Sunday, May 15, 2011

Health Disparities: Getting Worse or Getting Better?

Yes. Both. Simultaneously. With the exact same data.

For my general rip on health disparities research, see the previous (lower) post.

So the other day, I heard someone say that in almost all cases, (racial) health disparities are getting worse, not better. Which, of course fits perfectly with the scare tactics that much of public health relies on these days. I think when we public health types hear that, we think it means we need more resources to address health disparities.
But in the back of your head, doesn't it also say: all the work that's been done on reducing health disparities has been an abject failure, nothing we do works, and it's disheartening to contemplate moving forward? Wouldn't it be an even more powerful motivator to garner resources to be able to say "Hey, look at this, things are getting better". If you were a funder, wouldn't you rather build on success?

But the numbers are the numbers, right?
Actually... depending on how you look at the numbers, whether things are getting better or worse is all a matter of perspective.

Arguably, the most important health indicator of them all is mortality.
So, I went to one of my favorite websites,, and looked up trends in mortality by race and ethnicity over the past few years, and then threw it into a graph.
When I look at that graph, there are a couple things that jump out - first that there are rather enormous disparities in mortality by race in the US. Second that Hispanics and Asian/Pacific Islanders have much lower mortality rates than Whites. (How often do you hear about that?) And third, death rates for all racial/ethnic groups are declining fairly rapidly.
It isn't immediately obvious from this graph whether the racial disparities are getting wider or narrower, the differences between these lines looks pretty similar over this 9 year period.

So, let's look at the disparity using the official method.
You pick the healthiest group, then divide the other racial/ethnic groups by that group to get the relative disparity. In this case, the healthiest group is the Asians and Pacific Islanders, so you get this graph:
It's pretty clear that relative to Asians and Pacific Islanders, the disparity is essentially unchanged for Hispanics, and has increased for Whites and Blacks.
But I find this way of looking at things strange.
It obscures the fact that Whites are the dominant group in the US, so I did the same analysis, but using Whites as the reference group.
When you hear the term "racial disparity", what is the reference group that springs to mind?
At any rate, this picture is a bit different. The Black:White relative disparity in mortality is actually declining slowly over time, while the relative health advantage of Hispanics and also Asians and Pacific Islanders is increasing over time.
But the interpretation of whether
health disparities are getting better or worse over time depends on something besides which racial group represents your baseline - it also matters whether you divide (the standard method) or subtract.
When you plot the difference between Asian and Pacific Islanders rather than the relative disparity, you actually get a different interpretation:
that the difference between Asians and Whites has not been changing much over time, but the disparity between Blacks and Asians has been improving, the exact opposite conclusion one would reach from looking at the relative disparity.
And when you use Whites as the reference group for the difference, these results are pretty similar:
The Black:White disparity is clearly diminishing over time, the health advantage of Hispanics has been increasing slowly, and there is no apparent change in the health advantage of Asians.

I don't want to bore you with the math that explains all this, but I guarantee I haven't done anything funny. It's just that when you subtract, most health disparities appear to be on the decline, but when you divide (which is the standard method), most of the health disparities appear to be increasing.

So both are true simultaneously. Which raises the question as to why dividing has become the standard? I have two explanations - one rooted in history, the other rooted in computerized estimation methods. Neither rooted in any logic the least bit related to health disparities.

The first reason has to do with the history of trying to figure out if smoking caused lung cancer. In post-war England, a couple doctors (Sir Austin Bradford Hill and Sir Richard Doll) did a series of studies to investigate whether smoking could be causally linked to lung cancer. Not surprising in retrospect, they saw that smoking had a big influence on the development of lung cancer. Whether you divide or subtract, lung cancer was clearly more common in smokers than non-smokers. But there was a pesky finding in relation to heart disease. When you subtract, it looks like smoking causes about as many heart attacks as it does lung cancer.
To their thinking, it made sense that smoking would affect the lungs, but why the heart? Fortunately, when you divide, rather than subtract, it looks like smoking has a huge influence on the risk of getting lung cancer, but is associated with only about a 20% increase in heart attacks.
From that, they concluded that dividing must be the right way to think about it.
But subtracting is just as valid mathematically. All these numbers say is that smoking causes about the same amount of heart attacks as it causes lung cancer, but because lung cancer is so rare in non-smokers, that makes a huge relative difference, while heart attacks are pretty common among non-smokers, so a small relative difference still adds up to a lot of heart attacks.

The other reason that public health types divide rather than subtract is that computers like to divide. Well, that's not really true, they don't care. But the complicated statistical methods that we ask computers to do almost all rely on dividing as the one and only way to compare two numbers. The statistical methods that rely on subtracting as much harder to implement, and are often impossible. So what I like to do is throw the complicated statistical methods out the window and calculate the differences using more labor-intensive methods.

All of which is by way of saying that next time you hear that health disparities are getting worse, take it with a grain of salt. It probably depends on how you do the numbers.

My Rip on Health Disparities Research

A few days ago, I gave my final lecture in my epi class, partly an overview of social epidemiology, and partly a rip on health disparities research.
The short version:
Identifying and describing health disparities:
- is a great way to get grant money.
- seems like a great way to raise awareness-
--- in order to mobilize greater resources to address the problem &
--- in order to mobilize members of the 'target population' to take preventive action.
- can be demoralizing to the 'target population'.
- can induce feelings of helplessness in the 'target population'.
- can make ill health seem inevitable & expected in the 'target population',
--- potentially reducing the urgency for action.
- describes the result, not the causes-
--- if you believe genetics is the cause, the existence of health disparities supports that notion
--- if you believe lifestyle is the cause, the existence of health disparities supports that notion
--- if you believe racism is the cause, the existence of health disparities supports that notion.
- don't identify potential public health actions to address health disparities

Furthermore, the almost exclusive focus on adverse health disparities:
- is demoralizing to the 'target population'.
- enhances the sense of social distance between the 'target' and 'majority' populations.
- obscures the true picture of a mixture of adverse health disparities, health similarities and advantageous health disparities.

Ignoring advantageous health disparities
- blinds us to the possibility of understanding the mechanisms for these advantages
--- which could lead to more effective public health measures built upon those mechanisms to address the adverse health disparities
--- or could lead to building on those mechanisms to improve the health of populations other than the 'target population', including the 'majority' population.