I got some good news for the weekend, an opinion piece that I wrote together with some of the other post-graduate fellows at IHME was published online as a Science e-letter. It is titled U.S. Health Care Reform: The Case for Accountability and it’s about the measuring the outputs, outcomes, and impacts of the reform, whatever shape they end up taking.
The part that I was especially interested in adding to the discussion appears in paragraphs 3 and 4, about what these some of these statistics look like currently:
Disparities in health outcomes in the U.S. are unacceptable. A healthy life expectancy at birth in the U.S. ranks behind 28 other developed countries (1). Sizable groups in the United States have mortality risks resembling those in sub-Saharan Africa (2), including urban blacks between the ages of 15 and 64 living in counties with high homicide rates.
On average, Asian women lived 21 years longer than high-risk urban black males in 2001 (2). Although life expectancy for most American women increased between 1983 and 1999, life expectancy for women in 180 counties in areas such as Appalachia, the Deep South, the southern Midwest, and Texas decreased by 1.3 years (3).
I made some figures to accompany this, which Science didn’t print, so I’ve included them for you here:
Probability of a 45 year-old male dying before age 65, 2001, from Murray et al., Eight Americas: Investigating mortality disparities across races, counties, and race-counties in the United States. PLoS Medicine 2006.
Female life expectancy in US counties, 1961-1999 from Ezzati et al., The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PLoS Medicine 2008.
Healthy Algorithms 
Also, there were four post-graduate fellows who contributed a great deal to writing this, but were not listed on the e-letter. Proper respect to Farshad Pourmalek, Marie Ng, Mohammad H. Forouzanfan, and Stéphane Verguet! (as well as Katie and Samath.)
One could make the same criticism of the second plot that one hears about Africa’s low economic growth rate in the second half of the 20th century: maybe we are just picking losers ex-post, and just seeing patterns in noise.
The figure implies widening inequality. Is there really? One way to check would be to choose the red line to be the 180 counties at the low end of life expectancy in 1961 and see where they end up today, instead of vice versa. (Of course, by itself could also give the false impression of narrowing inequality.)
Very interesting post and statistics.
What do you mean exactly when you describe something as “unacceptable?” I think it’s sad that many people, in the US and elsewhere, don’t get to live as long or healthy lives as the most privileged, but what does it mean that it’s unacceptable? I’m just not sure what you think the policy implications are.
In any case, congratulations to you!
2 thoughts:
1) I’d love to see the chart suggested by Aram.
2) Could you publish the data you used? Perhaps to the excellent infochimps website?
aram: I was concerned about that too, but I think that “Reversal of Fortunes” paper looks at it as increasing life expectancy is clearly possible, so even the worst-off groups should be increasing. But more on this in my next comment.
rif: By “unacceptable”, I just mean that we shouldn’t accept these inequalities, and any changes in policy should attempt to reduce them. I saw a talk about “the spirit level” and the way inequality is hurting the rich as well last Friday, more details about that when I have a little more time to post.
Bill: I’d be interested in seeing this chart, too, but I don’t have time to make it right now. The data that went into Figure 2 is available as a Dataset 2 on Ezatti et al.’s PLoS Med paper. If I remember correctly, it doesn’t have the county populations, so I can dig for those if you want to make honest population weighted averages.
aram (part 2): I’ve been talking to my frequentist friends, and I think that there is some sort of null hypothesis approach to your question. Tell me what you think: if I look at the difference between the country life expectancy and the counties, then a null hypothesis is that this is a random walk (of some sort), and the widening inequality test is to say how small is the probability of seeing the lowest 180 random walks diverge this far from the mean.
I guess that the nature of the randomness in the random walk is pretty essential to this approach, is there a way to pick it that is sufficiently objective?
Yeah, that part is tricky, and also because of correlations between counties. I guess visually we might look at a scatterplot of life expectancy in 1961 vs life expectancy in 1999. But it would be more satisfying to have a statistical model that could be tested.
Oh, I’ve got it, in a bootstrap formulation I can understand. What random walk to use? The one we have observed. I.e. look at the year-to-year differences by county, and draw from this set randomly with replacement to get the steps in each the random walk. I wonder what the name for this is.
Sorry to only give non-constructive comments, but suppose life expectancy were completely constant over time, but measurements were noisy. Then this walk would diverge. Maybe that’s not a problem?
Also, what about national trends? I guess to handle that you could have the steps of the random walk taken in, e.g., 1974 be randomly selected from the 1974 differences.