Tag Archives: Autism

Dr John Briffa is wronger than wrong on vaccines and Hannah Poling

30 May

Media nutritionism is a crowded field, but John Briffa has managed to carve out a niche for himself. And Briffa’s take on vaccines stands out, even among media nutritionists. JDC takes a broader look at Briffa’s take on autism, but I’m going to focus on Briffa’s claim that:

the US Government recently looked at such evidence relating to just one girl (Hannah Poling) and concluded that vaccination had contributed significantly to her autism.

As readers of this blog can probably spot, almost every word of that statement is inaccurate: impressive work, indeed. Continue reading →

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Dore UK and Australia are now both in administration

23 May

As noted on this blog and elsewhere, Dore has been marketed as a treatment for ASDs (not to mention dyslexia, dyspraxia and ADHD) based on extremely limited evidence. Dore UK and Australia have now both gone into administration. In Australia, it is unclear whether clients and staff will get what is owed; in the UK, Dore has stated that they “are presently exploring alternative arrangements to ensure every client is cared for” without (as far as I can see) making clear whether staff will get paid. Continue reading →

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The Autism Epidemic Meme is Behind Almost All Autism Woo: A Call for Additional Research

13 May

After learning former US president Bill Clinton had indicated he believes that “the number of children who are born with autism [is] tripling every 20 years” (hat tip Orac), an understandable reaction might be to point out his ignorance. Understandable, yes, but I think we are looking at a bigger problem than lack of scientific literacy or political pandering in this case; a problem that is going to have to be addressed in a manner that is clear and generally convincing.

A lot of the discussion in the autism community centers around the anti-vaccination movement. It is true that anti-vaccination could potentially become a major problem for the world as a whole, and it is also true that it is a source of stigma for autistics. Some of us have taken it to be our fight, even though it should probably be the CDC’s or the WHO’s fight, if they were not, as it seems, asleep at the wheel. Nevertheless, I think the persistent autism epidemic meme is a much bigger issue as far as the autism community is concerned. Not only is the notion of an epidemic stigmatizing, but it results in ideas that are more than just theoretically harmful to autistics, such as the idea that autistic adults don’t exist. These ideas will be around regardless of the existence of an anti-vaccination movement.

In my regular blog I have discussed the evidence against the notion of an autism epidemic at length. If I may say so myself, I might have even managed to half persuade a few people from the other side of the debate.
What I want to do here, however, is to essentially critique the evidence I’ve discussed thus far. Let me explain why.

Those of us who are immersed in scientific discussions involving autism are well aware, for example, of diagnostic substitution, of an apparently high prevalence of autism in adults, of the changing characteristics of autistics over time, of regional prevalence differences that resemble time-dependent differences, of the stability of cognitive disability as a whole, of the stability (even the decline) of institutionalization rates, of what went on in the past, and so forth. Taken as a whole, this evidence is overwhelming and convincing to someone such as myself who has studied and perseverated on it for years. Fundamentally, though, it is evidence that has a number of problems: It is too numerous, complex, disjoint and most importantly, lacking in precision; none of it is decisive on its own. We are talking about many bits and pieces of evidence that need to be put together and thought through in order to arrive at the conclusion that there is no such thing as an autism epidemic. I don’t expect someone such as Mr. Clinton to be aware of this evidence, understand it, or think through it, much less be able to analyze some of the publicly available data that is not yet available through the scientific literature.

You see, there’s no such thing as an IOM report on the autism “epidemic.” While I’m personally not that fond of basing my beliefs on what authority tells me I should believe, I think a pronouncement by major authorities on the matter would help inform the general public of the state of the debate and the evidence. For this, however, I believe additional research that specifically addresses the matter in a clear way is needed. Allow me to propose some avenues of future research that could potentially answer the remaining questions once and for all. I encourage readers to propose their own ideas.

1) Replicate Lotter (1967). We know that the prevalence of autism as currently defined is relatively high. We also know that the prevalence of autism as defined in the 1960s was relatively low (4.5 in 10,000). What we don’t know is whether the prevalence of autism ascertained using Lotter’s operationalized criteria and methods is still relatively low in 2008. I think it should be feasible to replicate Lotter’s methodology and criteria today and find out the prevalence, not of DSM-IV autism, but of autism as it was thought of in the past. Without meaning to be disrespectful, this should preferably be done while Lorna Wing is still with us. She claims to know which kinds of children Vic Lotter considered autistic and which he didn’t.

2) Determine the prevalence of autism in adults. This one is non-trivial, as there are some ethical issues to consider, but it seems they will attempt it in the UK. I hope it’s not another case of trying to find how many adults are diagnosed with autism or receiving services under an autism category. This wouldn’t teach us anything new and would just be fodder for David Kirby’s blog. I also hope they don’t assume all autistics must be psychiatric patients, for example. They should find a lot of autistics in the general population, and there is evidence they should find many who might not be diagnosable with autism despite meeting criteria, for various technical reasons. Of course, they also need to look in institutions and group homes, since a ready rebuttal will be that “low functioning” autism must therefore be what’s rare in adults.

