The ketogenic diet has been a contentious topic since I first started writing about health. On the one side of the argument are the low-carb proponents, who insist that carbohydrates are the cause of pretty much all chronic disease. I’m not joking, that’s a real position that some people take. On the other side, you’ve got the establishment which—at least at first—took the position that ketogenic diets are high in fat and therefore even if they make you lose weight they are probably bad in the long term.

As is often the case, the truth is probably somewhere in the middle.

This brings us to the newest publication looking at ketogenic diets. According to the online buzz, the study proves that very low-carb diets are better for prediabetes, liver function, and general health. As The Independent put it, the weight loss method was “proven to be more beneficial than Mediterranean and low-fat diets”.

The trial that all of this news is based on is a fairly solid piece of research. So should we all be switching to ketogenic diets?

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In the ongoing culture wars, nothing is quite as attention-grabbing as transgender people. Even though trans and gender diverse individuals making up a tiny fraction of the population, they are given an outsized amount of concern no matter what the topic of the day is. Whenever a story pops up that touches on people who identify as something other than cis, the entire world has to have an opinion about it.

The latest is a study that has gone viral on The Bad Place because it draws a link between violent behaviour and what it defines as “gender ideology”. According to the headlines, the study has proven that people who support the broad concept of non-traditional gender identification are also more prone to be violent and support violent action.

As someone who has supported some form of “gender ideology” in the past, I was very interested in this argument. Could it be true that there is a social movement that has caused the “promotion of extreme narratives of harm for those who refuse to affirm transgender identities both socially and medically”?

Perhaps unsurprisingly, no. This is all based on a fairly ridiculous survey which was funded by the Trump administration. There’s no good reason to believe that people who support “gender ideology”, however you define it, are more likely to be violent extremists. If anything, the data suggests quite the opposite.

The Paper

The study that is being promoted so heavily on X is in fact a report commissioned by the US Department of Health and Human Services (HHS). This is the same HHS that released a farcical review into gender services at the start of the Trump presidency which had very little connection to science or evidence.

The report itself is quite simple. The authors recruited 1,192 people through an online research platform who answered a series of questions about their demographics and political identity. They then answered some short surveys asking about their agreement with what the authors termed “gender ideology”, their support of “left-wing authoritarianism”, and whether they supported several violent actions including the murder of Charlie Kirk, killing Donald Trump, setting ICE buildings on fire, and a few others. Finally, the authors ran a series of simple regressions looking at correlations between these questionnaires.

They found that there was a weak association between the support of statements such as “Parents should support their children’s transgender exploration.” and the belief that Donald Trump should be killed. There was a much stronger connection between what the authors called “left-wing authoritarianism”—we’ll get back to that in a second—and support for various violent acts against right-wing people or institutions. The authors of this overtly political publication concluded:

“People who strongly endorse gender ideology positions score dramatically higher on left-wing authoritarianism than their political identity alone would predict. Furthermore, both [left-wing authoritarianism] and [gender ideology] predicted support for political violence, these findings support the paper’s core claim: gender ideology may function as an ideological accelerant, telling left-wing authoritarian psychology who the enemy is, why the situation is urgent, and—in those already disposed toward force—that violence is legitimate.” (square brackets denote expansion of acronyms).

In other words, “gender ideology” makes left-wing people more prone to violence because it signals to the group the people deserving of that violence.

This is, in a word, ridiculous. The first obvious problem is what all of these terms actually measure. For example, “left-wing authoritarianism” was measured using the 13-item LWA scale, which asks people to rate these questions from 1-7 (with 7 indicating strong agreement):

1. The rich should be stripped of their belongings and status.

2. America would be much better off if all of the rich people were at the bottom of the social ladder.

3. When the tables are turned on the oppressors at the top of society, I will enjoy watching them suffer the violence that they have inflicted on so many others.

4. We need to replace the established order by any means necessary.

5. Anyone who opposes gay marriage must be homophobic.

6. People who are truly worried about terrorism should shift their focus to the nutjobs on the far-right.

7. The “old-fashioned ways” and “old-fashioned values” need to be abolished.

8. All political conservatives are fools.

9. Classroom discussions should be safe places that protect students from disturbing ideas.

10. University authorities are right to ban hateful speech from campus.

11. To succeed, a workplace must ensure that its employees feel safe from criticism.

12. I am in favor of allowing the government to shut down right-wing internet sites and blogs that promote nutty, hateful positions.

13. Getting rid of inequality is more important than protecting the so-called “right” to free speech.

Some of these are potentially arguable. You could say, for example, that supporting the replacement of the established order by any means necessary is support for authoritarianism, because it implies violent and suppressive action. Similarly, being in support of stripping “the rich” of all belongings and status does imply that you have some authoritarian leanings, even if the eventual aim is equality.

That being said, most of these questions just measure left-wing political beliefs. Most people of any political persuasion believe that their opponents are fools. Banning hateful speech from campus is a fairly complex political topic, especially in the US, and has a variety of meanings to different people. It’s not a straightforwardly authoritarian argument, except to a specific group of right-wing people who frame those ideas in a certain way.

In addition, some of the questions are just ignorant. Any reasonably well-educated person who was aware that the vast majority of terrorist acts in the United States are and have always been committed by right-wing individuals would at least partially agree with question 6. While it’s true that there are possible arguments against gay marriage that are not based on homophobia, the overwhelming majority of such arguments are unquestionably homophobic. A rational human being would therefore agree—perhaps at a 5 or 6 out of 7—with both questions 5 and 6, which calls into question whether the scale is measuring anything worthwhile.

It’s also important to note that despite most of the media discussing “gender ideology”, the correlation between support for transgender people and violent beliefs was very weak. In fact political affiliation - that is, voting Democrat - was a better predictor of support for violent action against Donald Trump, arson of ICE buildings, violence against the police, and the killing of Brian Thompson. Age appears to also have been at least as good if not better at predicting this perspective as well.

What Are We Really Measuring Here?

The main issue here is that the study was so nakedly political that it’s hard to infer any real meaning from the results. For one thing, the authors claim that their survey measured how much people agreed with “political violence”. But that’s obviously false—they asked whether people supported specific acts of left-wing violence.

There are plenty of forms of political violence that right-wing people generally endorse. For example, right-wing people are highly supportive of police violence against protesters, something that left-wing people on average disagree with. By only asking about violent actions that no right-wing person would ever endorse, and ignoring the violence that is generally popular in right-wing circles, the authors have created a useless, biased view of what “political violence” means.

There’s also no close connection between the questions asked about authoritarianism and actual support for authoritarian actions. They are more a marker of generic left-wing beliefs than any clear indicator of support for an authoritarian system of governance.

In my opinion, all the authors have done in this farce of a survey is measured political affiliation. Had they asked, say, whether the attack on Paul Pelosi was justified, they probably would have seen markedly different responses from their participants. Had they asked straightforward questions measuring adherence to authoritarianism, such as “should people who disagree with you be put in jail”, they might also have seen completely different answers.

Instead, what we’ve got here is a garbled, useless set of questions that mostly tells us one thing: the Trump administration continues to support pseudoscience. This is not particularly surprising, but it remains a very unfortunate fact.

In 2024, I wrote about a really bad paper that had just been published. It was causing a massive hubbub because the authors of the study were claiming that COVID-19 vaccines had likely caused a lot of deaths across the world in 2021 and 2022, and that COVID-19 itself was mostly harmless. While that was a common claim during the pandemic, this was a paper published in the leading journal BMJ Public Health, lending the ideas significant credibility.

The paper itself was terrible. The authors had simply lifted data from a previously published work called the World Mortality Dataset by Karlinsky and Kobak. They had then used Excel to poorly recreate some of the graphs from the original paper, and then added in their own opinions.

Karlinsky and Kobak showed mathematically that COVID-19 vaccines were not related to excess mortality, and that instead it was COVID-19 infections that were causing the vast majority of extra deaths. Instead of using this mathematical demonstration, or even discussing it, the BMJ Public Health paper implied that vaccines and lockdowns were the cause of excess mortality. The authors then spent several pages implying that governments were conspiring to keep this a secret, and saying that we should be doing massive autopsy studies to determine how many of the deaths were really caused by vaccines.

It was, as I said, a truly woeful paper. On the 11th August 2026, 874 days after the paper was published, it was retracted. You can read the full retraction notice here.

Obviously, the paper was terrible and COVID-19 vaccines save lives, but I’ve written about that many times before. There are additional things that are fascinating about this whole process that the BMJ notice lays out in detail. Let’s look at what happened with the paper:

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As a random white man born in London, I am not the right person to excoriate society for the issues here. I’m also not interested into delving into the question of whether a dead man made up aspects of his childhood in order to sound more impressive. It’s all ghoulish and awful and I want no part in it.

However, as a forensic metascientist—someone who examines academic work for serious issues such as fraud and the like—I have some thoughts about the published work and Arday’s treatment in the media. The entire story is remarkable because the facts have become completely overshadowed by the politics of the matter.

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There are two main things that I think are important about Arday’s story.

1 The Racism Of It All

It is not news when white men commit academic misconduct. It gets reported in a handful of stories, there’s a bit of media attention, but everyone quickly forgets about the problems. Many of the white dudes who do bad things at universities keep their positions for years after the allegations are proven, and some never face any real consequences for their actions. Specialty publications like Retraction Watch or the Chronicle of Higher Education will do some stories on these allegations or investigations, but there is almost no interest from the world at large.

