A study recently published by Irva Hertz-Picciotto and Lora Delwiche of the M.I.N.D. Institute, UC Davis, addresses the question of an apparent rise in the frequency of diagnosed autism in California.
This study is quickly becoming the focus of attention as the various factions with an interest in autism square off on assessing its validity. In the mean time, the study itself is rather modest in what it attempts and what it concludes.
Let’s have a look.
To date, there are three kinds of explanations given for this rise in Autism rate:
1) There is some artifact in the system such as changes in reporting or diagnosis, or changes in the definition of autism, that makes the rate of autism look like it is going up, but it is actually not going up;
2) There is a genetic change in populations, either because of local changes in the gene pool or immigration/migration causing this condition to increase in frequency;
3) There is an environmental cause of an actual increase in autism which is indicated in the increase in numbers of autism diagnoses.
The study by Hetz-Picciotto and Delwiche examines a number of different previously suggested explanations that fall into the first of these categories, and finds that these do have an impact on the apparent rise of reported autism, but according to these authors not enough to explain the entire phenomenon. The second hypothesis is also tested, or at least partly controlled for, by excluding immigrants (to California) from the study. While the authors are left concluding that explanations of the third category should be more closely investigated, they do not offer specific environmental explanations, and in fact conclude that “the extent to which the continued rise represents a true increase in the occurrence of autism remains unclear.
Taken at face value, this study seems to suggest an evening out of funding for autism research, which is allegedly highly biased towards genetic studies, to include more investigation into environmental causes. However, life is not so simple, and the following three considerations come to mind:
1) Previously, public interest groups had emerged which made a link between autism and vaccination. These groups asserted that specific chemicals in vaccines caused autism in some children, and that this accounted for the rise in autism that we see. These groups were largely funded and staffed by parents with autistic children who, understandably, wanted to find some entity to blame, but not so understandably, may have chosen a scape goat, with the cost of diminishing chances of finding a real explanation for autism (regardless of any rise in incidence). Various scientific studies seem to have debunked the vaccine link, the most important of which showed no decreases in autism prevalence after the most often blamed chemical found in some vaccines were removed from use.
2) It is possible that anti-vaccine denialist spokespeople, i.e, those who have all along asserted that the special interest groups mentioned above were wrong, have expanded their argument to include all or most environmental causes of the disorder, at the same time that those special interest groups have entrenched in an environmental cause camp. This makes it difficult to evaluate any research that simply tries to address the cause of the condition.
3) The research reported here was conducted by members of an institute funded by special interest groups that may have overlapped with those mentioned above. One has to consider the possibility of links between funding sources and researchers.
None of these three aspects of the problem should matter in the long run. The research reported here can to some extent be evaluated on its own terms, and attempts to replicate it or disprove it’s implications can and should (and likely will) be done by other research teams. But they do matter a great deal when it comes to discussing this issue. Recently, I posted a paragraph from a Scientific American article addressing this issue on this blog, without comment, and was instantly accused of being … I don’t know, some kind of anti-science denialist. Presumptions were made about what I was thinking, and not one person previously involved in this debate saw fit to be even remotely polite.
What does this mean? It means that if there are an anti- or non-scientific factions working hard in the wrong direction than necessary to nail down autism and related conditions, find out what causes them and work towards cure or relief, they will do better than they otherwise might in their efforts. Instant add-water-and-stir vitriol is stupid and counter productive. Go read the comments on my post and marvel at it all!
But what about the study at hand?
This is actually fairly complicated. This is an epidemiological study that looks at state databases showing autism diagnoses. The study notes and documents a remarkably high increase in autism rates, going from fewer than one per 10,000 children to close to 12 for the youngest cohort and 40 for the oldest cohort. For a broader context, other studies have shown rates of varying amounts, but close to (yet above) 10 per 10,000 for Autism, and a much larger value of near 50 or in some cases much more for the broader categories of diagnosis that include but are not limited to autism. The study consideres, but argues against, the idea that the shift seen in California is a change from a narrow diagnostic range to a broader one.
