>across cultures agree that the word “woof” refers to a dog
Mexican / Spanish its Guau-guau or "wow-wow". Russia its tyaf-tyaf. Mandarin Chinese its wāng wāng. Few if any cultures that don't speak English know what 'woof' is.
obviously the idea is not that all cultures have the same word for the sound that a dog makes, but rather if you ask people from cultures which is the sound a dog makes "woof" or "miaow" assuming they have these concepts for the animals they would say "woof".
I am not arguing this is the case, but the original poster obviously feels it is, connecting it to the top level post's idea of common ideas for what words describe over languages.
>I may have built a system that is above my own level of understanding.
I feel like that about a lot of code i did myself; If you don't structure things very logically and really think about your comments; A few months or years will leave you with a hell of a learning curve to understand what you created.
AI actually helps with this, if you have the right prompt injections. I feel like the correct way to handle AI is to take a step back in abstracting problems.
I'm very use to collapsing subroutines to make things readable, maybe even further back from this though, the issue is words become too vague to be useful at these scales.
>The model on its own figured out that the prompt belonged to exploitgym and decided to cheat the evaluator. That is in no way a valid interpretation of "complete the given task".
I think it is. When i ask for a solution to a problem, its like asking for a hack. And the more 'shortcut' like route that the AI returns the more i would give positive feedback, even if i ultimately don't use it. Example, i asked how to complete a problem in a game i was playing, and among the in-game solutions, came a hack to edit a file and by-pass the problem altogether. Its very helpful to point out when i can transcend a problem that i am dug into.
I suspect a prompt injection could reduce, or remove this behavior. But it would be to the detriment of the AI.
Taxi Drivers: The mean age at death is approximately 67.8 years.
General Population: in the same dataset, life expectancy averaged 74 years.
The average age at which patients are typically diagnosed with Alzheimer's disease is 79.
People in these jobs don't live long enough on average to get diagnosed, at the same rate. The same effect will happen in any job that lowers your life expectancy.
It pains me to see such a low-effort contrarian comment at the top: as stated in TFA taxi drivers are below other profession when controlling for age, and there are multiple professions with lower life expectancy that still have significantly higher Alzheimer rate than taxi drivers.
Fella has read the article, has an argument and some stats. What more do you want from a comment? He isn't running a study.
Besides, the point is a good one. There are a lot of professions. If we glance at the underlying study [0] Figure 1 suggests that we'd probably expect to see an outlier or two in that mess just through chance, we're expecting a few p<0.01. Taxi drivers doing unusually well doesn't seem especially impressive in that light, especially since it is one of the professions that seem to die off rather young and we might expect one of those professions to do rather well on Alzheimers.
But after the study controls for age the starkness of the effect more or less disappears (eyeballing Fig 1, the 2nd graph).
It turns out that there is a profession with a lowest risk of getting Alzheimer's (duh, one had to exist) and it looks like a slight outlier (it's the lowest, we'd expect that one to look like a slight outlier). As outliers go it isn't that impressive, it looks like it is part of a fairly boring distribution. We just plotted 400 data points on the graph, the 1/400 chance weird data point is going to look a little weird. We're actually just reading a lot into the fact that the 2nd data point in ambulance drivers which isn't all that convincing.
Not to knock the study, I'm sure it is a great study. Lots of science done. They do good graphs and all that. But the study appears to be telling me that Alzheimer's is random with respect to occupation and ideally don't spend so much time on the road if you want to live to see old age.
> But after the study controls for age the starkness of the effect more or less disappears (eyeballing Fig 1, the 2nd graph).
Adjusting for age, Alzheimers deaths were 1/100 for taxi drivers, and 1/60 for the general public. That's extremely significant. However you feel about the graphs means nothing compared to the actual numbers presented here.
> I'm sure it is a great study. Lots of science done. They do good graphs and all that.
Your choice to be dismissively anti-science helps to explain the bad point you're making.
What makes you think that is significant at all? Anytime you take a lot of small subsets of a large random sample you expect to find outliers that look a lot like this.
Outliers, sure. Exactly like this though? That depends. Since this is a population then assuming a gaussian (which could well be wrong for a variety of reasons I'm just guessing here) what's sigma? If 1/100 is within ~3 sigma then sure. Otherwise no.
It is an outcome from over 400 data points. We're expecting to see events outside a 3 sigma boundary. In fact, 3 sigma is basically the 1:400 boundary. Strictly it is 1 in 370 [0] for anyone who takes Wikipedia at face value.