Let’s take the example of Brian Wansink, former professor at Cornell University. After complaints about his papers were made public in early 2017, the university investigated and found no major issues with his work. Subsequently, sleuths identified dozens more problematic publications resulting in over a dozen retractions. In late 2018, Cornell announced that they’d conducted a more thorough investigation and identified “data falsification, a failure to assure data accuracy and integrity, inappropriate attribution of authorship of research publications, inappropriate research methods, failure to obtain necessary research approvals, and dual publication or submission of research findings”. Wansink was finally forced to resign, and left the university in June 2019. All of this got a fraction of the coverage that Arday saw, despite “data falsification” being worse than many of the allegations against Arday.

Or there’s Professor Dan Ariely. Several of his most well-cited studies were thrown into doubt when the data behind them was said to be fabricated. After a 3-year investigation, Duke University found that while the data behind a key paper was fake, there was no evidence that Ariely had knowledge of the fraud. The university has not elaborated further, and Ariely has kept his position despite one of his most impactful papers being retracted due to data fabrication and two more receiving expressions of concern.

Another case: Professor Tessier-Lavigne. He was the President of Stanford University when two papers where he was the corresponding author were retracted from the prestigious journal Science. This was after an institutional investigation that found that the papers had significant data manipulation in them, although again the university found no evidence that Tessier-Lavigne had manipulated the data himself. Tessier-Lavigne was forced to resign as the president of the university, but is still a full professor today despite dozens more of his papers being flagged with issues, several expressions of concern, and at least two further retractions since.

And then we’ve got Jason Arday. After allegations of plagiarism were made by self-described “race realist” Nathan Cofnas in July, Arday faced a wild storm of media criticism. More than 200 articles were written about him in the British media between the 24th of July 2026 and his death on the 14th of August 2026. The amount of public interest far outstripped anything that I’ve ever seen for any other case. Anaesthetist Joachim Boldt, the most retracted man on earth, whose fraudulent work almost certainly killed people, has had far fewer articles written about his papers since 2009 than Arday had in the space of a fortnight.

Arday was finally forced to resign on the 5th of August, just two weeks after the allegations were made public. He died nine days later.

This sort of massive media storm almost never happens for white academics who do naughty things, especially in the UK. They may get some pushback, but it fades far more quickly, and their careers often survive the investigations. Dr. William O’Reilly is still employed as a professor at Cambridge after he had “been found to have copied one of his undergraduate’s supervision essays” and the university had upheld the findings of “plagiarism” against him.

If Arday had not been a young, successful Black academic, the chances that anyone would’ve looked into his work are low. Even if someone did think to check his work, it’s likely that he could’ve just kept on going for years before there were any real consequences. On top of that, no one would’ve cared. Most of the time, even the most egregious plagiarists get one or two news stories about their misconduct and then are swiftly ignored by the world because it’s just not that interesting.

2 Arday Was Definitely Guilty

The second point is somehow even more frustrating. Yes, Jason Arday was hounded by a society that demands different standards for young Black men than white guys. Yes, the amount of scrutiny he received was obviously connected to the colour of his skin.

But also, he definitely did plagiarize. And not just a little bit. His work is riddled with clear, obvious, and extensive theft from other published academic papers.

Take his thesis. You can find it here. As many well-meaning left-wing people are saying online, Liverpool John Moores University—where Arday did his PhD—investigated this work and did not find any evidence of misconduct. The alleged plagiarism was “honest and reasonable error” caused by some deficiencies in his supervision.

This is absolute gibberish. You can very clearly tell that much of the thesis is straightforwardly copied from earlier work. There are sentences in the abstract of his 2015 thesis that are identical to sentences in the 2009 thesis from Brunel university by Paula Zwozdiak-Myers. Here’s a link to the 2009 thesis—download it and check for yourself. Arday’s work has extensive identical sections lifted directly from Zwozdiak-Myers’ work.

I had a quick go at identifying further plagiarism that has not yet been reported in Arday’s thesis, and it was trivially easy. Here’s a section starting at the bottom of page 44 of Arday’s thesis and taking up all of page 45. I’ve entered the text into a free online text comparison tool that flags it as 68% identical to segments of this 2010 paper.

I identified other clear examples of plagiarism on Threads. It was very easy to find, because large portions of the thesis are plagiarized.

You do not need to trust race realists or other unpleasant people. Just download the thesis and check for yourself. It is clear that a significant portion of the thesis was copied directly from other people’s work. This is not in doubt, it’s not in question, and the fact that so many people online are arguing that the plagiarism charges are false because a university said so is deeply depressing. It’s arguably the worst case of plagiarism I’ve ever seen.

There’s also extensive evidence about other papers by Arday. That’s the problem with plagiarism—it’s very easy to check. You can just search phrases from one paper and you’ll often find the source material.

The Whole Thing Is A Nightmare

I find this entire situation horrifying. Reading the dedication in Arday’s thesis—to his friends, wife, and children—was devastating. It is nightmarish that a pack of mean, nasty little men hounded Arday until he died. There is no question that much of this saga represents the sort of deeply unpleasant racism that requires anyone who isn’t white to be a paragon of integrity while white dudes often get away with warnings or slaps on the wrist.

However. In the context of Arday’s life and death, it’s understandable that people want to defend him. But his work is genuinely indefensible. He provably committed academic misconduct in a way that no one could possibly say was anything other than dishonest. You do not accidentally copy and paste lengthy sections from someone else’s PhD into your own. You certainly don’t craft your thesis out of a patchwork of identical text from other people’s work.

I don’t have any nice messages to end on. The entire saga makes me feel distinctly ill. Whatever your thoughts on plagiarism, it’s clear that Arday’s work harmed very few people. As he himself said, “I didn't murder somebody”. If he hadn’t been a Black academic and a poster child for diversity at Cambridge university, we’d probably have never known his name.

Arday was also failed by multiple institutions along the way. When he handed in his thesis in 2015, LJMU should’ve run it through plagiarism-checking software—which of course existed back then—and identified the issues. He would’ve been sat down in front of a very serious integrity committee and told to redo the thesis or be expelled. It happens. Instead, the university failed miserably and allowed this travesty to take place.

To be clear—he made his own bed. There is no excuse for plagiarism. It’s a truly ridiculous crime, and I’d expect any university to fire someone who was found to have substantial plagiarism in their PhD thesis and published work. But many others bear some responsibility for this enormous tragedy.

A man is dead and some of the most disagreeable people in the world get to celebrate. His poor family.

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This is the second part in my series of how to read a randomized trial. These detailed discussions of how to read and interpret the scientific literature are for paid subscribers only. If you like my work, please think about a paid subscription it really helps to keep me writing!

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Everyone loves a good anti-establishment story. The idea that all of those smarmy experts have been wrong all along is wonderfully compelling. There’s a sense of self-righteous indignation that you can only get for disagreeing with established wisdom for no reason at all only to be proven right years later. It’s why Joe Rogan’s podcast is so popular, even though at least 90% of the things said on it are some combination of nonsensical gibberish and outright lies.

For a long time, the expert consensus has been that we should probably eat less saturated fat to improve our heart health. One way to do this is to reduce your intake of full-fat dairy by drinking skim milk and choosing other low-fat options. This has been contentious, although it’s generally held up pretty well in scientific studies as a decent idea.

A recent study, however, may have overturned the consensus. Scientists have apparently shown that full-fat dairy has no negative impacts on heart health in a brand new randomized trial, the strongest form of research. The dietary guidelines were always wrong, and the contrarians were right!

Except, well, that’s not true at all. The reality is that the study had major issues, was substantially under-powered, and if anything showed that full-fat was worse for your health than skim. It was also funded by the Dairy Farmers of Canada, which wasn’t mentioned much in the media reporting.

The Issues

The newest paper was a trial where participants—overweight and obese Canadians aged 25-60—were randomly allocated to receive one of three interventions. The first group got given three servings of full-fat dairy products—yoghurt, cheese, and milk—per day for 3 months and were told to eat as much as they wanted. The second group got the same dairy products, but were counselled by a dietician to reduce their food intake a bit to account for the additional dairy. The final group were given no dairy, and were instead told by a dietician that they should reduce their food intake and only eat low-fat dairy where possible.

The researchers managed to recruit 107 people into their study, but only 74 completed it. They followed those people up for 3 months. At the end of the study period, they compared these participants on how much they weighed, as well as a wide range of dietary and other health outcomes.

The first issue with the reporting of the study is very obvious. The researchers pre-registered weight as their main outcome—this was the thing that they thought would be the most important when looking at dairy consumption and health. It’s the key outcome that usually they’d use to see if the interventions had a benefit or not.

And, it turns out, there was a benefit here. Specifically, people in the low-fat diet group lost about 1kg (~2lb) more weight than the other two groups over the course of the study. They also saw some modest improvements in waist circumference and BMI compared to the other groups. There were no other statistically significant differences between the groups across dozens of secondary outcomes including cholesterol, blood sugars, etc.

The authors have interpreted this in a truly bizarre way. Here’s the first paragraph from their discussion:

“The results support our hypothesis that adding 3 servings of full-fat dairy combined with counseling to follow the CFG would not adversely affect the blood biomarkers of chronic disease but would increase intake of limiting nutrients and decrease intake of food and beverages associated with chronic diseases.”

To be clear—the authors did not actually find that full-fat dairy had any impact on limiting nutrients or the intake of food and beverages associated with chronic diseases. The only thing that they found was that people in the dairy groups ate more dairy and had slightly more calories and calcium than those in the control. They also gained weight. In essence, the researchers proved that adding full-fat dairy to your diet increases your calcium intake and makes you a bit fatter.

