Sunday, June 19, 2016

Guns Don't Solve Problems. Guns Cause Problems.

    A week has passed since the Orlando Massacre. I've spend most of the time since grieving, seeking the comfort of others, and sharing what comfort I can. In the back of my mind has been a battle over how to "respond". Personally, publicly, politically. I'm still at a loss, but here's one partial, tentative response, among the many swirling in my head.
    So many things got "attached" in one way or another to the profound loss Orlando, the Nation, and beyond experienced: gay blood, the presidential race, violence prevention, overpolicing, I could go on.
    Perhaps the thing that most caught my eye was the idea that somehow now that "the gays" were activated, gun control might stand a chance of moving forward in ways it has been slow to in the wake of so many national tragedies. On one hand, I'm perplexed by the idea, but also inspired by the challenge - we've made such huge progress on the "gay agenda", what can we offer?

Levers of Power and Change
    What can "the gays" offer to gun control efforts? I guess I'd start with a basic breakdown of the levers of power and change in this country thatwe've appealed to at one juncture or another in recent years: the executive, legislative, and judiciary branches of government, the press, entertainment, science, and interpersonal relationships.
    It appears that the judiciary is, for the next 3-5 years anyway, unlikely to be of great assistance. The legislature (Federally) is hamstrung, and likely to remain so at least until the 2018 election, and probably beyond. Although some State and local governments have made heroic efforts in recent years, the porous nature of State boundaries put severe limits on what can be accomplished. The Federal executive has done about as much as it can in the face of strident opposition from the other to branches. The press is a strong advocate, and also a strong impediment. More on that later. Science is unlikely to be of great assistance - the basic work demonstrating that possession of a firearm greatly increases one's own risk of death is already well established. In my experience, science itself rarely moves political change. Narratives do what statistics appear to be incapable of. Coming out has been an incredibly powerful method for invoking change, starting at the most atomistic level of power: family and friendship. But frankly, I'm struggling to think of what the corollary in terms of violence reduction through gun control is.

Changing the Narrative
     I suspect the way to create the greatest momentum now, which can later be leveraged for policy change, is to work on the narrative of how we tell stories about guns. Guns don't solve problems. Guns cause problems.
    But in the movies and video games, guns are frequently portrayed as solving problems, from a distance, without consequence. Now I love me some violent video games and movies (Reservoir Dogs springs to mind), but to the degree that we don't tell an accurate story about how using guns screws everyone up, we're doing a disservice. We should demand that television and film makers don't turn to guns as a plot device to get rid of a problem to allow the protagonist to progress, but rather every use of a gun should endeavor to show how it makes everyone's life, including the shooter's, much more complicated and less comfortable. That would be a more accurate story.
    Narrative moves opinion more than statistics do. With that in mind, a committed, passionate press should tell more stories about the consequences of firearms use, more about the victims, and also more about what happens to those who carry and fire them. Get into the fears that drive people to carry, and to shoot, get into the legal consequenes, sure; and also the long term psychological consequences of what injuring or killing another human causes. Get the stories of people who used to carry and decided not to any longer - give current firearms owners a path out.

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.

Saturday, December 26, 2015

Coming out as a blood donor

I've been a surreptitious blood donor for 14 years.

It all started after 9/11. I was stricken with grief. The enormity of the tragedy taking place in New York City weighed heavily and I wanted to do something, anything, to help with the recovery.
A few days later, there was a blood drive at school, and I thought long and hard about donating. I desperately wanted to do something useful, but I also knew it would mean lying to do it.
Eventually, I decided that giving blood would be more important than the lie I'd have to tell to give it. It never felt right, lying, and I'm relieved that I won't have to do it ever again.

Giving blood is an important part of my life. I'm humbled by the idea that parting with a pint or two here and there can help others get through the worst day of their lives.
And frankly, selfishly, it feels good to feel 'healthy', that my body has something of value for others. That's not a message that gay men get often enough.

At this point, some of you may be shouting at the screen, wondering how I could be so irresponsible as to endanger the blood supply. I've seen what HIV can do. I've lost friends to it. I've seen what the meds can do. Believe me, I'm under no illusion that HIV is something minor. Sure it's treatable, perhaps even managable at this point, but I could never live with myself if I thought there was even a fraction of a chance I could give HIV to someone through a blood donation.
At the same time, the rule that the blood donation system used is ridiculous - permanent deferment for any man who has had sex with a man since 1978.

So, I had to make up my own rules. I decided to go with six months since last having sex, and then an HIV test just to make sure, before allowing myself to donate.
I guess I have sex so infrequently that waiting six months isn't a big deal. There were even a few opportunities I passed up because my (secret) identity as a blood donor wasn't worth putting on hold.

