It’s dense, y’all. So here’s the first dose.
It’s about race and health in public health research.
The U.S. is a multi-racial, multi-ethnic society, so we use race as a variable in all of our research. We do this partially because of the fact that racial differences persist in virtually every area of health interest, and partially because of convention – we publish statistics stratified by race, we control for race in research models, and we exclude individuals from analysis on the basis of race. What we (‘we,’ meaning me and my colleagues of health researchers… if I might take that presumptuous leap of status) don’t do is stop to question whether race is really an appropriate construct – what it means, what it really differentiates, and what it ultimately suggests.
This is really important because the use of race in public health research is very problematic. The idea is that using race categories controls for some sort of undisclosed differences in population genetics… or in fancier talk, the epidemiologic assumption is that there is a genotypic difference that is being controlled. But in reality, researchers aren’t in the practice of, say, taking gene frequency measures in their participants. And more to the point: they aren’t even in the practice of defining the criteria for assigning a person in one racial category to another.
Well, if you’re still with me, you might be asking about the standard. Because, surely, our medical researchers have come up with some hard and fast rule about the biologic concept of race in medicine.
Nope.
And as much as population geneticists will jump up and down screaming about things like ‘continental racial categories’ and the higher incidence of genetically-related disease in certain groups (say, sickle cell) – the bottom line? All our genome work has us coming back again and again to say that genetically, we’re all pretty much the same.
Richard Cooper (an MD and Epidemiologist at Loyola Med School in Chicago) is sort of the Master and Commander of this discourse and I’d be remiss to try and restate what he says so darn clearly:
Racial differences reflect different social environments, not different genes, even where two groups live side by side, as do blacks and whites in the United States. Race does not mark in any important way for genetic traits; rather, it demonstrates beyond question the paramount role of the social causes. We have much more to learn from that paradigm, rather than the one offered by ethnogenetics.
In short, when we’re studying race, we’re really not studying genotypic differences – we’re studying phenotypic differences. (e.g.: the differences that result in our environments, not our genetics.)
Okay then, but public health uses race all the time and finds all sorts of interesting results. What does all that mean??
For one, it means that the results might be screwy. The majority of public health research occurs statistically: where a model full of complex and overwhelming Greek letters spell out a variety of things (the independent variables) that predict what happens to an outcome (the dependent variable). Race is most often used as a dummy, or binary, variable – meaning that you are either black or white – so the lack of conceptual clarity about what in the world each of those categories means leaves a great deal of room for error… if you aren’t controlling for something very clearly within your model, it means that your variable is open to error. It could be measuring the effects of other things in your model, including things in the error term. This means it could be “endogenous,†which, in public health research, is a Really. Bad. Thing. Suggesting that using race as a binary variable presents a problem of endogeneity to statistical models is sort of like saying that that ‘vegetarian’ gravy your Mom has been feeding you for all your 20 years of vegetarianism is actually made from 6 different animals. It ruins everything you’ve ever done with it and colors your ability to use it in the future. It’s better to just not know. Or to ignore the reality. Or! To reinvent it!
Like, for example, saying that race doesn’t really mean what we think it means. Let’s get real, you say, we know that race is all messy! So when we’re talking about race disparities in health, we’re actually measuring other things… you know, like socioeconomic status, discrimination, cultural factors, stuff like this that we know have a racial component.
That’s all fine and good, I answer, but public health models shouldn’t be proxy for anything not clearly defined. That’s not good science. It’s more logic to argue that if race is a proxy for other factors, then we need to find better ways of measuring those other factors. If we’re going to intervene effectively, we need to clearly understand what is going on.
Let me give an example. Let’s say that you are a health researcher and you’re studying prenatal care utilization. You’ve got a great regression model controlling for a variety of factors and your results show a statistically significant coefficient for the race binary variable (that the mean number of visits is higher for whites than for blacks, even when you’re controlling for things like income, age, insurance status, etc.) You might fall into the trap of reporting (as is embarrassingly common in published research) that “race is a significant determinant of prenatal care utilization.â€Â Think about that for a minute. The color of one’s skin has nothing to do with how many times someone sees the doctor. How the world around someone reacts to them due to the color of their skin (or other individual factors) may very well impact how many times they attend a prenatal visit… but that is not what the model is measuring, nor what the data is suggesting!
Further, if you go along that route, you may filter that finding down to medical and public health practice. It may be unintentional or even unrealized, but your intervention could be focused on race, trying to address whatever it is about being black that means you go to the doctor less. You may not even think to see what is going on with the doctor, or the clinic, or the system because you’re so focused on intervening in on that race factor… and you’d be missing the point.
Public health science needs better conceptual precision about the measurement of race, period. At the very least, the lesson here is that we need to be clear on what we’re measuring and how we’re interpreting it.
eli | 05-Jan-09 at 4:31 pm | Permalink
Two questions:
1. Race may not be the direct cause of the problem but it is the independent variable influencing the problem. We found in our consortium that when we controlled for all the items you mention above race did seem unimportant at the skin level. BUT we did notice that it directly influence approach to housing, neighborhoods considered, attitude towards opportunities. So how can race not be considered? I think had we not seen the race issue we would have missed a major point. We picked up that race affected the provider not the user. (So in response to prenatal care affected by race…it might better read prenatal care affected by provider perception of race and cultural expectations.
