I have a Substack piece up today on the True Crime Times: What True Crime Can Learn from the Science of Getting Things Wrong.
This is an abbreviated version of my True Crime Replication Crisis series, albeit aimed towards a true crime audience rather than my usual folks here. I was pleased to see the first comment was from someone who also likes to rant about statistics. If you’re looking for that, I’m always here for you. Please see my Intro to Internet Science series for details.
When you talk about the economy and the state of young people today, you will almost always here about how young people are drowning with student debt. I took this statement at face value, until a few months ago when I saw someone make a comment that college cost creep had largely slowed down. I didn’t follow up much more on it until I saw the recent Astral Star Codex post about the Vibecession data, and he confirmed that the high water mark for student loan debt was actually 2010 (Note that this is debt per enrolled student, so those numbers aren’t impacted by changes in enrollment):
His theory is that 2010 is around when large numbers of people first started maxing out the $31k cap on many government loans, and that that cap hasn’t moved. I think there’s two other things going on:
Now you can find websites that say student loan debt is going up, but from what I can find these graphs don’t inflation adjust their data. The complaints about how a $100,000 salary isn’t what it used to be are accurate, but by the same token a $100,000 debt isn’t what it used to be. Looking at the top graph on this website for example, you wouldn’t know that a $17.5k loan in the year 2000 is actually about $34k today, remarkably close to the $35k they say 2025 graduates are taking out.
Ok, so that’s debt, but what about the sticker price? Well, the College Board puts together a pretty useful report on the topic that shows a few things:
For public universities, we see a slow down in tuition increases starting about a decade ago, and for private schools the change happens about 5 years ago. For all schools we see the inflation adjusted cost is currently lower than it was a decade ago. But wait, there’s more!
The above graphs are just the sticker price. The College Board also tracks the “real cost” after factoring in grants. Here’s the data for private 4 year colleges:
Note the “grant aid” line, which slowed during the 2008 crash, but then has been ticking upward starting in 2013 and hasn’t stopped. To emphasize, those are grants, not loans. That’s just money off the sticker price. According to the College Board, the net cost of attendance of a college in 2024 was less than in 2006. I won’t keep pummeling you with graphs, but for private 4 year, public 4 year and public 2 year colleges, the “real” cost peaked in 2016.
I am not an economist, but the numbers suggest a pretty clear story to me. When unemployment for those under 25 was high in 2009-2013, going to college, any college, seemed like a good financial move. For many, it probably was. Then, as employment picked up, students were able to get choosier and consider the cost of student loan debt in their choices. Very quickly colleges started upping the amount of grants offered, and then stopped increasing the sticker price. With recent inflation, the price increases actually dropped below the inflation line and now the real cost of college is dropping.
Additionally, technology improvements allowed online schools to start offering cheaper tuition at a large scale. This might have only made a small dent, except then the pandemic happened and traditional campus life was upended. This made the difference between going to a traditional college and an online college much smaller, and based on those I know with kids that age a lot of kids opted for at least a year or two at a cheaper online school rather than pay through the nose to sit in their dorm room all day. This put additional cost pressure on schools, and we see the prices tick down further.
All that being said, there is a not-small group of people who were pretty slammed by college costs: those who were coming of age during the 2008 financial crash and it’s aftermath. However, most of those people are actually in their late 30s now, and it’s important to note that state of affairs did not persist for those who came after them. Times change, sometimes for the better.
As 2025 comes to a close and we careen towards Christmas and giving season, I wanted to put in a plug for my favorite book of the year. The book is The Age of Diagnosis: How Our Obsession with Medical Labels Is Making Us Sicker, and I enjoyed it immensely. I found it originally when Jesse Singal did a Substack post called “Long Covid can be both Psychosomatic and Real”, and immediately forwarded it to my sister (an NP), who promptly got the book and then immediately called me to talk about it. She was annoyed I hadn’t actually read it yet, so I got the book and could see why she was calling. This is a book you want to talk about with people.
The author is a UK neurologist and a skilled writer, and she dares to ask the question “what is the point of diagnosing people with things”. She points out that diagnosis is supposed to be used strictly to inform treatment options, but we’ve completely overlooked the psychological impact a diagnosis can have. She starts with the example of Huntington’s disease, a fatal genetic disease that you can test for and diagnose, but for which there is currently no cure. Prior to the advent of testing, 90% of patients and their families said they would love to have a test. Once one was developed however, the decision to test or not proved a lot harder for people than they had expected.
