Research Worth Reading (4) - trans health in Massachusetts

Gunner Scott, Sewart Landers and pals have served up a very interesting paper in January's AJPH - the first time anyone anywhere has published anything peer-reviewed on a population-representative sample of transgender people.

In Massachusetts, the Behavioral Risk Factor Surveillance Study (BRFSS) has asked the adults it interviews "Do you consider yourself to be transgender?", and a whole lot of demographic and health-related questions.

Many studies in the past have sought out a transgender population to try to say something about the health of the group, but this is the first one to rely on a "random" sample, meaning calling people up at random; and that's probably the best way to be sure that you've got a study population that is fairly representative (at least of people with phones).

In addition to addressing trans health from a population perspective for the first time, this study is also the first to report simple basic demographics of the transgender population in the US as well, including the most basic one - how many transgender people are there?

The answer, in this study, is about 1 in 200 in Massachusetts, about 1 in 110 in a similar study from Vermont, and 1 in 170 in Boston. It is likely (for reasons I've discussed before) that these are overestimates, meaning that the true proportion is probably somewhat lower than that, but how much lower? That's hard to guess, it depends on how many nontrans people answer the wrong way because they are distracted or misunderstood the question. The only way to figure that out is to call back the people who said they were trans and ask them again.

The study is very interesting in that it validates some things trans health activists have known for years, but there isn't strong evidence to support all the health disparities that have been identified from "convenience" samples. Members of the trans population in this Massachusetts study were less likely to be employed, and more likely to be living in poverty than the nontrans population. The study also documented that 36% of the trans population were smokers, compared to 17% of the nontrans population. But markers of access to health care were not particularly different. The trans population was less quite a bit less likely to have health insurance (86%) than the nontrans population (94%), but this did not translate into not having a regular health care provider or not seeing a doctor because they couldn't afford it, and the trans population was even more likely to have had a checkup in the last 12 months (85%) than the nontrans population (75%).

Mental health measures did show some substantive differences: 70% of the trans population reported usually or always getting needed emotional support, but this was quite a bit lower than the 90% of nontrans people who got their emotional needs met; and 14% of the trans population reported being dissatisfied with their life, but only 6% of the nontrans population did.

The authors were very thoughtful about ways that these results might be misleading - for instance that trans people are probably less likely to be stably housed and have a telephone, so these figures may well present a rosier picture than a fully representative sample of trans people would be. And also, not knowing how many cis-gender (nontransgender) folks inadvertently classified themselves as trans, it is hard to know the degree to which true differences between the trans and cis populations are diluted by these inaccurately coded folks. Another possible source of bias might be people who have transitioned, but no longer consider themselves to be transgendered, although I suspect this is pretty unlikely to be a substantial part of the population, because the way the question in Massachusetts was asked, they made it clear that they meant "experience(d) a different gender identity from their sex at birth. For example, a person born into a male body, but who feels female or lives as a woman". But excluding people who no longer consider themselves to be trans would, probably, make the differences seem larger than they actually are. A similar bias would arise from trans people not feeling comfortable describing themselves as trans to a stranger on the phone.

Thanks Gunner & Stewart!

Redistricting - what's best for the gays?

California has just undergone a major redistricting effort, by far the most public process in recent memory.

And LGBT groups chimed in on how the lines should be drawn.

I have read a few of the many articles on the topic, and it got me to thinking - how should we want the lines drawn?

The consensus seems to be that we should try to keep the gayborhoods together, keep them from being split up into multiple districts. I suspect that the idea is to maximize our political power. The only other reason I could think of was if the gayborhood itself would split up if there was a voting district line drawn down the middle. But, I don't think that the Castro would lose it's sense of identity if it were divided into two State Assembly districts...

So let's go with maximizing political power.

And here's the conclusion I came to: split us up!

The idea of concentrating political power by maximizing the concentration of a group within a single district is predicated on a couple assumptions, among these are:

a) the group has a large enough presence to dominate a given district, and

b) a high degree of segregation allows one to draw geographic boundaries that largely coincide with where the group lives.

There's probably more to it than that, but I would argue that neither or those is true of LGBT populations, in which case, a fresh look at the strategy may be in order.

