Posts filed under General (2986)

Q: Did you see there’s no safe level of processed meat!

A: I saw the New York Post

Q: <side eye>Really?

A: Via Google. And RNZ and Stuff

Q: Didn’t we have this story a couple of months ago?

A: Not quite. That was ultra-processed. This is only processed.  And just meat.

Q: Is it true?

A: What did we say last time?

Q: “But think about it. How do they measure people’s consumption of ultraprocessed food down to the single bite level? How do they find a comparison group with just one bite less consumption? What does it even mean?”

A:  Exactly. Even though it’s not “one bite” this time, there’s still the question of what levels they actually compared

Q: So, um, what levels did they actually compare? And is there a research paper?

A: The paper is here.  They said (for diabetes, colorectal cancer was similar)

The mean relative risk (RR) of developing type 2 diabetes was 1.30 (1.12–1.52) at a daily intake of 50 g of processed meat compared with the theoretical minimum risk exposure level (TMREL; equal here to 0 g d⁻¹ or no consumption)… consuming processed meat in the range of the 15th to 85th percentiles of exposure (0.6–57 g d⁻¹), compared with consuming no processed meat, was associated on average with at least an 11% higher risk of type 2 diabetes.

Q: Is 50g a lot

A; About one hot dog

Q: So eating one hot dog will increase your risk of diabetes by 26%

A: One hot dog per day

Q: And they’re comparing to a “theoretical minimum risk” at zero hot dogs per day?

A: Yes

Q: Doesn’t that kind of assume there is no safe level

A: Pretty much.  They’d be able to see if moderate levels of consumption are actually protective, though if you think about how long people argued over that with alcohol, they obviously can’t tell very clearly

Q: So it’s not harmful at lower levels of consumption?

A: It’s correlated with diabetes (and cancer) at lower levels, too. Here’s the picture. The blue line is their estimate relative risk

Q: It gets a bit noisy down near 10 or 20g/day. Hard to tell the shape of the curve.

A: It is.

Q: And the red crosses?

A: The bluish dots are data; the red crosses are data they didn’t use

Q: So what does it actually mean that there’s no safe level?

A: If you’re eating hot dogs primarily for the health benefits, you’re doing it wrong.

• Health Nerd else has similar views about coffee as me last week
• Royal Statistical Society blog post on the future of the British Office of National Statistics
• Graeme Edgeler argues that getting rid of the Census may require amending entrenched provisions of the Electoral Act, which takes a 75% supermajority of Parliament
• The Economist/YouGov had a poll being run on bombing Iran at the time the US did it, and reported this interesting shift in opinions: republicans approved more; democrats approved less.
  
• As a StatsChat reader you should be looking at this and wondering where the uncertainty estimates. Owen Winter responded to my BlueSky query with this. If you take into account model uncertainty it’s a bit less impressive, but it’s not nothing
  

Team Ratings for Round 18

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 128 matches played, 76 of which were correctly predicted, a success rate of 59.4%.
Here are the predictions for last week’s games.

Predictions for Round 18

Here are the predictions for Round 18. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Week 18

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 134 matches played, 88 of which were correctly predicted, a success rate of 65.7%.
Here are the predictions for last week’s games.

Predictions for Week 18

Here are the predictions for Week 18. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for the Final

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 186 matches played, 143 of which were correctly predicted, a success rate of 76.9%.
Here are the predictions for last week’s games.

Predictions for the Final

Here are the predictions for the Final. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 17

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 120 matches played, 68 of which were correctly predicted, a success rate of 56.7%.
Here are the predictions for last week’s games.

Predictions for Round 17

Here are the predictions for Round 17. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Week 17

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 127 matches played, 81 of which were correctly predicted, a success rate of 63.8%.
Here are the predictions for last week’s games.

Predictions for Week 17

Here are the predictions for Week 17. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

• ‘Kids in sport stay out of court’ – Sport NZ to help curb youth offending from RNZ.  This is another one of these cause and effect ones. Is it that being pushed into sport makes kids less likely to offend? Is it that kids with the qualities — self-control, work ethic, parents who drive you to games — to engage with sport are less likely to commit dumb crimes? Or (as anonymous law commenter @StrictlyObiter suggests) is it that “promising young sportsmen” are more likely to get a discharge without conviction?  Or, more likely, all of the above in some complicated mixture
• From the Bennett Institute for Applied Data Science at Oxford, another example of counting being hard. They wanted to find out how much of each medication was used across the National Health Service
• On pizza as a leading indicator of US military activity
• It’s twenty years since XKCD did a big colour survey, showing people coloured patches and asking for colour names.  Nicola Rennie made this poster of the top (ie, most agreed on) colour names — click to embiggen.
  

A couple of studies out recently look at coffee and health.  One from Harvard(and reported by CNBC)  says coffee (but not decaf or tea) increases the chance of healthy aging in women. Another, from Tufts, (and reported by Newsweek and The Independent) says that unsweetened black coffee, or coffee with very small amounts of sugar or normal coffee amounts of milk, reduces death rates slightly, but not coffee with more sugar or milk (as in everything from a Kiwi small flat white to American-style lattes)

There are two problems with these studies.  The first is that I can’t see them.  One is an abstract from a conference presentation; the other is a paper in an academic journal, but not one the University of Auckland gives me access to.

Compounding this, the two abstracts only give information for their preferred beverages. It’s not possible to tell whether “Decaffeinated coffee and tea intake were not significantly associated with odds of HA nor any domains” means that there’s evidence the correlations were different for tea and decaf or whether there was just a bit more uncertainty around plausibly the same correlation.  Similarly, “However, the mortality benefits were restricted to black coffee [HR (95% CI): 0.86 (0.77, 0.97)] and coffee with low added sugar and saturated fat content [HR (95% CI): 0.86 (0.75, 0.99)]” doesn’t tell us what they found for other coffee types. Nor is the information in the press releases I could find. Since the difference between ways of drinking coffee was the main news tag for these studies, that’s a bit unsatisfactory.

I’ll also note that the Tufts team published an abstract in 2020 with a slightly smaller version of the data from the same survey series, and concluded “Adding milk/cream, alone or with sugar/sweetener, did not significantly change the results.”

A basic principle for studies like these is that conclusions about difference require evidence of difference.  This applies to conclusions in the paper, and even more so to conclusions you want the press to report.

Not necessarily endorsed by me, but many of these people do know what they are talking about.

• Initial reporting: Herald, Stuff, One News, Spinoff
• RNZ in more depth from Kate Newton
• With reference to the UK experience, at Stuff
• Len Cook at Newsroom The first request New Zealanders must have about these aspirational Statistics NZ changes is of transparency.
• Data Sovereignty and surveillance concerns from Māori scholars (RNZ, initially from The Conversation)
• Science Media Centre: diverse experts with varying views
• StatsNZ Future Census Evaluation Panel

I do also want to emphasize that no-one expert thinks this is a proposal to stop collecting data for the government. Administrative data already marks when you are born or die, when you enter or leave New Zealand, when you pay taxes or go to school or get health care.  This information is more reliably and rapidly collected administratively than in the Census. What we risk losing is not that, but other things.