I’m away from work for some really exciting family stuff, but while I wait on that, our paper on trends in smoking prevalence has just come out, along with a fun interactive data visualization of the results, and some media coverage that I think tells the story quite well.
What makes this work methodologically challenging is that the data comes from telephone surveys, but people who smoke stopped using landlines more than people who don’t smoke:
I’ve been having a good time following the development of the mpld3 package, and I think it has a lot of potential for making interactive data visualization part of my regular workflow instead of that special something extra. A few weeks ago, an mpld3 user showed up with an interesting challenge, and solved their own problem quite well.
I finally got a chance to look at it today, and with a little spit-and-polish this could be something really useful for me.
Last week I gave a talk on my work on the Iraq mortality survey. It was the first time that I’ve had a chance to talk about it since our paper was published. And since the data is all online and the scientific python tools are getting slick, I was able to make charts like this one:
See how little code it takes here.
I wish I had been more diligent in collecting the disease-specific papers that have come out following the Global Burden of Disease 2010 Study… here is the latest one to go into print: Moran et al, The Global Burden of Ischemic Heart Disease in 1990 and 2010: The Global Burden of Disease 2010 Study, in Circulation.
Last week, we read Chien et al, Efﬁcient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S. I’ve been very interested in these sort of “small-areas” spatial statistical methods recently, so it was good to see what is out there as the state of the art. I think I’ve got something to contribute along these lines some day soon.
Maps in small multiples look just lovely, too:
While I’m catching up on journal club reading, two weeks ago we discussed Chu et al, Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation, which takes on the question of how to decide when it is safe to stop a massive disease elimination program. This work must rely on some cool mathematical epi modeling, to say how many years of what level of coverage is necessary before you can hope the LF is gone.
We had a very different sort of research paper in journal club three weeks ago, and I was too busy to jot it down until now. Dewachi et al, Changing therapeutic geographies of the Iraqi and Syrian wars. This is certainly not our usual metrics-heavy approach, so it was good exercise to try to understand it.