Posts written by Thomas Lumley (2534)

From the Herald today (from the Daily Telegraph)

The story is somewhat better than the headline — for a start, it mentions in the second sentence that this is unpublished research being presented at a conference, so it’s really just a press release and there won’t be much further information available.  There’s a slightly more detailed story at Yahoo, from the Evening Standard.

You might think from the the headline that the research studied men who ran marathons after the age of 40, and that it came with recommendations not to do this.  Not quite. These were people aged over 40 who had taken part in more than 10 endurance events and had exercised regularly for at least 10 years, but quite a lot of their excessive exercise could have taken place when they were under 40.  More importantly, there wasn’t a “warning”:  the Yahoo piece quotes the researchers as saying

“In non-athletes, aortic stiffening is associated with heart and circulatory diseases.

“How this finding applies to potential risk in athletes is not yet fully understood, so more work will be needed to help identify who could be more at risk.”

As usual, this isn’t the first piece of research on the topic.

Earlier research of endurance athletes over 50, from 2020 (press release, paper) supports the current findings. It did find enlargement of the aorta in male but not female athletes, and like the current research it came with vague concerns rather than dire warnings

The first option is that aortic enlargement among masters athletes is a benign adaptation and another feature of the so-called athlete’s heart, where big is good. “The alternative is that being a lifelong exerciser may cause dilation of the aorta with the sort of attendant risk seen in nonathletes.”

A 2021 paper looked at the ‘aortic age’ in people doing their first marathon, at ages ranging from 21 to 69. It found

… a reduction in “aortic age” by 3.9 years (95% CI: 1.1 to 7.6 years) and 4.0 years (95% CI: 1.7 to 8.0 years) (Ao-P and Ao-D, respectively). Benefit was greater in older, male participants with slower running times (p < 0.05 for all).

That is, if these measurements have the same interpretation in athletes as in normal people, running a single marathon seems to be good for men over 40.

It might turn out that overindulgence in marathon running is bad for your cardiovascular system, but we’re not there yet.  If you’re running for fitness, you might be better off not going the full 42km. But if you enjoy it, go in (hopefully) good health.

• The WHO has released new estimates of Covid mortality.  Here’s Jon Wakefield, a statistician at the University of Washington, talking about them on PBS Newshour, Radio New Zealand, BBC’s More or Less, and Checks and Balance, at The Wire in India
• A nice new Australian electoral map, just in time for the election
• Some interesting sound-based data display from NASA. I’m not sure how well it works as communication
• The making of a data visualisation — all the plots that Georgios Karamanis made in the process of looking at some data and making a final graphic
• Media Council ruling on a terrible graph accompanying a column by Shane Reti in the Northern Advocate: “Opinion pieces must be based on a foundation of fact, and should not contain clear errors of fact.  There is no doubt that the graphs presented by Dr Reti as justifying his headline and comment in his column were misleading in that they did not fairly show the trend in cases through the period.”
• The sources of data for online panel surveys are more complicated and messy than many people expect: “We began wondering about survey data sourcing when we noticed that since 2018, the Cooperative Election Study  has included data from Dynata, Critical Mix, and Prodege. This was a shock to us because among academic researchers, the Cooperative Election Study is widely understood as a survey conducted by YouGov“
• A Twitter thread about Tesco Clubcard, perhaps the first store loyalty programme to focus on data collection

• The Herald has a story on a penis size ‘survey’ from a UK online pharmacy specialising in men’s health, who “used Google data to rank the average penis size of 86 countries – and New Zealand has placed 50th”. That’s not an indication of worrying privacy leaks in Android phones; it seems they did just Google for the results. The pharmacy obviously wants to get publicity because being unknown will be its biggest business problem. In the UK, they did well and got a mention in The Sun. Being in the Herald doesn’t do them much good, though.
• ESR is now publishing detailed wastewater Covid data, which is great — the big advantage of wastewater data is that the sampling is independent of Covid prevalence, in contrast to data on testing, where you tend to miss more cases as the prevalence goes up.
• One thing I don’t like about the current ESR graphs is that they use a log scale for viral load and a linear scale for cases, which is one reason the peak for wastewater looks so much broader than the peak for cases (the other reason is that wastewater measures total active cases, not daily new cases)
  
