In a Different Key: The Story of Autism (62 page)

Social scientists had a good idea why this was happening. The information that was being pored over had been gathered by educational authorities, not public health agencies. The Individuals with Disabilities Education Act had provided a standard definition of autism but had left it to each state’s Department of Education to create its own criteria for determining eligibility for special-education services.

Each authority built its own checklist, which ranged from as few as five items long to as many as seventeen. Some states required strict adherence to some version of the
DSM
criteria, others to the IDEA definition, and several to both. Some required diagnosis by a board-eligible psychiatrist or licensed clinical psychologist, but others did not. In some cases, the decision to provide services—which was not at all the same thing as a clinical diagnosis—was left to a group including the parents, school principal, and special-education teachers. All these disparities led to researchers dealing with data that was anything but uniformly derived.

Moreover, rather than revealing “true” prevalence, these numbers represented what social scientists called “administrative” prevalence. Counting autism by counting the people receiving services was like counting vegetarians on an airplane by adding up orders for meat-free meals. Just as there would be all manner of ways to miss the true “prevalence” in that scenario, administrative prevalence of autism was subject to various distorting influences. These included simple clerical or arithmetic errors, as well as the inherent subjectivity of a diagnosis based on the observation of behavior.

Even when the same criteria were being referenced, autism was still a diagnosis determined by a nonobjective measure—the opinion of whoever was asked to do the evaluation. Research showed clear geographic and socioeconomic trends in this regard. Diagnoses were more likely in communities that offered more services overall, and they were more commonly given to white and more affluent Americans than to
members of ethnic minorities or children from poor families. It was also possible for a child who was denied a diagnosis by one professional to receive it from another. Indeed, in some areas, parents shared lists of diagnosis-friendly evaluators who could be counted on to give an autism label to a child whose symptoms might be borderline.

Parents had a strong motive for such diagnosis shopping: thanks to their years of lobbying, schools had become much more responsive to the needs of children with an autism diagnosis than to those labeled with, for example, intellectual disability or some other kind of learning difficulty. Further, the autism label, again due to parent activism, had lost some of its stigma. It was known anecdotally that pediatricians and other professionals who held the power to label occasionally tilted the scale in the evaluations to ensure a child’s access to better programs and state services.

In 2007, sociologist Richard Roy Grinker quoted a senior child psychiatrist at the National Institutes of Health as saying,
“I’ll call a kid a zebra if that will get him the educational services I think he needs.” New York psychiatrist Isabelle Rapin, another prominent researcher in the field, was candid about this phenomenon.
“I admit up front that I have contributed to the ‘epidemic’ in New York,” she wrote in 2011, citing the example of a four-year-old patient she had diagnosed in the early 1990s as having “a severe developmental language disorder with serious behavioral problems.” Years later, his father phoned, seeking an autism diagnosis for his son. Based on that conversation alone, and the leeway afforded by a newer, less restrictive definition of autism, Rapin agreed to provide the young man the label of autism.

This so-called diagnostic substitution could certainly account for some of the apparent increase in autism numbers. In the 1970s and 1980s, after the label “learning disabled” came into use, numbers for learning disabled children in school soared across the nation as, simultaneously, the numbers of students labeled
“mentally retarded” dropped precipitously. This was due, in large measure, to children with mild intellectual disability being shifted into the category that carried less stigma.

The question of whether a similar dynamic was pushing up autism numbers fascinated a young social scientist in training named
Paul Shattuck in the early 2000s. Shattuck was a graduate student at the University of Wisconsin who was working toward a PhD in social welfare. He wanted to study
“the relationship between the rising administrative prevalence of autism in US special education and changes in the use of other classification categories.” Shattuck did not analyze or directly assess any children for his study. Instead, using data he collected from the US Department of Education, he looked at the annual state-by-state counts of children, aged six to eleven, with disabilities in special education.

