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:
GBD 2010: The Global Burden of Ischemic Heart Disease in 1990 and 2010: The Global Burden of Disease 2010 Study
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.
Journal Club: Efﬁcient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.
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.
Journal Club: Transmission Assessment Surveys (TAS) to Define Endpoints for Lymphatic Filariasis Mass Drug Administration: A Multicenter Evaluation
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.
Last week we had a talk from Rodrigo Moreno-Serra on Universal health coverage, equity, and health outcomes. This research used instrumental variables to show that universal health coverage is good for health. One day I will understand instrumental variables—I think there should be a simple way to explain it to combinatorialists who know some epidemiology.
It has been a while since I visited my pymc-examples repository, but I got a request there a few weeks ago about the feasibility of upgrading the Seeds Example of a random effects logistic regression model for PyMC3. It turns out that this was not very time consuming, which must mean I’m starting to understand the changes between PyMC2 and PyMC3.