In the study they seem put the P value of the taxi driver outcome at <0.01 (in Table 2, although I admit I haven't read the thing especially closely). For 400 data points, we expect there to be around 4 values with a P value less than <0.01.
It needs more statistical analysis than I have the background to do. However all signs look to another variation of the replication crisis, which is more an more my default assumption when I see an observational study.
You're handwaving. The xkcd point only applies if you're within ~3 sigma. This isn't a complicated statistical analysis it's just asking how far out the outlier is versus the standard deviation and size of the dataset.
To be clear I'm not claiming in one direction or the other. I feel that if you're going to attempt to discredit a piece of work it's on you to do such a basic check.
Really the whole point of that xkcd was to viscerally illustrate the practical effect of binary misclassification, or alternatively to make the point that a p of 0.05 isn't necessarily as rigorous as you might expect. Neither of those things directly applies here.
The article doesn't give enough information to answer your question. It isn't on me it is on the reporter's who shouldn't allow a handwave like that to pass without question. There are far to many cases of people hacking statistics in the world to accept that.
It's getting hard to find any study posted without a kneejerk "correlation does not equal causation" take as if that's not something researchers have to control for in order to even conduct the study in the first place, let alone publish
The institutional media departments, journalists, and headline writers add back in the insinuated causation and effect size as a routine matter. That way we can't find fault with the researchers for their clickbait study.
The article (and apparently the researchers too) advances the idea there's a causal link here, even though one is not shown by the research. They go so far as to generally prescribe spatial reasoning training as a way to reduce Alzheimers risk.
You might be tired of hearing "correlation does not equal causation", but it needs to be said here.
On hackernews I really don't think it needs to be said. Call me arrogant/too generous to this community, but I think most of the users here know the phrase and its meaning. Seeing 8000 variants of it doesn't contribute, and since it costs nothing to say "I bet they made a mistake" while elevating your own intellect people default to it far too much.
Read the comment I replied to. It claims the researches must have somehow controlled for correlation (whatever that’s supposed to mean). Which they haven’t. There are a bunch of other comments arguing there’s something beyond correlation shown here. But it hasn’t. The study just demonstrates correlation.
The misunderstanding here is widespread so it worth calling out.
It’s actually kind of funny that people are more sick of the mistake being pointed out over and over than of the mistake being made over and over.
Ok, if we’re going to play this game, what indicates they are making “correlation = causation” errors? What specifically about this study fell for that trap and has not shown something meaningful? You seem very confident they screwed this up, so there must be a reason. And no I’m not digging through your comments to piece together your argument lol I’m sorry but it is ridiculous of you to expect me to comb through your comments and synthesize your point. If your comments were so thorough and proved it then just copy and paste 1 or 2 here that are driving the point home with actual evidence.
Their argument doesn't show that at all though, they make an argument against an age bias when the article explicitly says age is being controlled for.
> Figure 1 suggests that we'd probably expect to see an outlier or two in that mess just through chance, we're expecting a few p<0.01.
That's a valid point, and a much better criticism than the one I'm calling out. That doesn't redeem the original comment though.
A distinction is that Alzheimer's is in an uncommon category of mystery diseases.
If a study found people who swim regularly are are less likely to get heart disease, that wouldn't be very surprising because know a lot about why people get heart disease. Therefore it would be valid to insist on more rigour for accepting (swimming => less heart disease) because the only use of that information is for health advice.
However even weak statistical correlations can be a valid lead for further research into Alzheimer's disease if they suggest where to look. You don't need to prove that signposts are correct in order to use them.
If we read OP's comment closely, it appears to be technically correct. I can imagine someone not upvoting it because it seems to be generic, but there isn't anything wrong or low effort about with it. And I don't see where the stats came from if he hasn't read the article.
maybe it would help this thread to have someone explain exactly how they control for age, if everyone in the range they want to study, is already dead.
What is the actual process to control for age? This comes up a lot, and the only response is "oh, well, we controlled for that", well exactly how?
Seriously, I'm curious.
//lot of downvotes, not a lot of anybody actually able to explain it.
From Article, the age question does seem sketchy.