Shattuck’s results, which he published in 2006, were attention-getting and controversial for a number of reasons. Seen in aggregate, the data he reported showed that, in forty-four states, big upticks in “administrative” prevalence of autism went hand in hand with downticks in the numbers for children labeled “cognitively impaired” and “learning disabled.” It was as if a group of children had walked from one end of a seesaw to the other. Shattuck’s conclusion was that, at least in these states, diagnostic substitution appeared to account for much of the apparent increase in autism.

Shattuck’s study had weaknesses, which he admitted and others pointed out as well. His reliance on school-based data, whose very credibility was so much in question, was a problem. He also did not dig down to the local level, much less to the even deeper level where he could track individual kids who had made the move from one category to the other. He also reported that a pattern of diagnostic substitution did not emerge in a handful of states, including California, and he had no explanation for this.

That said, his study—and even the criticisms of it—underscored an inescapable conclusion: no epidemic could be proven or disproven with the available numbers. The data was simply too much of a mess for anyone to be making either claim with even a hint of certainty. No credible scientist who looked at the numbers would disagree.

—

F
OR A TIME
in the mid-2000s, Australian TV viewers could see an emotional public service announcement in which a series of people spoke directly into the camera to describe the challenge of autism as it
affected kids in Australia. At one point, a woman declares: “One out of every one hundred and sixty-six children born
will
have autism.”

There was no place in that presentation to identify the 1 in 166 number for what it was—an American statistic, one that was often attributed to the CDC beginning in early 2004. As
Scientific American
put it three years later, the number had by then acquired a
“familiar ring,” a result of its ceaseless repetition by advocacy groups and media reports on autism. From India to Ireland to Argentina to South Africa, the 1 in 166 figure became the numerical expression of the epidemic story.

But the CDC never intended to make 1 in 166 the measure of the world’s autism rate.
The statistic burst into the spotlight only because of a skillful intervention by autism parent Peter Bell. The president of Cure Autism Now, Bell was one of several leaders seated around a table in Washington, DC, in 2003, at a meeting the CDC called to share information with the nation’s various autism organizations. During the discussion, someone brought up the awkward fact that each group present, in discussing the prevalence of autism, was using a different number. This was confusing to the public and threatened to undermine the credibility of all of them. Despite the sometimes rancorous relations among the players, they agreed that consistency was of paramount importance when discussing the scale of the epidemic.

That day, the group heard a presentation by a CDC official named Marshalyn Yeargin-Allsopp. Together with the American Academy of Pediatrics, the CDC had been reviewing recent epidemiological studies in order to come up with a more accurate estimate of prevalence for pediatricians. But as Yeargin-Allsopp told Bell and the others, the numbers were all over the map. One study she had led looked at the population of metropolitan Atlanta and found autism in approximately 1 in 300 of the children. Yet another undertaken in Utah found a far lower rate: 1 in 500. Three other studies—one covering a single New Jersey town, one an English county, and the third, the state of Illinois—yielded much higher rates: approximately 1 in 166. The prevalence question still had no simple answer, no single unifying statistic.

When the presentation ended, Peter Bell, who had worked in
corporate marketing for a dozen years, turned to Yeargin-Allsopp with a question. Was she telling them that this broad range represented “the CDC’s best estimate” of the prevalence of autism in the United States right then? Yeargin-Allsopp paused, looked around the room, and nodded. Yes, she confirmed, as of that date, the CDC had no better estimate. Now it was Bell who looked around the room, asking each person in turn whether his or her organization would agree to stick solely to one number, citing the CDC as their authority, in all future public discussion of the prevalence of autism. All agreed, and all rallied around the most alarming number in the range: 1 in 166. This became the number advertised by advocacy groups, and repeated by the news media. Soon it was widely accepted as hard fact.

Anyone visiting the CDC’s website would discover that the agency did not endorse this. In fact, they made it clear that there was no single number, and that all of the studies employed were relatively small-scale. It stated clearly what few wanted to be told:
“There is not a full population count of all individuals” with autism.

