Experts have challenged the medical case against Lucy Letby. What about the statistical evidence?

An international panel of medical experts have thrust Lucy Letby back into the spotlight. At a press conference convened by Letby’s legal team, the experts cast doubt over the former nurse’s conviction. Letby was given 15 whole-life sentences for murdering seven babies and attempting to murder seven more.

Speaking at the press conference in London, retired neonatologist Dr Shoo Lee told the assembled reporters: “In all cases death or injury were due to natural causes or just bad medical care.”

Why should we take Dr Lee’s word for it? Well, in part, because he is the author of a key paper on air embolisms, one of the methods that Letby was accused of using to kill babies, which formed a key part of the prosecution’s evidence at the trial.

He also claims that the paper’s findings were misinterpreted at the trial and that a newly updated version of the article would help exonerate Letby rather than convict her.

The Letby conviction has always attracted critical attention because there were no witnesses who could confirm they saw her attacking any of the babies she was convicted of murdering. Nor did anyone see her perform actions that could have constituted the attempted murders of seven others.

Consequently, the prosecution used statistics alongside the medical evidence the expert panel has now cast doubt upon. So how solid is that statistical evidence?

A key piece of statistical evidence is a chart which showed that Lucy Letby was on duty every time one of the crimes of which she was accused occurred, but that none of the other nursing staff were.

On the face of it, it seems quite damning. But when you think about it, it’s unsurprising that Letby’s column is the only one full of crosses. Any of the events at which she was not present she would not have been charged with and consequently wouldn’t appear on the chart.

A chart showing the shifts in suspicious activity was suspected and the nurses who were working on each shift.
Cheshire Police

This is an example of what is known in statistics as the Texas sharpshooter fallacy.

The fallacy is named for a story about a Texan cowboy who likes to head out to his barn after a few drinks for target practice. Invariably, the barn wall gets peppered with random bullet holes during the inebriated exercise, and purely by chance some of these holes are clustered.

One morning the savvy “sharpshooter” gets out his paint cans and daubs a target around this cluster of holes to give the impression of accuracy to anyone who didn’t see the process by which they were made and to draw attention away from the other more dispersed bullet holes.

The sharpshooter fallacy occurs when a conclusion is drawn based only on data consistent with a given hypothesis, ignoring data that doesn’t support the proposed conclusion.

Imagine, for example, you made a chart similar to the one used to convict Letby, this time including only those deaths at which a different member of the nursing staff was present. It’s entirely possible – for example, if they were present for deaths at which Letby was not – that their name would be above the only column full of crosses and not Letby’s.

Indeed, it later transpired that the table did not include six other deaths that occurred during the same period and with which Letby was not charged. The jury was not told about these other deaths.

As Jane Hutton, a professor of medical statistics at the University of Warwick argues: “If you want to find out what went wrong, you need to consider all deaths, not just a subset of them.”

She also points out that it’s important to consider how likely the other alternative causes of death were at the struggling Countess of Chester neonatal unit.

The prosecutor’s fallacy

The probability of so many deaths on a neonatal unit in such a short period should be quite low. At first glance, this might seem to make the alternative explanation of murder seem more likely. But this is a classic statistical error.

This mistake is so common in courtrooms that it is known as the prosecutor’s fallacy. The argument starts by showing that, if the suspect is innocent, seeing a particular piece of evidence is extremely unlikely.

For Letby, this is the assertion that if she was innocent of killing these babies, the probability of them dying due to other causes is extremely low. The prosecutor then deduces, incorrectly, that an alternative explanation – the suspect’s guilt – is extremely likely.

The argument neglects to take into account any other possible alternative explanations, in which the suspect is innocent, such as the death of these babies due to inadequate care. It also neglects the possibility that the explanation that the prosecution is proposing, in which the suspect is guilty, may be just as uncommon as the alternative explanations, if not more so.

By just presenting the low probability of these seven babies dying naturally, the inference that an untrained jury is invited to draw runs something along these lines: “The deaths of these babies from natural causes is extremely rare, so the odds that the deaths are the result of murder is correspondingly extremely high.”

However, it must also be taken into account, when weighing up the evidence, that multiple infant murders are also extremely uncommon. What really matters is the relative likelihoods of the different explanations. Weighing these very unusual events against each other is not an easy thing to do.

Criminal cases review

Other statistical issues with the case also deserve more attention: the high number of deaths at the Countess of Chester, even excluding the babies that Letby has been convicted of murdering. Or the possibility of false positive medical identifications of murder, for example.

Whether Letby’s team’s appeal to the Criminal Cases Review Commission will be successful or not remains to be seen. The statistical issues over the case, when taken alongside the doubts about the medical evidence, mean that there is certainly a possibility.