There just aren't enough gay people, pretty much anywhere, to dominate the population. Outside of a few square blocks of real estate in central San Francisco and West Hollywood, we simply aren't anywhere near a majority of the population.

And, we're not highly segregated, assuming that same-sex couples in the Census are a good representation. Particularly lesbians, who are very evenly distributed across the country.

So perhaps we should consider another approach to maximizing political power: splitting the gayborhoods into as many districts as possible in order to maximize the number of political candidates who need to consider our needs. That, and do exactly what we have had to do to get anything done politically, build allies.

Have I convinced you?

Images taken from http://swdb.berkeley.edu/gis/gis2011/

Thoughts on the Behavioral Risk Factor SURVEILLANCE Study

The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone survey conducted every year. It's been growing and growing every year, and it gives a lot of people (including me) a data woodie every year in April when the annual data dump comes out.

Nominally, the survey is about "behavioral health", things like smoking & drinking, seatbelt use, exercise, diet, getting your cholesterol checked and a mammogram done. It has become a cornerstone of our data surveillance infrastructure - used to track progress against the Healthy People goals, and to reiterate endlessly repetitive health disparity analyses.

In 2010, 429,630 people responded to the survey. That's almost the population of Wyoming. Sure Wyoming is the least populated State, but do we really need to call that many people every year to look at trends in how often people smoke, use seat belts, and eat five fruits & veggies a day? Imagine talking to each and every resident of Kansas City, Missouri in one year, asking them some 75 questions about their personal behaviors. That's the scale of this thing.

Since 1988, there have been almost 5 million interviews - about the population of South Carolina, or the combined population of the 7 least populous States: Wyoming, Alaska, North & South Dakota, Delaware, Vermont & Montana.

And it has been growing at a rate of about 8-9% a year, which means that is has been doubling in size about every 7 years or so.

One of the ways that BRFSS is complicated is that it tries to have about the same number of responses from each State. Think Senate vs. House of Representatives. So whereas the response burden in New York, Illinois and California is a relatively manageable 1 in 2,000 or so residents getting called in any one year, in New Hampshire and Hawai'i, about 1 in 200 people have to answer this survey every year. And in Vermont, it's as low as 1 in 92 people! That means you Vermonters probably know several people who get surveyed this year, and given the survey growth rate, it will be almost impossible not to get interviewed at some point in your life.

The record goes to the Virgin Islands, though, where about 1 in 35 people get surveyed every year. We're going to know every detail about every resident of the Territory before long!

This raises a couple of issues for me. There's the inevitable risk of some hacker breaking into the State Health Department and snagging detailed information on tens of thousands of State residents linked to their phone numbers. Although that's a scary idea, it doesn't get me too exorcised, because there is probably very little value to that information - it is hard to imagine who would want to know about your dietary habits, or even drug use or sexual behavior.

Another issue is just the level of surveillance, or monitoring of the population. Gathering information from a small number of people to keep tabs on trends in the population as a whole makes sense, but it seems to me that BRFSS is getting out of control, moving towards a degree of surveillance that is quite intrusive on a high proportion of the population. I mean, at this rate, it might make more sense to just mass mail the survey to every State resident every five years or something like that.

But the biggest problem I have with BRFSS is only tangentially related to it's size. It's the fact that it asks really boring questions. How that's related to its size is that by becoming the largest health survey in the country by an order of magnitude, BRFSS is where lots of people will look for answers to what is causing our public health problems. It's the centrality of "behavioral health" that I've got a gripe with.

Let's take obesity as an example.

BRFSS can demonstrate in very great detail the growth in obesity rates over time, in very minute detail. But it can't tell us much of anything about why any individual or group is getting heavier. Partly that's because it's a prevalence survey, so there is no way to track individuals over time (talk about invasive surveillance). There is no way to know if an overweight person became overweight recently, or even if they have lost a lot of weight recently. But mostly it's because the questions are boring.

You would think that we'd have learned by now that asking people about their behaviors doesn't tell us much about behavior change. And even when we do learn something about behavior change, we have learned that such efforts are incredibly difficult, time-consuming and often barely effective, especially when they are administered at an individual level.

I won't pretend to know what's causing the growth in obesity rates, but I can assure you that the answer won't come from asking 2 million more people about their exercise habits and vegetable intake. If the answer lay there, we'd have licked the problem a long time ago.