The American Academy of Pediatrics, on the other hand, dispensed with such nuance. The “Autism Alert” the AAP issued to pediatricians in the summer of 2004 delivered what most people were probably looking for anyway: a single number that made an immensely complicated story appear simple—and also, very frightening.

—

W
ITHIN A FEW YEARS
, the 1 in 166 figure became obsolete. By 2007, the CDC was operating what had long been lacking—a government-funded system for tracking the prevalence rates of autism over time. At regular intervals—roughly two years apart—the agency would be reporting new rates based on its own monitoring. That year, the agency announced that the measured rate of prevalence was now 1 in 150. It was a marked increase and a major headline in a world now primed to find evidence of an autism epidemic. In all subsequent reports, the CDC number kept climbing, to the point where the rate it represented was more than double the old 1 in 166.

The CDC’s new number was still not a “full population count” of people with autism. Such an effort would have been extraordinarily
expensive and beyond any possibility of true quality control. The CDC’s autism rate never had been, nor ever would be, an actual “autism census.” Instead, the monitoring program relied, as does most epidemiology, on population sampling. Specifically, to get a “national” figure, the CDC picked approximately 60 counties out of the nation’s 3,144, located
in just ten states, plus all the counties in Arkansas. Rather than establish a scientifically representative sample of the nation as a whole, these sites were chosen because roughly 10 percent of the nation’s population of eight-year-olds at any point in time lived in these communities. A panel of clinicians in Atlanta—all trained for the role by the CDC—got to know each of the eight-year-olds in these communities only on paper.

The obvious disadvantages inherent in long-distance diagnosis were somewhat balanced by the advantages of having all the evaluators applying a single, consistent set of criteria to the records of all the children from all eleven states. That way, local variations in how people viewed or diagnosed autism were less likely to skew the results. The CDC evaluators accessed school and health records and began searching through them for signs of autistic behaviors. In fact, they found themselves “diagnosing” autism, from their offices in Atlanta, in hundreds of eight-year-olds spread across the eleven states who had never been given an autism label in their lives.

And yet, in survey after survey, the CDC results still revealed major geographical inconsistencies in prevalence rates around the country. Its data for 2008 produced a higher-than-ever “national” rate of 1 in 88—
the most alarming statistic ever used by autism advocates. But this ratio disguised a huge spread in state-by-state rates, because it was merely the average of all the local results. That year, for example, the number reported for New Jersey was 1 in 49, more than four times higher than Alabama’s, which was 1 in 210. Moreover, Alabama seemed to be bucking the trend. Its reported prevalence in 2008 was 20 percent lower than in 2006—a fact few brought up amid all the talk of a worsening epidemic of autism.

Some saw the geographical disparities as a reflection of some sort of environmental contaminant in play, more active in some localities than in others. That could explain why a New Jersey address appeared
to be a higher risk than one in Alabama. This could be considered a rough road map for investigating a possible environmental driver for autism—something that no scientist, even the “epidemic skeptics,” would rule out: what is in New Jersey’s air or water that is not in Alabama’s?

Still, this approach would have to account for the role human behavior may have played in inflating the “epidemic” numbers. New Jersey, for example, had long been a magnet for autism families, who relocated there from surrounding states, adding to its prevalence rate moving van by moving van. The families were drawn by New Jersey’s superior offerings in state-funded special education for autism, which were among the best in the nation. The same could not be said of Alabama.

A competing explanation held that the rising numbers throughout the 2000s, rather than marking an epidemic, were a case of epidemiology catching up with reality. In this view, autism, regardless of the specific criteria, was probably always a part of the human condition, but one that it took Leo Kanner to bring into focus, followed by several decades of fine-tuning the definition. It was not that autism was spreading to a larger percentage of the human race than in the past, but that society, prior to 1999, had made no intensive effort to go find the people who were already living with autism among them.