• Ben Schmidt has made an interactive map of ethnicity in the US, based on Census data — in the sense that it has a dot for each of the 300-odd million people recorded in the Census
• Interesting Twitter thread about the case against the continued existence of Ivory-billed Woodpeckers in the US.  Not precisely statistical, but definitely statistics-adjacent.
• Somewhat dodgy graphic from Labour about housing consents. If this is an area graph, the axis should go down to zero.  It’s not as bad as some I’ve covered in the past, but it’s annoying. The second graph is an edited version where the axis does go down to zero and the areas are meaningful
  

Last month, the Italian region of Marche announced they had installed ventilation in schools and it had reduced Covid infections by a 82% (Reuters, Stuff, La Repubblica).  A report was supposed to follow, but this is all I’ve been able to find.  It’s not really surprising that Covid rates went down with improved ventilation, but what’s currently available is very low on detail. Ventilation was installed in 3% of classrooms (or for 3% of classes, I’m not certain), and this 3% was compared to those that didn’t get new ventilation.  The reported benefits were:

That’s great! But. Things you’d really like to know when you think about how much this should change policy in other countries:

• How were the schools with new ventilation chosen, and how were the different ventilation levels chosen? How did their Covid rates compare before the change?
• How was Covid measured? Was there any systematic testing or was it just a matter of who got sick and then got tested? Is this symptomatic infections or all infections? Do you know anything about their testing rates?
• Was there any attempt to decide if Covid cases were connected with school or were household infections or something else?
• Did the ventilation involve any measurement of air mixing and effective air changes, or does this study show you don’t need that?
• Were students wearing masks? What were the isolation rules for infections?
• What are the uncertainty intervals on those efficacy estimates? How many students and Covid cases in each group are the estimates based on?

In particular, the relationship between air changes and transmission risk looks very close to what you might expect from just diluting the air — but it really shouldn’t! The ventilation should only have changed Covid risk while students were at school; it shouldn’t have reduced the risk of transmission at home or in other places.  To get an 82.5% reduction in total infections, they must have been doing much better than 82.5% reduction in infections at school.  For example, if 82.5% of infections in the schools without new ventilation happened at school, you’d need to abolish those at-school infections completely to get 82.5% overall effectiveness.  If 90% of infections happened at school, you’d need 92% effectiveness in reducing at-school infections to get 82.5% overall effectiveness.

If the point of the Italian study is just that ventilation is beneficial, it really isn’t major news and it’s not all that helpful to other countries. If the detailed estimates are to be useful, we need to know what they are detailed estimates of.

Q: Did you see a study has found cannabis causes more cancers than tobacco?

A: Sigh. That’s not what it says

Q: Otago Daily Times: Study finds cannabis causes more cancers than tobacco

A: Read a bit further

Q: “shows cannabis is causal in 27 cancers, against 14 cancers for tobacco”. So it’s just saying cannabis is involved in causing more different types of cancer than tobacco? Nothing about more actual cases of cancer.

A: Yes, and if you’re not too fussy about “causes”

Q: Mice?

A: No, people. Well, not people exactly. States.  The study had drug-use data averaged over each year in each US state, from a high-quality national survey, and yearly state cancer rates from SEER, which collects cancer data, and correlated one with the other.

Q: Ok, that makes sense. It doesn’t sound ideal, but it might tell us something. So I’m assuming the states with more cannabis use had more cancer, and this was specific to cannabis rather than a general association with drug use?

A: Not quite. They claim the states and years where people used more cannabidiol had higher prostate and ovarian cancer rates — but the states and years where people used more THC had lower rates.

Q: Wait, the drug-use survey asked people about the chemical composition of their weed? That doesn’t sound like a good idea. What were they smoking?