3) Determine if regional differences in prevalence are real. When you study administrative databases in some detail, one thing that immediately jumps out is that there are huge disparities in the administrative prevalence of autism between certain regions, be it states, regional centers or counties. I have reasons to believe these differences are not real. If these differences are not real, I’d suggest it would be reasonable to hypothesize that time-based differences in administrative autism prevalence are of the same nature. I have suggested, for example, screening children with mental retardation from different regional centers in California to determine, at the very least, if there are real discrepancies in the prevalence of autism within the population with mental retardation. Another question that needs to be answered is why population density correlates so well with administrative prevalence (independently of things like environmental pollution, as I’ve recently found).

4) Explain the changes with a mathematical model. The plausible mechanisms that explain the rise in diagnoses of autism have been discussed at some length. They might include increased awareness, changes in official criteria, an increased availability of specialists, an increased availability of certain services, changes in cultural beliefs, and so on. I have even discussed the internet as a potential driving force behind increased awareness, particularly in the 1990s. But let’s face it, these are all essentially unproven mechanisms. No one has done a multivariate analysis that gives us a coefficient for each variable. Granted, some things are hard to quantify. It would be a lot like trying to quantify word of mouth. But some of this should be doable.

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Association Between Autism and Environmental Mercury Exposure Disappears Once Population Density is Controlled for

2 May

california-pollution-autism-analysis

[Correction 5/4/2008: Please see this comment. The trends and conclusions don’t change. The scatter of the graphs is not affected in a way that is noticeable, but the Y ranges do change. The adjustment formula also changes. See the corrected spreadsheet for details.]

This is a critique of Palmer et al. (2008), a recent study claiming to associate the administrative prevalence of autism in Texas school districts and proximity to coal-fired power plants, as well as mercury emissions. Normally I would just point out the likely problems of the paper, but this time I will go further and test a key hypothesis of my critique using California data in a way that is straightforward enough for readers to verify.

Background

Palmer et al. (2008) is not the first study of its kind. Palmer et al. (2006) claimed to document that “for each 1000 lb of environmentally released mercury, there was a 43% increase in the rate of special education services and a 61% increase in the rate of autism.” The more recent paper by Palmer et al. does not result in such remarkable estimates, considering its finding that “for every 1,000 pounds of release in 1998, there is a corresponding 2.6 percent increase in 2002 autism rates.”

Windham et al. (2006) is a case-control study done in the San Francisco Bay Area which claims to associate autism with emissions of Hazardous Air Pollutants (HAPs).

Then we also have Waldman et al. (2007), which I consider a study of the same type, except it associates autism with precipitation (as a proxy of television exposure) instead of environmental pollution.

My primary criticism of these types of studies is that they are attempting to find a cause for an epidemiological phenomenon that could very well not require an environmental explanation. That is, administrative data (special education data in particular) is not equipped to tell us if there are real differences in the prevalence of autism from one region to the next. No screening has ever demonstrated that substantial differences in administrative prevalence between regions are not simply diagnostic differences.

That said, the studies have been done, and they have found statistical associations. This usually means they either found a real effect or they have failed to properly control for some confound.

As I have noted repeatedly over the last couple of years, the glaring confound that most likely mediates these types of associations is urbanicity. The association between urbanicity and autism was documented even before these studies were carried out. It is plausibly explained by a greater availability of autism specialists in urban areas and by greater awareness in the part of parents who live in cities.

Palmer et al. (2008) does control for urbanicity, which might be one of several reasons why its findings are underwhelming compared to those of Palmer et al. (2006).

Is the control for urbanicity in Palmer et al. (2008) adequate?

There are two main problems with the control for urbanicity, described in the paper as follows.

Urbanicity. Eight separate demographically defined school district regions were used in the analysis as defined by the TEA: (1) Major urban districts and other central cities (2) Major suburban districts and other central city suburbs (5) Non-metropolitan and rural school districts In the current analysis, dummy variables were included in the analysis coding Urban (dummy variable 1, and Suburban (dummy variable2), contrasted with non-metro and rural districts which were the referent group. Details and specific definitions of urbanicity categories can be obtained at the TEA website http://www.tea.state.tx.us/data.html

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1. It is too discrete. Within the set of urban districts, some districts will be more urban than others. The same is true of rural districts. Palmer et al. (2008) is effectively using a stratification method to control for urbanicity, but this method is limited, especially considering the paper looks at 1,040 school districts. A better methodology would be to use population density as a variable.

2. Modeling for distance. The paper models autism rates based on distance to coal-fired power plants. It follows that a control variable should model distance to urban areas rather than urbanicity of each district. Granted, this would not be easy because, as noted, urbanicity is not a discrete measure. But it needs to be noted as a significant limitation of the analysis. Consider school districts in areas designated as “rural” that are close to areas designated as “urban.” Such proximity would presumably provide access to a greater availability of autism specialists than would otherwise be the case.