Speaking of secret identities, it was quite jarring to go back into the closet to give blood. And to stay in the closet about being a blood donor everywhere else. I wanted to ask for the pink gauze to wrap up my arm after the donation, but had to bite my tongue. I had to make sure I wore the t-shirts that they give you inside out, and I couldn't accept as a gift in exchange for donation anything that would visibly associate me with being a donor.

I frequently struggled with who, how and when to "come out" as a donor in the rest of my life. Half of me wanted to come out fully and fight against the injustice of the gay donor ban. Half of my wanted to fly under the radar and help as many people anonymously as I could with my donations. I can't say I ever felt like I made the "right" choice there, often flying a bit close to the sun trying to do both at once.

For those members of my family, my friends, my coworkers, I'm sorry I didn't feel comfortable coming out to you as a blood donor - I hope you won't feel betrayed that I kept this to myself. And of course to the nurses at the dontation centers I've given at, I apologize for lying right to your face.
But to the mucky-mucks at the FDA - screw you. You've made my life uncomfortable and duplicitous and prevented many valuable donations from being received by others.

Gentle readers, I'm curious to hear your thoughts.

Monday, December 15, 2014

Observations on 3-fold interactions

Sorry about how "mathy" this post is. I'm percolating about what to write about gay blood donors, but I need to think on that for another few days.

The last lecture for my epi class was about effect measure modification (interactions). Most people do it completely wrong, they use an interaction term in a statistical model (Y=a +b1X1 + b2X2 + b3X1X2), and then interpret b3 as though it's telling you something interesting. It isn't (except in extremely unusual circumstances).
What you really want to know is the degree to which being exposed to X1 and X2 produces more disease than you'd expect if all you know was the effect of X1 in the absence of X2 and the effect of X2 in the absence of X1.
Or, in mathy terms, let Rij be the rate of disease when X1=i and X2=j
We want to know whether (R11-R00), The difference that both make when working together, is greater or less than (R01-R00) + (R10-R00), the difference each makes in the absence of the other.

I'm going to skip right to the three-factor effect measure modification - here the idea is whether:
(R111-R000), the effect of all three together,
is comparable to the effect of each of the three in isolation:
(R100-R000) + (R010-R000) + (R001-R000).

First implication: In order to make that assessment, your study needs people with none of the exposures, all of the exposures, and at most one of the exposures. It does not need anyone with two of the exposures, so including any such subjects would be inefficient. That's bizarre.

Second implication: The fact that those people with two exposures are irrelevant actually points to the fact that there could be four quantities of interest: First, the one comparing each of the three in isolation to the effect of all three together, and then three iterations of comparing one in isolation with the other two in combination, i.e.
(R110-R000) + (R001-R000)
or (R101-R000) + (R010-R000)
or (R011-R000) + (R100-R000)
So, there are actually four interaction terms to compare to the joint effect: (R111-R000).

Third implication: I love how the math and the concepts circle around and inform one another. In this case, the fact that there is one comparison to make when there are two exposures, but four to make when there are three, suggests to me that our brains are not well suited to thinking about the issue of three factor interactions, and the whole idea should ideally not be attempted at all.

But hmmm, what happens when we go to four....
(R1111-R0000) would be the joint effect of all four.
The single factors adding up would be:
(R1000-R0000) + (R0100-R0000)  + (R0010-R0000) + (R0001-R0000)
Three together + one more would be:
(R1110-R0000) + (R0001-R0000)
(R1101-R0000) + (R0010-R0000)
(R1011-R0000) + (R0100-R0000)
(R0111-R0000) + (R1000-R0000)
Two together plus each of the other two alone would be:
(R1100-R0000) + (R0010-R0000)  + (R0001-R0000)
(R1010-R0000) + (R0100-R0000)  + (R0001-R0000)
(R1001-R0000) + (R0100-R0000)  + (R0010-R0000)
(R0110-R0000) + (R1000-R0000)  + (R0001-R0000)
(R0101-R0000) + (R1000-R0000)  + (R0010-R0000)
(R0011-R0000) + (R1000-R0000)  + (R0100-R0000)
And then two together plus the other two together would be:
(R1100-R0000) + (R0011-R0000)
(R1010-R0000) + (R0101-R0000)
(R1001-R0000) + (R0110-R0000)

Ai! 14 terms to keep in mind simultaneously.

Sunday, February 2, 2014

Interpreting interaction terms

I've had the great privilege of developing a class on social epidemiology this semester, and it's been a lot of fun so far. A ton of work, but fun.
There's a problem that keep cropping up though. A bunch of the articles I've picked out for my class to read have botched the interpretation of interaction terms. Even well-established leaders in the field of social epi routinely botch interpreting their interaction terms.

It may sound like arcane statistical mumbo jumbo, but interpreting interaction terms is really important in the following context. Let's say I want to see whether X causes more or less disease (Y) in group A or group B. That's a classic setting for an interaction term.

