Is Fear of Vaccines a Liberal Thing?

That's what I had always thought --- that the right wing can have their creationists, their global warming deniers, their opposition to stem-cell research, and whatever else, but we on the left have to claim the lion's share of the anti-vaccination crowd.

Photo taken by Flickr user captaincinema

I believed this mostly for two reasons: first, most of the anti-vaccination rhetoric I had heard focused on scary chemicals that may or may not be present in vaccines, and scary-chemical rhetoric is also a staple of diverse left-leaning causes ranging from the legitimate (i.e., environmentalism) to the kooky (alternative medicine, the cult of the "natural"). The second reason is that, to the extent that the anti-vaccine celebrities I'd heard of can be said to have politics, their politics tend to be Democratic. The one obvious example is Robert F. Kennedy, Jr., a Democrat and prominent environmentalist, and the two other anti-vaccine celebrities I can think of, Jenny McCarthy and Jim Carrey, aren't involved very much in U.S. politics, but the profiles of them I linked to suggest that they're Democratic-leaning.  

Anti-vaccine booth at the 2008 Netroots Nation convention in Austin, TX. Photo credit: Lindsay Beyerstein

The famous science writer Chris Mooney was also under the impression that anti-vaccine-ism is a crank ideology peculiar to the left, though I'm not sure he is anymore.

The mass freakout on the right over Gardasil made me reevaluate that impression, though.

It's true, Gardasil is a special case because it's a vaccine for adolescents --- and, initially, adolescent girls, although it's now recommended for all young people --- meant to protect against the cancer-causing strains of human papillomavirus, which spreads through genital contact. That puts it squarely in the middle of the Religious Right's Freakout Zone, which encompasses anything involving young women and sex.

I would've been perfectly content to accept just that explanation for the anti-Gardasil backlash, but then Michele Bachmann came out with her howler about Gardasil causing developmental disabilities. That sounded so much like what I had been hearing from Jenny McCarthy et al. that I started wondering whether anti-vaccine crankery was actually bipartisan.

There have been a lot of polls about people's attitudes toward vaccination, but I can't find very many that also include respondents' political affiliation. 

Chris Mooney wrote about two polls suggesting that anti-vaccine sentiments are spread evenly across the political spectrum: a USA Today/Gallup poll from 2009 that asked people to identify themselves as liberal, conservative or moderate and then asked them whether they had heard of Jenny McCarthy and whether they agreed with her or not; and a Pew poll, also from 2009, that asked, among lots of other things, whether children should be required to be vaccinated or whether that choice should be left to their parents.

With above photo, anti-vaccine protest signs at a Tea Party Express rally held on April 8, 2010 in St. Paul, MN. Photo credit: Fibonacci Blue

Larger version of printout attached to sign in lower half of the above pair of photos

Mike the Mad Biologist wrote about another Pew poll from 2009, this one asking people whether they would get the swine flu vaccine if it were available to them. It found Republicans and Independents more likely than not to refuse it (54% to 41%), while Democrats were almost 2:1 in favor of getting the vaccine. Republicans were also the most likely (54%), and Democrats the least (35%), to say that the news media were overstating the danger of swine flu.

Another poll, this one conducted just a couple months ago by You Gov, found that greater percentages of Republicans than Democrats said they were "not so confident" or "not confident at all" that the current vaccine schedule recommended by the Department of Health and Human Services is safe. Democrats were more likely than Republicans to say they were "very confident" or "somewhat confident," although strong majorities of both parties fall into those two groups.

That poll also asked people which conditions they think vaccines can cause, and Republicans were slightly more likely than Democrats to say vaccines cause autism.

I don't know what the New World Order is, but this graphic is a great example of attributing nefarious motivation to vaccine makers

It's also worth pointing out that there are different fears that underlie different people's opposition to vaccines. Some people might be afraid of Big Pharma profiting off their sickness; others might be afraid of the government telling them what to do with their bodies, or with their children's bodies, and other people might be afraid of the Scary Chemicals in vaccines. 