The authors claim that their study shows that full-fat dairy does not adversely impact things like cholesterol, but there’s a huge problem there—attrition. The original study was designed to find an effect on body weight between the groups, and the researchers said that they’d need 153 people to find that effect. This would not guarantee us seeing any differences between other outcomes, like cholesterol, but it’d be at least enough to be reasonably sure that not finding an impact on body weight was meaningful.

But they only recruited 107 people into the study. Of those, 33 dropped out. The final analysis only looked at 74 people, which is less than half the required sample size that the researchers set out to get. In this sort of situation, a lack of any statistically significant finding is mostly meaningless—we cannot say that the lack of difference between the groups was due to the full-fat dairy being fine for cholesterol, it could just be that the study wasn’t big enough to notice the effect.

In addition, this was what’s called a per-protocol analysis. What this means is that the authors only included people in their study who completed the experiment. It is very rare to see this sort of study published these days, because per-protocol analyses are known to be extremely problematic. The gold standard is to include everyone who was randomized into the study—those 107 people—and use statistical methods to infer any missing data.

Worse still, the dropouts were not random. Nearly half of the control group dropped out of the study, compared to just one quarter of the intervention groups. This is probably because the intervention groups got given free food for three months and the control group got nothing.

At best, what this study shows is that full-fat dairy makes you put on a few kilograms and very little else. But there are some even more serious issues that are similar to papers that have previously been retracted.

The Bad Stuff

This next bit is very much in the weeds. If you don’t care about numeric mistakes and serious data problems, you can skip down to the end.

Now we get to the really massive problems with the study. The first thing that I noticed is that the randomization the authors describe is impossible given the numbers. They say:

“Block randomization was per-formed prior to recruitment on SAS version 9.4 (SAS Institute Inc.) by the study dietitian to generate a random allocation sequence stratified by sex with a block size of 12.”

The way that block randomization works is that you create blocks with random sequences of the groups in them. Each block has the same numbers from each group, which is used to balance the numbers being randomized to each group overall—in pure randomization, you often end up with one group that is somewhat bigger than the others. With three groups (let’s call them A, B, and C) and a block size of 12, each block contains a sequence that looks a bit like this: AACABCABBCCB. At the end of each block, the groups are by design exactly the same size. Within each block, a group can at most be 4 bigger than the others, say if you had CCCCABABBBAA then A would be 4 smaller than C until you got to the fifth person in that block.

Stratification by sex means that the authors ran these blocks separately for men and women. This means that the maximum possible difference between two randomized groups is 8 people—if every person at the start of the male block was randomized to A, and the same for the female block, and then the study didn’t complete its randomization, you’d get an additional 8 people in group A compared to B and C.

But the study reports a bigger difference than this:

42-31 = 11. That’s bigger than 8, and therefore impossible. Either the randomization was done incorrectly, or the description is wrong, or both.

The study also keeps mixing up the sample size. LD-ER is the control, and the other two groups are the intervention groups. They report that 18 people did not complete the study in the LD-ER group, but 28 people had full food record data. Only 10 people did not complete in the 3D-AL group, but 14 people had missing data for their food records. So what does did not complete mean, and why is the sample size so different? The authors don’t explain this issue.

There are also mathematical errors in the paper. Here is table 7:

This is a very important table. It shows healthy eating behaviours measured at baseline between the groups. If the groups were very different in their reported baseline healthy eating behaviours—if the control group, for example, got takeout more than the other two groups—it would mess up pretty much all the study’s conclusions.

The p-values, fortunately for the authors, are not statistically significant. So there are no differences between these groups on their baseline healthy eating behaviours. Except, we can recalculate these p-values using a one-way ANOVA, and they are wrong. I used the aovsum command in Stata to check this—the correct p-values are 0.42, 0.0016, 0.0036, and 0.025. At baseline, the control group ate significantly more takeout, had fewer home-cooked meals, and used fewer healthy cooking methods than the intervention groups. And they still lost more weight. That’s a massive issue which largely if not entirely invalidates the study.

There are also a bunch of copy-paste errors in the paper. You can see it in the table above—the p-value 0.0939 appears twice for two completely different analyses. It is incredibly unlikely to have two identical p-values to four decimal places for different analyses in one table, making this quite likely to just be a case of someone accidentally copy+pasting the wrong value into a cell.

There are other mathematical impossibilities. Take Table 9:

Do you see the problem? Have a look at the total number of responses and then look at the percentages. You can’t have 32% of 67 people—21/67=0.313… and 22/67=0.328… This either rounds to 31% or 33%. This is a mistake. Most likely it’s another symptom of the authors not knowing their sample size.

The mean values are also impossible. You can’t have a mean of 6.83 for a questionnaire with integer values and 67 participants. 6.82 or 6.84 work, but not 6.83. It’s a bad sign when people have tables full of impossible values.

Let’s do one final problem. Look at this snippet from Table 6 of the paper:

Have a look at the baseline and weeks 11-12 mean values. Now look at the mean change scores. Do you see the problem?

Again, this is bizarre. The mean change does not match the baseline and final scores. For example, the LD-ER group went from 0.53 to 1.06 serves per day of whole grains. That’s an increase of 0.53 But the authors report this as a change of just 0.31 serves. In some cases—such as for fruits and vegetables in the 3D-EN group—the change is in entirely the wrong direction. Oddly enough, the p-values appear to be correct this time, which implies that either the baseline or final values are wrong.

There are other issues in this paper, but you have to stop with these things somewhere. We’re already at 2,000 words and half of you have probably already stopped reading. Suffice to say that it’s extremely borked and I would not trust any of the results in their current form.

Bottom Line

It is hard to see how this paper got published. The authors state in their conclusion:

“We found that consuming 3 daily servings of full-fat dairy did not lead to increases in weight, body fat, HbA1c, BG or lipids when compared with an energy-restricted diet with low dairy consumption.”

That’s straightforwardly false. They found that eating more full-fat dairy makes you gain a bit of weight. Not a huge amount, perhaps, but given the massive issues with the study it’s enough to make it a bad idea to drink full-fat dairy if your aim is to lose weight.

Why did the authors put such a positive spin on such a negative, problematic study? We will never know for sure. But it is worth noting that industry-funded studies are well-known to have skewed reporting, and this paper was indeed funded by industry.

As this study—and all of the previous literature—has shown, full-fat dairy is a bit worse for your health than low-fat options. The dietary guidelines were, and are, right. Full-fat is not going to kill you, but if you want to lose weight and lower your cholesterol, low-fat is probably the best way to go.

Fitness hacks are one of the most popular things around. We all want to be fit, but getting fit is such a chore. It would be lovely if there was some way to avoid spending hours every week slowly building muscle and doing cardio at the gym.

And according to recent news, there’s a really simple way to cut down on your time at the gym and still see those gains. A new study has shown that one workout a week can be as good for losing fat as three, which is great news for anyone who wants to get stronger but just doesn’t have the time.

Of course, the reality is nowhere near as simple as that. Working out is good for you, but if you really want to get buff you still need to spend a lot of time lifting weights.

Let’s look at the science.

Getting Fit Scientifically

The new study is a randomized controlled trial conducted in Hong Kong of overweight and obese adults. The participants were divided into three groups: High Intensity Interval Training (HIIT) once weekly, HIIT three times weekly, or a control group who got no intervention. HIIT is a type of exercise program where you intersperse very high and low intensity exercise. It’s been previously shown to help people lose a little bit more weight than other forms of exercise, although it’s unclear how strong that effect is.

In this study, the HIIT was running on a treadmill. The intervention group participants were given a personal trainer who helped them get set up and run for their exercise periods over 32 weeks. At the end of the study, those who worked out once a week and thrice a week had lost similar amounts of weight as well as fat around their waist, while the control group stayed the same.

This sounds amazing at first glance. You can cut your workouts by 2/3 and still see the same gains! Isn’t that fantastic?

Except, that’s not what the study showed at all. You see, the once-weekly HIIT sessions were three times as long. Both the once-weekly and thrice-weekly groups aimed to get 75 minutes of HIIT in, regardless of how many sessions it took. That makes the entire story sound a bit less impressive.

The benefits were also modest, as you would expect if you’ve been reading my articles for a while. On average, people in the HIIT groups lost 0.5kg (~1lb) over the 32 weeks of the study. They lost about 1.5cm in waist circumference, and got a little bit fitter as measured by their oxygen usage. They didn’t see any benefits to blood pressure, cholesterol, or blood sugar, although the participants were generally pretty good on these measures at the start anyway.

This is consistent with the literature on exercise for weight loss. You have to do a LOT of exercise to see real weight loss for a variety of reasons. Adding an extra hour and a quarter of HIIT to your week is great for your health, but it’s unlikely to translate into significant weight loss for most people.

I would never tell you not to exercise. Exercise is fantastic for your health, and it’s a very important thing to do if you want to live a long and happy life. But the evidence is very clear—taking up exercise is not a great way to lose weight. As someone who runs 30km a week, I can confirm this personally—it’s really difficult to shed kilos by exercising.

The key thing that matters about exercise for the average human is quantity. Unless you’re a professional athlete, slightly improving your workouts isn’t going to do much for your weight or overall fitness. What really matters is that you can consistently do the exercise, not that it’s 2-3% better than some other workout.

This study shows exactly that. Both groups got the same amount of additional exercise, and both groups lost almost exactly the same amount of weight. They saw the same general benefits. It doesn’t really matter what exercise you do: it matters that you can do it consistently long-term.

There’s a very common myth that I keep seeing online. It goes something like this:

“The reason we don’t have a birth control pill for men is because when they tested it men said they wouldn’t take it because of symptoms like aches, muscle cramps, and depression. The same symptoms women have had on the pill for DECADES.”

