-specific vaccination rates, only 22 countries were available the overlapped with his group. Unfortunately, this means we drop major countries like Italy, Spain, the UK, and Germany, but you work with the data you have.

For many sharing this and similar chart (such as charts with just one of those methods), the surprising (or not surprising) result to them is that Sweden comes out with almost the lowest excess mortality rate. Some approaches even put Sweden as the very lowest. Sweden!

Why is Sweden so important? Sweden has been probably the most debated country (especially by people not living in the country in question) in the COVID pandemic conversation. In short, Sweden took a less restrictive (some might say much less restrictive) approach to the pandemic. This debate was probably the most fevered in mid-to-late 2020, when some were even claiming that the pandemic was over in Sweden (it wasn’t). The extent to which Sweden took a radically different approach is somewhat overstated, especially in relation to other Nordic countries. And as is clear in both charts above, the Nordic countries all did relatively very well on excess mortality.

The bottom line from my first chart is that what really matters for a country’s overall excess mortality during the pandemic is how well they vaccinated their population. There seems to be a lot of interest on social media to rehash the debates about whether lockdowns (and lighter restrictions) or masks worked in 2020. But what really mattered was 2021, and vaccines were key. A scatterplot isn’t the last word on this (we should control for lots of other things), but it does suggest that a big part of the picture is vaccines (you can see this in scatterplots of US states too). It’s frustrating that many of those wanting to rehash the 2020 debates to “prove” masks don’t work, or whatever, either ignore vaccines or have bought into varying degrees of anti-vaccination theories. It’s completely possible that lockdowns don’t pass a cost/benefit test, but that vaccines also work very well (this has always been my position).

Why did Sweden have such great relative performance on excess mortality? Vaccines are almost certainly the most important factor among many that matter to a much smaller degree.

What About the US?

Note: for those wondering about the US, we don’t have the vaccination rate for ages 60-69 that I can find. Collyer also didn’t include the US in his analysis, it was only Europe. So, for both reasons, I didn’t include them in this post. The CDC does report first-dose vaccinations for ages 65+ in the US, though they top-code states at 95%. As of the end of 2021, here are the states that were below 95%: Mississippi, Louisiana, Tennessee, West Virginia, Indiana, Ohio, Wyoming, Georgia, Arkansas, Idaho, Alabama, Montana, Alaska, Missouri, Texas, Michigan, and Kentucky. These states generally have very high age-adjusted COVID death rates. Ideally we would use age-adjusted excess mortality for US states, but in the US that is horribly confounded by the rise in overdoses, homicides, car accidents, and other causes that are independent of vaccination rates (though they may be related to 2020 COVID policies — this is still a matter of huge debate).

Economic Recovery from the Pandemic

Jeremy Horpedahl3 Comments

How well have countries recovered from the declines in the pandemic? It’s actually a bit difficult to answer that question, because it depends on how you measure it. Even if we agree that GDP is the best measure, how do we measure recovery? One possibility is to simply ask whether the country has exceeded its pre-pandemic GDP level. Exactly which quarter to use as the baseline is debatable, but here is a chart that Joseph Politano made for G7 countries using the 3rd quarter of 2019 as the baseline.

But we know that absent the pandemic, most countries would have continued growing (absent a recession for some other reason), so just getting back to pre-pandemic levels isn’t necessarily a full recovery. But how much growth should we have expected? It’s a hard question, but here’s a chart along those lines from the Washington Post, using the CBO’s measure of “potential GDP” as what growth might have looked like.

Using either of these approaches, it appears that the US has recovered pretty well, although it would be nice to have a comparison across countries using the same approach as the Washington Post chart does. While there is no consistent measure similar to CBO’s potential GDP figure for all countries, a simple approach is to project growth forward using the average pre-pandemic growth rate. I have done so for a number of countries, using the average growth rate from 2017-2019. In the following charts, the blue line is actual GDP levels, and the orange line is projecting the 2017-2019 growth rate forward. Sorry that I can’t easily fit all these into one chart, so here come the charts!

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On Counting and Overcounting Deaths

Jeremy Horpedahl2 Comments

How many people died in the US from heart diseases in 2019? The answer is harder than it might seem to pin down. Using a broad definition, such as “major cardiovascular diseases,” and including any deaths where this was listed on the death certificate, the number for 2019 is an astonishing 1.56 million deaths, according to the CDC. That number is astonishing because there were 2.85 million deaths in total in the US, so over half of deaths involved the heart or circulatory system, at least in some way that was important enough for a doctor to list it on the death certificate.