One thing I guarantee that asking 2 million more people about their behaviors will accomplish is cementing in the minds of most public health researchers and practitioners that the answers to our public health issues lie in personal behaviors - and the corollary to that is that any health problems you have are because you have failed yourself. Is that the message we really want to send?

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, wonder.cdc.gov, 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.

Small-area variation in voting on Proposition 8

I've been looking at how people voted on California's proposition 8 (the one in 2008 that restricted marriage to mixed-sex couples) at the voting district level for a project to try to characterize normative heterosexuality within small areas.

These maps show voting behavior at the census tract level (census-defined neighborhoods of ~4,000 residents).

Yellow = 50% support
The darkest green = 35% or less
The brightest red = 65% or more
(white = fewer than 100 votes cast in that area - often airports, sometimes just very unpopulated)


San Francisco bay area.













Los Angeles area. It is surprising to me how polarized this issue was.

Nice camp, Google!

I wonder how long it will take the religious right to figure out that today's Google doodle has a second meaning.
Brilliant use of camp, Google!

TBLG Data Geek Nirvana is Coming - Are We Ready?

The year is 2015. An unprecedented amount of data is now available about the health, socioeconomic conditions, and familial relationships of bisexual-, lesbian-, and gay-identified Americans. We now have the opportunity to describe these populations in great detail.

Routine questions intended to allow people to identify as trans have been tested and deployed in a number of surveys, and our ability to describe the trans-identified populations of the United States is light-years ahead of where we were in the dark ages of 2011 when a tenuous estimate of the size of the trans-identified population was first reported from population-based surveys.

Although a fairly wide variety of national surveys had collected sexual orientation data since the mid-1990's, it was a haphazard process in which surveys would add a question on sexual orientation largely based on lobbying by lesbian and gay researchers with strong personal relationships with survey administrators. Not only that, but the idiosyncratic method meant that there was little, if any, co-ordination between surveys, and each one asked about sexual orientation in it's own slightly peculiar way, leading to much methodological navel-gazing, and debates about which results were different because of question wording vs. which differences were the result of population differences, or some other factor.

Then, in 2011, given a push from a prestigious Institute of Medicine report, the ground shifted.

Rather than the default position being that one had to justify adding questions about sexual orientation to a skeptical research committee, survey administrators would now feel pressure to justify _not_ including sexual orientation questions, and have to get creative about how to reliably assess transgender identity in general population studies.

Furthermore, a growing consensus about the precise wording used to assess sexual orientation and gender identity had developed, leading to a much greater ability to compare results across studies, and even to combine multiple studies together in order to overcome the problem of not having sufficient numbers of sexual and gender minority individuals, and also to enable analyses of ever more tightly defined sub-populations - such as elderly Asian-American lesbian and bisexual women, bisexuals in their 20's and those in their 50's, heterosexually-identified men who profess same-sex attraction but have not had sex with another man, and so on.

Not only that, but the National Institutes of Health had made substantial investments in supporting analyses of this data bonanza, and had also invested in programs designed to provide up-and-coming students of this field with mentoring and training to an unprecedented degree.


In short, TBLG data geek Nirvana had arrived!


Since that scenario, or one not far from it, is likely to be in our 5-10 year future, it is worth spending a bit of time getting past the salivating, and doing some serious critical thinking about some potential side effects, so that they can be anticipated and prepared for, rather than an unanticipated surprise and source of frustration.
And that's why I'm writing this piece. I'm as eager as the next TBLG researcher to get my hands on the tsunami of data headed our way. We all want to surf that wave, rather than get pummeled by it.

It's going to be great, and...
I want to think about the "and..." part for a minute.

I have been able to anticipate a few side effects of this coming wave, but I don't want to claim that I can see the future. Some of these may not happen. Undoubtedly other things I haven't considered will catch me by surprise.

The more research, the better.
Well, it depends on the research. See my previous posts for more on this. In short, it depends on what the goals of that research are. More than likely, we're going to see a huge expansion of rather thoughtless research. Specifically in terms of health research, we're going to see a lot more "health disparities" research oriented towards only adverse health disparities. We're going to see a lot of "intersectionality" research oriented towards showing cumulative disparity and adversity. Both of these approaches will miss the interesting exceptions: what health advantages do TBLG populations have? When does the assumption of cumulative adversity inherent to much intersectionality work fail to capture the points of resistance and resilience that provide opportunities for effective health promotion and community pride?