You can see appeals to all of these different fears in anti-vaccine rhetoric: most obvious (to me, at least) is the Scary Chemicals rhetoric that emphasizes what kinds of scary-sounding things are in vaccines (or, in the case of thimerosal, used to be in vaccines), but there's also the tactic of discrediting anything a medical professional says about vaccines by saying they're being paid off by the pharmaceutical companies. 

One type of anti-vaccine rhetoric I hadn't noticed before I started writing this post is the kind that objects to mandatory vaccines. Even when there's a very good reason for it, like requiring health-care workers to be fully vaccinated because they're in contact with lots and lots of 1) sick people and 2) people whose immunity is compromised.

People protesting a proposal to make swine flu vaccination mandatory for health care workers in New York state in 2009. Both photos taken by Louise McCoy for The Epoch Times

It doesn't take a lot of imagination to see how a libertarian-minded person, probably a lot like the people in the two Tea Party pictures above, might see mandatory vaccination as yet another unwarranted government intrusion into their affairs.  

This old article in the Seattle Weekly about the antivaccination movement in Washington state makes note of the movement's bipartisan appeal:

A closer look at [Washington state Department of Health] data reveals the potent mix of demographics that makes vaccine resistance such a sturdy presence in the state. Some of the highest [vaccine] exemption rates are in eastern Washington, where any kind of government mandate --- whether immunization or taxation --- is viewed with hostility. 

At the opposite end of the political spectrum are the liberal enclaves of western Washington, which are also resistant to vaccines. At Vashon Island's public elementary school, 25 percent of students have skipped at least one vaccine. At the Seattle Waldorf School, ... the number is a whopping 47 percent. 

These schools are part of well-educated and affluent communities that one might think would be most likely to follow the recommendations of scientists and doctors. But in fact, as journalist Seth Mnookin points out in his new book The Panic Virus, they perfectly reflect the base of today's anti-vaccine movement. Its constituents are part of what you might call the suburban counterculture --- parenthood and affluence mixed with creative aspirations, a crunchy-chewy lifestyle, and an inclination to question authority.

Finally, let's look at laws making it easier for people to opt out of vaccination.

Here is a list of states whose laws allow parents to refuse to vaccinate their children for "philosophical" reasons:

• Arizona
• Arkansas
• California
• Colorado
• Idaho
• Louisiana
• Maine
• Michigan
• Minnesota
• North Dakota
• Pennsylvania
• Ohio
• Oklahoma
• Texas
• Utah
• Vermont
• Washington
• Wisconsin

As you can see, it's a pretty mixed bag of "red states" and "blue states."

One More Air-Pollution Study

In the last post I confused this study (PDF) with an earlier one by the same group of researchers; I wrote about the earlier one, but linked to a post on Paul Whiteley's blog about the more recent one, which was published just last November.

(I also started describing this one, and then switched to describing the earlier one; in my last post, only one of the studies I mentioned used air-pollution data from the EPA's air-monitoring stations. The earlier study by these authors only used proximity to high-traffic roadways as their variable indicating pollution exposure.)

This paper combined the methods of the two studies I wrote about in the last post; it used the same pool of children born in California between 1997 and 2006 and drew on two sources of data on air pollution at the time and place those children were born: the EPA's air-quality data that I wrote about yesterday, and a computer model of average traffic flow, and exhaust emissions, along California's major roadways.

To some extent, you could see it as a more geographically dispersed version of the study I described yesterday that looked at prenatal exposure to air pollution in just Los Angeles County. 

(Weirdly, the LA-County-only study, though restricted to a smaller geographic area, involved way more people than the two traffic-related studies: 7,603 autistic and 75,782 control subjects, as opposed to this study's 279 autistic and 245 control subjects.)

But the relatively straightforward EPA data, which are direct measurements of the concentration of various pollutants at regular intervals, and which are only abstracted from each child's actual prenatal exposure in that 1) they do not measure what concentration of those pollutants actually got into the mothers' bodies, much less the fetuses', and 2) they were taken at sites some distance away from where the children actually live. 

