I’ve been talking a lot about excess mortality lately, so I wanted to post about something that’s much harder to quantify: the number of people being left with excess medical debt. While we know that nearly a million excess deaths have occurred in the past 2 years, what we don’t know is how many people required intensive/expensive medical care but didn’t end up dying.

Assuming that number is likely to be up as well, I’d like to point people to a great charity called RIP Medical Debt. They buy medical debt in bulk and pay it off for people, meaning your gift can go 10x as far as it usually would. They have a gift matching special going on today, and if this last 2 years has been good to you, please consider throwing something in. You never know who it could make a difference for.

Read more about them here.

Well hello there! Another month has flown right by, and it’s time for another US excess mortality update. I’ve actually spent my week arguing about Portugal’s death counts (substance use related, not COVID related), but that’s a story for another post.

It’s been quite the month, and I was interested to look back and realize that when I first started doing these posts in August, the estimates for excess mortality put us at between 595,688 and 758,749 excess deaths since 2/1/2020. As of the most recent data release earlier this week, the US is now somewhere between 791,202 and 962,125. The 1 million mark is getting pretty close. If you have questions about these numbers, see prior posts.

Excess Mortality in 20-50 year olds

After my last post, bluecat57 left me a few comments about the concerns about excess mortality specifically in the younger adult age ranges. These are concerning deaths because 1. They are on the rise and 2. There is debate where they are coming from. It will be a while until all the data is in, but here’s the US trend for deaths through the end of September:

So basically during the pandemic we gained about 600ish extra deaths in the 25-44 year old age range, with a sudden jump in July of this year that took us to about 1800 extra deaths every week over baseline.

One of the links went to a clip of someone speculating on the reasons for these deaths, with several possible explanations. One explanation is that this is COVID. One is that something else is killing young people. One was that the vaccines were killing younger adults. Now this was an interesting claim, as the timeframe doesn’t entirely match up. July was on the later side for most people getting vaccinated, but perhaps the claim is there’s a delay. I decided to take a look at my 3 states I go back to: Massachusetts, Arizona and Tennessee. These are interesting states because they are in 3 totally different areas of the country, and have very different vaccination rates. In Arizona, 53.7% of the population is fully vaccinated. In Massachusetts, 70.3% and in Tennessee it’s 48.8%. That’s of the total population (kids excluded until recently) so we can assume that translates in to a somewhat higher number for this slice of the population. So how are these states doing? Here you go:

Starting around May of 2020, Arizona and Tennessee show gains here, whereas Massachusetts does not. Per the state report, around 80% of 25-44 year olds in Massachusetts are vaccinated, and we do not see much of a gain (if any) in young person deaths. As someone in that age group, it is of course relieving to see that my risk of death is basically unchanged since 2019. In Arizona and Tennessee, there are points where your risk of death was 1.5-2x as likely.

It seems unlikely the vaccines are causing these deaths, when we see bigger spikes in more unvaccinated populations than in largely vaccinated ones. I wanted to compare these to the overall deaths in each state, so here is that:

Interesting that each spike in overall mortality correlates to a similar spike in 25-44 deaths except the initial Massachusetts spike. This backs up the theory that the very first COVID spike hit nursing homes hard, and that has not been as much of a problem since then. I was also interested to note that despite COVID appearing fairly seasonal, death rates for Arizona and Tennessee exceed those of Massachusetts last winter.

So basically, I think it’s unlikely that vaccines are playing a role. I would speculate many of these are COVID deaths, or something else potentially related to COVID. As far as I’m aware, Massachusetts had the toughest lockdowns/restrictions of the 3 states (though lockdowns have been done in MA for over a year, unless you count mask mandates as lockdowns) so I’d suspect it’s not lockdown related either. Let me know if you have evidence Tennessee or Arizona were tougher though, as I obviously know my own state better than those two.

Excess Mortality Over Average Updates

Okay, so here’s the visual:

Who moved the most this month? Not a lot of major changes

Of note, New York fell off the top ten list entirely, and currently sites at #17. It’s good to see the top states slowing down though, it’s been a tough go of it.