She goes on to cover many other areas of medicine: COVID, chronic Lyme, autism, ADHD, cancer screenings, and points out repeatedly that there are two ways to be wrong. Missing a diagnosis you could have treated is obviously bad, but giving someone a diagnosis they may not have also carries a risk. It’s that second risk she explores for both physical and psychological illnesses. What does happen if you think you have a disorder that you don’t? Does disorder creep carry a cost? If your diagnosis makes you feel better about yourself but actually doesn’t improve your objective functioning or even worsens it, should it really have been given? Shouldn’t we be, you know, studying some of these questions?
I liked this book because I’ve spent a lot of time in the last 7 years or so thinking about the purpose of diagnoses and what they’re good for. Back in 2019 I wrote about my lengthy journey to getting diagnosed with chronic migraines (they had an atypical presentation at first), and it was a great relief to finally getting a name to my issue. However, it still took years to get a treatment regimen that worked, and I still have problems. I also have a new appreciation for psychosomatic illness because the migraines have messed up my sense of pain quite a bit. I now have to let every health care provider I have know that my sense of pain is not a great guiding light, in either direction. I have felt pain in places that appeared to have nothing actually wrong with them, and failed to recognize pain in other places because I thought it was part of the regular pain I have. Not having your senses work predictably is a huge disadvantage in diagnosis, but there are more people this happens to than you think. One highlight of the book was when she notes many people experience psychological pain as physical pain, and get slapped with every escalating numbers of diagnoses while trying to treat it. This isn’t good for anyone.
A related read this week was Accommodation Nation in the Atlantic, which points out that now over 20% of students at elite universities have a disability on file. This is a rate far higher than less elite universities, and the disabilities are primarily autism, ADHD and anxiety, and again makes us wonder what a diagnosis is really for. If the best and brightest are claiming to be disproportionately impaired, what are we really looking at here?
What The Age of Diagnosis highlights, sometimes uncomfortably, is that our institutions haven’t caught up to the psychological and social power of a label. In an era where traditional communities seem to be shrinking, we run the risk of allowing diagnoses to take a disproportionate role in the way we define ourselves. Books like this don’t offer easy answers, but they do give us the vocabulary to ask better questions about how we allocate care, how we define impairment, and what we actually want our diagnostic categories to accomplish in a world where they shape so much of public and private life.
There’s a Bret Weinstein speech going around on Twitter where he makes a comment about how birth control and abortion changed the game around sex, commonly known as the sexual revolution that occurred in the 1950s-1970s. I have not listened to his speech so I have no comment on what he was saying specifically, but in reading some of the comments I was interested that when people discuss “what changed” during the 1950s through the 1970s, they seem to focus on just abortion and birth control on repeat. Even the Wikipedia page for the sexual revolution only mentions these two. Those things absolutely changed behavior, but I think there’s two more things that need to be a bigger part of the discussion:
Paternity testing started out with blood testing in the 1920s, but hit it’s stride in the 1960s with HLA testing. Prior to that, you had to use social rules and general vibes to determine paternity. It largely relied on people’s own truthfulness. Prior to paternity testing, marriage was the most surefire way to ensure no one questioned whose kids were whose, but after we got a better method the number of kids born to single moms went from 5% to 40%. You can see that as good/bad/neutral, but that almost certainly doesn’t happen without the ability to identify a father accurately.
As for antibiotics Penicillin was discovered in 1928, but WWII sped up the perfection of antibiotics for treatment of bacterial infections, and widespread for the public use came in during the 1950s. From 1935 to 1968, 12 new classes of antibiotics were launched. Prior to this, basic STDs like syphilis were actually killing people at a rate similar to suicide today:
And that’s just deaths from syphilis, not cases. That figure comes from this analysis, which notes that prior treatment methods may have been as effective, but they were expensive and time consuming, and penicillin just made everything easier. Of course, syphilis is just one of the diseases people were dodging, chlamydia and gonorrhea also would have been issues. Antibiotics changed the game here.
I bring these up not to take any particular stance on any issue, but to point out that the past was very different in ways we don’t often think about. Even if somehow birth control and abortion were wiped off the face of the planet today, antibiotics and paternity testing would still ensure our population level practices around sex were different than they were 100 years ago. Sexual mores were never just about pregnancy, they were also about ensuring you could establish paternity and avoid STDs.
