The article seems to be taking things further than is necessarily possible right now, but while it might be hard to know precisely what you buy at a supermarket, it's a pretty fair bet that if you made a purchase at McDonalds you were buying junk food; if you made a purchase at an off-licence you were probably buying alcohol. And so on.
The data might not be perfectly accurate but it can leak a lot of probable information.
If you ran a machine learning algorithm on the data, without knowing how much anything cost and just wanted to correlate certain purchase amounts with whether people tended to get sick or not, you would probably find correlations with certain amounts that happen to correspond to things like cigarette purchases.
This is especially likely because people often tend to buy only cigarettes, or maybe a couple of other things, rather than only buying them along with a larger group of items that would tend to disguise the purchase.
It should be pretty easy to spot the difference in average prices between someone buying a packet of cigarettes, vs a bar of chocolate, vs a weeks' worth of shopping. It might not be super accurate for each data point, but given enough data it's likely some fairly consistent patterns will emerge.
In fact, one of the things about ML is that it's good at spotting all sorts of correlations. Those don't prove the existence of a causal link, but often that doesn't matter: the fact a correlation exists is enough. So simple things like buying patterns might be correlated with certain tendencies or risk factors, regardless of what the contents of the purchases actually are (of course this is purely hypothetical).
The data might not be perfectly accurate but it can leak a lot of probable information.
If you ran a machine learning algorithm on the data, without knowing how much anything cost and just wanted to correlate certain purchase amounts with whether people tended to get sick or not, you would probably find correlations with certain amounts that happen to correspond to things like cigarette purchases.
This is especially likely because people often tend to buy only cigarettes, or maybe a couple of other things, rather than only buying them along with a larger group of items that would tend to disguise the purchase.
It should be pretty easy to spot the difference in average prices between someone buying a packet of cigarettes, vs a bar of chocolate, vs a weeks' worth of shopping. It might not be super accurate for each data point, but given enough data it's likely some fairly consistent patterns will emerge.
In fact, one of the things about ML is that it's good at spotting all sorts of correlations. Those don't prove the existence of a causal link, but often that doesn't matter: the fact a correlation exists is enough. So simple things like buying patterns might be correlated with certain tendencies or risk factors, regardless of what the contents of the purchases actually are (of course this is purely hypothetical).