Who will be the gatekeepers?
"Nobody" is the short answer. Which we should welcome with open arms. Expanding access to data about TBLG people will be the inevitable result of this process of asking more questions. The thing to watch out for here is that people who have not in the past had any particular interest in TBLG populations will be chiming in for the first time, and making rookie mistakes in interpreting what they look for, what they find, and how they report it. The other phenomenon to wacth out for related to this is people who have had an interest in TBLG populations, but will now feel empowered to look at data across a variety of disciplines other than where they have spent most of their time to date. Of course that is to be welcomed and encouraged, but there will be bumps along the way.

A shift in prerogative
Survey administrators will feel the shift from having to justify adding sexual orientation and gender identity items to surveys to including these questions unless there is a strong reason not to. Similarly, people analyzing these surveys and studies will feel an expectation that they should at least try to see if there are differences across categories of sexual orientation.
And, when they do these exploratory analyses, a variety of possibilities may arise. They may see a disparity that it truly there and is significant in a statistical sense. Since the idea of disparity and deficit fits well with assumptions about the social order, these exploratory analyses are more likely to get published than health similarities or health advantages that are truly there and also significant in a statistical aspect (technically a similarity can't be significant, but the point is that if no difference is seen in a large sample, that is evidence of little difference). Disparites that are truly there, but not significant in a statistical sense may also be commented on, and disparities that are observed as statistically significant, but in reality aren't disparities at all are also likely to be reported out as adverse health disparities. The probability that a health advantage that is not significant would be reported is, I propose, vanishingly small.
As a result, we are likely to see reports of a wide variety of health disparities, many of which will be "real", and also many of which will be spurious findings that are less likely to be replicated in other groups of people. But we won't be able to tell which is which until replication studies are done. And the way science works, debunking a falsely significant finding that jibes with the overall assumption of TBLG people being deficient can take several, even dozens of failed attempts to reproduce that finding in other populations.
Conversely, findings that go against the notion of inherent deficit (health similarities and advantages) will be viewed as tentative, and may require dozens of significant findings before being viewed as worthy of comment.

The upshot of all that is that as we shift from analyses designed to examine TBLG health from a theoretically-oriented perspective to a time when routine, but atheoretical analysis of these populations become commonplace, we should expect to see a large increase in spurious findings of TBLG deficits.

How Come Queer Health Research Can Only See Unhealthy Queers?

Recently, the prestigious Institute of Medicine came out with a tremendous compendium, The Health of Lesbian, Gay, Bisexual, and Transgender (LGBT) People: Building a Foundation for Better Understanding.
The goal of the book was to describe the state of knowledge on queer health, identify priorities for future health research, and make specific recommendations to the US National Institutes of Health.
And as far is goes, it does these tasks very well. The literature review is very thorough and will be useful for anyone writing a grant, among other things. The chapter on context is a great 30 minute romp through our nation's homophobic history.
The recommendations it makes are, in my opinion, a mixed bag, from laudable goals to some that I really can't get behind at all. All are the inevitable result of the mainstreaming of queer health research and bringing it into the NIH funding model.

If you've followed this blog in the past, you probably can guess that I don't think it goes far enough. I have to admit that I have not yet been able to read each and every sentence in the thing. It's huge. But I have read the executive summary, the intro and the recommendations, and I doubt that there is anything buried in the rest of the document that would alter my critique. (I hope I'm wrong, and look forward to being pleasantly surprised).
I also have to say that lots of this report makes for wonderful reading. If I had more time to devote to this, I would balance a lot of this criticism with things that the report does really well, like acknowledge that homophobia hurts straight people too.
I'm going to break my critique down into two sections. The first will address two broad deficiencies of the report: a failure to fully appreciate the health similarities and even advantages of TBLG people. What do we do well, for ourselves, one another, and for the society at large. A failure to understand the role of social context in the lives of queer populations except in a very limited and limiting way. Second, I'm going to tackle the recommendations one at a time..

