Health disparities for under-represented racial and ethnic groups with autism have persisted for
decades. For example, children who are Black and Hispanic with autism are identified at later ages than
White children despite there being no biologically plausible reason for delays in diagnosis. This suggests
that other systemic barriers likely contribute to lower prevalence rates and delayed identification. There is
a growing awareness that developmental screening instruments could potentially contribute to these
disparities. That is, many front-line screeners perform poorly when the child is Black or Hispanic (e.g.,
Dahl, et al, in press; Moody, et al, 2018; Rosenberg, et al, 2019). While previous research has found that
these instruments are not as accurate for autism identification in Black and Hispanic children, there is no
research available determining whether items within these screeners contribute to the poorer
identification, or if differential item functioning (DIF) impacts the overall performance of these screeners.
This project will use factor analytic and item response theory methods to identify specific items
that differentially perform across Black, Hispanic, and White children on the two most widely used
screening tools for children 3 to 5 years old (Social Communication Questionnaire and Social
Responsiveness Scales). We will then use a recently published method to quantify the impact that DIF has
on overall instrument performance. To do this we will 1) determine if the SRS and SCQ factor structures
are consistent across groups; 2) use Item Response Theory (IRT) to measure test level and item level effect
sizes; 3) use a new method by Gonzalez and Pelham (2021) to determine if items that have Differential
Item Functioning (DIF) impact the overall performance of the instruments; and 4) conduct a sensitivity
analysis to determine if race, ethnicity, socioeconomic status or other demographic variables impact the
outcomes.
This will be a secondary analysis of data from the Study to Explore Early Development (SEED), the
largest case-control study of autism risk factors in the world. SEED has an extremely well characterized
and diverse sample. Data come from six states (California, Colorado, Georgia, Maryland, North Carolina,
and Pennsylvania) and includes over 7,271 children. This research will allow for more effective
developmental screening and reduce early identification health disparities for Black and Hispanic children.
Findings from this study will reduce the public health burden of later identified autism in Black and
Hispanic populations by leading to improved screening algorithms that improve the early identification
process resulting early intervention for young children.