Throughout all this, it’s important to remember the families affected by the events at the Countess of Chester Hospital. Whatever the ultimate truth of the matter, this ongoing case will undoubtedly make dealing with their grief more difficult. Läs mer…

Forget BMI – there’s a 2,000 year-old technique for measuring body fat that’s more useful

It’s the start of another year and the body mass index (BMI) is being criticised again. This time a Lancet-commissioned group of experts is denouncing it as a diagnostic tool for obesity. They say that doctors should look at the overall health of a patient when diagnosing obesity, not just rely on this one flawed metric.

BMI is calculated by measuring a person’s mass in kilograms and then dividing that by the square of their height in metres. For recording and diagnostic purposes, anyone with a BMI below 18.5 is classified as “underweight”. The “normal weight” range extends from 18.5 to 24.5 and the “overweight” classification spans 24.5 to 30. “Obesity” is defined as having a BMI above 30.

Given the health implications related to a diagnosis of obesity, or even of being overweight, you might have assumed that the metric used to diagnose these conditions, the BMI, would have a strong theoretical and experimental basis. Sadly this is far from the truth.

While it’s true that fatter people typically have a higher BMI, it does not work well as a diagnostic criterion. One of the main problems with BMI is that it can’t distinguish between muscle and fat. This is important because excess body fat is a good predictor of heart disease risk. BMI is not.

A recent study suggested if the definition of obesity were instead based on high-percentage body fat, between 15 and 35% of men with non-obese BMIs would be reclassified as obese.

However, it turns out that BMI both under- and over-diagnoses obesity. The same study found that up to half of the people that BMI classified as overweight and over a quarter of BMI-obese individuals were metabolically healthy.

BMI is clearly not an accurate indicator of health. Instead, it would be useful to access a direct measure of the percentage of body fat that is so closely linked to cardiovascular disease. To do that we need to borrow a 2,000-year-old idea from the ancient city-state of Syracuse on the island of Sicily.

Read more:
Body mass index may not be the best indicator of our health – how can we improve it?

This one weird old trick

Around 250BC, Archimedes, the pre-eminent mathematician of antiquity, was asked by Heiro II, king of Syracuse, to help resolve a contentious issue. The king had commissioned a crown of pure gold. After receiving the finished crown and hearing rumours of the metalsmith’s less-than-honest reputation, the king worried that he had been cheated and that the metalsmith had used an alloy of gold and some other cheaper, lighter metal. Archimedes was charged with figuring out if the crown was a dud without taking a sample from it or otherwise disfiguring it.

The illustrious mathematician realised that he would need to calculate the crown’s density. If the crown were less dense than pure gold, he would know the metalsmith had cheated. The density of pure gold was easily calculated by taking a regularly shaped gold block, working out the volume and then weighing it to find its mass. Dividing the mass by the volume gave the density. So far, so good.

Archimedes ‘eureka’ moment probably didn’t happen quite like that.
Avi Horovitz / Alamy Stock Photo

Weighing the crown was easy enough, but the problem came when trying to work out its volume, because of its irregular shape. This problem stumped Archimedes for some time, until one day he decided to take a bath.

As he got into his extremely full tub, he noticed that some of the water overflowed. As he wallowed, he realised that the volume of water that overflowed from a completely full bath would be equal to the submersed volume of his irregularly shaped body. Immediately he had a method for determining the volume, and hence the density, of the crown.

Vitruvius tells us that Archimedes was so happy with his discovery that he jumped straight out of the bath and ran naked and dripping down the street shouting “Eureka!” (“I have found it!”) – the original eureka moment.

Sadly, it is unlikely that this is actually how Archimedes solved the problem. Instead, it is more likely that Archimedes used a related idea from hydrostatics (the mechanical properties and behaviour of fluids that are not in motion), which would later become known as Archimedes’ principle.

The principle states that an object placed in a fluid (a liquid or a gas) experiences a buoyant force equal to the weight of fluid it displaces. That is, the larger the submerged object, the more fluid it displaces and, consequently, the larger the upward force it experiences. This explains why huge cargo ships float, providing the weight of the ship and its cargo is less than the weight of water they displace.

Using this idea, all Archimedes needed to do was to take a pan balance with the crown on one side and an equal mass of pure gold on the other. In air, the pans would balance. However, when the scales were placed underwater, a fake crown (which would be larger in volume than the same mass of denser gold) would experience a larger buoyant force as it displaced more water, and its pan would consequently rise.

It is precisely this principle from Archimedes that is used when accurately calculating body-fat percentage.

A subject is first weighed in normal conditions and then reweighed while sitting completely submerged on an underwater chair attached to a set of scales. The differences in the dry and underwater weight measurements can then be used to calculate the buoyant force acting on the person while under water, which in turn can be used to determine their volume, given the known density of water.

Their volume can then be used, in conjunction with figures for the density of fat and lean components of the human body, to estimate the percentage of body fat.

While it clearly isn’t as easy to use as the basic BMI measurements, and there may be better ways to assess body fat, this 2,000-year-old trick can certainly provide a more useful assessment of health risks. Läs mer…