I think this is important for both cultural conservatives and cultural liberals to remember, as at times we can look at the past as either a golden era of morality or a deep pit of oppression. But in prior “moral” eras, a lot of sexual behavior was kept in check by people lying or threatening to lie about true things, and paternity testing stopped that. Conversely, things like religion may never have had quite the level of influence we attribute to them, they were often coping with very real issues around STD control in an era when the medical community couldn’t help much. When those things changed, behavior changed. It’s a good reminder that most social changes have several causes, and are not just related to one thing.
To note: the things I mention above are those I believe had a direct impact on sexual issues in the 1950s-1970s specifically. There’s a few other advances that probably changed sexual behavior in a slightly less direct fashion: cars (teenagers could go see each other more easily), at home pregnancy tests (earlier identification of pregnancy, no doctor needed), mass distribution of porn (TBD), dating apps (thank God I missed that era).
Anything else I missed?
Earlier this week, the Assistant Village Idiot tagged me in one of his link roundups:
Off With Her Hair Women tell attractive women to cut their hair. The study’s authors are all female. I wonder what it is like for women studying female intrasexual competition. Is it harder to get along, or easier? Bethany, you need to get in on researching the women who research women.
I’ll admit I got a kick out of this, in part because I love a good gender study, and in part because I have REALLY long hair. I mostly wear it up, but it’s the kind of hair that people actually say “whoa, I had no idea it was that long” if I take it down. I call it homeschool hair. The last time I wore it down for an extended period of time, someone (who I knew) stopped me and asked if she could take a picture of it. I have no particular attachment to this style, but I actually don’t like haircuts, so here we are.
I hadn’t yet had a chance to dive in to the study, when a Tweet popped up on a similar topic:
It actually came to my attention because a few people immediately pointed out that these women were in a no win situation: if they’d told their coworker “she looked like shit” they would be considered catty, but if they tell her it looks good they are intrasexually competitive. Additionally, they were coworkers of hers, not friends, and it’s pretty weird to expect that all women at all moments must be aiding every other woman they know with her appearance. I suppose there’s an option where they could have tried to be pleasant but not endorse the haircut, but that’s a very hard tone to hit correctly and honestly? I’ve also seen plenty of male coworkers say things “looked great” when other males came in proud of some new thing they did/purchased/whatever. Why start conflict with a coworker for no reason?
All of this prompted me to deep dive in to this study, to see what they found. Ready? Let’s go!
So the basic set up of the study is that 200ish (mostly college aged) women were recruited for a series of two studies. In both, they had a series of female faces cropped to the shoulders like this:
The women studied were supposed to suggest how many centimeters (they were Australian) they were supposed to cut off. They were given the picture of the woman, an assessment of the hair’s condition and then how much hair the woman was comfortable cutting off. Those last two were a binary: hair condition was either good/damaged and the requested length of cut was either as much as needed/as little as possible. After that they asked women to rank themselves on a few different scales, including one that measured intrasexual competitiveness.
What’s intrasexual competitiveness you might ask? Well, it’s apparently a 12 question measure that asks you stuff about how you feel about those of your gender who might be better than you on some level. The questions they mention are things like asking you to agree/disagree with statements like “I just don’t like very ambitious women” or “I tend to look for negative characteristics in attractive women”. Highly intrasexually competitive women are those who answer that they strongly agree with questions like that.
They hypothesized that women who scored high on this scale might be more aggressive with their recommendations to other women about how much hair the should cut off, under the idea that men like long hair and this would be sabotaging other women who might be competitors to them. And to be honest, this sounds like a pretty plausible hypothesis to me! These are women who just answered a bunch of questions reiterating that they really didn’t particularly like other women, I would imagine they’d actually end up being meaner to other women than people who disagreed with those statements. It reminded me of someone who recently pointed this out about introvert/extravert tests: they will ask a bunch of people if they like big groups of people, and then you call those who said “no” introverts, then we declare that we found introverts don’t really like parties. I mean, that makes sense! But it does at times seem like most of the sorting already took place before we even got to the study itself. But I digress, let’s keep going.
Ok, so the first thing that caught my eye is that the primary finding of the study is that all women, regardless of scale ranking, first and foremost based their haircut recommendations on two things:
So to be clear, it was found that even women who stated they didn’t much like other women primarily based their recommendations on what was best for the other woman and what they other woman wanted. And it wasn’t even close. Every other effect we are going to talk about was much smaller both in absolute value and in statistical significance. Here’s the graph:
To orient you, the top panel is the recommendations for healthy hair, the bottom is the recommendations for unhealthy hair. As you can see, in general the difference in recommendations based on that condition alone is quite large, around the 2cm (a bit under inch for us USA folks) range for all conditions. The second biggest impact was what women wanted, which made a difference of about 1-1.5cm in the recommendations. Then we get to everything else.