"Although proportional mortality analyses do not provide information about the population at risk, with careful selection of controls and risk adjustment for factors that may affect competing risks (eg, age, sex, and social class), these values can still serve as a useful indicator of variations in disease frequency across different occupations.16 17"
You build a model that describes how likely people are to get diagnosed with Alzhimer's as a function of sex: maybe 12 women out of 100 get it, while 8/100 men are diagnosed. You can do the same thing for age: almost nobody is diagnosed before 30, it's very rare before 40, and sadly common (~1 in 10) after 65 years of age. There are all sorts of mathematical tricks to include multiple variables, account for the fact that you can only be diagnosed once, or that data is "censored" (i.e., missing) at some ages because people have already died.
Based on that, you can then ask if the prevalence of Alzheimer's Disease among cab drivers is surprising, given their demographics. For example, if we know that they skew male and younger, we'd expect that number to be a bit lower than a naive estimate of 10%. In fact, we can calculate that number and then see if it's unexpected given the number we actually see. In practice, you'd actually do this by fitting two models: one containing job and one that doesn't, and see which one best describes the data and how, specifically, the job factor affects the outcome.
However, how well these adjustments work depends on the quality of your data and your modelling. Your model could be missing important factors or have the wrong structure: (e.g., you assume risk is directly proportional to age, but it actually increases more rapidly as you get older). Your data could have problems too: maybe women are more likely to go to the doctor (and thus get diagnosed), even if the actual prevalence is the same.
As long as a researcher said "we controlled for confounding variables," there are plenty of people willing to take it at face value.
It's really difficult (maybe not possible) to do that in these large retrospective studies. Shingles vaccine preventing dementia, GLP-1 drugs preventing just about everything, etc, etc. There are plenty of good faith critiques of this kind of work from actual medical researchers making essentially the same point as the OP.
True. But, these are still odd outliers in terms of low life expectancy, and that's something that the actual BMJ article cited (https://www.bmj.com/content/387/bmj-2024-082194) or TFA talks about at all. The LE is low enough for both professions to be awkward.
I'm also a little concerned that the BMJ plot of risk adjusted mortality still shows a clear positive correlation with age at death, which I strongly suspect would turn out to be statistically significant. The adjustment does not appear to be correct, therefore, and while that might still preserve these odd findings, it's definitely not ideal.
The much better occupation, from the graph, is the third outlier -- mining and geological engineers. Life expectancy: 80.3, risk-adjusted mortality: 1.1%. Or, alternatively, economists are close too (80.6; 1.19%).
Aircraft pilots, btw, really were the opposite end of the scale (78.1; 2.3%), surprising enough to be noteworthy beyond a control (the BMJ article does discuss this a little, but seriously -- that's very surprising for a highly-educated profession).
Worth mentioning that controlling for confounders is hard, a reasonable prior is the null effect, and any time a researcher makes a mistake it produces a publication claiming a novel effect. You observe a set of publications concentrated with false positives as a form of selection bias. I wonder how many of the causal studies are reproduced by independent teams using independent datasets.
Plausible scenario. Individuals predisposed to Alzheimer’s experience different mental sharpness from birth and this makes them enjoy taxi driving less (more intense than long-haul trucking), and so they pursue taxi driving as a career at lower rates. Under this scenario, driving has no effect, it just induces a selection bias.
A better low effort contrarian comment - isn't it that early symptoms appear less often with people with a "reserve"?
Being mentally active (or physically active?) IIRC makes actual plaques both less likely, but also less detectable - a taxi driver can forget 90% of what they know and still not get diagnosed because they don't get lost on the way to the shops. Also they are often masters of small talk.
This would suggest they'd need to control for method by which the alzheimer's was discovered. A quick glance seems to indicate they did not do this. This is not information available I guess..
I would suspect this would eliminate a lot of the experiments participants, as only people who go for regular cognitive check-ups might be eligible?
I cannot imagine there would be many people left who'd be eligible. I can only think of 1 person that does it.. and he obviously does it for the pure satisfaction of acing those cognitive tests, and not because he is showing worrying amounts of decline in his mental faculties.
I do wonder if you can truly control for it in this case, or if doing so doesn't in and of itself introduce a survivar bias that masks a reversal of causality. That is: could the taxi drivers who are already predisposed to have a lower chance of developing Alzheimer for other reasons also be the ones who are more likely to live to be older than the average taxi driver?
If the "study" is interesting at all, it's because of the implication that driving a taxi somehow wards off Alzheimers... but at the cost of killing you before you get it.
Every top-level thread is like a multiple choice armchair takedown.