You see this all the time. Here’s a recent post about the 96% effective male hormonal birth control and how it was shelved. It’s a very compelling narrative, so it goes viral all the time—women have been dealing with side-effects for decades but men can’t handle them for one measly trial just to get the birth control approved. Weak!

It’s just not exactly true.

Most of these posts are referencing this 2016 phase 2 study looking at a hormonal injection for male contraception. It was indeed stopped early because of concerns about side-effects, which were pretty similar in some ways to the side-effects that women have to deal with when taking hormonal contraception. But the reality is far more complicated and less satisfying than the online outrage.

Let’s look at the science.

The Study

The study in question was testing a combined injection of norethisterone enanthate and testosterone undecanoate in men with partners over the course of 12 months. It was a massive international effort that recruited people from 7 countries, and involved 10 different institutions.

The researchers recruited healthy men aged 18-45 with no fertility issues who had female partners aged 18-38. They had to have been in a stable monogamous relationship for at least 1 year, and have no desire for a pregnancy in the next 2 years following the study.

The treatment in this study works by suppressing sperm production through a combination of synthetic hormones—testosterone and progestogen—but it’s not an immediate effect. Men got injections of the study drugs every 8 weeks, but they had a suppression phase which lasted up to 26 weeks before the treatment was deemed effective. The men had to have semen tests every 2 weeks to see if their sperm count had declined to low enough levels that they were unlikely to cause a pregnancy.

Of the 320 men who started the suppression phase of the trial, 274 were successfully suppressed. Of the men who were not suppressed—meaning they still had high sperm counts after starting treatment—37 discontinued the trial for various reasons and 9 took the injections as designed and they simply didn’t work.

This is the first place where the reality diverges from the social media outrage. For one thing, the treatment is quite involved. You can’t just get a prescription and be assured that there are no babies in your immediate future. You have to take it for up to 6 months, getting regular semen tests, before it is effective, and even then there is a 3-14% chance that it’ll never work at all.

On top of that, there’s the question of efficacy for the men that reached the suppression threshold. The common statement is that the treatment was 96% effective, which is partly true. 96% of the men who continued to use the treatment long-term had their sperm suppressed to levels of less than 1 million/ml, which was the defined efficacy threshold for the study. But there were also 4 babies born to partners of the men during the study, and some men whose sperm rebounded unexpectedly. The combined efficacy rate taking all of these into consideration was 92.5%, which is a fair bit lower.

If we take the entire sample as our basis, that means that for every 100 men who use this drug combination, it will fail for at least 10 of them. Possibly more. It will still reduce their risk of getting someone pregnant, but we don’t really know how much. This is one of the difficulties in developing hormonal contraceptives for men—for women, you have to stop one egg, but in men you have to get to an acceptably low number of sperm which is more of a judgement call than a specific threshold.

This brings us to the next issue—side-effects. Here’s the full table of side-effects reported in the study, which I think is quite informative:

99% of the side-effects reported by participants were mild or moderate—there were very few severe adverse events. There were a few unfortunate issues—for example, 5.6% of the men reported gynaecomastia or growing breast tissue—but overall the side-effects were expected and not all that much of an issue. Most of the men experienced at least one problem, most commonly acne, libido changes, and some form of pain, but it was mostly pretty mild and in line with what you’d expect from any hormonal medicaiton.

Importantly, the men didn’t care about the side-effects. We know that because they said so. 85% of the men who used this hormonal contraceptive said that they would keep using it. 74% were satisfied or very satisfied with their outcomes. Despite the relatively unpleasant experience of taking the hormonal injections, and the general discomfort of a clinical trial, most of the men in the study were happy with the contraceptive.

So why was the study stopped? The authors described this in their paper. I’ve bolded the important part for emphasis:

“An independent Data Safety and Monitoring Committee (DSMC) was established by WHO/RHR and CONRAD before the start of the trial. At the scheduled meeting on January 17, 2011, the committee reviewed the interim analysis data and determined the study met all criteria for continuation. As part of WHO/RHR’s continuing monitoring review of all its ongoing studies, the department’s Research Project Review Panel (RP2), an external peer-review committee, met in March 2011, reviewed the same data and determined that, for safety reasons, recruitment should be stopped and enrolled participants should discontinue receiving injections and be transitioned to the recovery phase. Sperm recovery and other data collection and analyses were to continue. This decision was based on RP2’s review of study AEs and conclusion that the risks to the study participants outweighed the potential benefits to the study participants and to the increased precision of the study outcome findings from having the full cohort contribute to the final analysis. The AEs of concern to the RP2 were reports of mood changes, depression, pain at the injection site, and increased libido. The study DSMC, the investigators, and the study participants were informed of this decision by the study sponsors.”

Basically, the independent committee thought that the authors had enough data and didn’t need to enrol any more participants. The risks were also significant enough that the committee thought that the men currently taking the medications should stop. It was a somewhat odd decision, given that the men and their clinicians were fine with the drugs and happy to continue, but it was also not quite the same as stopping the trial “early”.

The decision makes a lot of sense when you realize that the study only set out to recruit 227 men in the first place. They had already gone nearly 100 men over their planned sample size, and therefore had very good data on the risks and benefits of the injections. The committee clearly believed that there was no strong scientific reason to continue the study, as the researchers already knew enough to say that the treatment was effective and to know the side-effect profile with quite a lot of confidence.

As someone who sits on an ethics committee, I fully understand this decision. Without looking at the actual data, I don’t know if I agree, but I understand it. At a certain point, every study has to end. If the committee thought that the researchers here had already gathered all the information they needed about the drugs, then the most ethical thing to do would be to halt the trial.

Where Are We Now?

So the treatment worked reasonably well, men liked it, and even though the trial was halted it wasn’t early per se. Where does that leave us with male contraceptives?

Honestly, it’s hard to find any good data on the topic. It looks like the whole thing was abandoned. In part, that’s probably because this isn’t an easily profitable medication. The two treatments are off-patent, and while some of the researchers did file a patent for the combination back in 2001, it was abandoned in 2006. I’m not a patent law expert, but that seems problematic if you’re looking to turn a profit. Even the trial was funded by the UN and charities, not by a pharmaceutical company looking to make some money.

There’s also a relatively limited market for the treatment generally. It’s primarily aimed at men who are in stable relationships, because there are obvious trust issues with men telling their partners that they’re on contraception in random hookups. It’s also a lot more involved than many of the options for women, including fortnightly checkups to make sure the treatment is still working. While there are many men who would like the option, I would not be surprised if people thought the investment was not worth it.

My personal theory is also that the myth that men wouldn’t tolerate any side-effects at all has played a role in killing male hormonal contraception. I think that potential investors saw the headlines and reactions, and assume that it’s probably true. It’s impossible to know if this is how it went, but it seems like something that’s probably part of the problem.

There are non-hormonal male contraceptives being tested, and personally I hope that some of them are successful. As a man in a long-term monogamous relationship with two kids, it’d be nice to have a less final option other than vasectomy for my own contraceptive needs. Technically they can be reversible, but it would be great to be able to pause my fertility for a year or two while we decided whether we wanted a third.

The stories online about men hating contraceptives get a lot of attention, but the science is both more interesting and much more complicated. We know that the majority of men would be happy to risk side-effects to prevent children, but as yet there is no way for them to do so.

There are many circular stories in the medical media world that are extremely irritating to me. Every 3-6 months we have a scare about artificial sweeteners and it’s almost always a non-event. The Environmental Working Group regularly puts out misleading press releases making a huge deal out of barely-detectable residues of heavy metals in some consumer product. Someone, somewhere, is already writing an article about how alcohol is secretly good for you for some obscure and unconvincing reason.

But not all circularity is annoying. My favourite thing is when food producers get together and run studies trying to prove that their produce is healthy. I’ve covered Big Blueberry numerous times—they fund at least one study a year trying to show that blueberries can save your life. I’ve seen Big Kimchi, the literal secretive government entity trying to get you to buy more kimchi, Big Walnut, and even Big Pasta. It’s always a fun story to tell.

The latest in the round of Big Food is Big Avocado. If you believe the news, avocados may be the secret to improving your heart health and not dying of a stroke. In reality, the data doesn’t show much of a benefit for the vegetable at all.

The Habitual Diet And Avocado Trial

The newest study is an analysis based on the Habitual diet and Avocado Trial (HAT, because everything needs a fun acronym). The HAT study randomized people to either get 6 months of nothing—eating their normal diet—or to get an avocado per day. Most of the people in the avocado group ate their avocados on schedule for the study period. At the end of the study, the researchers looked at various measures of heart health, in particular cholesterol levels.

The new paper looked specifically at different types of cholesterol molecules. We tend to say cholesterol as a single entity, but it’s really divided up into various different types—good, bad, etc—and then further subdivided into some quite specific categories. In this study, the authors found that an avocado a day reduced the total amount of low density lipoprotein (LDL) particles in people’s blood by around 2% over the course of the 6 months. This is bad cholesterol that is known to cause heart attacks, and so a fall is generally seen as a good thing, although given that statins reduce LDL by around 50% it’s not a particularly impressive reduction.

There were no changes, however, in any other kind of cholesterol. No increase in good cholesterol, no modification of triglycerides, nothing. There weren’t even changes in the specific subtypes of LDL, which was sort of the entire point of this article. So at best it’s a modest improvement in one type of cholesterol that may or may not be related to long-term heart health.

But that’s not the real issue. The bigger problem is that this is just a substudy based on the original trial. The original HAT paper came out in 2022, and found no benefits on any of the primary outcomes including weight loss, fat loss, blood pressure, blood sugar, or quality of life. So avocados don’t improve heart health based on most of the pre-specified outcomes that the authors thought were important before they started the trial.

