Category Archives: global health

Math Epi Ebola Models

I’ve been watching as much as I can the Ebola outbreak in West Africa, and the role of epidemiological modeling in the response. The figure that most approaches start with is from here:

f1

An important extension to the model appeared in a recent paper:

f2

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Digging into GBD 2010 Risk Factors

I have to make the old DisMod-III website disappear (is it still here?). It is beautiful, but it is not available, and so I have to turn away researchers who want to use it for their own work. Fortunately, I can send them to a GitHub repository of DisMod code that they can use. But recently, it was not really DisMod that the emailing researcher wanted. I think they were really interested in digging into the details behind a figure like this one:
rf

For that, there is a non-dead website I can offer: GBD Compare. Finding your way around it can be a bit of a challenge, though, so here is a link straight to the relative contribution of each nutritional risk factor for Germany and USA: http://ihmeuw.org/2c6t ; to see from which specific diseases the risk factor DALYs are attributed, you can use a different part of this tool, linked to here: http://ihmeuw.org/2c6v

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Age- and sex-specific death data

A colleague asked recently where to find different estimates of age- and sex-specific death counts for comparison purposes, and I told her that there should be some available on GapMinder. This was wrong (although you can compare child mortality rates there), so I did a little digging. Here are the results, in case they are useful for you, too:

If you want to explore the IHME data, here is a little notebook that you can use to get started.
Capture

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Change Seminar: Measuring Mortality in Iraq

I gave a seminar last week for the UW computer scientists interested in doing good with technology. It’s a fun crowd. Here are the slides, requested by a regular attendee who couldn’t be there.

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Fact checking with GBD Compare

I’ve been developing a habit of comparing health statistics I hear in the media with the results in GBD Compare. It is nice when they agree, such as in a recent ScienceMag focus on chronic kidney disease, corroborated here: http://ihmeuw.org/1v7i . It would be even better if the cause was known, and the burden could be removed.

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IHME Seminar: Overdiagnosed

I have fallen way behind in noting the IHME weekly seminars, but I was just thinking of this wonderful one from last semester, and I couldn’t wait any longer to link to it: Overdiagnosed: Making people sick in the pursuit of health by H. Gilbert Welch.

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Smoking trends by US counties

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:

smoking_brfss_nhis

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Open data and the scientific python ecosystem is making my life easier

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:

dc_pct

See how little code it takes here.

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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.

ihd_compartments

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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.

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