Tag Archives: Epidemiology

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.