A: No, the chemical composition data came from analyses of illegal drugs seized by police.

Q: Isn’t the concern in the ODT story about legal weed? <reading noises> And in the research paper? Is that going to have the same trends in composition across states

A: Yes. And yes. And likely no.

Q: So their argument is that cannabidiol consumption is going up because of legalisation and prostate cancer is going up and this relationship is causal

A: No, that was sort of their argument in a previous study looking at cancer in kids, which is going up while cannabis use is going up.  Here, they argue that ovarian and prostate cancer are going down while cannabidiol use is going down.  And that it’s happening in the same states. In this map they say that the states are basically either purple (high cancer and high cannabidiol) or green (low both) rather than red or blue

Q: Um.

A: “The purple and pink tones show where both cannabidiol and prostate cancer are high. One notes that as both fall the map changes to green where both are low, with the sole exception of Maine, Vermont and New Hampshire which remain persistently elevated.”

Q: What’s the blue square near the middle, with high weed and low cancer?

A: Colorado, which had one of the early legalisation initiatives.

Q: Isn’t the green:purple thing mostly an overall trend across time rather than a difference between states?

A: That would be my view, too.

Q: How long do they say it takes for cannabis to cause prostate cancer? Would you expect the effect to show up over a period of a few years?

A: It does seem a very short time, but that’s all they could do with their data.

Q: And, um, age? It’s older men who get prostate cancer mostly, but they aren’t the ones you think of as smoking the most weed

A: Yes, the drug-use survey says cannabis use is more common in young adults, a very different age range from the prostate cancer. So if there’s a wave of cancer caused by cannabis legalisation it probably won’t have shown up yet.

Q: Ok, so these E-values that are supposed to show causality. How do they find 20,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 times stronger evidence for causality with cannabis, not even using any data on individuals, than people have found with tobacco?

A: It’s not supposed to be strength of evidence, but yes, that’s an implausibly large number.  It’s claiming any other confounding variable that explained the relationship would have to have an association that strong with both cancer and cannabidiol.  Which is obviously wrong somehow. I mean, we know a lot of the overall decline is driven by changes in prostate screening, and that’s not a two bazillion-fold change in risk.

Q: But how could it be wrong by so much?

A: Looking at the prostate cancer and ovarian cancer code file available with their paper, I think they’ve got the computation wrong, in two ways. First, they’re using the code default of a 1-unit difference in exposure when their polynomial models have transformed the data so the whole range is very much less than one. Second, the models with the very large E-values in prostate cancer and ovarian cancer are models for a predicted cancer rate as a function of percentile (checking for non-linear relationships), rather than models for observed cancer as a function of cannabidiol.

Q: They cite a lot of biological evidence as reasons to believe that cannabinoids could cause cancer.

A: Yes, and for all I know that could be true; it’s not my field. But the associations in these two papers aren’t convincing — and certainly aren’t 1.92×10¹²⁵-hyper-mega-convincing.

Q:  Russell Brown says that the authors are known anti-drug campaigners. But should that make any difference to getting the analysis published? They include their data and code and, you know, Science and Reproducibility and so on?

A: Their political and social views shouldn’t make any difference to getting their analysis published in Archives of Public Health. But it absolutely should make a difference to getting their claims published by the Otago Daily Times without any independent expert comment.  There are media stories where the reporter is saying “Here are the facts; you decide”. There are others where the reporter is saying “I’ve seen the evidence, trust me on this”. This isn’t either of those. The reporter isn’t certifying the content and there’s no way for the typical reader to do so; an independent expert is important.