It’s important to note that despite how this topic often gets introduced, there was no significant effect found based on attractiveness in general. This is notable because like the Tweet above shows, this stuff is often portrayed in popular culture as something “women” do, and we don’t have much proof that it is! They did find an attractiveness effect for the women with healthy hair being judged by regular and highly competitive women, but it went the opposite way: it was actually unattractive women who got the recommendation to cut off more hair. And again, the difference was a fraction of the impact of the other two factors: somewhere between .1-.2cm. For those of us in the US, that’s less than 1/10 of an inch. A quick Google suggests that’s less than a weeks worth of hair growth, and certainly not enough for anyone to notice.
I think it’s good to hammer on this because if I told you someone was out to sabotage you, you might be worried. But if I told you someone was out to sabotage you but they’d first do what was best for you, then follow what you wanted, then would sabotage you so subtly it would be imperceptible to the naked eye…..well, you’d probably calm down substantially. Much like when we see studies like “eating eggs will double your risk of heart disease in your 50s (from .001 to .002 per thousand)”, we need to be careful when we are quoting results like this that find a near imperceptible difference that can be fixed with 5 days of regularly hair growth.
But back to the finding that attractive women didn’t actually get penalized and instead the slight increase in hair cut recommendation was aimed the other direction, the study authors conclude the following:
This suggests that appearance advice may act as a vector for intrasexual competition, and that such competition (in this scenario at least) tends to be projected downward to less attractive competitors.
I will admit that annoyed me a bit, because this means that ANY variation is now considered to prove the thesis. They stated this was ok because there was no active “mate threat”, so they would expect it to go this way, but I will point out if attractive women had been penalized it would have also been considered proof. Having just finished our series on the replication crisis, I will point out that explaining every finding as proving your original thesis is a big driver of non-replicated findings.
Moving on to the second study though, the study authors did a few really smart changes to their set up. First, they provided participants with a picture of a ruler and a credit card up front so they’d actually have a reminder of what different lengths meant. They also changed from using a text box for the answers to “how much hair would you recommend they cut off” to using a Likert scale type set up where you had to recommend a whole number 1-10 cm. I liked that these changes were there because it showed a good faith effort to improve the results. In this condition, they added faces that were considered “average” to the mix and repeated most of the same experiment.
The findings were similar. The biggest variations were based on hair damage and client wishes, with relatively small differences .1-.2cm appearing across different individual groups. The graph that got the headline though is this one:
This is the graph they used for the title of the study, and it comes from dropping the whole clients wishes/hair damage thing and just looking at the overall amount of hair these women suggested be removed for anyone. You will note again the variation across attractiveness levels is .1-.2cm, but indeed the “high” intrasexual competitiveness women recommend more than the other two groups. The highest recommendation is about .8cm higher than the lowest value. That’s about 1/3 of an inch. Not enough for you to visually notice, but still something.
What caught my eye though was that we only really saw variation with the high and low group, which got me wondering how many women were in each category. And that’s where I found something interesting. In the first study, they defined “high” and “low” intrasexual competitiveness as being 1 SD from the mean. Assuming a normal distribution, that would mean about 16% of the sample were in the high/low groups, and the remaining 68% were in the average group. For this study though, they changed it to 1.5 SD, which means a little less than 7% of the group are in the high/low groups. Given the sample size of around 250, we’re looking at about 17 people in both the high and low group (34 people total) and 216 or so in the average group. By itself that will lead to higher variation in the groups with smaller sample sizes. You will note there is very little variation in what the group with most of the participants answered.
So like I said at the beginning, I find this study’s conclusion fairly plausible. The idea that women who specifically state they don’t like other women will give other women worse advice just kind of makes sense. But a few thoughts:
Ok, well that’s all I have! Thanks for reading, and I’m going to go consider cutting my hair an amount no one will notice, just for fun.
I unfortunately have a packed weekend and have not been feeling well, so no True Crime post today. Instead, I would like to mention something I was wrong about.
For years I have disliked the whole “science for girls” thing, believing that it was fairly condescending to slap pink on something sciencey and to declare it “for girls”. I believed this right up until I had to buy a present for a precocious 4 year old girl I know who is obsessed with all things pink, sparkly, and unicorn adorned. It had been requested I try to find something sciencey, so I decided to take a chance with, well, a pink sparkle unicorn science kit for girls.
