Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools

Abstract

Whether and to what extent sociodemographic disparities in school-based autism identification have been occurring in U.S. elementary schools is currently unclear. We investigated for disparities attributable to race, ethnicity, biological sex, family income, and language use by analyzing repeated cross-sectional data collected on very large samples of U.S. fourth graders participating in the National Assessment of Educational Progress from 2003 to 2022 (ns = 103,150–205,860). Multivariable logistic regression models accounting for potential confounds including student-level academic achievement and school-level resources repeatedly indicated that students of color, females, students from low-income families, and multilingual learners (MLs) are less likely to be identified with autism while attending U.S. elementary schools. These disparities have been largely stable over time, particularly for Black students, females, and MLs. Health and educational policies that ensure equal access to autism supports and services in U.S. elementary schools including by students from historically marginalized communities are warranted.

Plain Language Summary

Students of Color, Females, Students from Low-income Families, and Multilingual Learners Are Less Likely to be Identified with Autism While Attending U.S. Elementary Schools

Whether students who are Black or Hispanic, females, from low-income families, or who are multilingual learners have been less likely to be identified with autism while attending U.S. elementary schools is currently unclear. Prior work reports conflicting findings and has often been unable to approximate contrasts between similarly situated students including those displaying the same levels of academic achievement and who are attending the same schools. Such contrasts of similarly situated students are necessary to better evaluate for the possibility of differential treatment due to biased or discriminatory practices. We used statistical methods to account for potential alternative explanatory factors (e.g. differences in family income, language use, or academic achievement) to better approximate contrasts between similarly situated students. Doing so provides stronger evidence of disparities in school-based autism identification attributable to race, ethnicity, biological sex, family income, and language use and not instead to alternative explanatory factors. To investigate how these disparities have changed across time, we analyzed very large cross-sectional samples of fourth-grade students from 2003 to 2022. These analyses repeatedly indicated that students who are White, boys, those from higher-income families, or students who are English-speaking are more likely to be identified with autism than students of color, females, those from low-income families, or students who are multilingual learners including among those who are displaying similar levels of academic achievement and who are attending the same schools. Although autism prevalence rates have increased for students from historically marginalized communities, students from these communities are still less likely to be identified with autism while attending U.S. elementary schools. Efforts are needed to ensure equal access to autism services and supports among students attending U.S. elementary schools.