• BBC’s More or Less on the actual evidence for mask use. Probably not 53% reduction in risk. But is it worthwhile? Yes, they say.
• I’m the The Conversation on the benefits of vaccines: modest against infection but pretty good against serious illness
• People are (quantatively) bad at estimating the population income distribution — and everything else
• The USA may be about to stop daylight saving time changes. The Washington Post shows where the impact will fall if they do.  It’s probably a good idea, but it’s going to be a pain for some people.
• Trip planning for Ancient Rome
• The problem with caring too much about university rankings is that it may be easier to improve the ranking than to improve the university. Columbia University allegedly submitted dodgy data to US News & World Report to get a better ranking.  Now, there are differences between universities, on lots of metrics, and it’s good for people outside the academic or social elites to be able to learn about these differences. The problem is folding them into a ranking and then treating the ranking as the important thing.  And if you’re running a ranking system as valuable as that one, you should probably be doing a bit of data checking.
• “When covid testing flipped to home RATs, there was a big drop in the children reported as having covid relative to parents compared to professional tests. Then came people sharing advice on how to swab kids, then easier reporting, and now we seem to be back where we were.” @thoughtfulnz on Twitter
  
• Phrasing of poll questions is important. Here’s one from the UK about a “no-fly zone in Ukraine” that lays out some of the risks and gets different results from polls that didn’t. (I’m also glad to see a high “Don’t Know” proportion reported.)
  

This came from Twitter (arrows added). I don’t have a problem with the basic message, that when people are packed into a smaller area it takes less energy for them to get around, but there are things about the graph that look a bit idiosyncratic, and others that just look wrong

The location of the points comes from an LSE publication that’s cited in the footnote, which got it from a 2015 book, using 1995 data (data not published).  The label on the vertical axis has been changed — in both the sources it was “private passenger transport energy use per capita”, so excluding public transport — and the city-size markers have been added.

One thing to note is that you could almost equally well say that transport energy use depends on what continent you’re in: the points in the same colour don’t show much of a trend.

Two points that first really stood out for me were San Francisco (lower population density than LA) and Wellington (higher population than Frankfurt, Washington, Athens, Oslo; same general class as Manila and Amsterdam).   In this sort of comparison it makes a big difference how you define your cities: is Los Angeles the local government area or the metropolis or something in between? In this case it’s particularly important because the population data were added in by someone else to an existing graph.

In some cases we can tell. Melbourne must be the whole metropolitan area (the thing a normal person would call ‘Melbourne’), not the small municipality in the centre.  The book gives the density for Los Angeles on a nearby page as the “Los Angeles–Long Beach Urbanized Area”, which is (roughly speaking) all the densely populated bits of Los Angeles County. Conversely, San Francisco looks to be the whole San Francisco-Oakland Urbanized Area, which has rather lower density than what you’d think of as San Francisco. The circle looks wrong, though: the city of San Francisco is small, but the San Francisco area has a higher population than Brisbane or Perth.

The same happens in other countries. Manila, by its population, should just be the city of Manila, but that had a population density of 661/ha in 1995 so the density value is for something larger than Manila but smaller than the whole National Capital region (which had a density of 149/ha and a population of 9.5 million).  If it’s in the right place on the graph, its bubble should be bigger. The time since 1995 also matters: Beijing is over 20 million people now, but was under 10 million at the time the graph represents. We’ve seen that the San Francisco point is likely correct, but the size is probably wrong.  The same seems to be true for Wellington: the broadest definition of Wellington will give you a smaller population than the narrowest definition of Washington or Frankfurt.

As I said at the beginning, I don’t think the basic trend is at all implausible. But when you have data points that are as sensitive to user choice as these, and when the size data and density data were constructed independently and don’t have clearly documented sources, it would be good to be confident someone has checked on whether Manila really has the same population as Wellington and San Francisco is really less dense than LA.

When we look at the impact of particular government strategies in Covid, it’s important to compare them to the right thing.  The right comparison isn’t, for example, pandemic with lockdowns vs no pandemic — ‘no pandemic’ was never one of the government’s options. The right comparison is pandemic with lockdowns vs pandemic with some other strategy.

Along these lines, Stuff has a really unusual example of a heading that massively understates what’s the in the story. The headline says Covid-19: Pandemic measures saved 2750 lives, caused life expectancy to rise, based on a blog post by Michael Baker and his Otago colleagues. As you find if you read on, the actual number is more like 17,000 or 23,000 (or even higher).