Here's an interesting counterpoint. "Unthinkable" presented studies of navigation tasks in 2D/3D and testing/training. Hippocampus measurements correlated with skill and training. Interestingly, this skill and training and measurements all correlated with beneficial responses in a disaster (as in, not panicking). Weird right?
But on some level I understand the immediate low level criticism. Of course, it’s stupid to criticize something without reading about it. However, I’ve seen waaaay more studies which was made with even lower effort.
For example, the other day I wanted to figure out what’s the current state about whether gender equality causes measurable benefits for companies… and the studies are terrible. All of them. Most of them was about Norway, and almost all of them had a reference point in 2008, one of the largest economic crisis, and somehow most of them was even worse than this. They openly distorted statistics. Depending on what they wanted to achieve to one way or another. Even the most cited ones. It’s disgusting. The best ones could prove only that inequality is not inherent of economics, but social. But the agenda was different for them too, so they tried to lie something bigger, all the time, while this would be more than enough to support it.
The first question you ask when you read a paper like this is whether you think the authors actually did control for what they say they controlled for. Usually, they didn't.
And how would you control for the causal possibility that those who will eventually develop Alzheimer's early on might tend to have undetectable memory issues that prevent them from becoming taxi drivers in the first place? You can't control for profession, since that's behind the very claim itself.
Its even worse than that, i can imagine people with undiagnosed early Alzheimer's self selecting for car accidents and cardiac issues through additional stress of mental fatigue while their brain tried to compensate for loss of function.
So you have people self selecting out of the profession, self selecting for early death within the profession, and on top of that, a general life expectancy discrepancy that is near impossible to adjust for.
Fewer car accident deaths, leading to more elderly people, which inevitably means a higher population of people with Alzheimers.
(Unless people highly predisposed to dying in car accidents are also highly predisposed to mudering people who will have, but don’t yet have, Alzheimers. But that seems sufficiently unlikely for the purposes of this hypothetical.)
Jokes aside, the serious answer depends on whether the link is causative or merely collerative.
I'm still waiting on someone unbiased to study the data and tell me something. Those who have obvious biases tell me self driving cars are good, but their bias makes them suspect sources. So far I've not found someone without an obvious bias who had the data and is talking.
At one point the amount of human induced accidents will increase since no human can account for sudden erratic brakes of a self driver, even though it predicted a correct incoming accident with another V2V connected car.
As the paper points out that's accounted for in the analysis, because shockingly enough to most internet commentators researchers know that they need to control for confounding variables
It's not necessarily about intellectualism. Science in modern times is plagued by publish or perish. Researchers just need to get things published to keep working, and negative results don't get published. So I think people often publish stuff without proper controls/missing confounding variables/inversion of causality/etc not out of ignorance, but because they know the conclusion is dubious - but if they don't publish, they're going to be out of a job.
I don't think many realize how huge the replication crisis is. You have 75% of studies in a leading social psychology journal failing to replicate. [1] Another replication effort on preclinical medical studies found only 11% were able to be replicated, a replication effort on cancer studies found the average effect size to be 85% lower than published, and much more. If you assume most science headlines, at least outside of the traditional hard sciences, are false - you're substantially more likely to be right than wrong.
> It's not necessarily about intellectualism. Science in modern times is plagued by publish or perish. Researchers just need to get things published to keep working, and negative results don't get published. So I think people often publish stuff without proper controls/missing confounding variables/inversion of causality/etc not out of ignorance, but because they know the conclusion is dubious - but if they don't publish, they're going to be out of a job.
This is very true. In my PhD research, I managed to perfectly predict the placebo effect using a random forest. Now, this was actually due to sampling variance, and the finding went away when I averaged over splits.
I often wonder if I'd still been in academia when this happened, if I would have been able to resist the siren call of a Nature/Science paper (and the consequent career benefits).
They got trained to said assumptions by bad sensationalist 'science journalism'. Remember how power lines caused brain damage in children? Not through the magnetic fields or anything like that, but the lowered property values.
About that old Slate Star Codex article, I find it amazing that no one in that 70+ comment section of presumably smart and Rational people, bothered to look up to see if there were interventional studies on bullying.
Dan Olweus did several of them in the 70s and 80s, and fought to his dying day against programs which didn't have casual intervention studies backing them.
The 1/60 to 1/100 difference could be explained by just one small confounder that is impossible to cover for. Therefore in my opinion making any conclusions based on this study wouldn't be fruitful at all.
Alzheimer's is an entirely different beast than normal mental atrophy and there are plenty of stories of otherwise intelligent people ending up with it.
