Disparities in Autism Spectrum Disorder Diagnosis: Examining Individual and Socioeconomic Predictors of Diagnostic Age and Provider Type

Abstract

Autism spectrum disorder (ASD) can be reliably diagnosed as early as 18-months-old, however, the average age of diagnosis is 48 months or later. It is critical that individuals suspected of having ASD are evaluated as early as possible, as early intervention has been shown to have the most robust long-term outcomes. Individual and systemic barriers prevent underserved populations from receiving a timely diagnosis of ASD. While these disparities have been outlined in the literature, to date, a large-scale study has not examined the impact of these individual predictors, along with access to resources, has on the timeliness of diagnosis. As such, the present uses linear modeling with data from the SPARK study to examine how individual and socioeconomic predictors impact the timing of diagnosis, particularly for participants from traditionally underserved groups. Additionally, the present study will examine the role of school psychologists in the identification of ASD based on individual and socioeconomic factors. To achieve this, logistic regression was used to model the probability of school-based ASD diagnosis. Results indicate that cognitive impairment, symptom severity, diagnosis year, and BIPOC status were predictive of diagnosis age. Additionally, interactions with cognitive impairment and symptom severity were significant. For school-based diagnoses, BIPOC individuals were more likely to be diagnosed, while females were less likely. Age band and diagnosis year also interacted with the likelihood of receiving a school-based diagnosis. These findings highlight the importance of addressing both individual and socioeconomic factors that contribute to diagnostic age for ASD, particularly for underserved populations. The results suggest that greater attention is needed to ensure equitable access to timely evaluations and interventions, especially for BIPOC individuals and females, who are at risk for later diagnoses despite early indicators of ASD. Additionally, the role of school-based identification is crucial, as individuals from undeserved communities are more likely to receive diagnoses in the school setting.