The entire point of the new paper was to see if the modest reduction in LDL cholesterol seen in the 2022 paper also caused changes in the subtypes of LDL particles which are categorized by size (small, medium, large). This is important because the different sizes of LDL particles are thought to have different risks when it comes to heart health, so a small overall reduction in LDL might be very impactful if it is also causing a huge reduction in a specific subtype of LDL particle. But the paper found no differences in LDL subtypes by avocado consumption, and indeed no other improvements in any other measure of cholesterol.

In other words, this is a total nothing of a finding. They repeated a secondary analysis from their 2022 paper and found the same result, with all of the additional analyses showing no benefits for avocados.

Bottom Line

The original 2022 HAT study is a fairly robust, impressive piece of research. The authors convincingly showed, in a randomized sample of 1,008 people, that avocados have no benefits for heart health on most of the metrics that we use.

There was a modest benefit in LDL cholesterol specifically. Given the number of things that the authors were measuring, it’s not unlikely that this is a statistical quirk, but it’s possible that eating an avocado a day may improve your LDL a bit. It’s unclear if a 2% reduction is enough to actually improve your health, but it’s not nothing.

The new study adds nothing to that discussion. Given the fairly comprehensively null findings of the HAT trial, I think it’s fair to say that eating an avocado a day almost certainly won’t stop you from having a heart attack.

That’s not a reason to avoid avocados. They’re still a delicious food with an important place in a good brunch plate. But despite the efforts of Big Avocado, they probably don’t improve your heart health.

With the popularity of my recent series looking at how to read and interpret systematic reviews and meta-analyses, I thought I’d write a bit about the second most important type of research* in the medical literature: randomized controlled trials. They are a very finicky and surprisingly complicated type of study that most people struggle to really understand, even after spending a semester in classes focused on study design. This piece is for paid subscribers only, so if you enjoy my work please do consider signing up!

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There’s nothing we love to hate more than a scary chemical in our food. Any study that finds some sort of harm for a food additive is immediately thrust into headlines worldwide. There’s just something wonderfully simple about modern food ingredients being bad for our health—intuitively, we all sort of assume that stuff we don’t recognize must be harmful, so we love to hear that it is.

The latest study to hit the headlines is no exception. Based on a paper from French researchers common food preservatives found in the majority of foods you can get in a modern supermarket may be causing heart disease. As Wired put it, “Food Preservatives May Increase the Risk of High Blood Pressure and Cardiovascular Disease”.

The “may” is, of course, doing a lot of work there, but it still sounds terrifying. Food preservatives are pretty essential to our modern lifestyles. They are a key part of the way in which we store and consume food, and if we had to get rid of them there are many foods that would probably disappear from supermarket shelves entirely.

Fortunately for the world, the research is less than convincing. There is a certainly a paper showing some associations between preservatives and disease, but the likelihood of there being anything to worry about is very low. In fact, this whole debacle is a repeat of an almost identical story from 6 months ago.

Let’s look at the data.

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There are obvious cons to being bald. As someone who started losing their hair in his 20s, I can tell you that I definitely miss hair sometimes. I occasionally regret not dyeing my hair just to see what it would look like. You can always get a wig, but it’s just not the same as having your own hair.

But there are also pros. For one thing, I don’t spend money on hair cuts any more. I just shave it with a razor in the shower—quick, easy, and simple. No fancy shampoo or conditioner needed, I just wash with shower soap. It makes my life at least 5 minutes easier each day, probably more.

And, according to new research, it might be improving my health. As the New York Post is reporting, there appears to be a “surprising health benefit of being bald”. Apparently, being bald can reduce your risk of cancer.

The reality is, of course, a bit hairier than that. The truth is that going bald may be a signal of cancer risk, but it is very unlikely to improve your health. Let’s look at the data.

Bare Naked Truth

The newest study to hit the headlines is a case-control investigation looking at men with and without prostate cancer. This is a type of research where you identify people based on whether they have the disease you’re investigating, and it’s commonly used to try and identify things that may raise your risk of relatively uncommon conditions. For example, the issues posed by asbestos were primarily identified through exactly this sort of study.

In this case, the authors looked 1,001 men diagnosed with prostate cancer and compared them to a group of similar controls. They asked these men whether they had lost hair when they were 30, and 1 year before their inclusion in the study. They then ran a series of statistical models looking at whether reported hair loss was associated with prostate cancer risk—essentially, whether men who had prostate cancer were more or less likely to say that they had hair loss at 30 or have no hair when diagnosed than men the same age.

After correcting for a range of factors that can cause both baldness and cancer, such as age and family history, the authors found that men who said they were losing their hair at 30 were less likely to get cancer. However, hair loss at the time of cancer diagnosis was not associated with prostate cancer in any of the models.

This is obviously inconsistent. If hair loss is related to prostate cancer, then it should be related to the disease regardless of which age it happens at. Also, the age 30 variable is by far the weaker one—most of the men in the study were over age 60. This creates a very big risk of recall bias. This is exactly what it sounds like—people get worse at remembering stuff the longer it has been since it happened. It is unlikely that the average 60-year-old could remember the exact time that they started to lose their hair, which means that the estimate of hair loss at age 30 is pretty murky.

It’s also not an especially robust study. It’s not tiny, but it’s not huge, and the authors didn’t have a great deal of information on the men in the dataset. There are all sorts of potential biases that they simply couldn’t control for in this analysis.

Hair Loss and Cancer

This brings us to the evidence-base more generally. This is hardly the first time that anyone has looked into whether hair loss might be connected to prostate cancer. There’s actually a fairly plausible biological connection, since both male baldness and prostate cancer are influenced by hormone levels, and therefore they could very well be connected to some extent.

Unfortunately, the evidence does not really support an association between the two. The biggest study I could find on the topic which enrolled men in their 70s and asked them to remember whether they were bald at age 45 did not find an association between balding and prostate cancer. A large prospective trial from the US where men had their baldness assessed in 1971-4 by a dermatologist and were then followed up in 1992 found an increased risk of cancer in men who were going bald. A systematic review from 2018 including 15 studies in total did not find any association overall.

In other words, while this new study adds a little bit to our knowledge-base, the reality is that we have not seen a consistent association between baldness and cancer. It’s possible that there is an association that we haven’t discovered yet, but given the current studies I’ve seen published I don’t think it’s very likely.

Also, none of these studies claim to show that baldness protects against cancer. The entire concept is that hormones may influence both cancer risk and hair loss, not that losing your locks somehow prevents cancer cells from forming in your body.

Baldness doesn’t prevent cancer. We all knew that, I think. Perhaps more interestingly, despite the recent headlines it doesn’t seem to be associated with prostate cancer either.

This is the third in my series teaching you how to read and interpret scientific studies. This time, we’re looking at meta-analyses. These are one of the most important types of statistical analyses that you’ll come across, and there are a few really simple things you need to understand to not get tricked by them.

Part One

Part Two

These pieces are for paid subscribers only. If you like my work, and want to learn more about interpreting biomedical research, consider subscribing!

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Smoking was a remarkable public health success. A single, simple behaviour that on its own increased your risk of dying by a truly staggering amount. Despite the addictive nature of nicotine it’s still relatively straightforward to get people to change a single facet of their lives.

These days, everyone is trying to find the new smoking. We all want to identify another simple trait that we could fix to save millions of lives. One thing that pops up regularly is the idea that sitting is the new smoking—that spending long periods of time sitting down is a simple, easy-to-change behaviour that makes us sick.

The newest study seems to support this idea. Sitting down is, it seems, causing us to get cancer and die more often. If you just replace an hour of sitting with some movement every day, you’d live a lot longer, according to the headlines.

There are many issues with this idea. Yes, sitting down a lot probably isn’t ideal for your health, but there’s no reason to buy a standing desk just yet. Let’s look at the science.

The Data

The new paper that’s making headlines is a large observational study based on the large UK Biobank cohort. The authors had data on about 100,000 people who wore accelerometers—things that measure your movement, similar to a Fitbit—for 7 days between 2013 and 2015. In a model correcting for a range of common confounding factors, they found that sedentary behaviour—sitting down—was associated with an increased risk of dying from various cancers. They also found that people who had more “interrupted sedentary time”—defined as getting up frequently during periods of sitting—were less likely to die from cancer.

The numbers were, however, a bit unimpressive. People who spent the most time sitting down—on average 13+ hours per day—had a 2.8% risk of dying from cancer in the decade of the study. People who spent the least time sitting down—less than 10.7 hours per day—had a 1.3% risk of dying. That’s not nothing, but it’s not quite the sort of health impact that you’d see from smoking.

The findings were also quite complicated. The authors ran what’s called a replacement analysis where they looked at what might happen if people were to replace one activity with another. They found that replacing prolonged sedentary time with interrupted sedentary time actually had no impact unless the interrupted sedentary time included exercise. That is, standing up was not associated with better health unless you went for a walk or did some star jumps.

This sort of study also has all of the usual issues that you see in observational research. For one thing, the authors only had movement data at one time point. They got 7 days of physical activity for each person sometime between 2013-2015, and had to use that to define their movement for the entire study.

This creates some obvious issues. Reverse causality, for example, is when your outcome variable—in this case, cancer—is causing the intervention variable. In other words, people who were experiencing the early stages of cancer or other health problems that would eventually lead to cancer might have already been spending more time sitting down at the start of this study. That would make the results here largely meaningless, and it’s a possibility the authors do not appear to have looked into.