Where's the Health?
Given that the report draws on the most frequently published research, it should have come as no surprise that the report concentrates heavily on health disparities in a very particular (and peculiar) sense. In virtually every case, "health disparities" means a health outcome (like depression, asthma, etc.) that we do worse on than heterosexuals. Very little attention is paid to those areas where we do about the same, and none (that I've found yet) of the things that we do better on than straights.
For example, gay men tend to have lower body mass index than straight men, but you won't find that anywhere in this document. Instead you'll find that lesbians are more likely to be heavy than heterosexual women repeated in at least three places, and a complete silence on the health advantage of gay men in this area.

Why focus only on adverse health disparities? As near as I can figure it, the rationale for focusing exclusively on adverse health disparities is that these fit nicely into the narrative that homophobia is bad, and homophobia affects TBLG people by making them less healthy in a variety of ways. Fair enough, but if homophobia is the thing making many of us sick, why not measure homophobia directly?

What's the danger of focusing exclusively on adverse health disparities?
1) It's not an accurate portrayal.
2) It's demoralizing - it reinforces the very effects of homophobia by making 'health' seem unattainable for queers.
3) It makes the radical religious nuts in this country smile from ear to ear - they love pulling out data about how unhealthy we are when they try to deny us various civil liberties (if not our existence entirely).
4) It doesn't explicitly differentiate whether these disparities are due to homophobia (as we would like to think), or whether they are due to some inherent, inborn insufficiency (as the nuts would like to think). Both interpretations are supported by these data.
5) We can't learn anything from the health advantages that we can build upon and use to improve the health of our communities if we can't call 'em when we seem 'em.
6) We can't really offer anything useful to other populations (heterosexuals, as well as other minority groups) as success strategies, if all we identify and talk about are what's adversely affecting us.
7) We can't learn how homophobia affects heterosexuals with this approach.

By analogy, the first century or so of research into health disparities affecting racial and ethnic minorities has largely fallen into the same trap, of spinning out a relentlessly monotonous string of adverse health disparities. These health disparities get interpreted by most of us as evidence that racism affects health adversely, and by bigots as evidence that they were right all along. Health disparities allow both interpretations.
A nearly exclusive focus on adverse health disparities has prevented us from seeing what it is that racial and ethnic minorities do well, what lessons can be learned to promote the health within these communities, and what lessons can be exported to the wider population as well?
For decades, epidemiologists have pondered and shrugged off the "Hispanic paradox", which is that many of the adverse health disparities one sees when comparing Blacks to Whites are not there when comparing Hispanics to Whites. And in many cases, (such as birth weight and longevity), Hispanics' health generally exceeds that of Whites in this country. Not looking into health similarities and advantages has really stalled both understanding the true nature of racism and how it operates in the US (it's a lot more complicated than 'being a minority'), and also being able to build on the record of strength that various communities have already demonstrated despite or perhaps because of living in a racially hostile social climate.

So, when this report focuses so exclusively on adverse health disparities, it really does us all a disservice. It's not an accurate picture of our health. It is demoralizing and depressing to read that litany. It supports our most adamant opponents' views of us. It doesn't get to the root cause. It doesn't show us what we've done well for ourselves, each other, or the communities (geographic, not identity-based) that we live in. It doesn't point (efficiently) to solutions. It doesn't address the degree to which cis-gender heterosexuals are affected by the same forces.

Social Context Involuted
I had the great honor to listen to a panel discuss the report at the 10th anniversary celebration for the Williams Institute on Friday. Stephen Russell was one of the panelists, and he really brought home the message that the report had been insufficiently curious about the role of social context in the document. I was practically giddy with excitement as he spoke, because I no longer felt like the lone curmudgeon in the room. Not that Stephen was a curmudgeon, not at all, I just was beginning to feel out of place as speaker after speaker lauded the report rather uncritically.

What do I mean by "insufficiently curious about the role of social context"? Well, I mean that the report does talk about the importance of social context, and much more so than the other documents of this type. One of the "cross-cutting perspectives" that the report recommends be used to understand TBLG health issues is:

A social ecological perspective--An individual's health is affected by community and social circumstances. LGBT health research should consider both the individual and the various contexts, including interpersonal relationships, in which the individual lives. (Summary, page S-6)

Not only that, they devote an entire chapter to describing the historical and social context of homophobia in these United States that is well worth reading.
Not only that, but also they have a terrific discussion of how stigma operates at various levels.