The 2750 is the difference between the number of deaths we’ve seen during the pandemic period and the number we’d expect with no pandemic measures and also no pandemic.  The fair comparison for the impact of pandemic measures isn’t this, it’s the comparison to what we’d expect with a pandemic and the sort of pandemic measures used in other countries.   According to Prof Baker, we are at minus 2750 excess deaths per 5 million people, the US is at about 20000 excess deaths per 5 million people and the UK at about 13700 excess deaths per 5 million people.  The difference: 13700- -2750 or 20000- -2750 is the impact of having our pandemic measures instead of theirs.

There’s room to argue about the details of these numbers.  The UK is more densely populated than NZ and was run by Boris Johnson, so you might argue that the UK deaths were always going to be worse . Alternatively, the UK and US have more capacity in their medical systems than NZ, so you might argue that NZ deaths with a similar outbreak would have been worse. What’s important, though, is to compare our choices with other choices New Zealand could have made. No pandemic wasn’t one of those options.

• Like a lot of news outlets, the Herald and  Newshub reported the case of a US teen needing amputations after eating some dodgy leftover lo mein.  Fortunately or unfortunately, it’s not true — well, the “after” is true, but the implied “because of” isn’t. The victim had meningoccal disease, which isn’t foodborne, and the connection came only from YouTube.  As the Boston Globe and Ars Technica report “The article never mentioned the leftovers again—because the food wasn’t linked to his illness. The lo mein was simply a red herring that the doctors dismissed, according to the article’s editor and director of the clinical microbiology laboratory at Massachusetts General Hospital, Eric Rosenberg.”
• As Siouxsie Wiles says in Stuff, we could really use a Covid prevalence survey now that case counts aren’t a reliable way to assess infection numbers and allow hospitals to predict what they’re going to see in a week or so.  As a stopgap, we could use various existing data sources to cobble together an estimate, but a proper random survey like the one the UK has been running would be better.  The UK is stopping theirs; the president of the Royal Statistical Society writes about why this is bad
• Interesting US political research: I’ve mentioned quite a few times that opinion polls have a problem with the difference between what people believe and what they say.  This research looked at people who say they believe the 2020 US election was really won by Donald Trump, and concludes that most of them actually do believe it.
• On the other hand, YouGov finds substantial differences between the proportion of people who support vaccine mandates for schoolkids and the proportion who think “parents should be required to” have their children vaccinated.

Stuff (from the Sydney Morning Herald) reports that a baby was born at exact 2:22 and 22 seconds on February 22nd. An Australian maths professor is quoted as saying

“It’s about 1 in 30 million is the chance of being born at that precise second,”

Maths nerds will recognise that as the number of seconds in a year (roughly π times ten million), and music nerds will remember 525,600 as the number of  minutes in a year and multiply by 60.  So, 1 in 30 million is the chance of picking a particular second if you pick one randomly from a year. It seems a bit strange to give that as the answer for the baby’s chance of being born at that precise time.

If you had picked this specific baby, Bodhi, in advance, his chance of being born at a particular second depends on how much variation there is in birth times.  They’re roughly a Normal distribution with a standard deviation of 16 days, and it turns out this gives a chance of about 2.5% of being born five days early and so about one in 3.6 million of being born in a particular second on that fifth day early.

But we didn’t pick this specific baby in advance and look at when he was born. We picked the time and looked for the baby. There are nearly 300,000 births per year in Australia; about one per 100 seconds.  There would be about a 1 in 100 chance that some baby in Australia is born at one particular second.

So far we’ve been saying there’s one particular second. But the baby was born at 14:22:22 and presumably 02:22:22 would have done just as well. Or maybe 22:02:22 or 22:22:22.  It’s not really one special second in the day.  And on top of that, a baby isn’t really born at a single second — there’s at least a small amount of flexibility in how you define the time, and you know someone is going to take advantage of the flexibility.  What would be really surprising would be a birth recorded as 2:22:19pm on 22/2/2022.