(PS11-D80) Autism Symptom Severity, over Race, SES, and Rurality, Serves as Best Predictor of Early Diagnosis

Introduction: Early identification of autism spectrum disorder (ASD) and intervention can make contributions to lifelong functional, social, and developmental outcomes (Bryson et al., 2003). Nationally, the average age of diagnosis (AOD) is around 52 months. However, AOD is often much later than the age at which parents begin having concerns about their child’s development or question the possibility of ASD. This delay is due to a myriad of reasons including access to specialists, long waitlists, need for clear therapeutic pathways and inclusion of stakeholder input (Baio et al., 2018; Vivanti et al., 2018). This study aims to examine the influence of certain demographic factors on age of diagnosis.

Methods: Data were collected from a university-affiliated ASD specialty clinic. Participants include parents of children diagnosed with ASD who consented to their evaluation data being used for research purposes. Demographic variables include Medicaid status (as a metric of socioeconomic status), race, and rurality, which was measured based on families’ zip code compared to population estimates from the 2010 U.S. Census. ASD symptoms severity was measured using the Autism Spectrum Rating Scale (ASRS) .

Results: 109 parents of children aged 2 to 15 years old (M= 5.60, SD= 3.49) completed assessments. About 55% of the sample self-identified as racially minoritized and 74% of the children were male. Initial correlations showed that age of diagnosis was significantly correlated with the Unusual Behaviors (r= 0.366, p< .001) and Social Communication ASRS subscales (r= -0.153, p< .001). Analyses examining the relationship between AOD and demographic variables showed that race was significantly related to AOD such that minoritized children received diagnosis at an earlier age (t= 2.171, p= .020). Medicaid status was not a significant predictor of age at diagnosis. An ANOVA revealed that the relationship between rurality and AOD was marginally significant (p=.066), such that children who lived in very rural areas received diagnoses later than those living in urban and rural areas. A multiple regression examined the relative influence of ASD traits (step 1) and rurality and race (step 2) on AOD. Adding rurality and race did not significantly improve the model, DF(2, 104)=1.794, p=.171, DR²=.028, although the overall model remained significant F(4, 104)=6.472, p< .001, R²=.169, suggesting that social communication and unusual behaviors served as stronger relative predictors.  

Discussion: Although rurality and race appear related to AOD, the Social Communication and Unusual Behavior subscales served as a stronger predictor of age of first diagnosis. It’s possible that higher levels of ASD symptom severity cause greater disruptions that lead families to seek services. Understanding how certain sociocultural factors and behaviors can influence timing of initial diagnosis is critical for early detection. Early intervention is widely accepted as a necessity for ASD youth, and studies that increased knowledge on access and barriers to care can serve as building blocks to improving early intervention and providing maximum treatment benefit.