Most of the states with the biggest gains this month actually did not make the top 10. The top 5 gainers were Idaho (#32, +596), Puerto Rico (#31, +563), Montana (#13, +535), Kentucky (#12, +516) and Wyoming (#36, +500).

Excess Mortality Over Upper Bound by State

Graphical representation here:

Okay, here’s the top 10 list:

Again, not a lot of motion except for NY running off the list. This seems fairly consistent with a falling peak: states simply aren’t adding huge numbers at the moment. The biggest changes are again (mostly) coming in states that have actually had lower pandemic numbers up until now: Idaho (#29, +521), Puerto Rico (#40, +496), Kentucky (#15, +457), Montana (#23, +396), Tennessee (#8, +361).

Again, given the nature of COVIDs seasonal interaction here, I’m going to continue to keep this updated for at least Dec/Jan/Feb. With acquired immunity and vaccines going on, it’s hard to know if we’ll see a substantial 4th (or 5th, depending how you count) wave of deaths in the US. Stay healthy everyone!

I don’t often make New Year’s resolutions, but this year I’ve decided to join Gretchen Rubin (of happiness project fame) in resolving to go on a 20 minutes walk every day in 2020. Her theory is that if you aren’t getting much exercise, resolving to get a little bit daily will provide big benefits. She has research on her side on this one, and it doesn’t hurt that walking seems to be the only form of exercise that makes my migraines better rather than worse. We’ll see how this goes.

This got me thinking about New Year’s resolutions in general, and wondering what the most common ones were. there appears to be a lot of selection bias in the studies, but healthy eating/exercise/weight loss and saving money seem to be the most common in America.

I tried to find some from other countries, and it seems like Germans may put stress reduction and family time at the top of their list. My googling for other European and north and south American countries didn’t turn up much.

I did however, find this blog post from Duolingo, that had some really interesting insights about one particular New Year’s resolution. Duolingo is an app that helps you learn a second language, and they have a distinctive peak in sign ups and account usage just before the first of the year. They discovered that the countries their users normally came from changed a bit around the first of the year:

Apparently users who sign up around the first of the year actually are slightly more likely to continue using the app than those who sign up at other times.

Overall, I’ll admit I was a little surprised that I couldn’t find more research on the subject of New Year’s resolutions. It seems like this would be an interesting study in how priorities change across countries or time. If anyone knows of any good resources that I didn’t find, please let me know! In the meantime, happy new year everyone!



Back when this blog was in its first incarnation, I used to occasionally do some challenge questions. I stumbled across one this week that seemed like a good candidate, and since my computer is still broken I figured I’d throw it out there.

As part of their religious landscape survey, Pew Research has put together a racial diversity ranking of religions and major denominations in the US. Six groups were found to be more diverse than the U.S. general population. What are they?

A few clarifications and one hint to help:

1. The diversity ranking measures the spread across 5 racial groups: White, Black, Asian, Latino and Mixed/Other. A perfect score would be 20% in each category. In other words, a group dominated by one group would not be considered diverse even if that group was a minority group in the US.

2. Pew breaks Christianity down in to major denominations and includes several types of unaffiliated (aka not religious) groups in their survey. If you want to see the groups included, see this page.

3. The survey also only looked at people in the US, so diversity is only based solely on that. Groups may have more diversity in other countries, but only their US members were counted.

4. If you need a hint: 3 of the top 6 groups are Christian or Christian-adjacent* and 3 were other religions or unaffiliated groups.

For the answer, see the list here.

*For purposes of this question, Christian adjacent means that the members of the group might consider themselves Christians, but a majority of Christians in other denominations do not.

My computer is still having problems, so another short post today. I saw this graphic on Twitter this week, and thought it was interesting:

Our voting certainly leaves a wide margin of error with regards to public opinion.

What’s interesting of course is that we have no idea how those people would vote if they were forced to, though many people seem to think they know. From experiences in other countries it seems like it might increase support for left leaning policies and higher tax brackets. However in other countries it boosted fringe third parties, and doing away with it increased support for major parties. Other countries have not seen a difference.

Point being, a non-random sample doesn’t always tell you much about what’s not in the sample. Keep that in mind with any initiatives aimed at changing voting requirements.