She loved it. Last I heard she had told one of her parents to “go away, I’m doing scientist things”.
Worth every penny.
I was on Twitter/X recently and ended up following two rather interesting conversations started by Tweets with strong claims. Of course I can’t find them again, but one asserted that teachers unions were the cause of our educational problems in this country, and the other asserted that the only way to fix the falling birth rate was to raise the status of men in this country. Both of these were interesting enough for me to start following the thread, and people quickly countered them both.
For the teacher’s union claim, it was pointed out that there are 5 states where teachers can unionize, but they are not allowed to do any collective bargaining: Texas, Georgia, North Carolina, Virginia, and South Carolina. One would assume without this power, these unions are pretty weak and indeed that’s what we see. So are these the areas with the best educational outcomes? No. According to this ranking they are 40th, 30th, 34th, 9th and 38th respectively. Those rankings are all over the place, but it’s hard to see a compelling case in that data that defanging a union is a simple solution to our educational woes.
Similarly with the claim that raising the status of men will raise the birth rates, we can actually look at the countries that are worst on the gender equality index. Yemen is currently considered the worst country in the world for this, and here’s how their birthrate has gone in the last few decades:
Here’s Afghanistan, Syria and Iran:
Now while these birthrates are higher than the US, it’s hard to miss that they are also falling. Even if one could overthrow the US and install Afghanistan style gender relations, it’s not clear that line would stop falling at all.
Now none of this is to say these hypotheses had no value, changes to unions or to male status might actually help the intended issues, but it’s important to be aware of their limitations from the jump. In both of these tweets these solutions were presented as clear and obvious with no caveats, but a quick look at real world data suggests they are not going to be silver bullets. For example, you say that Afghanistan is confounded by the unrest there, but how do you suggest you boost male status more effectively than the Taliban with less unrest? What damage are you suggesting unions are doing outside of their collective bargaining? Whatever your hypothesis is, let’s find a place that’s already doing it and see if it worked. If a strong effect doesn’t jump out at you, you have to temper your expectations.
We see this all over the place too, to the point where it’s a good first test for any hypothesis you have. Do you think people aren’t having babies because we don’t have a generous social safety net, free daycare or ample maternity leave? Let’s find some countries that have those things already:
Uh oh, that’s lower than the US. Again, maybe those things will help, but they clearly can’t be the full problem.
The Assistant Village Idiot recent put up a post about the belief by RFK Jr and others that psychiatric meds. The countries with the top anti-depressant consumption are Belgium, Greece, Spain, Iceland and Canada. These countries don’t come anywhere near the US for public mass shootings. We have 8-9 times Canada’s population and 27 times the number of public mass shootings. So we now have to explain why these medications would have a large impact in one country but not another. It’s possible! But it does involve invoking a bunch of other factors not part of the original statement.
Got time for one more example? I’ve spent the past 2+ years hearing that my state (Massachusetts) had a super corrupt police force and an aggressive justice system based on one high profile case that happened in my town. It might surprise you to hear then that Massachusetts actually has the lowest incarceration rate in the country. We incarcerate 241 people per 100,000 residents. I’ve had to talk about this with people from Virginia (679 per 100k), Alaska (744 per 100k) and Illinois (433 per 100k). None of them were aware how high their incarceration rates were, comparatively speaking. Can we come up with a story where Massachusetts has an incredibly corrupt police force and aggressive justice system that only incarcerates people at a fraction of the rate of other states? Maybe, but it’s challenging. Add in the fact that we are bottom 5 for police involved killings, and your job gets even harder.
Like I said, state and country level data may not entirely disprove a hypothesis, but it does give a good first glance in to how compelling your case is. When I’ve pointed this out to people in various situations in real life, the first response I often get is “oh well of course there are other factors”. But normally we started this whole conversation because they made a statement that there was just one compelling factor. We can certainly back off to “it’s a bunch of things”, but that’s not typically where the conversation started. I understand in the social media age declarative statements are king, but state and country comparisons are pretty good at moderating conversations rather rapidly. Highly recommended.