There’s also the ever-present issue of unmeasured confounding. People who spend less time sitting were are different in lots of ways to people who don’t sit down much at all. In this study, the people who spent the least time sitting down were younger, smoked less, drank less alcohol, exercise far more, were thinner, and were generally much healthier than people who sat down a lot. It’s likely that there were some things that the authors simply could not measure which also influenced the cancer risk, which makes it hard to know if the increased risk of cancer related to sedentary time was caused by the sitting or by something else that the study couldn’t control for.

Exercise Is…Good?

The bottom line here is that the study doesn’t really tell us anything new. People who get more exercise are healthier than people who get less. There’s probably some causal relationship there, because we know that exercise improves your health in a multitude of ways.

Whether this is really related to sitting down or not is up for debate. I think you could argue that the results from the replacement analysis prove that sitting has very little to do with the issue—instead, it’s how much exercise people are getting and how much of that exercise is vigorous vs light.

At best, the study shows that if you sit down a lot it’s probably better to go for a walk once every 90 minutes or so than to stay sitting down the entire time. That is, I think, not really news to anyone. Yes, sitting down for your entire day probably isn’t ideal for your health, but there’s still no strong evidence that standing up for the same period of time is good for you. The real trick is to get more exercise, but unfortunately that sort of boring health advice rarely makes headlines.

In the world of science, the main process we have to deal with seriously bad research is called retraction. This hearkens back to the old days of scientific publishing when articles were in paper journals read by a small handful of academics. They would issue a retraction notice in the newest issue of the journal to let everyone know that the editors no longer trusted an article for whatever reason—obviously, in paper journals, you couldn’t simply remove everyone’s access to the article from a previous issue.

These days, the process is much simpler, because most scientific articles are published online. While some journals do still exist in print, and will issue old-style retractions, most will simply update the online page to note that the journal has retracted the paper and slap a big red RETRACTED watermark on the PDF. This process is used for everything from studies with serious mathematical errors to plagiarism to numeric fabrication—it’s mostly reserved for the biggest mistakes or crimes of academia.

I’ve written before about how this whole process is a bit broken. It often takes years for journals to do anything about even the most obvious misconduct, and sometimes they simply never do. I was once told by an editor that he wouldn’t retract a paper even after the author sent through a clearly fake dataset because he thought readers would still be interested in the work.

It’s a system that relies on trust to the point that you might call it painfully naive.

But what about the worst of the worst? People who’ve had dozens of retractions for all sorts of problematic issues, and whose papers are undeniably unreliable? Not all of their work gets retracted, largely due to the endless swathe of red tape involved, but surely no one would cite their work?

We set out to look at precisely that question. The answer is a bit depressing.

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Positivity is everywhere these days. You can’t go more than a mile without seeing a bunch of posters extolling the benefits of always looking up, or having an activewear-clad smiler tell you that all you need to do is put a smile on your face and the world will improve. This is not helped by the now-debunked—but still common—belief that simply smiling would make you feel more happy.

People love the idea of positivity. If all you need is a sunny outlook to make your life improve, that makes everything so much easier. Which means that journalists leap on any study showing even a slight benefit for positivity like starving wolves sighting a lame elk.

The most recent example of this phenomenon is a series of headlines saying that being positive is the one secret trick to aging well. Apparently all you need is a “healthy mindset” and you can improve your brain and body as you get older.

Unfortunately for everyone who is reading this with a forced smile on their face, the evidence is incredibly weak. There’s no reason to believe that simply changing your thoughts can prevent age-related decline.

Let’s look at the data.

Positive Age Beliefs

The new paper that has everyone all agog about positive thinking is a large epidemiological study looking at “positive age beliefs” in a cohort of people in the United States. The authors took a group of people enrolled in the Health and Retirement Study, and analyzed those participants who had scores at baseline and follow-up for cognitive function—brain health—and physical activity. While the original study included more than 20,000 people aged 65 and above and was nationally representative, this new paper only had brain measurements for 11,000 and physical measurements for 4,000 of the initial sample.

The first thing that the authors noted was that a reasonable portion of the sample improved either physically or cognitively. Indeed, 45% of those that they had data for showed a higher score at the end of the study than at the start on either physical or cognitive scales.

But that’s not the headline finding. At the start of this study, people were asked a series of five questions about how they feel about aging. Some examples of these: “The older I get, the more useless I feel and I am as happy now as I was when I was younger”. The authors dichotomized the responses—splitting people into either high or low positivity—and looked at the correlation between these five questions and their long-term physical and cognitive ability. In a model that controlled for various confounding factors, they found that people who had more positive age-related beliefs had 4% (p=0.049) higher odds of having physical and/or cognitive improvement over the 12 years of the study than people who had fewer positive beliefs.

And from this, headlines.

There are so many huge gaps here that it’s quite hard to know where to start. The entire thing is a bizarre example of what happens when no experts are involved in communicating scientific studies at any point in the process.

For one thing, there’s the issue of selection bias. The authors only had information on half of the study for any outcomes—what happened to the other 9,000+ people? It’s quite likely that people who experienced serious health problems dropped out of the cohort, which would cause a huge problem for this sort of analysis.

Also, while 45% of people saw some improvement in either of their scores over time, the average results dropped. A lot of people had some improvement in one, but a drop in another. So even in this highly-selected group, the overall result was a decline in both physical and cognitive ability over a decade of aging.

The result is also hilariously weak. A 4% increase in odds that you have any improvement in physical or cognitive ability is basically meaningless. It’s the sort of effect size that’s only noticeable in huge cohorts, because it’s so tiny. Also, the authors didn’t include key confounding factors like cancer and respiratory disease in their statistical model, so there’s every chance that minor changes to the modelling here would render the results null.

There’s the issue of reverse causality. People who are 65 and already have a negative view of aging are probably not doing that great. It’s perhaps entirely understandable that such people would end up doing worse on measures of physical and brain health over a decade.

The positivity scale is very inadequate. It mostly measures how people feel about aging, but that’s closely tied to all sorts of things. Whether this actually measures a positive outlook or not is entirely up for debate.

And on, and on. It’s not a very good study, and at most it tells us that there is a very weak correlation between answers on this particular subscale of the Philadelphia Geriatric Center Morale Scale and long-term physical/cognitive scores in this highly-selected cohort of people. What that means for aging is anyone’s guess.

Healthy Aging

We’ve known the “secret” to aging well for decades. It’s the same simple, boring health advice that you hear all the time—exercise more, don’t drink alcohol, don’t smoke, eat less food. Most other things turn out to be more noise than signal, and this looks to be no different.

If you want to try and have a positive outlook on life, go ahead, but the evidence that thinking positively will actually improve your health is not particularly strong. This study in particular mostly just shows that mediocre research can still make headlines as long as no one looks too closely at the data.

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It feels like there’s a new cure for aging every week. Where once we talked about health in general terms, or wanted stuff that enhanced our wellbeing, the new quest is to break the bonds of time and stop people from getting older. Or, at least, to stop us all from experiencing the health problems that happen when you reach a certain age.

The newest story that has everyone very excited about no longer getting older is about a nasal spray. A new study has, it seems, shown that we can prevent age-related disease with nothing but a couple of puffs up our noses. If true, this would be revolutionary for anyone who is concerned about their own mortality.

Sadly, the data does not hold up to even the most basic of scrutiny. This is yet another example of extremely early research that is unlikely to ever be used in real people to improve their health.

Let’s look at the science.

The Study

The recently published piece of research from the journal of extracellular vesicles has the rather fascinating and difficult to pronounce title “Intranasal Human NSC-Derived EVs Therapy Can Restrain Inflammatory Microglial Transcriptome, and NLRP3 and cGAS-STING Signalling, in Aged Hippocampus” .

The specifics of what the researchers did are quite convoluted, but in essence they looked at what happens when you give nasal sprays of human stem cells to areas of the brain. They gave these sprays in old age, and examined how that impacted the brains of the treated patients. When dissecting the brains at the end of the study, they found that those who had received the nasal sprays of these stem cells had less inflammation and fewer negative brain changes than those who had received another type of treatment that acted as the control.

That sounds impressive, or it would if the patients were people. Of course, you can’t really do dissections of brain tissue in most human studies, so the clever among you have probably already realized the issue here: the study was in mice. Specifically:

“The study comprised two cohorts of C57BL/6 mice: young adult (3 months old) and late middle-aged (18 months old).”

So what we can say is that there are some interesting findings—and to be clear, the findings are quite interesting—that show that these intranasal sprays may have an impact on mouse brains. This has, unfortunately, very little meaning for the average person who is interested in their brain health.

Most treatments that are tested in mice fail to make it to humans. Sometimes the mice just react differently, sometimes the treatments are unexpectedly deadly to people for some reason, and sometimes we don’t really know why they aren’t working—whatever the explanation, fewer than 1% of treatments that show some benefit in rodents end up treating real humans in the real world.

Bottom Line

I’m not trying to attack basic science here. We need studies of new chemicals and techniques in rodent brains otherwise we’d never develop anything that works in people. It’s vital that we do studies like these, because they are what eventually leads us to things that work.

That being said, this is very early days. Giving some mice nasal sprays is the very first step in a decade-long process that might, if the researchers are very lucky, lead to something that works. Chances are it won’t end up curing aging either, because real life is rarely that neat. The most likely outcome by far, however, is that this does not pan out and we never hear about it again.

I’ve covered dozens of these stories in my decade working in the science communication space. The main problem is that we hear about them at all. It’s interesting research, but of the thousands of similar stories only one or two will end up being anything other than a footnote in a report about failed treatments that we’ve already tried.