I'm not saying you shouldn't try using your brain more but this is just like saying Stephen Hawking should have tried exercising more.
The article doesn’t say taxi drivers are immune to Alzheimer’s. It specifically talks about having more cognitive reserve, which means having “more brain” to atrophy. Essentially Alzheimer’s makes their hyper-developed hippocampus become that of an average’s person because they started off higher up.
Being intelligent doesn’t mean you still learn new things as you age. Using your brain doesn’t do anything, it’s using your brain in novel ways.
That companion is also bunk. Two entirely different diseases.
Could just be that whatever factor causes certain cabbies to live much longer than their cohort is also protective from Alzheimer’s. I guess they might have accounted for that too — I’ll have to look at the study.
Or, that if you can survive the thing that kills the rest of their cohort, it has beneficial tradeoffs. As in, perhaps smoking protects from Alzheimer's, if it doesn't kill you first.
Controlling for age in a profession with a high fatality rate like this seems like controlling for high alertness, consciousness, quick reaction times, and well restedness/low stress, low recreational drug and alcohol use.
Of those, sleep issues are highly correlated with Alzheimer's and also with auto accidents. Controlling for age alone here would select for low incidence of sleep issues.
The claim was learning is important. Not the ability to communicate with it. There are more than 7000 known languages in the world, study a different one every week...
Back in college (around 15 years ago), I read a few books on eminence by Dean Kieth Simonton. One thing that I remember, and that has always been in the back of my mind, is that certain studies found that (in the USA), children who grow up in bilingual and bicultural households tend to have a much greater chance of achieving some measure of eminence in their adult lives. I am not sure if this is true globally (nor if it is still true in the USA). But I suspect that there is a reason[0] why so many eminent people came from Austria-Hungary for _generations_ (even the generations after the collapse of that particular empire).
[0]: There were 10 distinct languages, making up 98% of all languages in the empire. My understanding is that most citizens had to learn German, even though only 20% of the population spoke German as a mother tongue. Also a few different language branches (Germanic, Ugric, Slavic, Romance), with one of those branches (Ugric) not being Indo-European at all. https://en.wikipedia.org/wiki/Ethnic_and_religious_compositi...
> After adjusting for age, sex, race, ethnicity and education, roughly 1 in 100 taxi and ambulance drivers died of Alzheimer’s, compared with 1 in 60 people overall.
No. There are a lot of other comments on why many do not believe it.
There is no reason to believe they have enough data to adjust for any of their controls. It looks to me like they plotted everything and then found the "outlier" which statistically we know will exist in a true random sample.
| After adjusting for age, sex, race, ethnicity and education, roughly 1 in 100 taxi and ambulance drivers died of Alzheimer’s, compared with 1 in 60 people overall.
They claim to control for these factors in the 5th paragraph of the article here. Which is a standard thing to do in these kind of studies.
So you're point is either not relevant or that they didn't control for age correctly?
Is every case of dementia diagnosed accurately? Is there a difference between professions how likely their dementia is to be diagnosed accurately?
Is there a correlation between dying early and being pre-disposed to have developed Alzheimer if having lived longer? If there is, then the "correction" for age is not correct, because the correction assumes the fraction of the group that died early were equally likely as the rest of the group to develop the disease if they had not died early.
I don't know anything about Alzheimer but it doesn't seem unthinkable that the factors that cause people to die early also could also play a role in causing Alzheimer.
You could imagine that pre-diagnosis confusion relating to Alzheimer's increases the risk of traffic accident and kills taxi drivers, too. Might not operate for truckers as much because they're more likely to survive accidents.
> After adjusting for age, sex, race, ethnicity and education, roughly 1 in 100 taxi and ambulance drivers died of Alzheimer’s, compared with 1 in 60 people overall.
So no indication they accounted for the expected lifespan of the profession.
It’s insufficient. For example, the people who develop Alzheimer’s may for whatever reason may be less likely to survive the profession where Alzheimer’s is the cause of death (eg getting into a fatal car accident where undiagnosed early onset is the problem). Bus drivers typically drive the same route over and over and probably have safer trips overall than taxi and ambulance drivers.
It’s a plausible hypothesis but it’s really really hard to tease things out and controlling for age is the obvious thing but that doesn’t get you the conclusive result you think it does.
Indeed. I feel that although many people try to learn new languages, the intellectual stimulation provided by doing so is understated. Especially learning languages very different from your native language (grammatically, morphologically, etc.). It’s almost like finding new ways to think.