2. I’m curious how our genes (I’m not an MD so educate me on this one) are not influenced by race. Essentially, the majority of jewish people I know are lactose intolerant which has to be some type of a genetic mutation at some point based on their religious and racial background.
admin | 05-Jan-09 at 7:10 pm | Permalink
Hi Eli, I’m glad you’re asking questions.
For 1. In reference to health research, there is never one “direct” cause to any problem. In a multi-variate model, the idea is to understand how the myriad of issues contribute to a problem and determine effective interventions based on what the data show. And like any scientific approach, you must have complete control over your variables at the most elemental level in order — that means you must know what they mean, know that they are measuring exactly what you intend for them to measure, and know that you are measuring absolutely everything that is contributing to your outcome. Only then can you be sure that your results are not skewed. We view racial categories so routinely that we rarely question what they mean — when in reality, they mean NOTHING, or at least, they mean nothing in terms of what we think they are suppose to mean. Even worse, when we set out to do research, we don’t clearly define what we are attempting to measure within that variable. Plain and simple: it’s not good science. You shouldn’t take anything for granted — you should know what it is and what it is measuring. And question, question, revisit, change, and question again. That is the practice of a good scientist.
For example, in your study — granted, I’m guessing it was for market issues, which is much different both in epistemology and in how you were conceptually approaching your study hypothesis — but none the less, in your study, how did you define race? Was it on skin color? If so, then what about Black Latinos? Was anyone that speaks Spanish, regardless of skin color, categorized as Hispanic? If so, then was race based on linguistic differences, or skin color? What about folks from the Indian subcontinent? Were they lumped with Asians? How about Hawaiians? Who was included and why? In health research, these are the kinds of considerations that come into play. In health research, where we are looking for what makes certain people more unhealthy than others, who you put in which groups matters a lot. In your example, it sounds like you are approaching race from a race ecology viewpoint (i.e.: race as defined by environment — versus public health, where the epidemiologic assumption has been that we are studying race as a genotypic phenomenon). Race ecology is a more popular and growing science that is just reaching public health studies. (Actually, I would LOVE to apply race ecology principles to health outcomes — probably something like birthweight — stratified by race and neighborhood through the NOLA area… studies have suggested that the more racially segregated an urban area, the worse the health outcomes for ALL races. NOLA is just about the perfect place to test this… IMO.) ANYWAY. The biomedical model public health works from is somewhat antiquated in how it approaches stuff like race — mostly because the hold on the excuse of genetics is just way too easy. Tempting to just chalk it up to genetics and move on, rather than really be forced to study the nuances of environment and social causes of health inequalities. The bottom line is that the “why?” of persistent health inequalities is simply much too difficult to easy capture in a statistical survey, we simply don’t have the tools or conceptualization of the mechanisms to really know how to measure it… so it’s easier to just say it’s genetics and move on. But the policy implications can be terrible.
2. Population geneticists have been working with theories of “continental races” as the reasons for things like sickle cell (1 in 12 African Americans are carriers) — nonwhites are commonly lactose intolerant (highest in Asians, if I remember right?) But the bottom line is that the science is completely inconclusive. I really respect Richard Cooper’s work on genetics, race, and public health — he is a cardiology specialist and epidemiologist and approaches science ethically and rationally. There is an article that specficcally addresses your question that I like… it’s a few years old now (2003) but very succinctly brings all of the issues together. I’m pretty sure it’s on his website (or I can send you the article in PDF) — the link that you want is the 2003 International Journal of Epidemiology Commentary on Race, it’s called something like “New Wine, Old Bottles”. http://www.meddean.luc.edu/depts/prevmed/Main/Faculty/RSC.htm
lisa paul | 05-Jan-09 at 8:20 pm | Permalink
Whew! Lots of this was over my head, but I’m glad I read through it (twice). It’s good to know a researcher is asking these kinds of questions. And I like one of your conclusions (warnings) that seeing a different pattern by race may not mean the simplistic conclusion. In other words, you don’t necessarily go to the doctor less frequently because you are black. But there may be subtle societal pressures or differences in care/attitudes in the health care profession that influence, by race, how frequently you go to the clinic. Lots of food for thought here.
jenny | 06-Jan-09 at 9:34 am | Permalink
lovely first post – i’m looking forward to reading more, holly.
eli | 06-Jan-09 at 10:20 am | Permalink
Thank you for the clarification. The consortium actually broke down race into the following (and yes it was more market issue based but none-the-less we didn’t just go black, white, hispanic)
Latino-US Born
Latino-Foreign Born
Latino-Spanish dominant
Latino-Mixed, Spanish preferred
Latino-Mixed, English preferred
Latino-English dominant
We then broke out education level and urban vs. rural.
We then also broke out african american by education, urban vs. rural, AND this was the interesting one that brought up a fun (roll eyes here) but difficult reality check…marital status! We found single women in this group with education were more likely to be homeowners than married women with educaton.
We didn’t need to go into birth weight or those factors as we were much more concerned with how survey results in general are skewed by the respondents language, ethnic and socioeconomic background etc. It was eye opening.
God I wish I lived near you so we could go have a good discussion about this over coffee…
MOVE TO LA to do some research!
magpie | 06-Jan-09 at 1:16 pm | Permalink
This is fascinating.