California Analysis

This time around I thought it would be a good idea to run some actual numbers in order to test this population density confound hypothesis that up to this point has been simply theoretical. I will use county-level data from the state of California, which was fairly easy to obtain on short notice. The data used is the following.

  • Special education autism caseload data at the county level for 2005 was obtained from a California resident who had requested it from the California Department of Education.
  • County population and density data for 2006 was obtained from counties.org.
  • Atmospheric mercury concentration data was obtained from the EPA’s 1996 National Air Toxics Assessment Exposure and Risk Data for 2006.
  • All of the raw data, intermediate data, formulas, and resulting charts can be found in this spreadsheet which I am making available for readers to verify and tweak as needed.

Population Density vs. Autism

Autism prevalence was calculated by dividing the special education autism caseload of each county by its population (Column G). This is not a precise determination, of course, but it should not affect the analysis. In any given California county, the population under 18 is roughly a fifth of the total population of the county.

A first attempt at modeling population density vs. autism prevalence (Chart A) suggested the relationship was logarithmic. So I modeled log(population density) vs. autism prevalence, which resulted in the clear correlation you see in Figure 1 (Chart B).

Pop. Density vs. Autism Prevalence

Figure 1: Pop. Density vs. Autism Prevalence

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This is as expected. You will note, however, there is one significant outlier in the lower-right quadrant. That is San Francisco county. Presumably, because of its peculiar geographic characteristics, its population density is the highest in the state. Nevertheless, San Francisco is an important data point since it is a significant urban area which happens to have a relatively low special education prevalence of autism. Let’s leave it in and see how it affects things.

I will use a simple standardization method of adjustment for population density. Basically, I will standardize autism prevalence in each county, such that population density is no longer a factor. Think of it this way. If the population density of each county grew such that its log were now about 3.5, how would we expect autism prevalence to be affected? The following formula is what I came up with.

Adjusted(Y) = Y + 7 – 1.93 * X

The fact that the adjusted prevalence (Column H) is not dependent on population density can be verified graphically (Chart C). Readers can click back and forth between Chart B and Chart C to better understand the effect of the adjustment. I will come back to this adjusted prevalence.

Mercury Exposure Concentration vs. Autism

I obtained atmospheric mercury exposure concentrations for each county from 1996 EPA data (Column I). More recent data would’ve been better since our population density data is from 2006, but it is not clear if newer data is available. I learned of the 1996 data because that is what Windham et al. (2006) uses. I’m working under the assumption that changes in population density in the last decade have been roughly uniform across the state.

Let’s first look at Figure 2 (Chart E), a graph of log(mercury exposure) vs. autism prevalence, without adjustment for population density.

Pop. Density vs. Autism Prevalence

Figure 2: Mercury Exposure vs. Autism Prevalence

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There is a graphically noticeable trend in Figure 2, which is not surprising. The question is, does the trend remain after adjustment for population density?

Pop. Density vs. Autism Prevalence

Figure 3: Mercury Exposure vs. Autism Prevalence Adjusted For Pop. Density

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Figure 3 (Chart D) is a graph of log(mercury exposure) vs. standardized autism prevalence; that is, autism prevalence adjusted for population density as previously calculated. In this figure we see there’s no longer a graphically discernable correlation between environmental mercury and autism. In fact, Excel produces a linear fit that indicates there’s somewhat of an inverse correlation between environmental emissions and autism prevalence.

Granted, if we were to remove San Francisco as an outlier, the trend would be pushed upwards. But then in this graph there appear to be two additional outliers in the middle upper part of the graph, Orange county and Los Angeles county. Keep in mind we have not adjusted for wealth. Regardless of how we might adjust the analysis, I fail to see that the graph would support a statistically meaningful association between mercury exposure and autism.

Further Confirmation

So far I have provided evidence that, in California, an association between environmental mercury exposure and autism disappears once we control for population density. This is clear to my satisfaction, but I thought it would be a good idea to attempt an inverse exercise as an illustration of the adjustment method. That is, let us try adjusting prevalence for mercury exposure, and see if the correlation with population density remains.

This is similar to what I did previously. A linear model is discerned from the correlation between log(mercury exposure) and autism (Chart E). This is used to derive an adjustment formula (Column K) whose validity can be verified graphically (Chart F). The new adjusted prevalence (Column K) is used in a new graph of log(population density) vs. autism: Figure 4 (Chart G).

Pop. Density vs. Autism Prevalence

Figure 4: Pop. Density vs. Autism Prevalence Adjusted For Mercury Exposure

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What Figure 4 (Chart G) tells us is that even after we control for mercury exposure, there is still a clear correlation between autism and population density. In other words, population density wins bigtime – I believe that is the epidemiological term.

Conclusion

An analysis of California data suggests that correlations between the administrative prevalence of autism and environmental mercury emissions are fully mediated by population density. Palmer et al. (2008) suggests there is a real effect in Texas, but its results are not convincing primarily because its control for urbanicity is limited and inconsistent with the hypothesis the paper tests.

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