I am now at the age where I, along with most of my friends, have retired parents. This has led to a natural increase in the discussions of the problems of aging, some of which I expected and some of which I did not. One thing I’ve been surprised by is the number of people I’ve had mention to me recently that their parent(s) have a problem with hoarding. This piqued my interest because I have no direct experience with this (my parents have made a big point of continuously going through their stuff), but when I started to mention that I was hearing this a lot more, I started to get more stories of people’s parents or friends parents who were struggling with this. And these stories were bad. This wasn’t “moms house is a little more cluttered than I’m comfortable with”, these were stories of rooms being rendered fully unusable, important things going missing, and fears of having to be the one to clean it up after they pass away. So what’s going on here? Is this a case of increased awareness, expanded definitions or a real uptick? Turns out it might be all three! Let’s dig in.
Hoarding disorder is actually a fairly new diagnosis, first introduced in to the DSM in 2013. Prior to that it was considered a subset of obsessive compulsive disorder. The full criteria is here, but it’s basically the psychological inability to get rid of stuff in a way that ends up negatively impacting your life or health. People keep accumulating stuff whether through compulsive overbuying or just refusal to discard anything in such a way that their homes fill up. The estimates are that about 19 million Americans reach the criteria. It’s estimated about a quarter of all preventable fire deaths happen due to hoarding.
One of the more interesting things I found while looking in to this is that a group called Hoarding UK actually publishes something called the “Clutter Index Rating“, a visual guide to what level of clutter might require help or intervention. They recommend that a 4 or above might require help. Here’s an example of their visual for the kitchen:
I was relieved to discover my house does not fall in the problem zone.
Well, a few reasons. Between the reality show “Hoarders” debuting in 2009 and the new diagnosis being added in 2013, the public did start having a new level of awareness of this disorder. This led to more people talking about it, which tends to lead to more people identifying that their dads inability to throw out any newspaper he’d ever gotten had a real name.
Next, there’s the obvious issue that stuff is easier to accumulate now than ever before. Could you fill up a house with random stuff back in 1900? Sure but it would have taken a lot longer. Interestingly this post was inspired by someone encountering a (likely) hoarder who tried to pick up some stuff they’d left our for free by the side of the road, and despite her whole car being full of random stuff, she started asking if they had anything else laying around she could look at.
But finally, hoarding is not evenly distributed across the lifespan: it is far more common in those over 65. People who just had a clutter problem in their younger years may turn in to full blown hoarders later in life, so as the baby boomers cross age 65 we can expect to see an increase in those impacted. Interestingly despite the initial link to OCD, it actually seems it’s more closely linked to depression. People who have divorced, lost a spouse or are otherwise isolated may be even more vulnerable. Unsurprisingly, this also means that the pandemic boosted the problem, though it’s not clear if that persisted. Sadly, some major cases of hoarding aren’t discovered until the affected person passes away.
Well, much like any difficult psychological problem, there’s not one clear answer. My local council on aging has resources and my state also supplies support, particularly to landlords who may need to evict a hoarder. There are 12 step programs and traditional therapy options, there are services that will clean your house out. However, it is noted that cleaning the house out has a 100% recidivism rate if no other support is given. My state provided this interesting little decision tree, which I appreciated:
But overall this will depend a lot on local resources and exact circumstances. Not an easy spot to be in if you’re a loved one.
On my last post, I gave a few scattered thoughts about the UKs healthcare system vs the US system. In the comments, a very astute commenter mentioned that life expectancy was not a great way of measuring how well your health care system was working. This is an excellent point that I think deserves some discussion.
If you start looking in to the US healthcare system, you will very quickly run across a graph like this one that shows health care spending vs life expectancy:
There’s a variety of these charts but they all show the same thing: the US spends the most on health care per capita by a good margin, but does not have the highest life expectancy in the world. We’re about 5 years behind a country like Japan (84.7 years vs US 79.3 years), despite us spending 3 times what they do ($4k vs $12k per capita). I think it’s worth diving in to why this is, and why it may or may not be an accurate measure of how our healthcare system is doing.
There’s a actually a few different ways to calculate life expectancies, and the exact details of what you’re trying to do matter quite a bit. But one thing most ways of calculating it have in common is that they are all impacted quite a bit by people who die young. This is an issue a lot of us are familiar with when looking at historic life expectancies, which tend to be weighed down by the high number of children who died before their 5th birthday. This is a big enough issue that the UN actually looks at both life expectancy from birth and life expectancy at age 15, just to account for both child mortality and mortality at older ages.
So the point is, if you’re in a developed country and you want to understand why your life expectancy looks like it does, the first thing to take a look at is what kills your young people. So what kills young people in the US? Guns, drugs and cars.