However, if you Google “heart disease deaths US 2019,” you get only 659,041 deaths. The source? Once again, the CDC! So, what’s going on here? To get to the smaller number, the CDC narrows the definition in two ways. First, instead of all “major cardiovascular diseases,” they limit it to diseases that are specifically about the heart. For example, cerebrovascular deaths (deaths involving blood flow in the brain) are not including in the lower CDC total. This first limitation gets us down to 1.28 million.

But the bigger reduction is when they limit the count to the underlying cause of death, “the disease or injury that initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury,” as opposed to other contributing causes. That’s how we cut the total in half from 1.28 million to 659,041 deaths.

We could further limit this to “Atherosclerotic heart disease,” a subset of heart disease deaths, but the largest single cause of deaths in the coding system that the CDC uses. There were 163,502 deaths of this kind in 2019, if you use the underlying cause of death only. But if we expand it to any listing of this disease on the death certificate, it doubles to 321,812 deaths. And now three categories of death are slightly larger in this “multiple cause of death” query, including a catch-all “Cardiac arrest, unspecified” category with 352,010 deaths in 2019.

So, what’s the right number? What’s the point of all this discussion? Here’s my question to you: did you ever hear of a debate about whether we were “overcounting” heart disease deaths in 2019? I don’t think I’ve ever heard of it. Probably there were occasional debates among the experts in this area, but never among the general public.

COVID-19 is different. The allegation of “overcounting” COVID deaths began almost right away in 2020, with prominent people claiming that the numbers being reported are basically useless because, for example, a fatal motorcycle death was briefly included in COVID death totals in Florida (people are still using this example!).

A more serious critique of COVID death counting was in a recent op-ed in the Washington Post. The argument here is serious and sober, and not trying to push a particular viewpoint as far as I can tell (contrast this with people pushing the motorcycle death story). Yet still the op-ed is almost totally lacking in data, especially on COVID deaths (there is some data on COVID hospitalizations).

But most of the data she is asking for in the op-ed is readily available. While we don’t have death totals for all individuals that tested positive for COVID-19 at some point, we do have the following data available on a weekly basis. First, we have the “surveillance data” on deaths that was released by states and aggregated by the CDC. These were “the numbers” that you probably saw constantly discussed, sometimes daily, in the media during the height of the pandemic waves. The second and third sources of COVID death data are similar to the heart disease data I discussed above, from the CDC WONDER database, separated by whether COVID was the underlying cause or whether it was one among several contributing causes (whether it was underlying or not).

Those three measures of COVID deaths are displayed in this chart:

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“Let whoever needs to die, die”:  China’s Abrupt COVID Reopening To Achieve Rapid Herd Immunity and Resumption of Industrial Production, at the Cost of a Million Deaths

Scott Buchanan3 Comments

I noted a month ago that President Xi and the CCP have taken credit for relatively low (reported) deaths from COVID, due to strict lockdown protocols. By “strict” we mean locking down whole cities and blockading residents in their apartment buildings for months at a stretch. However, public protests rose to an unprecedented level, and so the Chinese government has done a surprising full 180 policy change, towards almost no restrictions.

According to Dr. Ezekiel Emanuel in the Wall Street Journal, the way this policy is being carried out has the makings of a mass human tragedy:

Zero Covid was always untenable and had to be ended. But it could have been done responsibly.

Among other things, that would involve buying Pfizer and Moderna bivalent vaccines and administering them to the elderly and other high-risk people, and purchasing Paxlovid and molnupiravir to treat those who test positive. Supplies of these products are ample. Authorities could continue mask mandates to reduce transmission. And China could institute a rigorous wastewater testing program to identify potential SARS-CoV-2 variants as soon as possible – and commit to sharing the data with the world.

Due to nationalistic pride, China has spurned the purchase of effective mRNA vaccines from Pfizer and Moderna, pushing instead the less-effective in-house vaccine.

Readers may recall in the early days of COVID spread in the West, masking and social distancing were promoted, not because they would prevent everyone from ultimately becoming infected, but because these measures would “flatten the curve” (i.e. reduce the peak load on hospitals at any one time, but instead spread it out over time). China is headed into a very un-flattened infection curve; some 800 million people (10% of the world’s population) may get COVID in the next 3 months, overwhelming hospitals and leading to over a million deaths. Besides the near-term human costs, this concentration of active COVID cases is likely to lead to a slew of new, even more virulent variants which will affect the rest of the world, along with China. What should help mitigate the situation is that the newer, most virulent variants of COVID may be somewhat less fatal than the original strain.

