Autism more common in hi-tech areas

Autism is more common in geographical areas with a high proportion of engineers and IT specialists, a Cambridge University study has found.

And, they say, this may be partly because autism is linked to skills seen as desirable in a tech-savvy society.

The team had previously discovered evidence for a link between autism and systemization – the drive to analyse how systems work, and to predict, control and build systems. They found that fathers and grandfathers of children with autism spectrum conditions (ASC) were over-represented in the field of engineering.

In addition, they found that mathematicians were more likely to have a sibling with ASC, and that students in the natural and technological sciences, including math, show a higher number of autistic traits.

Building on this, Simon Baron-Cohen and his team wanted to discover whether ASC would also be more common in geographical areas associated with the high-tech industries.

They examined ASC diagnoses in over 63,000 school-aged children in the Netherlands’ Eindhoven – known as the tech hub of the country – along with control areas Haarlem and Utrecht-city.

And they found that the reported rate of ASC in Eindhoven was 229 per 10,000 – way higher than in Haarlem, at 84 per 10,000, or Utrecht  with 57 per 10,000.

The reason, says Baron-Cohen, may be that there’s active selection going on in these areas for the sort of IT skills that are associated with autism.

“These results are in line with the idea that in regions where parents gravitate towards jobs that involve strong ‘systemizing’, such as the IT sector, there will be a higher rate of autism among their children, because the genes for autism may be expressed in first degree relatives as a talent in systemizing,” says Baron-Cohen.

“The results also have implications for explaining how genes for autism may have persisted in the population gene pool, as some of these genes appear linked to adaptive, advantageous traits.”

His team is now planning a follow-up study to look at the causes of the variation in more detail.