You could make B the reference group and model it as:
Y = a + b1X + b2A + b3A*X
Or you could make A the reference group and model it as:
Y = a+ b1X + b2B + b3B*X
You should get the same interpretation either way.
You do when you interpret things correctly.
A lot of people interpret b1 as being the effect of X in the referent group (it is), and b3 as being the effect of X in the comparison group. Sometimes it is, but usually that's not the case.

Here's some real data. Let's say we were looking for the effect of state tax revenues per capita on mortality among Blacks vs. Whites in the 10 most populous states. (full disclosure, I started out with income inequality, but the data didn't look good. I figured state tax revenues per capita are probably a good indicator of redistributive potential)

Age-adjusted mortality in the populous states with the highest tax revenues (CA, MI, NY, NC, PA) was 776.9 per 100,000 Whites per year, and 985.2 per 100,000 Blacks per year.
Age-adjusted mortality in the populous states with the lowest tax revenues (FL, GA, IL, OH, TX) was 806.4 per 1000,000 Whites per year, and 1,026.0 per 100,000 Blacks per year.

Let's make White the reference group, as is standard practice. Then, let's do the standard logistic model.

Mortality = -4.85761 + 0.037268*low tax base + 0.237533*Black + 0.0033101*low tax base*Black

According to the flawed interpretation, the effect of having a low tax base among Whites is exp(0.037268) = 1.038, or a 3.8% increase in mortality in low tax base states, and the effect among Blacks is exp(0.0033101) = 1.003, or a 0.3% increase in mortality in low tax base states, so sloppy interpretation would lead you to think that living in a low tax base state has more impact on Whites than it does on Blacks.

But what happens when you switch the reference group to Blacks?

Mortality = -4.62008 + 0.040578*low tax base -0.23753*White - 0.0033101*low tax base*White

Using the flawed approach, we would get that the effect of having a low tax base among Blacks is exp(0.040578) = 1.041, or a 4.1% increase in mortality in low tax base states, and the effect among Whites is exp(-0.0033101) = 0.997, or a 0.3% decrease in mortality in low tax base states, so the sloppy interpretation suggests that living in a low tax base state increases mortality more for Blacks than Whites, and might even be beneficial for Whites (laying aside for the moment the very important issue of the role of stochastic error in the measures).

What's the real answer? Well it's right there when we look at the two models next to each other. Whites in a low tax base state have a 3.8% increase in mortality, but Blacks have a 4.1% increase in mortality. Not much difference, but the effect appears to be slightly stronger among Blacks than Whites. There is a way to get both the 3.8% and the 4.1% from only one model, but that's a bit more complicated than I want to get into in a blog post...

Saturday, January 11, 2014

Do homophobes really die sooner?

Two weeks ago, I posted in my Research Worth Reading series about an article that found that heterosexuals harboring ill-will towards gays lived shorter lives. It seemed like a methodologically sound article, but one thing nagged at the back of my brain. The un-adjusted results were huge, and after controlling for a few sensible factors, the adjusted results were still impressive, but much smaller.
That always makes me worry about uncontrolled (or poorly controlled) confounding, and I figured I'd look into it. There were a bunch of analytic choices I would have made differently, but none of them seemed like they'd be a big deal.
I got excited by their analysis and writeup, and wanted to play with the same data myself, try out a few different things, maybe look at different sub-groups, that sort of thing. I also thought it was a great approach, looking at the degree to which people harboring hatred may lead shorter lives.
So, I downloaded the same GSS files the authors used and fiddled around with it myself.

The results I got were not quite as impressive as theirs, and suggest that nearly all the main effects can be explained easily by routine confounding factors. Rather than starting out with a 187% increased death rate that is reduced to 25% after adjustment, my analyses showed a 70% increased death rate that was reduced to 8% after adjusting for similar factors.

There are a few important differences between their approach and mine, but it would be a lot more re-assuring to see similar results despite slightly different approaches, and I'm tempted to put this finding on hold until some replication in another dataset comes forward.

Thursday, January 2, 2014

Research Worth Reading - Homophobia Shortens Lives

Mark L. Hatzenbuehler, Anna Bellatorre, Peter Muennig. (2014). Anti-gay prejudice and all-cause mortality among heterosexuals in the United States. American Journal of Public Health. Published online ahead of print, Dec 12. 2013.

I'm so glad someone has finally done this study!
We all know that homophobia is bad for your health. It could be as direct as gay-bashing, or societal disapproval leading to depression, and less directly by causing high blood pressure and that sort of thing.
But what about the haters? What are the ill effects on people who are themselves homophobic?
In this paper, the authors used 20,226 people who answered the General Social Survey to figure out how much anti-gay prejudice people feel, specifically heterosexuals, then followed them for 5 to 20 years after the survey to see whether straight people who harbor anti-gay prejudices die sooner than those who don't.