Why is the Chinese government doing it this way? Well, the sooner the country gets through mass exposure to the virus, the sooner everyone can get back to their factories and start producing stuff again. If in the process a bunch of (mainly older) people die, well, that’s just the price of progress. Let ‘er rip…

From MSN:

[U.S.] Epidemiologist and health economist Dr Eric Feigl-Ding estimate that 60 per cent of China’s population is likely to be infected over the next 90 days. “Deaths likely in the millions—plural,” he added.

According to Eric, bodies were seen piled up in hospitals in Northeast China. “Let whoever needs to be infected infected, let whoever needs to die die. Early infections, early deaths, early peak, early resumption of production,” the epidemiologist said terming it to be summary of Chinese Communist Party’s (CCP) current goal.

But don’t expect any acknowledgement of mass death from the official Chinese media. Just as the initial COVID outbreak was denied and censored by the Chinese propaganda machine, so the current surge is being minimized. From Barrons:

On Friday, a party-run newspaper cited an official estimate of half a million daily new cases in the eastern city of Qingdao. By Saturday, the story had been amended to remove the figure, an AFP review of the article showed….

Several posts on the popular Weibo platform purporting to describe Covid-related deaths appeared to have been censored by Friday afternoon, according to a review by AFP journalists.

They included several blanked-out photos ostensibly taken at crematoriums, and a post from an account claiming to belong to the mother of a two-year-old girl who died after contracting the virus.

Posts about medicine shortages and instances of price gouging were also taken down, according to censorship monitor GreatFire.org.

And social media users have posted angry or sardonic comments in response to the perceived taboo around Covid deaths.

Many rounded on a state-linked local news outlet after it reported Wu Guanying — designer of the mascots for the 2008 Beijing Olympics — had died of a “severe cold” at the age of 67.

Perhaps we should not be surprised that the Chinese Center for Disease Control and Prevention just reported zero COVID deaths for December 25 and 26.

Protests Erupt Across China Over COVID Policy But Lockdowns Continue: Why?

Scott Buchanan1 Comment

Headlines in today’s financial news include items like “Clashes in Shanghai as COVID protests flare across China“ from Yahoo Finance. There have been widespread protests this week, which are normally a rarity under the authoritarian regime, and are being suppressed by any means necessary. Apple stock is down about 4% in the past two trading days on fears that iPhone shortages will get worse due to worker unrest in the giant Foxconn factory in Zhengzhou. Wall Street keeps hoping the China will loosen up, since the lockdowns on the world’s second-largest economy are a drag on global markets.

China has pursued a “zero-COVID” policy, of strict mass lockdowns to halt any spread of the virus. Residents have been confined to their apartments for over 3 months in some cases. Whole cities with tens of millions of people have been locked down for months at a time whenever a number of cases are spotted. China’s economic growth has stagnated, and unemployment among young people has risen to 20%, which has helped fuel unrest.  Chinese people are aware that the rest of the world has moved on from mass lockdowns, and may be realizing the futility of thinking that lockdowns can stave off the virus indefinitely.

Given its discomfort with widespread discontent and protests, why does the Chinese government persist in this policy? An article in the Atlantic by Michael Shuman answers that question: “Zero COVID Has Outlived Its Usefulness. Here’s Why China Is Still Enforcing It. “  Back in 2020 when COVID first swept through the world, the strict lockdowns (readily enforced in an authoritarian regime) seemed like a big win for the Chinese leadership:

When the outbreak began in Wuhan in early 2020, the virus was unknown, vaccines were unavailable, and China’s poorly equipped health system could have quickly become overwhelmed by a sweeping pandemic. Yet the policy had a political element from the very beginning as well. The Communist Party is adept at sniffing out threats to its rule, and it quickly identified COVID as one of them. A major public-health crisis, with millions dying, would have raised serious doubts about the regime’s competence, which is, in effect, its sole claim to legitimacy.