In 2023, I started a series for paid subscribers looking at some basics of epidemiology and evidence appraisal. Stuff that you probably wouldn’t know unless you’ve done a masters of public health or similar, but which is really important for understanding how the world of scientific evidence works. For various reasons—mostly other stuff coming up—I stopped writing the series and haven’t picked it up since. Fortunately for you all, a devoted reader looked at those early pieces and asked if I’d resume the series. I think it’s a great idea, so thanks reader!

If you haven’t read part 1 of systematic reviews you can find it here:

I’d recommend reading that first. I go into the history of the subject, and why you should care about this sort of scientific study. Now, on to the meat of the issue. If you come across a systematic review, what should you do?

Read more

It’s been two years since the Cass review into gender identity services in the UK came out. Since then, the UK has progressively restricted access to gender clinics of all kinds for trans teens, now offering only a handful of appointments a year. The new clinics exclusively focus on psychological and psychosocial interventions, with the UK government having banned the use of puberty blockers entirely based largely on Cass’ recommendations.

The latest foray into removing access to medical interventions for transgender youth is the new NHS policy on hormone therapy. The draft policy proposition from NHS England states:

“NHS England has carefully considered the evidence reviews conducted by Solutions for Public Health (2026) and has identified and reviewed any further published evidence available to date. We have concluded that there is not enough evidence to support the safety or clinical effectiveness of feminising and masculinising medicines to make the treatment routinely available to children and young people at this time. This includes both individuals who identify with a binary, or a non-binary gender. “

This is based largely on a series of reviews conducted by Solutions for Public Health (SPH, an NHS evidence reviewing body), which are also linked in this policy proposal. The organisation has conducted a remarkable 10 systematic reviews and meta-analyses looking into a relatively simple question, and come to the conclusion that there is no good evidence that hormones are beneficial for transgender young people.

This is interesting, because the previous NHS-commissioned review, conducted by the University of York as part of the Cass review, found that there was “moderate quality” evidence suggesting a benefit for hormones. These new systematic reviews are, in theory, an update on that data, but somehow found substantially less evidence that hormones help transgender teens.

I was commissioned by Gender Plus, a private clinic for gender identity treatments in the UK, to look into these studies and provide a report on them. They are, in my opinion, largely useless as a guide for policy in the UK or anywhere else. They have serious weaknesses, and do not allow us to make useful decisions about, say, whether to ban hormones for transgender teens in the UK’s gender clinics.

Methodological Weaknesses

The first thing that I found in my report is that the reviews are quite weak from a methodological perspective. Specifically, they don’t report quite a lot of the important things that you’d expect systematic reviews to report. A systematic review is a way of aggregating all of the evidence together on a specific topic, and the methodology is therefore basically just how the authors identified studies and grouped them together. Usually, you report things like how you downloaded the studies, who did the extraction, and how exclusions were made.

These 10 reviews do not report any of those things. Instead, they refer to an internal NHS document called “Guidance on conducting evidence reviews for Specialised Services Commissioning Products’ (2020)”. There’s no information on what that document contains, and I could not find it online, which means that we simply do not know important things about these reviews.

For example, one common issue in systematic reviews is that it is not simple to decide whether a study meets or does not meet the inclusion criteria. It can be easy to miss specifics that might cause a study to be dropped from the analysis. That’s why we usually get two people to independently review the studies we’ve identified in our searches, and if they disagree on what gets included we have a third person on standby to be the deciding vote.

But in these SPH reviews, they don’t say that they did this. There’s just that reference to an internal document that may or may not include this important step. It’s possible the authors did do their reviews using standard methodology—it’s also possible that they did everything completely wrong. We just don’t know.

There are many basic measures of good systematic review practice that these SPH reviews don’t describe. That’s a serious deficiency, and makes it much harder to trust the results at the outset.

Excluding Most Evidence

The methodological weaknesses are bad, but much worse was the way the reviews put together their questions. The studies that they actually included. In all 10 reviews that they conducted looking at hormones for gender diverse teenagers, they included a total of just 11 studies. 6 of the reviews included no studies at all. In contrast, the York University review included 53 studies on the same broad question. How is this possible?

The issue comes down to the specific questions the SPH authors asked. A systematic review is fundamentally about finding the literature looking at a medical question. The method used by SPH to define these questions is called PICO—Population, Intervention, Comparator, Outcome. For example, we might want to know what impacts metformin, a diabetes medication, has on mental health compared to a placebo in people with pre-diabetes. That would be:

P: people with pre-diabetes

I: metformin therapy

C: placebo pills

O: mental health

There’s an important point here though. It is quite easy to make your PICO framework too broad—there are probably quite a few studies on the above question, making it a VERY tedious process to try and comb through them—but it’s also possible to make the PICO too narrow. If we changed the above PICO framework to something rather more specific:

P: women aged 40-55 with pre-diabetes and no history of mental health disorders who do not drink alcohol, take a statin, and exercise regularly

I: metformin taken twice a day

C: matched placebo pills taken in the same schedule as the metformin

O: a composite outcome including depression and anxiety scores

We would probably end up finding no studies at all looking at our new question. But the issue here is not that there is no research which might inform us, it’s that we’ve chosen a ridiculously narrow question to ask of the data. There are probably no randomized studies of metformin on this extremely specific population, but we have lots of randomized trials including many different groups that would almost certainly give us a strong answer as to what metformin would do in this situation.

In the SPH reviews, they made a simple mistake. Their questions were FAR too narrow. The example I picked in my report was review 2417k. This review had the PICO:

P: Children and young people (up to their 18th birthday) who have gender incongruence as defined by the study and identify as non-binary and wish partial physical masculinisation.

I: Masculinising medicines comprising testosterone monotherapy.

C: One or a combination of: 1. Psychological and psychosocial support 2. Social transitioning to the gender with which the individual identifies OR 3. No intervention

O: Impact on gender incongruence, Impact on mental health, Impact on Quality of Life, Masculinising physical changes, Psychosocial impact, Fertility, Feasibility of masculinising genital surgery, Cognitive outcomes, Detransition after receipt of masculinising medicines, Regret after receipt of masculinising medicines, Safety, Cost-effectiveness

This is patently absurd. For one thing, I have not been able to find anyone else who uses the phrase “partial physical masculinisation”. The concept of giving hormones to non-binary youth to cause partial changes in their gender expression is mostly theoretical at this point, and there are no studies looking at what this might mean in practice.

In addition, the ages are defined quite tightly. There’s little practical difference between hormone use for 17 year olds and 18 year olds, but the authors decided that they would exclude any study that looked at people over the age of 18 (this is also bad in another way—we’ll come back to that).

On top of all of this, they are only looking at “testosterone monotherapy”. That means that kids who ever got puberty blockers are excluded from this and all of the other SPH reviews that discussed monotherapy. The SPH team did run 4 reviews on combination therapy—hormones and puberty blockers—but they only included studies where the hormones and puberty blockers were provided concurrently for all patients in the paper.

This is ridiculous because any study where some portion of children got puberty blockers before hormones was automatically excluded from all the SPH reviews. That’s a large portion of the evidence-base on hormone use in trans teens. Worse still, it was standard NHS policy to require puberty blockers be given before hormones for children in the UK for nearly a decade, meaning that SPH automatically excluded all data from their own country as part of their review process. The reviews noted that “The generalisability of the findings to the UK is limited.”, but this was entirely due to the authors disregarding any UK evidence as a key part of their protocol.

It’s also problematic because most studies combine both gender transitions in their analyses. That is, they look at the impact of gender-affirming hormones for both young trans men and women at the same time. The biggest single cohort of trans youth taking gender-affirming hormones, for example, does not break out its analysis by gender, which means that it was excluded from all of the SPH reviews as well. One of the largest analyses of trans youth—which showed a reduction in suicidality for teenagers prescribed gender-affirming hormones compared to those who did not get hormones—was excluded because results were not published separately for those receiving testosterone vs oestrogen.

As I pointed out in my report, there are at least a dozen papers that show benefits for hormone use that SPH excluded in this manner. They defined their questions so narrowly that basically no studies could be included in the reviews, and then concluded that there was insufficient evidence to support the treatment. But the lack of evidence was just due to the mistakes in their own review process.

Inconsistencies

The final issue with the reviews that I discussed in my report was the inconsistency in how they applied their own inclusion criteria.

Remember the thing about age from above. Technically, the authors stated in their methodology that they would exclude any study that had people over the age of 18 in it. However, 7 of the 11 included studies had people >18 as part of the sample. What the SPH team appears to have done is decided that they could include studies with an average age <18. So a paper where the average age was 17.92, and which included a few dozen people over 18, would be included, but a paper where the average was 18.01 would not.

This is, obviously inconsistent. SPH was extremely rigorous with their application of the hormone prescription criteria. There were dozens of papers excluded because those papers had some teenagers in them who were given puberty blockers before hormones with no question about the average child.

But when it came to age, the reviews were much less stringent. There’s no information on how and why this choice was made.

Similarly, the reviews were divided into binary and non-binary groups. Every question was asked both for a binary physical transition—say, male to female—and for a non-binary physical transition (which was not defined by the SPH team). But there are virtually no studies that separate out their patients into those who wanted a binary and those who wanted a non-binary physical transition—they do occasionally just exclude non-binary people entirely—largely because the phrase “non-binary physical transition” appears to have been introduced by the SPH authors as part of these reviews.

The authors noted for all 11 of their included studies that they did not report whether these teenagers were given hormones for a binary or non-binary transition. But they still included them.

Had the SPH authors treated all of their PICOs as similarly important, they would’ve included no studies at all in any of their reviews. That’s rather problematic.

Should Teenagers Be Able To Access Hormones?