The study looks at the following possible causes of the increase: Younger ages of diagnosis, migration of autism-rich populations into the state, changes in diagnostic criteria, and inclusion of milder cases into the category as time goes by.
The study concludes that while any of these could account for some of the changes, the sum of these effects is insufficient to explain the data. In particular, this study suggests that a 56% increase can be explained by inclusion of milder cases, and a 12% increase can be explained by changes of age at diagnosis.
The problem is that this analysis does not (and can not) do what we really want, and that is to measure the actual number of children with the same exact criteria at two or more points in time. What we are left with instead is a difficult game of measuring rates. Rates are always tricky.
Fourty is 400 percent of 10, so a shift from 10 to 40 cases per 10,000 sounds like a large increase. But what if that ’10′ was off a bit, and it was actually five? What was 400 percent is now 800 percent. But what if that 10 was off in the other direction, and was actually a 15? Now we’re talking about 270%.
This is the problem with extended ratios. Small changes at one end or the other and especially changes at the ‘short’ end of the ratio, can make large changes in the key index variable (the percent increase).
Even more dramatic would be a comparison of ‘then’ vs. ‘now’ in which we assumed that some of the effects occurred early vs. later. That could get very complicated.
By the most extreme reasoning in one direction, the increase in new cases per 10,000 can be explained at one order of magnitude by diagnostic and reporting factors, but is occurring at one more level of magnitude higher. By the most extreme reasoning in the other direction, cumulative effects of diagnostic and reporting artifacts can easily include the increase.
There is another way to think about this. Take the data at face value. In particular as shown in this figure from the paper:
Is this a genetic change? No. Too fast. This is way sub-generational, and migration has been excluded from the analysis. Is this an environmental change? It looks like one would expect for certain environmental changes, but if it is, then what factor in the environment is so different spanning some 20 years. Such a thing should be obvious. This does resemble, in my view, a secular change in effect of the kind one might expect with differences in diagnosis and reporting, because such changes take years to take hold, but can have a large influence.
In other words, other than ruling out in situ evolution, I don’t think you can tell what causes this. The fact that a big chunk of this variation is probably explained by changes in reporting and diagnostic criteria suggests that more of the same sort of effect may be sufficiently explanatory. The fact that a careful look at reporting and diagnostic effects does not readily explain the level of magnitude of the change we see here suggests that more explanation is needed.
In the absence of a correlation between these data and a list of causal effects (which could then lead to some effective hypothesis testing) it important to keep an open mind about what causes autism. I can think of no reason that this study’s validity or lack thereof informs us in this regard. Those who wish to insist that no matter what there is no increase in autism rates are no less a failure at explaining autism as those who see a real increase in graphs like this one.
Meanwhile, the authors of this study and others are looking into the data further to test for environmental links. According to a press release from UC Davis:
Hertz-Picciotto and her colleagues at the M.I.N.D Institute are currently conducting two large studies aimed at discovering the causes of autism. Hertz-Picciotto is the principal investigator on the CHARGE (Childhood Autism Risk from Genetics and the Environment) and MARBLES (Markers of Autism Risk in Babies-Learning Early Signs) studies.
CHARGE is the largest epidemiologic study of reliably confirmed cases of autism to date, and the first major investigation of environmental factors and gene-environment interactions in the disorder. MARBLES is a prospective investigation that follows women who already have had one child with autism, beginning early in or even before a subsequent pregnancy, to search for early markers that predict autism in the younger sibling.
“We’re looking at the possible effects of metals, pesticides and infectious agents on neurodevelopment,” Hertz-Picciotto said. “If we’re going to stop the rise in autism in California, we need to keep these studies going and expand them to the extent possible.”
Irva Hertz-Picciotto, Lora Delwiche (2009). The Rise in Autism and the Role of Age at Diagnosis Epidemiology, 20 (1), 84-90 DOI: 10.1097/EDE.0b013e3181902d15