This month, the Institute for Health Metrics and Evaluation released a county-level study of life expectancy in the United States from 1987 to 2007. They compared their information to an average of the 10 nations with the world’s best life expectancies.
“We are finally able to answer the question of how the US fares in comparison to its peers globally,” wrote Dr. Christopher Murray, IHME Director and one of the paper’s co-authors. “Despite the fact that the U.S. spends more per capita than any other nation on health, eight out of every 10 counties are not keeping pace in terms of health outcomes. That’s a staggering statistic.”
I’ve mapped their data for Minnesota counties. But first, some discussion of the data.
What’s the story here? Compared to large chunks of the United States, Minnesota is doing pretty well on life expectancy.
That’s a story in itself, but it’s not what we’re looking at here.
We’re looking at how Minnesota stacks up against other nations, especially those with the best life expectancy numbers. In the data I used for these maps, it’s referred to as the “international frontier.”
Why should we compare ourselves to an “international frontier”?
Simple. As the doctor said, the United States spends more per capita on health care than any other nation, yet we rank 37th in the world for life expectancy of both women and men. It’s a shameful discrepancy, and one we should highlight at the local level, which is what I’ve done with these maps.
What do the shades of red mean?
Each county is shaded according to its relationship to the international frontier of life expectancies for women and men.
If a county is shaded light red, it is not far at all behind the international frontier. If it’s dark red, well, let’s take the darkest red on the male life expectancy map; Beltrami County is 18 years behind the frontier. That means that the most advanced nations reached Beltrami’s levels in the early years of the Arne Carlson administration.
How bad is that?
In Minnesota, for men, it’s as bad as it gets. Women have fallen further still in Pine and Benton counties, where female life expectancy is 20 years behind the frontier.
Among counties nation-wide, it could be worse. The Institute for Health Metrics and Evaluation found that “U.S. counties range from being 15 calendar years ahead to 50 calendar years behind for men” and about the same for women.
The best Minnesota ranking for men was Stearns and Washington counties, which matched the frontier, and Stearns again for women, at just one year behind.
How do you explain these discrepancies?
No surprise here: it’s complicated. Usually the discussion flies straight into issues of poverty and inequality, but it’s very difficult to pin down causes. A Minnesota Department of Health report on life expectancy disparities in 2009 stayed clear of any attempt to explain the discrepancies, noting, for example, that “life expectancy in some older age groups in the Twin Cities suburbs is significantly lower than the state average, even though overall suburban life expectancies are close to or exceed the state average.”
Without a clear explanation of the discrepancies, what use is all of this?
The IHME researchers call for a new discussion, one that goes beyond what has become an almost static, if crucial, discussion of race and class. They point to additional factors, some peripheral to the race and class discussion and others transcending, such as tobacco smoking, hypertension, diabetes, physical inactivity, obesity, LDL cholesterol, diet and alcohol.
“At the national level,” they write, “these risk factors together lead to close to one million premature deaths” each year.
So couldn’t there be a solution at the national level? Through legislation perhaps?
Here are the researchers on that: “A single national solution may not be the most effective for all risks. What will work to increase the effective coverage of hypertension management in Native Americans on the Pine Ridge and Rosebud reservations and in Hispanic communities in Miami may be very different.”
Would a better health care system, created at perhaps the national level, fix these issues?
A health car system that focuses more on preventative care, certainly, but the researchers argue there has to be a more holistic approach. The providers are key, but so are individuals, communities, and governments:
National, state, or even local policies may be effective for banning trans-fat and regulating salt in packaged and prepared food, tobacco and alcohol taxes and control, increasing financial and physical access to healthier diets such as omega-3 fatty acids and fruits and vegetables, and authorizing the use of incentives by employers, insurers, and others for risk factor modification.
Community intervention may be important for promoting physical activity and tailoring screening for hypertension, blood sugar, and cholesterol to local culture and context.
Expanded and enhanced primary care can be the key locus for more aggressive management of hypertension, cholesterol, blood sugar, and personalized interventions for tobacco and alcohol.
Major limitations to prioritize preventable causes of death include the need for more primary care physicians and implementation of research efforts to improve adherence.
A health system push on preventable causes of death would not be easy, but it is a target that is technically possible and could make a major impact on U.S. health and life expectancy rates at the national and local levels.
Now to the map. Whatever questions that pop up for you, please share them in the comments. I’ll find answers and add question and answer to this post.
Here are the maps: female life expectancy / male life expectancy
Next: We’ll look at racial disparities in life expectancy for the Twin Cities
Correlation does not establish causation, but it would still be interesting to see the correlation between county median (a better measure than the average, which can be skewed by extremes) income and life expectancy.
I love the work you are doing mapping data. Suggestion, if management will not invest in a mapping solution besides Google maps, please investigate stylized maps (http://code.google.com/apis/maps/documentation/javascript/maptypes.html#StyledMaps). This can be leveraged to remove the unnecessary landmarks on the map, e.g. roads. Also, consider changing the canvas popups from an onclick action to an onhover action. These small usability tweaks would greatly improve the presentation, IMO.
@David Thanks for the note! I’m actually doing lots of work with Fusion Tables Layers and other API features. Experimenting with listeners also, to get the data next to the map rather than requiring the popup window. What I’m learning/building may not show up on the site until our redesign is complete, however. I hope you’ll be happy with the changes when they begin to appear! I’ll apply them to the maps already published. Map party!
Jeff, glad to hear you are exploring these things, sounds like you and I agree on the point raised. I am looking forward to seeing the redesign. Is there any articles/timelines available on the site somewhere?
The Minnesota data is interesting — but would be more meaningful if compared to other locations in the U.S.. It would also be informative to see in graphic or tabular format the disparity between the cost of health care and outcomes (at least as measured by longevity of life) on a nation by nation basis. The limited information I have seen on this comparison blows away the often repeated claim that the U.S. has the “best health care in the world”.