Bizarre Things Purported to Cause Autism: Early Exposure to Air Pollution

A quick note: my (very) occasional "Bizarre Things..." series was never intended solely as a crank roundup. No, in my mind I resolved to cover every autism hypothesis*, however well- or ill-founded, plausible or implausible, that I ever heard of and thought "well, that's weird!"

Indeed, I have tagged (though not titled) a post discussing a pretty rigorous, well-thought-out study investigating something that's been common wisdom for a long, long time with the "bizarre hypotheses" label just because its tentative conclusion --- that there may be something more to the relationship between autism diagnoses and social class than the fact that rich people can afford to get their children seen by specialists and poor people can't --- surprised me.

So sometimes bizarreness is in the eye of the beholder, I guess.

Anyway, on to the business at hand.

Paul Whiteley has written a couple of posts on the idea that exposure to air pollution might play a role in determining whether a kid develops autism; he talks specifically about these two studies working with the same two data sets: the state of California's Department of Developmental Services' records of all children diagnosed with autism in the state, and where they lived at , and also data from the federal Environmental Protection Agency's Ambient Air Monitoring Program.

I've written about the former data set before, but not the latter. The EPA measures six major outdoor air pollutants (I'm sure they do a lot more, too, but these six are the ones that affect air-quality indices): two sizes of "particulate matter" (dust, ash, soot, smog), the "coarse" particles measuring between 2.5 and 10 micrometers in diameter or length, and "fine" particles smaller than 2.5 micrometers; carbon monoxide; nitrogen oxides; sulfur dioxide; lead; and ozone. They have monitoring stations set up all over the country, particularly thick on the ground near big, sprawling cities. How often the stations record measurements varies with what kind of equipment is being used to take them; some pollutants can only be measured daily, or once every few days, though some can be measured hourly. Either way, it's a huge volume of data.

The authors of the more recent study (PDF) restricted their analysis to Los Angeles County, so I can actually tell you how many monitoring stations' data that would encompass.

Map showing the locations of all the EPA air monitoring stations in LA County --- made by me!

This PDF lists, among lots and lots of other things, all the EPA air-monitoring stations in California and where they are located. Within Los Angeles County, it looks like there are nineteen: one in Commerce, one in Azusa, one in Burbank, one in Industry, one in Compton, one in Glendora, one at the Los Angeles International Airport (LAX), two in Long Beach, two in Los Angeles, one in Pasadena, one in Pico Rivera, one in Pomona, one in Vernon, one in Reseda, one in Santa Clarita, one in Santa Fe Springs, and one at the Van Nuys Airport. So I guess that's technically five that are somehow part of LA or attached to it. Not all of them measure every one of the six pollutants, either, but you can see which of them track what in the PDF I linked earlier.

The earlier study** focused on families living near highways throughout the state of California, and while I might theoretically be able to put together a list of all the cities and towns that have highways running through them, and cross-check that with the EPA's records of where their monitoring stations are (assuming they publicize them all), it sounds like more armchair detective work*** than I want to do. So I can't tell you how many stations are contributing their data to this study, but I'd guess that it's more than the LA County-only study used.

Anyway, both studies involved matching the geographic location of each autistic child in the study with some other spatial variable: for the earlier study, this other variable was distance between where they were born and a freeway or major road; for the more recent study, it was the EPA monitoring station nearest to where they were born. In that study, the researchers looked at the measurements for each pollutant recorded nearest to each child's birthplace averaged over each trimester of gestation.

Both studies compared their autistic subjects to same-age, same-sex peers from the same general area (LA County for the one study, not specified for the other); the ratio was 1:1 in the smaller, older study and 10:1 (control:autism) in the bigger, newer one. So the idea was, I guess, to check whether kids living in the same county, city, suburb, or whatever as a kid without autism tended to have lower prenatal exposure to air pollution (new study) or live somewhat further away from the closest high-traffic road (old study), than the kid with autism.

