Tag Archives: health metrics

November 29, 2016 · 8:00 am

2016 Stephen Stewart Gloyd Endowed lecture: Sanjay Basu

Dr. Sanjay Basu gave the 2016 Stephen Stewart Gloyd Endowed lecturer on May 19, 2016. He spoke on the challenges and importance of measuring the health impact of government programs and policies. The Stephen Stewart Gloyd Endowed Lecture was established in 1982 to recognize Dr. Park Willis Gloyd. The lectureship was renamed by the family to recognize Park’s son, Stephen Gloyd, who is the Associate Chair for Education and Curriculum for the UW Department of Global Health, and a professor in Health Services at the School of Public Health.

Sanjay Basu, MD, PhD, is an Assistant Professor of Medicine at Stanford University. He is a primary care physician and an epidemiologist, focusing on the study of how population health is influenced by social and economic programs. Dr. Basu received his education from MIT, Oxford, and Yale, and serves on advisory panels for the United Nations, World Health Organization, the American Heart Association, and the Global Burden of Disease Project. In 2013, he was named one of the “top 100 global thinkers” by Foreign Policy Magazine, and in 2015 he won the New Innovator Award from the Director of the National Institutes of Health. Dr. Basu is the co-author of “The Body Economic: Why Austerity Kills,” which discusses recessions, budget battles, and the politics of life and death.

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May 12, 2016 · 8:00 am

NAP Report to Read

Metrics That Matter for Population Health Action: Workshop Summary
http://www.nap.edu/read/21899/chapter/1

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April 15, 2013 · 8:00 am

Methods debates

An interesting exchange has been going on regarding the GBD 2010 estimates of deaths due to HIV, published as part of our GBD work last December, along with mortality estimates for 234 other causes of death. The UNAIDS reference group comment, and my colleagues respond.

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June 27, 2011 · 7:00 am

Life Expectancy by County in US

This recent study by my colleagues has been making headlines a lot last week, but I’m just getting to write about it now.  While I was busy, stories about it appeared in high-profile outlets like NPR and the Statistical Modeling, Causal Inference, and Social Science blog.

As I’ve been thinking for two years (according to the ancient post I pushed out the door yesterday), life expectancy is a weird statistic. Life expectancy at birth is not, as the name might imply, a prediction on the average length of the life of a baby born this year. It is something more complicated to describe, but easier to predict. I like to think of it as the length of life if you froze the world exactly the way it is right now, and the baby today was exposed to the mortality risk of today’s one-year-olds next year, today’s two-year-olds in two years, etc. Although, as a friend pointed out two weeks ago, this is not a really good way to look at things either, if you push the analogy too hard. Currently Wikipedia isn’t really helpful on this matter, but maybe it will be better in the future.

There is another interesting thing in this paper, which is the validation approach the authors used. Unfortunately, it’s full development is in a paper still in press. Here is what they have to say about it so far:

We validated the performance of the model by creating small counties whose “true” underlying death rates were known. We did this by treating counties with large populations (> 750,000) as those where death rates have little sampling uncertainty. We then repeatedly sampled residents and deaths from these counties (by year and sex) to construct simulated small-county populations. We used the above model to predict mortality for these small, sampled-down counties, which were then compared with the mortality of the original large county.

I believe that this is fully developed in the paper which they cite at the beginning of the modeling section, Srebotnjak T, Mokdad AH, Murray CJL: A novel framework for validating and applying standardized small area measurement strategies, submitted. From what I’ve heard about it, I like it.

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May 11, 2010 · 5:15 pm

Network Theory in Health Metrics

The heavy-tailed/small-worlds crowd had a big impact in health research recently, and now it’s drawing criticism from the theorists. Slate covered the story well a couple weeks ago, and interviewed Russ Lyons at length about the methodological shortcomings of the evidence that obesity, smoking, and loneliness are socially contagious. The Slate article even links to Russ’s preprint on the matter, which is some pretty technical stuff to point a general audience towards. Go Slate!

When I passed on some of Russ’s concerns to my experienced health metrics colleagues, one replied that the idea of social contagion is important enough that it doesn’t matter if the methods are wrong. Interesting perspective. It reminds me of Gian Carlo Rota’s ordering of mathematical results: most important are definitions, less important that that are theorems, and much, much less important than that are the proofs.

I’ve been in meetings for almost 3 weeks now, and meanwhile more good papers on networks for health are pouring out. Christakis and Fowler have posted a preprint to arxiv, showing how network thinking improved flu surveillance of Harvard undergrads. So maybe the idea was the important part. Meanwhile, the Cosma Shalizi and Andrew Thomas have an additional critique preprint, to be put in the same category as Russ’s. I asked Russ what he would accept as evidence of social contagion, and I didn’t find out, but the paper by Shalizi and Thomas says maybe nothing can be convincing: Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.

For me, it’s time to get back to that meeting!

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