Sunday, January 24, 2016

New Data. New Opportunities.

So, I'm working on a new analysis, and my plan is to describe what I'm doing, why I'm doing it, and what I'm learning along the way, as it happens.

Well, I guess it's not entirely "new", depending on how you count it, I've been working on this for years already.

It's an extension of prior work I've talked about a lot on this blog, the extraordinary finding that gay men are more likely to be in "excellent" health than heterosexual men.

I've looked at this issue in many datasets, and published a paper on it in one particular dataset, the Behavioral Risk Factor Surveillance Survey, where I looked at the self-reported general health of men in same-sex and mixed-sex couples, based on their marital status and household composition.

In that study, I found that about half the general health advantage of men in same-sex couples was explained by the facts that men in same-sex couples were younger on average, better educated, and wealthier. When I looked at some of the specific health characteristics, men in same-sex couples were less likely to be overweight or obese, but more likely to smoke. Taking all the health factors I could into account didn't explain why man in same-sex couples were healthier than men in mixed-sex couples.

But what do I want to do now, in this new analysis? Well, it may yet shift and wiggle from this idea, but I'd like to do a couple of things. First, there's a lot more data to work with now. Instead of having to look at marital status and household composition, we can now look at sexual orientation directly, because a number of states have started to ask people not just who you live with and your marital status, but also whether you think of yourself as heterosexual, gay, lesbian, bisexual, or something else.
Second, because we now have both sexual orientation and marital status, and same-sex couples can get married in a lot more places than when I did the earlier analyses, I can look at single gay, lesbian and bisexual people, bisexual people in same-sex and mised-sex relationships, and a variety of household composition structures.
Third, I'd like to try to fold three different surveys into a single analysis, the BRFSS (which is the largest of the surveys, but the sexual orientation questions are asked in only select states), the National Health Interview Surveys (which are smaller, but comprehensively asked all across the country), and the National Health and Nutrition Examination Surveys (which are smaller still, but have a great deal of depth to them).

There are other ideas I'd like to look at too, like the degree to which heterosexuals would be misidentified as being sexual minorities because they live in same-sex households (like my heterosexual roommate). And what geographic and demographic factors predict that misclassification.
Also on the issue of misclassification, the BRFSS asks interviewers to guess the sex of the person they're on the phone with, and these data give us an estimate of how often they guess wrong.
Another set of issues I could look at with these data are things like basic demographics - Who do gay men and lesbians live with? How many are in relationships? Believe it or not, these basic issues have barely been touched in the academic literature, because data like this has not been available before.

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