But, notice what happens in that definition of a social ecological perspective above - the definition sounds very inclusive of understanding how homophobia operates in the social environment, but the single specific context they mention is a highly individualized one "interpersonal relationships". Likewise, there are several points in the article which confuse "structural" with "institutional" or even "interpersonal" levels, where the interactions between patient and provider are characterized as "structural".

And when they get to the recommendations, this trick of collapsing social context down to the most proximal surroundings of a person happens every time the concept is mentioned. And nowhere in the recommendations is it suggested that a goal for understanding LGBT health should be to develop or use measures of heteronormativity and/or homophobia at any level beyond how it affects individuals in these very limited, proximal contexts.

I decided to do a bit of a quantitative analysis of this bias in the document (sorry my qual friends, I just don't know how to do your style well). I searched for the terms "stigma", "discrimination", "prejudice", "homophobia", "heterosexism", "heteronormativity", "homonegativity", and slight variations on these words.
The terms "stigma" (and "stigmatizing", etc.), "discrimintation" (and "discriminatory", etc.), and "prejudice" (and "prejudicial",etc.) occurred fairly often, including in the recommendations section. "Homophobia" and its kin occurred considerably less often, most often in the chapter on context, and the literature review, when other authors were being cited. It was completely absent from the recommendations, as were the other terms in my search list. About half of the occurrences of the term "homophobia" were in the titles of papers cited in the reference section.
OK, so they focused on "stigma" and "discrimination" in the parts of the document where they were proactively describing what they thought should be done in the future.
But every time these terms were mentioned in these sections, it was as though stigma and discrimination just happen to people, that there is no identified source. We should of course be measuring the experiences of discrimination and stigma that affect our health, but shouldn't we also try to identify those who do the discriminating? Those who perpetuate the stigmas?

Otherwise we run the risk of repeating what has been done in the "perceptions of racial discrimination" literature, where the source of that discrimination is rarely named (except in general terms like "at work" or "in health care"), and the use of the term "perceptions" invites the interpretation that this is somehow all in people's heads, that they are perceiving discriminatory acts whether or not they were intended to be discriminatory, and rarely delves into the pervasive issue of discriminatory actions that are not perceived as such (doesn't everyone get watched in those big mirrors when you're shopping?).

The Recommendations
The report has seven recommendations. Here's my take on them (wow that sounds egotistical. But it is egotistical, so I might was well own it instead of pretending that I represent a broad swath of TBLG opinion on these issues).
In general, these recommendations are written on behalf of people researching TBLG health, who are invested in the model of getting funding from the government to do so, written to the government agency that they seek to influence (and which itself seeks to be influenced).
As a result, they tend to look out for the interests of the researchers, even when these interests may conflict with those of many individuals in TBLG communities (although they are generally in concert), and they are intended to be appealing to a government funding agency that is notoriously shy about engaging in politically-charged areas.

The first recommendation is that "NIH should implement a research agenda designed to advance knowledge and understanding of LGBT health", which sounds pretty innocuous. What else should a group of researchers appealing to a government agency for more funding start out with. And who could object to more research?
I guess for me it comes down to what research are we getting more of? Demoralizing work on adverse health disparities (of which I would classify almost all the items listed in their specific recommendations in table 7-1, aside from the demographic items), or a richer exploration of health similarities and health advantages as well? Research that is highly individualized, dominated by the well-established methods of mass survey techniques, or research that starts with what we want to learn, then develops the methods most suitable to achieving those goals.
This part of my critique applies to the vast bulk of health research funded by the NIH, and isn't specific to TBLG health research at all.
More research, sure... but how about the right research?
There are lots of sub-recommendations under this one which I can't get into right now. Some of these have the potential to be great ("social influences on the lives of LGBT people"), and some leave me cold ("inequities in health care"), but that's for another day.