Ok, so before we go any further, I want to acknowledge that the topics of guns, drugs and cars tend to get people a little worked up. Given this, I want to clarify why I’m going in to this. I am NOT attempting to recommend any particular policy solution to the things I’m talking about below. I’ve done some of that in other posts over the years, but in this post I am specifically focusing on 1. If guns, drugs and cars kill people in the US at rates higher than in other countries and 2. If those deaths can be stopped by healthcare spending. This is important because again, that graph above gets used All. The. Time.
If life expectancy has some factors going in to it that cannot be fixed with healthcare spending, then that is a reason to take that graph a little less seriously next time you see it. Alright, with that out of the way, let’s look at some data!
Since 1981, the single largest killer of those under age 44 in the US has been “unintentional injuries”. This is a large category that includes drowning, poisoning, falls, motor vehicle accidents and “other” accidents. 90% of them are motor vehicle accidents or poisoning, and “poisoning” is the broad category that includes (and indeed is dominated by) recreational drug overdoses. Here’s a quick comparison of the top causes of death for those age 1-44 in 1981 vs 2023. Note: these are raw numbers, not population adjusted. ChatGPT suggests the under 44 population probably went up by 22 million people during the 42 years covered here.
| 1981 | 2023 | |
| Unintentional injuries | 58,500 | 83,300 |
| Malignant neoplasms | 22,000 | 17,400 |
| Homicide | 17,900 | 16,900 |
| Heart Disease | 16,400 | 16,100 |
| Suicide | 15,900 | 23,400 (now #2 cause) |
You can quickly note that the two categories here that the healthcare system has the most control over malignant neoplasms (cancer) and heart disease both went down during the timeframe we’re looking at here. Homicides also went down, but suicide and injury deaths went up. Given that in the US suicides are about 50% firearm deaths and homicides are about 80%, we can pretty accurately sum up the top killers of young people as “guns, drugs and cars” So how does this compare to other countries? Well the Global Health Data Exchange visualization tool can help us there. I picked a few countries that show up as having higher life expectancies than the US for less money to compare us to on the top causes of death, and here’s what I got. Note: I had to pick one age category for the visualization, and they didn’t have exactly the age 1-44 used above, so I used 15-49. We’re just getting a sense of the differences here. Anyway, here’s what I got:
Road injuries: the US sees twice as many deaths per capita as the next closest country, and substantially more than the lowest comparison countries I picked.
Drug abuse deaths (aka overdoses): again, we lead substantially here.
Suicide: we are one of the top here, but are much closer to other countries
Homicide (aka “interpersonal violence”): again, we are top
Cancer (aka “neoplasms”): we are middle of the pack
Heart disease: back at the top
So again, guns, drugs and cars appear to have a rather substantial impact on our mortality in younger people, and it’s not clear what our healthcare system could do differently to stop this. For motor vehicle accidents and murders, the health care system is mostly involved after the fact. There’s some argument that we could maybe improve our care of severely wounded people, but I don’t think anyone is really making the argument that our trauma care in the US isn’t as effective as that in Japan. It seems more likely that there’s just a lot more car accidents and violent incidents here. Healthcare spending can’t stop that.
For suicides and drug overdoses, one can argue perhaps that a better funded mental health/rehab system could help things, but as anyone who has dealt with a suicidal or addicted family member knows that it’s not quite as simple as that.
I will note that I often hear obesity thrown out there as another issue the US faces, and I think this is true based on the cardiovascular disease numbers. The only reason I don’t include it in “the big three” is because it is mostly taking out people in later years, and while we are above most other countries, our problem isn’t twice as bad like it is with road deaths, homicides or overdoses. We could definitely add it in though, and we’d still get back to healthcare spending not changing much. New medications like Ozempic might change that math, but up until recently that was pretty true.
I also leave it out because honestly I’ve heard waaaaaaaaaaaaay too much “if we stopped spending money on medication and let everyone go to the farmers market, we’d be great!” type stuff. That’s a nifty idea but it’s still not gonna change car crash deaths, overdoses or homicides, and so the bulk of our problem remains.
Ok, so what does this do to life expectancy, and how do we know this is the major driver? Well the Financial Times did an interesting analysis here. It’s paywalled, but the author did a Twitter thread here. Some graphs were included, like this one that shows that US citizens over 75 basically have the same life expectancy as our peer countries, whereas those under 40 have a much greater chance of dying:
This graph shows a similar thing, the probability of dying at a particular age is much higher for young people in the US vs peer countries, and similar for older ages:
If you look at the actuarial tables from the Social Security Administration, you can see this as well. Those tables look at a hypothetical cohort of 100,000 people born in the same year and show how many will still be living at each age. The UK releases similar data:
| US – male | UK – male | US – female | UK – female | |
| Age at which 1 in 100 of the cohort are deceased | 16 | 24 | 21 | 34 |
| 1 in 20 | 35 | 50 | 49 | 57 |
| 1 in 10 | 50 | 60 | 59 | 66 |
| 1 in 5 | 62 | 69 | 69 | 74 |
People in the US are just more likely to know someone who died young.