Worse, the party could have faced a populace that directly blamed it for the outbreak—with good reason. The Chinese authorities at both the national and local levels botched their initial response to the novel coronavirus, suppressing information about its discovery by a Wuhan doctor and acting far too slowly to contain the initial spread. Sensing its potential vulnerability, the party shifted into anti-COVID overdrive, shutting down large swaths of the country, with the result that it did succeed in snuffing out an epidemic in a matter of weeks, even as it spread to the rest of the world.

That success allowed the Communist Party to transform a potential tragedy into a public-relations triumph. Within weeks of the Wuhan outbreak, China’s propaganda machine was touting the wonders of its virus-busting methods. And as the U.S. and other Western countries struggled to contain the disease, Beijing’s big win became even more valuable as evidence that its authoritarian system was more capable and caring than any democratic one. Beijing and its advocates pointed to rising case and death counts in the U.S. as proof of China’s superiority and American decline.

A number of other countries including Australia and New Zealand also implemented strict (stricter than in the U.S.) lockdown measures in 2020, and, like China, experienced far less impact from the virus in that timeframe than seen in the U.S. However, most of these measures were lifted in 2021. The widespread application of mRNA vaccines like those from Pfizer and Moderna in the West has served to mitigate the severity of the viral infection. Also, some measure of herd immunity has been achieved by the widespread exposure to COVID in the population; antibodies persist for at least eight months after contracting the disease. So, what’s up with China?

China has resisted using Western vaccines, relying instead on homegrown vaccines which are less effective, though they do give some measure of protection.  Also, “The additional layers of high-tech surveillance adopted in the name of pandemic prevention can be used to enhance the tracking and monitoring of the populace more generally,” which is another win for the government. However, the major factor is that the Party, and especially President Xi, cannot afford to loosen up now and risk an embarrassing explosion of cases that would overburden the healthcare system and probably lead to millions of deaths:

The victory of zero COVID was claimed not just as the party’s but as Xi Jinping’s in particular. The State Council, China’s highest governing body, declared in a 2020 white paper that Xi had “taken personal command, planned the response, overseen the general situation and acted decisively, pointing the way forward in the fight against the epidemic.”

This narrative became entrenched. If Beijing loosened up and allowed COVID to run amok, the Chinese system would appear no better than those of loser democracies, and Xi would seem like another failing politician, a mere mortal, not the virus-fighting superhero he was painted as. Zero COVID’s failure would be a disaster for the Communist Party’s veneer of infallibility.

So the leadership insists on zero COVID and damn the consequences.

What’s Killing Men Ages 18-39?

Jeremy Horpedahl3 Comments

The all-cause mortality rate in 2021 for men in the US ages 18-39 was about 40% higher than the average of 2018 and 2019. That’s a huge increase, especially for a group that is not in the high-risk category for COVID-19. What’s causing it?

Some have suggested that heart disease deaths, perhaps induced by the COVID vaccines, is the cause. This is not just a fringe internet theory by anonymous Twitter accounts. The Surgeon General of Florida has said this is true.

What do the data say? The first thing we can look at is heart disease deaths for men ages 18-39.

The data I’m using is from the CDC WONDER database. This database aggregates data from US states, using a standardized system of reporting deaths. The most important thing to know is that in this database, each death can one have one underlying cause, and this is indicated on the death certificate. Deaths can also have multiple contributing causes (and most deaths do), and the database allows you to search for those too. But for this analysis, I’m only looking at the underlying cause.

Here’s the heart disease death data for men ages 18-39, presented two different ways. First the trailing 12-month average. Don’t focus too much on that dip at the end, since the most recent data is incomplete. Instead, notice three things. First, there was a clear increase in heart disease deaths. Second, that rise began in mid-2020, well before the introduction of vaccines. Third, once vaccines started being administered to this age group in Spring 2021, the number of deaths leveled off (though it didn’t return to pre-pandemic levels).

Here’s another way of looking at the data: 12-month time periods, rather than a trailing average. I created 12-month time periods starting in March and ending in February of the following year. I’ve also truncated the y-axis to show more detail, not to trick you. But don’t be tricked! The deaths are up 2-3%, not a more than doubling as the chart appears to show.

We can see in the chart above that the rise in heart disease deaths for young males completely preceded the vaccination period. Something changed, for sure, but the change wasn’t the introduction of vaccines. Heart disease deaths (by underlying cause) are only up 2-3%, while overall deaths are up around 40%.

So, to repeat the title question, what is killing these young men?