The second recommendation is that "Data on sexual orientation and gender identity should be
collected in federally funded surveys administered by the Department of
Health and Human Services and in other relevant federally funded surveys". More data = good... mostly. This one gets my unqualified support. The more surveys ask about us, the more we will be fairly represented. With sexual orientation, this is pretty easy, because the questions are there. Gender identity is a bit more difficult, because very little is known about how questions about gender identity perform in general population surveys. There are some dangers here, though. It is likely that the first few surveys that ask about gender identity will "get it wrong", but not in the way you're thinking - the biggest problem will be that a small number of people who have never given their gender a conscious second thought will answer the question incorrectly and be counted as transgender. As a result, a substantial proportion of the transgender-identified population will actually be inattentive or confused cis-gender folks, maybe even half or more.
But, as Gary Gates told me, if you don't have any questions on there, there won't be the incentive to fix them and make them more accurate.

The third recommendation "Data on sexual orientation and gender identity should be
collected in electronic health records" seems ill-advised to me. The research potential of asking these questions is phenomenal, but I'm not at all convinced that there are sufficient protections around this (or any) medical information to make it a standard field. And even when the information is protected sufficiently, there are many circumstances where being "out" on one's medical forms can be downright dangerous. I understand the potential for benefits in terms of getting more appropriate care, but that assumes that appropriate care is available, and that we even have a good sense of what appropriate care would consist of. I'm not convinced that we do know what TBLG appropriate care means in a broad enough set of circumstances to warrant having this information collected routinely on medical records. Should people who feel that it is important to their care have it recorded in their charts? Of course! But do we really want a young trans lesbian in Colorado Springs having that information on her record when she walks into see a provider she's never been to before?

The fourth recommendation is to "NIH should support the development and
standardization of sexual orientation and gender identity measures". Yes, of course. But at the same time, there is a risk that these standardized questions (much like the OMB directed race/ethnicity questions) will result in less curiosity about how people self-identify, and re-inforce the "normality" and reality of a small set of mutually exclusive categories. I still think it's a good recommendation. I just worry about cookie-cutter inclusion of the question in an un-thinking manner all over the place.

The fifth recommendation, that "NIH should support methodological research that
relates to LGBT health" is one I am fond of, but I think that is because I am really into methodology and want to see a certain set of methods (especially ecologic methods) built up, although most of it will probably be devoted to trying to turn various forms of "convenience" sampling into "probability" sampling. Boh-ring!

The sixth recommendation, "A comprehensive research training approach should be
created to strengthen LGBT health research at NIH" Sure, why not.

The seventh recommendation scares the pants off me. "NIH should encourage grant applicants to address explicitly the inclusion or exclusion of sexual and gender minorities in their samples". Sure there are abuses where TBLG people are inadvertently (and in some cases very intentionally) excluded from various research projects. But the discrimination we face in this regard is tiny compared to the historical discrimination against women and racial/ethnic minorities in particular in clinical trials. And a similar remedy for this past exclusion has had limited success and some strange side-effects (see Stephen Epstein's book Inclusion: The Politics of DIfference in Medical Research).
This formulation makes it sound like participating in health research is a civil right, and while exclusion from trials without a good basis should be discouraged, I'm not sure that adding another administrative check-box on the NIH grant forms will really achieve that goal, it may raise false hopes of being able to study TBLG folks in the vast majority of health studies. Perhaps most worrisome, by drawing attention to sexual orientation and gender identity as categories across which one should compare results, we are on the verge of being flooded with falsely positive chance findings that will crop up in various studies and which may or may not be able to be shoved back into the bottle with future research (as examples of this, I would cite the "gay gene" and "gay brain" studies, which, no matter how many times they have been subsequently disproven, still hang in the public imagination as valid). Another example of this (I can't remember if Epstein gets into it or if it came up after his book was published) was the spurious finding that an HIV vaccine candidate appeared not to work over-all, but did appear to work in a bizarre atheoretical grouping of African-American and Asian-American men. That finding was widely reported on, despite obviously being irrelevant to how the vaccine worked. It was a statistical fluke. And how often should we expect a statistical fluke? About 1 in 20 times. So if we start encouraging people to look at every possible health-related thing in every study, one in twenty statistical flukes is going to start adding up into an avalanche pretty darn quick.
In my opinion, if recommendation 7 is implemented, then a lot of researchers who have never given a second thought to TBLG issues are going to feel not only empowered to do these analyses, but encouraged to do them, and we're going to be up to our eyeballs in damage-control mode beating back spurious research results.