Moving Beyond Queer Health Stereotypes

Head Shot Scout 2014 lo res by Scout, Ph.D.

“Epic fail,” my friend texted me Thursday. I clicked on the attached link to see what he was talking about. Oh. Washington Post had published a story, Who still smokes in the United States — in seven simple charts, and in it they decided to ignore one point from the original study — the numbers showing LGB people smoke at rates over 40 percent higher than others.

To understand how upsetting this story was let me frame it a bit for you. Years ago I was one of several people in small meetings with officials at the federal government strategizing how we could get LGBT data collection on the most often cited federal health survey. There was some front room dealing, some back room dealing and lots of public pressure to get these data collected. Then I stepped through a painful process whereby the feds started then stopped testing questions for trans data collection. But at least we got LGB data collection on the survey, so I tried to console myself with that win while still working for the missing T data. Now the results from that survey are starting to hit the streets, and as predicted, it’s giving us a much better picture of at least LGB health than we’d had before. Like we always say in our meetings, you don’t fix what you can’t see.

The problem is, the Washington Post story pulled from a one-page summary of these data that did clarify that LGB people have profound smoking disparities. So why didn’t we rate an eighth chart?

Unfortunately, we see this phenomenon often. When you think of LGBTQ health what comes to mind? Suicide, mental health, HIV, & bullying perhaps? These were the top four health concerns from a recent state LGBTQ needs assessment we conducted with a group of local community centers. Suicide and bullying and HIV are the stories that make the papers about us. Every single HIV program I know of at a state and local level has specific outreach to at-risk sexual and gender minorities. Increasingly, suicide and bullying programs also include outreach to LGBTQ youth and it’s about time. But when we ranked state tobacco programs on their LGBTQ outreach and inclusion, the average grade was a C. Too many of them ignored us altogether and that teaches us that this isn’t a big health issue for our communities. Now consider that tobacco is easily the largest health burden our communities experience. I ask: why aren’t we routinely included in tobacco control work?

LGBTQ health is more than just HIV & suicide risk. Yes, please pay attention to those critical issues but that’s not where our health problems stop. What about ourprofound access to care challenges? What about the fact that 80 percent of first year medical students display implicit bias against us? What about the systemic discrimination too many of us experience if we are faced with a cancer diagnosis? What about our higher risk for addictions, including the one that lops so many years off our lives, smoking? What about the daily safety risks so many trans women, especially trans women of color, face? What about issues of social support and isolation? What about the constellation of larger health disparities experienced by those marginalized within our communities: bisexuals, LGBTQ of color and transgender people among them?

We’ve been fighting so hard for data collection but it looks like we have to fight just as hard to be seen even after data are collected.

We deserve an eighth chart.

2 thoughts on “Moving Beyond Queer Health Stereotypes

  1. Reblogged this on Busy Nurse Research and commented:
    These stereotypes are so pervasive it is painful. I remember when my advisor told the class I wanted to study STDs in sexual minority populations, when we had just had a conversation about how I wanted to study LGBT health. That was how her mind auto-translated it, and stuff like that happens because of stuff like this.

    So, step 1: collect the data. We are making strides there. You would think step 2 would be to use the data, but as you can see from this reblog, that is really step 3. Step 2 is to actually see the data.

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