So it's not a perfect data source, but it's still a lot more directly reflective of reality than this computer model seems to be:

The principal model inputs are roadway geometry, link-based traffic volumes, period-specific meteorological conditions (wind speed and direction, atmospheric stability, and mixing heights), and vehicle emission rates. Detailed roadway geometry data and annual average daily traffic counts were obtained from Tele Atlas/Geographic Data Technology in 2005. These data represent an integration of state-, county-, and city-level traffic counts collected between 1995 and 2000. Because our period of interest was from 1997 to 2008, the counts were scaled to represent individual years based on estimated growth in county average vehicle-miles-traveled data. Traffic counts were assigned to roadways based on location and street names. Traffic volumes on roadways without count data (mostly small roads) were estimated based on median volumes for similar class roads in small geographic regions. Meteorological data from 56 local monitoring stations were matched to the dates and locations of interest. Vehicle fleet average emission factors were based on the California Air Resource Board's EMFAC2007 (version 2.3) model. Annual average emission factors were calculated by year (1997-2008) for travel on freeways (65 mph), state highways (50 mph), arterials (35 mph), and collector roads (30 mph) (to convert to kilometers, multiply by 1.6). We used the CALINE4 model to estimate locally varying ambient concentrations of nitrogen oxides contributed by freeways, nonfreeways, and all roads located within 5 km of each child's home. Previously, we have used the CALINE4 model to estimate concentrations of other traffic-related pollutants, including elemental carbon and carbon monoxide, and found that they were almost perfectly correlated (around 0.99) with estimates for nitrogen oxides. Thus, our model-based concentrations should be viewed as an indicator of the traffic-related pollutant mixture rather than of any pollutant specifically.

So, to arrive at an estimate of how much of a certain category of air pollution (traffic-related air pollution) each mother and child in their study had been exposed to, they used a computer model to come up with average emissions for vehicles all over the state, traveling at various average speeds corresponding with their various categories of roads, for each year in their study. Then they entered that, along with all the other types of data mentioned above (winds, atmospheric conditions, traffic volume, road layout) into another computer model to arrive at the final answer.

I'm not criticizing their model; it actually seems like a pretty good one to my untrained eye. But my point is that there's a lot of extrapolating, averaging, assuming that what's true for location x will also be true for location y, and other things that make the model work but aren't grounded in direct observation and thus might not actually be true. 

That will be the case for any model, and this one has a few serious gaps in its data pool. They're missing eight years of traffic data from their eleven-year "period of interest," so they have to guess at what those numbers might be based on expected growth in traffic volumes. They're also missing traffic counts for some roads, so they estimate them based on the counts for other, similarly-sized roads.

It bears repeating that this model was not their only source of data on pollution exposure; they also used direct measurements taken by the EPA air-monitoring station(s) nearest to study participants' houses throughout the study period.

For traffic-related air pollution --- the type of pollution exposure they modeled rather than measured directly --- they found a difference between the highest- and lowest-exposure groups (with the former three times as likely to develop autism as the latter), but no difference between the lowest-exposure group and the two groups in the middle.

For the specific pollutants measured at EPA air-monitoring stations --- coarse and fine particulate matter, nitrogen oxides, ozone --- they found an increased likelihood of autism associated with greater exposure to particulate matter and nitrogen oxides, but not ozone. This effect was strongest during the third trimester of pregnancy. 

Unlike the other study I described that used the EPA air-quality data, this one did not find any change in the pattern when they adjusted for sociodemographic variables like child's sex, race/ethnicity, parents' educational level, mother's age, or mother's smoking during pregnancy.

Volk, H., Lurmann, F., Penfold, B., Hertz-Picciotto, I., & McConnell R. (2013). Traffic-Related Air Pollution, Particulate Matter, and AutismAir Pollution, Particulate Matter, and Autism JAMA Psychiatry, 70 (1) DOI: 10.1001/jamapsychiatry.2013.266

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.