They found that heterosexuals with a high degree of anti-gay prejudice were much more likely to die, dying at a rate nearly 3 times as fast as heterosexuals with a lower degree of anti-gay prejudice. That may seem implausibly high, and it is. People who harbor anti-gay prejudice tend to have less formal education, and tend to be older, and both of those factors strongly predict mortality.
But even after adjusting for age and educational attainment (and a few other things), they found that heterosexuals with a high degree of anti-gay prejudice died about 25% faster than heterosexuals with lower anti-gay beliefs. That's more reasonable, but still higher than I'd expect. I suspect that at least some of that difference is due to the fact that the General Social Survey is so long and tedious for respondents that there's a fairly high rate of non-sensical responses in there.

Promising work, but when you see over 80% of the apparent effect (an excess hazard ratio of 187% dropping to 25%) after being "explained" by control factors, what's left has to be treated very skeptically.
I'll be eager to see how this line of inquiry pans out in other datasets, although this is clearly the best dataset to start with, and it may be challenging to find another than could produce comparable results for quite some time.

Well worth reading: the language is pretty accessible even if you're not steeped in the public health world. The methods are a bit challenging, but you can skip the most confusing parts because they don't really make much difference anyway.
Methodologic critique
This study is actually really well done, much better than most public health research these days. Despite the inherent flukiness of the GSS, the authors used methods that should be pretty robust despite the relatively high rates of non-sense that you find in the GSS.
Having given high praise overall, I'll move on to the relatively minor things I'd quibble with... First, the measure of whether a person has a high degree of anti-gay prejudice is based on some questions that are horribly out of date, and were horribly out of date when they were asked, from 1988 to 2002. The items are taken from a series of questions designed to assess general social attitudes about communists, atheists, homosexuals and other "undesireables", so the questions can sound a bit strange to us today, especially the first three, which are probably more about civil liberties than prejudice:
  1. "If some people in your community suggested that a book in favor of homosexuality should be taken out of your public library, would you favor removing this book, or not?"
  2. "Should a man who admits that he is a homosexual be allowed to teach in a college or university, or not?" 
  3. "Suppose a man who admits that he is a homosexual wanted to make a speech in your community. Should he be allowed to speak, or not?"
  4. "Do you think that sexual relations between two adults of the same sex is always wrong, almost always wrong, wrong only sometimes, or not wrong at all?"
If I were doing the study, I'd probably ignore the first three as anachronistic and focus just on the fourth one. But what they did is collapsed the fourth one into a yes/no of "not wrong at all" vs. any of the other responses, and then (as best as I can tell), said that a "yes" to any of the four indicated a high degree of prejudice. It's possible that someone had to say "yes" to any two or more to make it into the high prejudice category. At any rate, it would have been re-assuring to show some kind of dose-response curve from lower endorsement to higher endorsement, and also a check to see if the pattern held when just looking at the fourth item, which is most clearly related to prejudice.
Of course, it would also be nice to have some sort of response codes indicating a positive inclination towards us, rather than assuming that our words and deeds have only the potential to offend.

In terms of potential confounders adjusted for, they used pretty much the same list I would have, but I would have modeled some of them a bit differently. Rather than treating age and education as continuous, I'd want to look at them in categories first to make sure that a linear trend makes a logical fit. And I wouldn't use household income itself, but adjust it first to the size and composition of the household relative to poverty. $20,000 for a single person in 1988 would be a lot more comfortable than $20,000 for a household of four in 2002, and log-transforming the household income doesn't help with those issues at all.
Most importantly, I'd want to explore the year of the survey in a bit more detail. The surveys were conducted from 1988 to 2002, and the follow-up for death ended in 2008, so someone from the early part of the survey could be followed for up to 20 years, while someone interviewed in 2002 might be followed up for as few as five years. They used Cox proportional hazards, which should account for these differences in the length of follow-up, but the fact that anti-gay prejudicial attitudes have shifted rapidly over the same time period makes me less confident that the model did what it was supposed to do. You can probably think of someone who would answer those questions differently in 2002 than they would have in 1988. But the model assumes that they would have answered the same way at both points in time, or at the very least that someone giving a certain answer in 1988 had the same level of prejudice as someone giving the same answer in 2002, despite the fact that it became much less acceptable to express anti-gay attitudes over this time period. It might screw up the model a bit to add year of interview in as a potential confounder, but I'd give it a try anyway, because it's quite possible that what we're seeing is just an artifact of the fact that as the population has developed fewer anti-gay attitudes, they've also been followed for a shorter period of time, and are thus less likely to be seen dying, despite the beauty of the Cox proportional hazards approach in dealing with censored data.