Category Archives: global health

Matching Algorithms and Reproductive Health: Part 3, A Stylized Virginity Pledge

It’s been three weeks and one IHME retreat since I wrote about matching algorithms and virginity pledges, and I think I now understand what’s going on in Patient Teenagers well enough to describe it. I’ll try to give a stylized example of how the minimum-weight perfect matching algorithm makes itself useful in reproductive health research.

I think it’s helpful to focus on a concrete research question about the virginity pledge and its effects on reproductive health. Here’s one: “does taking the pledge reduce the chances that an individual contracts trichomoniasis?” If the answer is yes, or if the answer is no, people can still argue about the value of the virginity pledge programs, but this seems like relevant information for decision making. Continue reading

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Matching Algorithms and Reproductive Health: Part 2, Matching and Virginity Pledges

I might have been a little over-ambitious with this series. I wrote a little bit about the how matching theory emerged from the social sciences two weeks ago. But then I got really busy! And that was the part I actually knew something about ahead of time. The promised connection between matching algorithms and reproductive health (and more generally, how matching is being used in quasi-experiment design) is the part that I have to do some reading on before I can write knowledgeably about.

However, I have a plan: I’d like to “crowd-source” my library research. Continue reading

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Matching Algorithms and Reproductive Health: Part 1, Matchings Emerge from Social Science

Earlier this week, I was inspired by current events to launch a bold, crazy-sounding series about matching theory and its application to reproductive health.  This first installment is a quick social history of the development of matching theory, largely influenced by (and fact-checked against) Lex Scrijver’s encyclopediac Combinatorial Optimization: Polyhedra and Efficiency.  His paper “On the history of combinatorial optimization (till 1960)” contains similar information in an easy-to-download form.

On to the story:  how social science applications drove the  development of matching theory. Continue reading

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Matching Algorithms and Reproductive Health: Part 0

One of the first things on Obama’s agenda after being sworn in as President last week was lifting the “global gag rule”, a Regan-era innovation that tied US aid to strict anti-choice regulations. Meanwhile, the TCS reading group at UW has been studying matching problems and Edmond’s blossom algorithm. Together, this has been the motivation I needed to launch a series of posts about applications of matchings in reproductive health metrics. Part 1 will have more about matchings.

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Holiday Viewing

It’s been snowing in Seattle for a week now, and that never happens. Things were already getting quiet around here for the holidays, but now there are almost no cars on the roads and it’s been really quiet. I’ve been watching healthy algorithm videos to pass the nice, quiet time:


Gaussian Process Basics
David MacKay


GP Covariance Functions
Carl Edward Rasmussen


Unnatural Causes
California Newsreel


The Trap
Adam Curtis

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Filed under global health, probability, videos

Combinatorics of Malaria Eradication

Malaria infecting a mosquito

I’ve got at least 3 interesting blog posts worth of material on TCS applications for fighting malaria, but I haven’t had time to pen even one of them. Here is an abbreviated version:

Malaria is a major disease, something like the #3 infectious disease globally, and the #1 cause of both death and disability in many parts of Southern Africa.

The Gates Foundation is leading the charge to attempt to eradicate malaria from the world, and many national governments and NGOs are also involved in the fight.

There is a history of malaria eradication attempts, and the historic lesson is this: don’t start a fight with malaria unless you’re going to win.

Continue reading

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IHME and the two words of good journalism

The investigative journalist I.F. Stone once told an assembly of aspiring writers, “I am going to tell you a number of things, but if you really want to be a good journalist you only have to remember two words: governments lie.”

Exaggerate is a more diplomatic way to put it, and that’s how the headlines read regarding a new IHME report that came out in Lancet on Thursday. Here is the local edition, from the Seattle Times: Continue reading

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Florence Nightingale: Health Metrics Pioneer

Diagram of the causes of mortality of the army in the east

Nightingale's Coxcomb


Science News recently ran an article on the health statistics work and data visualization work of Florence Nightingale.  It’s fun for me to learn about this history, since I am such a recent immigrant to the land of health metrics. Nice quotes from Nightingale’s statistical mentor in the piece, too:

You complain that your report would be dry.  The dryer the better. Statistics should be the dryest of all reading.

The graphics in the Science News article are from an educational project of the Statistics Lab at the University of Cambridge called Understanding Uncertainty. It seems like Nightingale’s coxcomb it is a well debated form of infoviz over at the Edward Tufte Discussion Board.

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Midwest Theory Day and Me

I’ll be talking about “Theoretical Computer Science in Global Health” as the invited speaker at the Midwest Theory Day this Saturday (Dec. 6). It sounds like it will be a fun workshop, a one day deal at Northwestern Univesity. If you’re in the area, I think you should come on by.

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Google Flu

Have yinz already seen Google Flu? It’s a project by google.org, in collaboration with the Center for Disease Control and Prevention (CDC). It’s been getting healthy press coverage for the last two weeks or so. And, if you want to dig deeper, a draft manuscript on their approach is also available.

The headline result of this approach to tracking flu outbreaks is that it is fast: google.org can observe flu trends two weeks before the CDC. And it is accurate enough, with correlations of 0.85-0.98 between the search-result-based estimate and the gold-standard rates produced by the CDC.

I’ll tell you what’s wrong with it, but first let me praise it.

Continue reading

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