What did they find? It's complicated; in the newer, just-LA-county study, they found that exposure to some of the six pollutants was actually associated with a slightly lower likelihood of having an autism diagnosis, at least in the (more****) raw data. The only pollutant to show a significant increase in odds ratio (a measure of how much more likely a child exposed to a given pollutant is to develop autism than an unexposed child; if it's less than 1, it means less likely, more than 1 means more likely) before logistic regression was ozone, which gave a 1.19 odds ratio. (That's also the biggest increase found anywhere in this study, regression or no).

After adjusting for a bunch of variables they had expected to correlate with pollution exposure (maternal age, maternal education, race/ethnicity, gestational age and others), the odds ratio for ozone went down while the odds ratios for the other pollutants (nitrogen oxides, carbon monoxide and particulate matter) went up. Where they had been sitting at about 0.8 or 0.9 (i.e., maybe ten, fifteen or twenty percent less likely?), they moved to about 1.05. 

I have only the faintest notion of what a logistic regression actually does, but my understanding of it is that, when researchers have a lot of variables closely intertwined with the variable they're trying to study, they use a logistic regression to "correct for" those other variables. It's like a way to try and zero in on the one strand in the snarl that you're trying to trace.

Anyway, they also did another regression, this time by maternal education only, and compared odds ratios for autism risk associated with each pollutant among three groups: mothers with less than a high-school education, mothers with a high-school education, and mothers with more than a high-school education. Except for ozone, the odds ratio for each pollutant went up slightly as maternal education increased, with the biggest differences between the least- and most-educated categories. 

Somehow, the mother's level of education affects how strongly exposure to these airborne pollutants predicts whether her children develop autism.

I have absolutely no clue what to make of that, if I'm even reading it right.

The roads study's outcome was less brain-twisting: they found a correlation between living near a freeway (i.e., a state or interstate highway) and getting a diagnosis of autism, but they found no such correlation for other high-traffic roads. Since it's probably not the case that different kinds of pollutants are being spewed out by vehicles on highways vs. other big, heavily-traveled roads*****, I'm going to follow David Gorski's lead and write off the freeway association as an artifact of data-dredging.

Becerra, T., Wilhelm, M., Olsen, J., Cockburn, M., & Ritz, B. (2012). Ambient Air Pollution and Autism in Los Angeles County, California Environmental Health Perspectives DOI: 10.1289/ehp.1205827

Volk, H., Hertz-Picciotto, I., Delwiche, L., Lurmann, F., & McConnell, R. (2010). Residential Proximity to Freeways and Autism in the CHARGE Study Environmental Health Perspectives, 119 (6), 873-877 DOI: 10.1289/ehp.1002835

*Yes, I have yet to do posts on the big ones, like mercury, thimerosal (really part of the mercury one, but some people propose mercury-based explanations that don't mention thimerosal, so they probably deserve separate posts --- anyway the thimerosal one would be long enough even if it weren't folded into a larger post on mercury compounds), MMR. It's less a matter of not knowing what I want to say than it is a matter of figuring out whether, or how, to marshal all the available evidence.

**I belatedly find out that this study is actually a precursor to the one Whiteley is writing about, not the same one. The one he writes about takes an approach that looks to me like a blend of the two I'm writing about now: looking at both air-pollution data and proximity to high-traffic roads.

***Actually, it's not even armchair detective work, as I am not in an armchair when I use the computer. It's armless-hard-wooden-chair detective work, which is somewhat more grueling than armchair detective work. 

****I'm not sure how much, if any, of the data in this paper can be considered "raw" when you consider how much tinkering they did with the air-pollution data.

*****You could perhaps make an argument that it could be the case, what with there probably being more trucks on the highways than not, and with trucks using diesel instead of gasoline. I am unsure if that would make a difference in how much particulate matter or nitrogen oxides are produced, and also quite skeptical that such a difference would show up at all in a study like this.