2016 Gatlinburg Conference Poster
PS-33
Title: Examining the Influence of Race and Gender on Screening for Autism Spectrum Disorder: A Self-Organizing Map Approach
Authors: Marlene V. Strege, Reina S. Factor, Luke E. K. Achenie, Angela Scarpa, Diana L. Robins, Scott McCrickard
Introduction: Research suggests delays and inaccuracies in autism diagnoses for racial minorities. For example, one study found
that Caucasian children received a diagnosis earlier (6.3 years) than African American children (7.9 years; Mandell, et al., 2002).
In addition, there may be gender differences in ASD presentation. ASD is predominately diagnosed in males. Researchers have
implicated social and emotional differences as potential explanations for the imbalance in male to female diagnoses, which may
result in an under-diagnosis of ASD in females (Head, et al., 2014). Following this, it would be beneficial to explore the role
gender plays in ASD symptom manifestation and screening.
Machine learning (ML) may serve as an optimal data-analytic approach to examine potential differences between race and
gender groups. Unsupervised ML techniques utilize algorithms to "learn" from data to make predictions and detect patterns of
responses within the data. The self-organizing map (SOM) algorithm is an unsupervised ML analysis that clusters data according
to similar responses (Bock, 2004).
Methods: The current study examined potential race and gender response differences on the Modified Checklist for Autism in
Toddlers, Revised (M-CHAT-R; Robins et al., 2009). The sample consisted of four groups of toddlers (16-30 months): (1)
Caucasians (N=11,156), (2) African Americans (N=2,425), (3) males (N=8,246), and (4) females (N=7,437). Toddlers determined to
be at risk were referred for a comprehensive ASD diagnostic assessment.
SOM (MATLAB SOM Toolbox) was applied to each group's responses separately, resulting in clusters of varied levels of risk for an
ASD diagnosis within each group. Item analysis was used to further explore potential race or gender differences in symptom
manifestation.
Results: Clusters of varying risk levels were identified in each group. Risk level was determined based on the number of elevated
ASD traits. Results demonstrated that SOM resulted in similar cluster results for Caucasians and African Americans. In both
groups, three distinct risk status clusters (Typical, Low Risk, and High Risk) were revealed. The application of SOM in the male
group also resulted in clusters of three levels of risk status; however, SOM failed to provide these risk differentiations for the
female group, resulting in only Low Risk and High Risk clusters. In all groups, 100% of the ASD cases (determined by follow-up
testing) fell into the High Risk clusters. Item analysis demonstrated overall consistent symptom manifestation across all four
groups.
Discussion: Similar results for Caucasians and African Americans suggest race may not be a differential factor in M-CHAT-R
screening. This may indicate that race does not directly contribute to potential diagnostic differences between races, but may be
the result of other factors (e.g., SES, maternal education, or access to services). In contrast, SOM results differed between males
and females. Specifically, SOM was able to differentiate risk status groups between males but was not as successful in
designating risk status in females. Poorer performance in females may be because females with ASD tend to show symptoms
later than males (Lai, et al., 2014) often not showing clear ASD features until school age when presented with more social
opportunities (Robinson et al., 2009).
References/Citations:
• Bock, T. (2004). A new approach for exploring multivariate data: Self-organizing maps. International Journal of Market
Research, 46(2), 189-204.
• Head, A. M., McGillivray, J. A., & Stokes, M. A. (2014). Gender differences in emotionality and sociability in children with
autism spectrum disorders. Molecular Autism, 5(1), 19.
• Mandell, D. S., Listerud, J., Levy, S. E., & Pinto-Martin, J. A. (2002). Race differences in the age at diagnosis among
Medicaid-eligible children with autism. Journal of the American Academy of Child & Adolescent Psychiatry, 41(12),
1447-1453.