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.
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.
It's not terrible to not make a good point, but it's not a good point. The not good points should not be ranked highly.
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.
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.
They aren't being anti-science and it's low-quality to suggest they are.
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.
That is https://xkcd.com/882/ seems to better fix the data just as well.
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.
[0] https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rul...
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 (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.
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.
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.
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.
It's absolutely not worth publication with clickbait title and distorted description to the general public.
Then not closely enough? The study adjusts for age, which is the core of OP’s criticism.
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.
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.
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).
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.
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.
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.
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?
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.
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.
(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 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.
[1] - https://en.wikipedia.org/wiki/Replication_crisis#In_psycholo...
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).
I doubt OP's jaded response was alluding to your insight, but still thanks for that, it's genuinely good to know.
'Why ‟controlling for a variable” doesn't (usually) work' - https://dynomight.net/control/
Other things that don't work, including four other 'controlling for a variable' entries at items 39 through 43: https://dynomight.net/things/
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.
Delay your aging by using your brain a fuck ton, all the time, learning brand new things nowhere near anything you already know.
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.
/s
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.
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.
Language skills atrophy surprisingly fast.
I have no idea what the truth is here...
[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.
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.
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.
So no indication they accounted for the expected lifespan of the profession.
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.
then don't post
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?
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).
2. The original study didn't just study taxi drivers, but also ambulance drivers. I doubt that their life expectancy is that much lower.
Define "hard job". Carrying heavy objects?
Odd hours, sitting too much, inhaling too many exhaust fumes, having people yell at you if _they're_ late.
Sounds like a fairly hard job.
There are jobs stressful on the mind too - and some of them have very low "life expectancies".
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!"
Also, older ones were exposed to a ton of cigarette smoke.
Sure they might be better and per unit of time they drive they must have miniscule amount of accidents.
But if there's a 0.001% chance per hour driven, then over enough hours a few accidents are inevitable.
Pedestrians are a lot more at risk. So that's unsurprising that annually 1/2 taxi driver might be fatally injured in one.
But I would assume the ratio of other drivers to taxi drivers would be high enough that this number should be zero as well.
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.
"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."
Where? What country/ies?