Posts from October 2014 (47)

If you’d like to comment on or debate any of this week’s Stat of the Week nominations, please do so below!

• Two more programs to simulate various sorts of colour blindness, so you don’t make your graphs completely useless: Sim Daltonism and Chromatic Vision Simulator

• From the New York Times: Can Big Data Tell Us What Clinical Trials Don’t? (complies with Betteridge’s Law) (via Roger Peng) [and more from KQED, via Mark Ferris]

• Interactive picture of NZ electricity generation, usage, and price (via JJ Eldridge)

Earlier this week, the Herald had a story about antibiotics and childhood obesity. No-one involved really covers themselves in glory here, but probably the reporter comes out best.

The headline (Antibiotics ‘link to child obesity’) with appropriate claim quotes, and the lead

Children who are given antibiotics in the first two years of their lives are at greater risk of becoming obese in early childhood, a study suggests.

are fine, though there’s a sub-headline “Study finds some treatments kill bacteria that affect rate at which toddlers gain fat” that manages to be wrong in every respect.

The story goes on

The United States research is based on the medical records of 65,000 children between 2001-2013.

Researchers found infants who were given broad-spectrum antibiotics at least four times in their first two years were 16 per cent more likely to be obese by the age of 5.

That is in the research paper, but it’s much less impressive in context

The circles are the observed ratios of rates of obesity, the lines give the margin of error. You can see that, compared to no exposure, one exposure seems bad, two or three not so bad, and four or more worse.  They’ve looked at a lot of comparisons that don’t show a clear pattern, and picked out the biggest number.

There are other quibbles, for example, ‘broad spectrum’ doesn’t mean what it usually means, it means ‘everything except penicillin and amoxicillin’, but the biggest problem is confounding.  There are lots of things related to obesity and to antibiotic prescription, and it wouldn’t be at all surprising for one of them to explain this relationship.

More importantly, there’s no way this explains any meaningful fraction of the increase in childhood obesity.  Only about 3% of children were in the ‘4+ broad spectrum exposures’, so even if the ratio of 1.16 was true, the antibiotics would only be responsible for about half a percentage point of the obesity rate.  Even less of the the obesity increase would be explained, since antibiotics aren’t actually completely new.  Differences between countries also don’t seem to fit this as an explanation. For example, South Korea used to have a serious antibiotic overuse problem, which was reduced by new regulations in 2000. They don’t have much of a childhood obesity problem.

Still, there’s no reason the result couldn’t be true to some extent.  Antibiotics do affect gut bacteria, and gut bacteria are important in metabolising food. It might be true, but it’s definitely being oversold, and there are more urgent reasons not to over-use broad spectrum antibiotics.

The other part of the story that’s disappointing for the light it casts on science communication is some of the response

Some New Zealand experts are sceptical. Fight the Obesity Epidemic founder Dr Robyn Toomath noted that the latest study was funded by the American Beverage Foundation for a Healthy America, founded by the soft-drink industry.

“This is the industry buying crap science,” she said. “People who are poor get sick and get more antibiotics. They are more likely to be fat and a lot of other things as well.”

I’m in favour of scientists commenting on public issues, and I don’t see anything wrong with advocacy, but I think public allegations of intellectual dishonesty need a bit more detailed backup than this. You’d almost get the impression that the research hadn’t looked at number of doctors visits or any indicators of poverty.

Team Ratings for the Grand Final

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

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 200 matches played, 117 of which were correctly predicted, a success rate of 58.5%.

Here are the predictions for last week’s games.

Predictions for the Grand Final

Here are the predictions for the Grand 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 9

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

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 32 matches played, 24 of which were correctly predicted, a success rate of 75%.

Here are the predictions for last week’s games.

Predictions for Round 9

Here are the predictions for Round 9. 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 8

Here are the team ratings prior to Round 8, along with the ratings at the start of the season. I have created a brief description of the method I use for predicting rugby games. Go to my Department home page to see this.

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 54 matches played, 34 of which were correctly predicted, a success rate of 63%.

Here are the predictions for last week’s games.

Predictions for Round 8

Here are the predictions for Round 8. 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.

The Upshot blog at the New York Times had this interactive map of the flooding risk from climate change. It lets you see the number affected in each country, and also lets you vary the estimates for how much CO2 will be emitted and how sensitive ocean levels are to CO2.

It’s missing something, though: not just New Zealand, but all the Pacific Island countries. We often get maps cut off at about 167 E longitude, and we can just whinge quietly about the New York view of the world

but in  this context, where some of these islands will cease to exist if sea levels rise as predicted, leaving them out seems more inappropriate than usual.