Why inferring autism’s causes from epidemiology is dangerous

In the past few years, I’ve seen several reports that suggest that health disparities in autism are related to social factors such as culture, race, ethnicity and socioeconomic status.

For example, claims abound that a mother’s ethnicity is associated with genetic differences that predispose her children to autism. Or that her country of birth means she was exposed to harmful environmental factors that may lead to autism in her children. Or even that the stress associated with immigration increases her risk of having a child with autism.

Most recently, a study published earlier this month reported that in Los Angeles County, children born to mothers who immigrated from Central or South America, the Philippines or Vietnam have an elevated risk of autism. It also concludes that African-American and Hispanic children have higher rates of autism than do Caucasian children¹.

At first glance, these ‘just-so’ stories resemble well-established associations between some single-gene disorders and certain races or ethnic groups — for example, the link between Tay-Sachs disease and the Jewish population. But it is far more challenging to identify and explain such disparities in conditions such as autism, which result from multiple genetic and environmental risk factors.

While working on a World Health Organization-commissioned review of the global prevalence of autism, I came across many other just-so stories linking factors such as ethnicity, nativity and race to the prevalence of the condition². But association does not imply causality, and prevalence data cannot be used to infer underlying genetic, biological or environmental differences.

Out of Africa:

I was particularly struck by a claim in the review that autism is a rare or even nonexistent condition in Africa. I was even more surprised to find the origins of this claim in a misinterpretation of Victor Lotter’s pioneering and insightful case series in Africa³.

A well-known epidemiologist in the 1970s, Lotter traveled to a number of African countries and described cases of autism that looked remarkably similar to what he had seen in his home country, the U.K.

Lotter is often misquoted as suggesting that there is a lower prevalence of autism in Africa than in the U.K., or that autism is associated with high socioeconomic status. He was in fact open about the fact that he had looked at only a small group of people of high socioeconomic status. He attributed this to the fact that these families were probably more likely than others to seek help in urban clinics.

As weput together the puzzle pieces of race, culture and biology as risk factors for autism, it is worth making sure that we aren’t perpetuating this type of misunderstanding. If we do, we risk repeating the past mistakes of social Darwinism, which attributes individual differences in intelligence, personality and cultural and social characteristics to a genetic basis.

For example, African-Americans as a group may score consistently lower on tests of intelligence than other ethnic groups, even after controlling for a wide range of social and economic confounds⁴. This has led some people to attribute these results to innate (genetic) differences between races. But prominent critics of this perspective have challenged not only the quality of the evidence on which these claims are based but also their underlying assumptions⁵.

“Mistakenly attributing racial differences [in autism risk] to biology offers a convenient excuse for political apathy.”

Complex social phenomena cannot be reduced to measurable concepts such as intelligence quotients. And the association of these measures with race does not imply causality. The lower test scores could be a result instead of biases in the tests themselves, which in the case of IQ tend to reflect the person’s ability to take tests in general. What’s more, the skills measured in these tests are by no means culturally universal.

Mistakenly attributing racial differences to biology offers a convenient excuse for political apathy, in lieu of sustained efforts to eliminate health and social disparities where possible.

In autism, many studies have similarly tried to link autism risk or prevalence to race, ethnicity and country of origin.

The stories often lead in two curious directions: Mothers take the lion’s share of the blame. And the increase in autism risk and severity is seen mostly in non-Caucasians, or those who are born outside the U.S. or Northern Europe.

In the past decade, social and advocacy pressures have revealed many disparities in autism prevalence. Indeed, in our global review we found that prevalence estimates are highly variable across geography and culture. Others’ findings suggest differences in severity of symptoms or in functioning across racial groups.

We and others have attributed this variability to a range of social factors that influence the measurement of autism prevalence. These include broadening of the diagnostic criteria of the condition, the rise in awareness, improved identification and stronger advocacy, alongside the many methodological differences in prevalence studies.

If differences in prevalence across diverse groups were truly the result of genetics, they would be immutable. Instead, prevalence estimates are highly variable and amount to snapshots within a given community at a certain time period.

False assumptions:

The most comprehensive evidence from the U.S. Centers for Disease Control and Prevention confirms that prevalence across racial groups in the U.S. is a moving target. The pattern of change suggests a ‘catch up’ in diagnosis in groups of individuals who were initially underdiagnosed. This makes prevalence estimates powerful advocacy tools to signal the unmet needs of various subgroups.

Further problems with claims about differences in prevalence relate to the validity of their constructs. For example, how is ‘foreign birth’ a biologically meaningful construct? Who or what is the person foreign to? Where did she come from? Did she choose to leave her home country or was she driven out by natural or political circumstance?

Similarly, U.S.-defined race categories are limited in capturing the complexity of individual differences both in biology and in culture. For example, ‘black’ encompasses African-Americans alongside immigrants from African countries who are a socioculturally distinct group. Neighboring Canada has dozens of government-recognized ethnic categories that are not collapsible into the U.S. categories, despite the overlap in ethnic origins between the populations of the two countries.

More often than not, ‘U.S.-born Caucasian’ is used as a reference group of convenience, rather than one that is logically or statistically justified. Rather than measuring individual differences, we tend to measure how different everyone else is from this ‘prototypical’ Caucasian Anglo-American or European group.

What’s more, disparities in access to care and clinician biases are documented phenomena that may be driving the reports of differences in prevalence. And the lack of a typical comparison group in most studies leaves open the possibility that tests used to measure IQ or language skills underestimate these skills in children from certain racial and ethnic groups.

If we accept that race and ethnicity are signs of biological differences in autism, this also opens to the door to using a child’s skin color to decide his or her prognosis or treatment. We should instead focus on making access to care more equitable for all children.

Rather than relying on frequency counts, studies investigating questions of culture and ethnicity need to formulate solid hypotheses based on well-grounded assumptions and the highest-quality data. For now, evidence suggests that where these disparities exist, they are unlikely to relate to underlying causes.

As with social Darwinism, our epidemiological just-so stories may inadvertently have socially and ethically questionable implications. The reality is that what underlies these stories is probably as complex as life itself.

References:

1: Becerra T.A. et al. Pediatrics 134, e63-71 (2014) PubMed

2: Elsabbagh M. et al. Autism Res. 5, 160-179 (2012) PubMed

3: Lotter V. J. Child Psychol. Psychiatry 19, 231-244 (1978) PubMed

4: Herrnstein R. and C. Murray (1994) The Bell Curve: Intelligence and Class Structure in American Life New York: Free Press

5: Gould S.J. (1981) The Mismeasure of Man New York: W.W. Norton