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GDP Growth and Excess Mortality in the G7

Jeremy Horpedahl1 Comment

Two weeks ago my post looked at GDP growth during the pandemic. But of course, economic growth isn’t the only important outcome to look at in the pandemic. Health outcomes are important too, and indeed I have posted about those in the past alongside GDP data.

Today, my chart looks at the G7 countries (representing roughly half of global wealth and GDP), showing both their economic performance (as measured by real GDP growth) and health performance (as measured by excess mortality through February 2022).

The US has clearly had the best economic performance. But the US also had the highest level of excess deaths per capita (not all of this is from COVID — US drug overdoses are also way up — but even using official COVID deaths, the US still tops this group).

Japan had the best health performance, in fact amazingly no cumulative excess deaths through February 2022 (this has risen very slightly since then, but I stopped in February so all countries had complete data). However, Japan also had slightly negative economic growth.

Which country ends up looking the best? Canada! Very low levels of excess deaths, and at least some positive economic growth. Not as much growth as the US, but Canada is the second best performer in the G7.

To give some context of just how low the level of deaths have been in Canada, first recognize that the US had 1.1 million excess deaths in the pandemic through February 2022. If instead our excess deaths had been roughly equal to Canada on a per capita basis, we would have only had 180,000 excess deaths in the US, saving over 900,000 lives.

Some of Canada’s COVID policy have been overly restrictive, such as the vaccine mandates that sparked protests in February 2022. But by then, Canada had already largely achieved it’s COVID victory over the US and most other G7 nations. Compare excess mortality in Canada with the US: the only big wave in Canada that came close to the US was the Spring 2020 wave. After that, Canada was always much lower.

Market Concentration & Inflation

Zachary Bartsch2 Comments

We are living in volatile times. With covid-19, big federal legislation packages, and the Ruso-Ukrainian conflict disruptions to grain, seed oils, and crude oil, relative prices are reflecting sudden drastic ebbs of supply and demand. I want to make a small but enlightening point that I’ve made in my classes, though I’m not sure that I’ve made it here.

Economists often get a bad rap for being heartless or unempathetic. Sometimes, they are painted as ideologues who just disguise their pre-existing opinions in painfully specific terminology and statistics. Let’s do a litmus test.

Consider two alternative markets. One is a perfect monopoly, the other has perfect competition. All details concerning marginal costs to firms and marginal benefits to consumers are the same. In an erratic world, which market structure will result in greater price volatility for consumers? Try to answer for yourself before you read below. More importantly, what’s your reasoning?

Extreme Market Power

A distinguishing difference between a competitive market and a monopoly concerns prices. While firms maximize profits in both cases, the price that consumers face in a competitive market is equal to the marginal cost that the firms face. There is no profit earned on that last unit produced. In the case of monopoly, the price is above the marginal cost. Profits can be positive or negative, but the consumer will pay a price that is greater than the cost of producing the last unit.

Below are two graphs. Given identical marginal costs of production and benefits that the consumers enjoy, we can see that:

  1. The monopoly price is higher.
  2. The monopoly quantity produced is lower.

But static models only go so far. What about when there is volatility in the world?

Volatile Costs

Oil and gasoline are important inputs for producing many (most?) physical goods. Not only that, they are short-lived, meaning that they disappear once they are used, making them intermediate goods. Therefore, changes in the price of oil constitutes a change in the marginal cost for many firms. If the price of oil rises, or is volatile otherwise, then which type of market will experience greater price and quantity volatility?

Below are two figures that illustrate the same change in the marginal cost. We can see that:

  1. Monopoly price volatility is lower (in absolute terms and percent).
  2. Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).

The take-away: While monopoly does constrict supply and elevate prices, Monopoly also reduces price and output volatility when there are changes in the marginal cost.  

Volatile Demand

That covers the costs. But what about volatile demand? A large part of the Covid-19 recession was the huge reallocation of demand away from in-person services and to remote services and goods. What is the effect of market power when people suddenly increase or decrease their demand for goods?

Below are two figures that illustrate the same change in demand. We can see that:

  1. Monopoly price volatility is higher (in absolute terms, though no different as a percent).
  2. Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).

Monopolies Don’t Cause Inflation

Economists know that inflation can’t very well be blamed on greed (does less greed beget deflation?). Another problematic story is that market concentration contributes to inflation. But the above illustrations demonstrate that this narrative is also a bit silly. Monopolistic markets cause the price level to be higher, it’s true. But inflation is the change in prices. Changing market concentration might be a long term phenomenon, but can’t explain acute price growth. If demand suddenly rises, monopolies result in no more price growth than perfectly competitive markets. If the marginal cost of production suddenly rises, monopolies result in less price growth.

