Assessing the heterogeneity of autism spectrum symptoms in a school population.
Autism traits in schools form a single slope, not separate boxes—ditch categorical cut-offs and track severity instead.
01Research in Context
What this study did
Morales-Hidalgo et al. (2018) looked at autism traits in regular school kids. They used a method called Factor Mixture Analysis. This lets data show if symptoms fall into neat groups or spread along a line.
The sample came from mainstream classrooms, not clinics. The goal was to test if ASD is a yes-or-no category or a sliding scale.
What they found
Autism traits formed one smooth curve, not separate boxes. Kids with many traits and kids with few traits sat next to each other on the same line.
The old idea of ASD vs SCD categories did not fit the data. Severity mattered more than labels.
How this fits with other research
Sajith et al. (2008) used a similar math tool but found discrete ASD subgroups. The clash is clear: one paper sees boxes, the other sees a ramp. The gap likely comes from where the kids lived. G et al. studied clinic patients; Paula et al. studied everyday classrooms.
Shuster et al. (2014) reviewed 36 factor studies and saw two steady clusters: social-communication and restricted behaviors. Paula et al. agree those clusters exist, but show they sit on one continuous slope, not two separate islands.
Root et al. (2017) meta-analysis warns that SCQ accuracy drops in young or convenience samples. Paula et al. add a new warning: even in schools, cut-offs should be dimensional, not categorical.
Why it matters
Stop using yes-or-no checklists to place kids in ASD or SCD buckets. Use a severity score and watch movement along that line over time. This shift can reduce false negatives in mainstream classes and keep kids from losing services because they miss a label by one point.
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02At a glance
03Original abstract
UNLABELLED: The aim of the present study was to assess whether the nature of the main autistic features (i.e., social communication problems and repetitive and restrictive patterns) are better conceptualized as dimensional or categorical in a school population. The study was based on the teacher ratings of two different age groups: 2,585 children between the ages of 10 and 12 (Primary Education; PE) and 2,502 children between the ages of 3 and 5 (Nursery Education; NE) from 60 mainstream schools. The analyses were based on Factor Mixture Analysis, a novel approach that combines dimensional and categorical features and prevents spurious latent classes from appearing. The results provided evidence of the dimensionality of autism spectrum symptoms in a school age population. The distribution of the symptoms was strongly and positively skewed but continuous; and the prevalence of high-risk symptoms for autism spectrum disorders (ASD) and social-pragmatic communication disorder (SCD) was 7.55% of NE children and 8.74% in PE. A categorical separation between SCD and ASD was not supported by our sample. In view of the results, it is necessary to establish clear cut points for detecting and diagnosing autism and to develop specific and reliable tools capable of assessing symptom severity and functional consequences in children with ASD. Autism Res 2018, 11: 979-988. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The results of the present study suggest that the distribution of autism spectrum symptoms are continuous and dimensional among school-aged children and thus support the need to establish clear cut-off points for detecting and diagnosing autism. In our sample, the prevalence of high-risk symptoms for autism spectrum disorders and social-pragmatic communication disorder was around 8%.
Autism research : official journal of the International Society for Autism Research, 2018 · doi:10.1002/aur.1964