I actually couldn’t find a comprehensive source for top issues with our life expectancy in the US, but I did finally think to use ChatGPT to ask, a resource I’m still not used to. I was pleased that despite not using it until this point in the post, the top causes it listed that are making the biggest impact are drugs, cars and guns. I asked it a few different ways how much we could add to our national life expectancy if those were closer to peer nations, and it suggested we’d add 2-5 years, which if you’ll recall would put us up much closer to the top.
After it listed those causes, we got in to a few (cardiovascular and metabolic disorders) which can be tied to obesity. It also added in smoking, maternal health, and general mental health. Racial differences, socioeconomic status and access to healthcare were listed last, with an estimate we could get back about a year of life expectancy if we fixed all of that.
To reiterate the point that things that impact young people count a lot more than things that impact older people, ChatGPT estimated that “solving” the opioid crisis would give us back about a year of life expectancy for our entire population. “Solving” obesity? About half a year. Stunning when you consider how many more obese people there are than opioid addicts, but again, one death of a 22 year old takes off 56 life years, as much as 11 people dying at 74 rather than 79.
One weird data point I encountered while doing this work is the differences in how countries count non-citizens. I couldn’t verify how each country counted immigrants/illegal immigrants/refugees, but it seems likely that how they do that counting could impact their overall numbers. I don’t know for sure but I would guess that those raised in third world without adequate access to nutrition or health care may always have higher medical needs (including translation services) and lower life expectancies than those who have always lived in a first world country. Differences in counting is going to matter quite a bit here.
So finally we loop back to the ultimate topic: are we really spending more money for worse outcomes? Well yes, sort of! But it’s not really the healthcare systems fault. If you have two countries with the same exact health care system but one country has people who get in lots of car accidents and the other doesn’t, life expectancy will be lower and costs will be higher. External injury deaths are a huge driver of mortality in the young, and if they are not equal across populations their outcomes will be unequal. The healthcare system mostly cannot prevent these deaths, they are just dealing with what comes across their door.
It’s worth noting that in addition to the deaths counted above, there are also going to be a bunch of people impacted by car crashes, drugs and guns who won’t die but will end up with health problems that will both cost money and shorten their lifespan. Many people I know who were in bad car accidents when they were younger end up with early arthritis in the impacted joints or other issues. Former drug users also may carry long term issues like Hepatitis C or HIV infections. Basically the pool of people who died under 50 is just the center of a much larger group of those injured early on who may have issues. These will also run up healthcare costs.
Again, none of this is to say what, if anything, we should do about these risks. But it is important to know when you see the spending/life expectancy graph exactly what we’re dealing with, and what can or can’t be fixed simply by throwing healthcare dollars at it.
I’m sure this is obvious to most of my readers, but it makes me feel better to put it in writing.
If you’re not familiar with Gell-Mann amnesia, it’s a cognitive bias described as “the tendency of individuals to critically assess media reports in a domain they are knowledgeable about, yet continue to trust reporting in other areas despite recognizing similar potential inaccuracies.”
The original statement about it was about newspapers, and yet I now see people doing this all the time with TikTok. “They saw it on TikTok” is used in most of my circles with an eyeroll and an obvious implication that whatever opinion was offered was uninformed (at best), and (at worst) completely made up. Now wait 5 minutes and change topics and some of those same people will turn around and seriously cite TikTok as an authority on different topics they know much less about.
So here’s my PSA: if you do not think topics you care about can be adequately summed up on TikTok in 2 minutes, assume that things you know less about can also not be summed in 2 minutes. If you think TikTok is rife with misinformation on topics you care about, assume it is also rife with misinformation on topics you are unfamiliar with.
Newspapers were never perfect, but in general they had more to lose when publishing incorrect or defamatory information. TikTokers can hide their identity, delete videos, and individual creators often are “judgment proof” or have too little money to sue. While there is good information on TikTok, always do the due diligence you’d want others to do if they came across a video in your field of interest.
Stepping back off my soapbox, happy Saturday!
graph paper diaries 