All of this analysis entirely ignores welfare. Also, no market is perfectly competitive or perfectly monopolistic. They are the extreme cases and particular markets lie somewhere in between.

Did you guess or reason correctly? Many econ students have a bias that monopolies are bad. So, in any side-by-side comparison, students think that “monopolies-bad, competition-good” is a safe mantra. But the above illustrations (which can be demonstrated mathematically) reveal that economic reasoning helps to reveal truths about the world. Economists are not simply a hearty band of kool-aid drinking academics.

The Latest GDP Data: First Quarter 2022 in the OECD

Jeremy Horpedahl2 Comments

Today two data releases for Gross Domestic Product were released. The first release was for the United States, giving us the third and “final” release for first quarter 2022 data. It was down 1.6% from the prior quarter (though we knew this two months ago — not much has changed since the “advance” estimate). That’s not good (but see this great Joseph Politano newsletter for some more detail).

The second release was the annual 2021 GDP data for the European Union. The release showed strong growth in 2021 (+5.4%), but that’s relative to the bad year of 2020. So compared to the pre-pandemic level of 2019, the EU was still about 0.8% below this more accurate baseline. Comparatively, the US was already 2% above 2019 with the annual 2021 release (everything in these two paragraphs is adjusted for inflation). Of course, within the EU, there is a lot of variation, but overall the US looks comparatively well.

Let’s break down that variation in the EU and include the first quarter of 2022 data to make the best comparison with the US. To bring in some more relevant comparison countries, I’ll use data from the OECD for a complete comparison. Note: I’ve excluded Ireland, because their GDP is weird. I’ve also excluded Turkey, because even though all the data here is adjusted for inflation, Turkey is in a highly inflationary environment, making the data a little difficult to interpret.

Here is the chart, which shows the change in real GDP from the 4th quarter of 2019 up through the 1st quarter of 2022 (I use the volume index, which is similar to adjusting for price inflation). I have highlighted in orange the largest economies in the OECD (anything with about $2 trillion of GDP or larger, with Spain and Canada at about that level).

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COVID Deaths, Excess Deaths, and the Non-Elderly (Revisited)

Jeremy Horpedahl8 Comments

While we know that COVID primarily affects the elderly, the mortality and other effects on the non-elderly aren’t trivial. I have explored this in several past posts, such as this November 2021 post on Americans in their 30s and 40s. But now we have more complete (though not fully complete) mortality data for 2021, so it’s worth revisiting the question of COVID and the non-elderly again.

For this post, I will primarily focus on the 12-month period from November 2020 through October 2021. While data is available past October 2021 on mortality for most causes, data classified by “intent” (suicides, homicides, traffic accidents, and importantly drug overdoses) is only fully current in the CDC WONDER data through October 2021. This timeframe also conveniently encompasses both the Winter 2020/21 wave and the Delta wave of COVID (though not yet the Omicron wave, which was quite deadly).

First, let’s look at excess mortality using standard age groups. For this calculation, I use the period November 2018 through October 2019 as the baseline. The chart shows the increase in all-cause deaths in percentage terms. It is also adjusted for population growth, though for most age groups this was +/- 1% (the 65+ group was 3% larger than 2 years prior).

A few things jump out here. First notice the massive increase in mortality for the 35-44 age group (much more on this later). Almost 50% more deaths! To put that in raw numbers, deaths increased from about 82,000 to 122,000 for the 35-44 age group, and population growth was only about 1%. And while that is the largest increase, there were huge increases for every age group that includes adults.

Also notice that the 65+ age group certainly saw an increase, but it is the smallest increase among adults! Of course, in raw numbers the 65+ age group had the most excess deaths: about 450,000 of the 680,000 excess deaths during this time period. But since the elderly die at such high rates in every year, the increase was as large in percentage terms.

One related fact that doesn’t show up in the chart: while there were about 680,000 excess deaths during this time frame in the US in total, there were only about 480,000 deaths where COVID-19 was listed as the underlying cause of death. That means we have about 200,000 additional deaths in this 12-month time period to account for, or a 24% increase (population growth overall was only 0.4%).

That’s a lot of other, non-COVID deaths! What were those deaths? Let’s dig into the data.

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