In my opinion, these SPH reviews are a total waste of time. You can’t possibly produce 10 reviews including just 11 studies and expect people to take the work seriously. The authors excluded most of the work looking at hormones for transgender teens, and ended up publishing what I would call completely useless systematic reviews.

They also excluded all evidence on simple, boring questions like whether hormones cause physical changes. This lead to the remarkable statement that the SPH team was unsure of whether giving children cross-gender hormones would cause changes to their bodies. Does giving testosterone to someone assigned female at birth change their body? How could we possibly know???

Which brings us to the real question: what does the data say?

We could go back to the York review, which found that there was some positive evidence showing that hormones improve the mental health of trans teens. We could also look at some of the data that’s come out since then, which also appears to be quite supportive of providing hormones to 16 and 17 year olds who want them. We could even read the only randomized trial of hormone therapy for transgender people, which was in young adults and found a fairly large benefit for mental health, and extrapolate to these older teens.

Unfortunately, the evidence is still just not that strong either way. As I’ve said many times before, I have no firm opinions on medication use for teenagers with gender dysphoria. The current best evidence is certainly supportive of hormone use for trans teens, but we still need better data before we can be really sure.

Regardless, what we can say with a great deal of certainty is that NHS England is currently relying on terrible evidence to make decisions about trans healthcare. It’s not just the SPH reviews—the NHS policy guidance document says:

“NHS England commissions the specialist NHS Children and Young People’s Gender Service. The service provides a focus on psychosocial, psychological and psychoeducational support following individual and family assessment.”

But their own evidence review—conducted as part of the Cass review—found that there was literally no evidence looking at these psychological, psychoeducational, or psychosocial interventions. Based on their own data, the NHS is implementing the treatment paradigm with the lowest quality evidence, all while publicly arguing that evidence is key.

I don’t know whether giving 16 year olds with gender dysphoria hormones is necessarily the best thing to do, but what I can say is that it has a lot more evidence behind it than the current NHS proposal.

The question of how many people get Long COVID has always been contentious. The reason for that is pretty obvious—as COVID-19 has decreased in acute severity, causing far fewer hospitalizations and deaths, the argument has become almost entirely about Long COVID and the possible impacts it may be having on our health. If COVID-19 causes few deaths, but leaves lots of people sick with a new chronic disease, then it may still be something that we need to spend time on even now in 2026.

The newest estimate of Long COVID rates has just come out, and it’s remarkably high. A new paper has estimated that 1 in 6 people who catch COVID-19 end up with Long COVID, and that the number is actually increasing over time. If true, this would mean at least a billion people with long COVID worldwide, likely more.

The number is, however, absolutely ludicrous. There is absolutely no plausible way that this many people get Long COVID after an infection—even in 2020 the high-quality data showed much lower percentages.

The Study

The new paper that has caused a furore on Bluesky is called “Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals”. It’s a fairly complicated calculation of Long COVID rates based on a dataset of nearly 500,000 people across 58 hospitals in four US states.

The authors developed what they describe as a “custom artificial intelligence algorithm” which identifies whether people had Long COVID or not. Based on this algorithm, they classified 16.28% of the patients in their cohort as having had Long COVID after their infection.

This estimate is one of the worst that I’ve seen in years.

The first issue is the algorithm. While the authors describe it as artificial intelligence, in practice it’s quite a simple mathematical calculation. The approach initially measures temporal correlations between various conditions. Basically, it identifies whether two things are likely related in the cohort in question, so for example whether chronic heart failure is related to chest pain.

The algorithm itself then uses these correlations to determine whether disease codes—the way that hospitals record the health problems that you have—are related to something an individual had before their COVID-19 infection. For conditions on the list of things that the authors have decided constitute Long COVID, if there is no other defined cause—so for example if someone has chest pain but no chronic heart failure beforehand—and they last for 2 months or more, they are considered to be Long COVID.

This sounds complicated, but in practice it’s really quite simple. There’s a list of conditions that the authors think are all part of Long COVID. If someone experiences one of these conditions for 2+ months after their COVID-19 infection, and it can’t be explained by another health condition that they had before their infection, it is considered Long COVID.

This means there is absolutely no assessment of causality in the study. This is just an estimate of the proportion of people who experience one of the conditions that the authors put on their list after having a COVID-19 infection. There’s a little bit more to it than that, but in essence that’s what the study shows.

In addition, there’s the list itself. While some of the things on the list make sense—for example, chronic fatigue—some of the conditions are truly bizarre. Here are some examples of International Classification of Diseases (ICD) codes that are included in the authors definition of “Long COVID”:

F14.180 Cocaine abuse with cocaine-induced anxiety disorder

O90.4 Postpartum acute kidney failure

D84.821 Immunodeficiency due to drugs

H53.50 Unspecified color vision deficiencies

E86.0 Dehydration

R19.6 Halitosis

E55.0 Rickets, active

J15.5 Pneumonia due to Escherichia coli

The list goes on. While you could argue in some cases that COVID-19 could be a contributor to some of these things, most of them are very straightforwardly not related to a coronavirus infection in any plausible way. Dehydration has a vast array of potential causes. Pneumonia due to E. Coli is, well, caused by E. Coli. Postpartum disease is by definition related to the pregnancy.

This is one of the inherent issues with Long COVID that I’ve been writing about for years. If you define a disorder as pretty much any symptom that could occur, you’re going to find a lot of that disorder in any population. But most of these things are almost certainly not related to COVID-19, and it makes no sense to include them in a disease definition for Long COVID.

On top of this, the authors have proven in their paper that the algorithm they used was terrible at estimating prevalence. The methods includes this paragraph:

Algorithm Validation Through Distributional Robustness

In the absence of site-specific EHR review, we assessed algorithm validity through distributional robustness testing.^28,29 Consistent performance across demographically divergent populations provides evidence of robustness,^30,31 whereas systematic miscalibration would be expected to manifest as divergent prevalence estimates across sites with different demographic compositions.³² The 58 hospitals across 4 regions represent substantially different populations in terms of racial and ethnic composition, comorbidity burden, and health care system scale, providing a rigorous test of algorithm generalizability beyond the development site.

In essence, they are validating their “artificial intelligence” algorithm by checking that different populations end up with similar numbers of Long COVID patients. If you have one group of people with COVID-19 where the algorithm finds a rate of 50%, and another where the rate is 5% then there are probably significant issues in how the algorithm is working.

The authors never again refer to this paragraph in the paper. Which is shocking, because the algorithm clearly fails the test of distributional robustness. Despite similarities in age and disease characteristics, nearly double as many people were classified as having Long COVID in California than Pennsylvania using the algorithm. In New England, only 5% of Long COVID ‘cases’ had endocrine issues, while in Texas 15% of ‘cases’ had these problems. The p-values for these differences—the likelihood that they would happen if the regions actually had the same distributions—are essentially 0.

In other words, the data fails the authors own stated test for robustness, which they never bothered to check. This makes the estimates even more dubious even as an interesting description of the diagnostic codes that people who once had COVID-19 eventually received.

COVID-19 Is On The Decline

I wrote about Long COVID rates using the existing best evidence way back in 2023. At that time, the data indicated that around 1% of people who were being infected with COVID-19 were going on to have long-term symptoms of the disease. The number appeared to be declining, but it was hard to say where exactly it would end up.

That analysis hasn’t really changed. The best evidence on Long COVID rates remains the high-quality studies conducted in the UK in 2021, 22, and 23, which suggested that around 10% of people in the initial 2020 outbreaks had long-term symptoms. This declined to around 1% of people by the Omicron waves, likely due to a combination of infection-derived immunity and vaccination.

I could try and estimate the current incidence of Long COVID—the percentage of infections that result in long-term symptoms. I would be surprised if it was higher than it was in 2023. But there’s really not that much point, because COVID-19 is on the decline anyway.

COVID-19 infections have plummeted in recent years. In 2022, there were around 1.5 infections per person, meaning almost everyone was infected once and many people had more than one infection. In 2023, the number was around 1. In 2024, we stopped recording really good statistics on the number of infections, but every indicator we had showed another huge decline. By 2026, we are looking at record low rates of COVID-19 across hospitalizations, deaths, wastewater data, and cases.

It’s hard to know precisely how many people have had a COVID-19 infection in the last 12 months, but we can look at the data to get a clue. The number of COVID-19 deaths has been halving every year since 2022, and is still falling. It’s likely that only 10,000 people will die of COVID-19 in the US in 2026, compared to half a million in 2021. There are currently no people hospitalized in Germany due to COVID-19, something that has not been true since before the virus initially broke out. Wastewater detections of COVID-19—the number of viral particles showing up in sewerage—show rates that are less than 0.1% of the 2022 waves in Denmark, the US, and elsewhere.

While we can’t put exact figures on the number of COVID-19 cases over the last year, we can say with a great deal of certainty that it is very low. And falling.

That makes all of this discussion about Long COVID incidence a bit pointless. It’s highly unlikely that even as many as 10% of people who catch COVID-19 will experience long-term symptoms, but even if the rate was that high it would still not be especially concerning with COVID-19 rates this low. More realistically, your risk of getting Long COVID today is quite close to 0%.

None of this is reassuring for people who have Long COVID from previous waves. I really feel for the community of people who have been permanently disabled by a disease that is no longer of great interest to most of the world. It sucks to have a condition that very few people want to spend money researching.

That being said, the ludicrously high estimates of Long COVID help no one. The 16.28% figure doesn’t pass the basic sniff test, and it’s clearly wrong. The true rate of Long COVID in 2026 is likely to be at least an order of magnitude lower, if not more.