I am pretty sure that a dataset controlling for that would (laughing here) not negate the fact that fewer people who lived past the mean date that are taxi drivers died of a related disorder.
That said, suggesting it is related to the career as opposed to, say, the fact that a higher percentage of taxi drivers carry some other set of genes or genetic markers that either led them in some way to become a taxi driver or contributed to a lack of development of the disorder, is pretty weak. In the United States and I think a few other countries the number of taxi drivers which are immigrants from a handful of countries outnumbers any other type.
And, the world has changed a lot. Are 1980s cab drivers and 1950s cab drivers and 1965 cab drivers and cab drivers from 2007 comparable? are car service and livery drivers?
That's very interesting but why do taxi drivers have a job that lowers life expectancy? Is it because they're sitting too much? Or because they're inhaling CO from cars too much? Or staying inside (the car) too much? Or is it just stressful?
For from the look of it it doesn't look like an exceptionally hard job (btw a brother of mine did years of "VTC": "Vehicule de Tourisme avec Chauffeur", basically high-end taxis without the "taxi" logo).
I did it, briefly, twenty-some years ago. Lousy job—sitting all day, irregular pay, danger, nasty fares, drunk fares, belligerent fares, fares who seem to have nothing better to do with their days than to find ways to cheat a driver. But the thing that really sucks is the hours: shifts are twelve hours long, either ending or beginning at 3:00am, and because your rental for the cab is highest at the beginning of your work week, descending every successive day (free on day seven), the compensation is structured so that you're making good money only by working a ton of hours.
1. They are hardly in the bottom quintile, where the income effect is large enough that it could actually shave 7 years off their life. I'd expect nightly work, irregular sleep patterns, too much fast food and maybe a higher frequency of smoking to do their part.
2. The original study didn't just study taxi drivers, but also ambulance drivers. I doubt that their life expectancy is that much lower.
might turn out that the next company they apply to is run by street urchins who don't take too kindly to the stereotype? or possibly humorless scolds who don't want their employees telling jokes in their free time?
>People in these jobs don't live long enough on average to get diagnosed
not to mention the other devil in these studies: some professions are statistically going to have the lowest rate of every possible disease. you can't just cherrypick outliers and declare "causal!"
It's not violence from the job but their job was generally bad for their health in other ways. Sitting all day without getting up and driving around with windows down getting all this UV.
Also, older ones were exposed to a ton of cigarette smoke.
I could imagine taxi drivers have fewer fatal/grave road accidents than the average person or at most slightly more. They tend to drive in cities, are more trained, can't drink or do drugs on the job. For London only 1 taxi driver has died in a traffic accident since 2020.
I'm gonna go out on a limb and say that not even a double digit percentage of taxi drivers' careers are ended due to violent attacks. Taxi driver doesn't even appear in the U.S. Bureau of Labor Statistics top 20 most dangerous jobs.
That's actually crazy if that's as low as you dare guess. I would have easily guessed it was like, a fraction of a percent. 1 in 10 would be fucking nuts.
" (...) After adjusting for _age_, sex, race, ethnicity and education, roughly 1 in 100 taxi and ambulance drivers died of Alzheimer’s, compared with 1 in 60 people overall."
"For each occupation, we first calculated the percentage of deaths due to Alzheimer’s disease and the mean age at death in years (ie, average life expectancy). We plotted the association between these two variables, with each observation reflecting a single occupation. The purpose of this analysis was to illustrate the need to account for the person’s age at death since the risk of Alzheimer’s disease rises with age and therefore Alzheimer’s mortality would naturally be lower in occupations with a lower life expectancy."
In the US, but you’d likely see a similar effect in other places. Taxi drivers are highly sedentary and occupationally exposed to a lot more road pollution than most jobs.
Isn't the mean age simply brought down by the number who die at a very young age due to car accidents, robberies, and other reasons?
Not that cab drivers only live to 67 - many would still live to 75, 85, 95, but that occasionally a driver dies at 25 or 35 bringing the mean figure way down.
I like how for example if I say contractors can be bad or even awful at their jobs and we'll all nod and agree, but to merely suggest a doctor or someone with a PHD may be, it's blasphemy.
Mexican / Spanish its Guau-guau or "wow-wow". Russia its tyaf-tyaf. Mandarin Chinese its wāng wāng. Few if any cultures that don't speak English know what 'woof' is.