Maternal and Neonatal Birth Factors Affecting the Age of ASD Diagnosis

Early diagnosis of autism spectrum disorders (ASD) enables early intervention that improves long term functioning of children with ASD but is often delayed until age of school entry. Few studies have identified factors that affect timely diagnosis. This study addressed how maternal education, race, age, marital status as well as neonatal birth factors affect the age at which a child is diagnosed with ASD. This study involved a retrospective analysis of 664 records of children treated at one of the largest autism treatment centers in the United States from March 1, 2009 to December 30, 2010. Logistic regression and Cox proportional hazards regression were used to identify maternal and neonatal factors associated with age of diagnosis. Infant gender, maternal race, marital status, and maternal age were identified as significant factors for predicting the age of ASD diagnosis. In the Cox proportional hazards regression model, only maternal race and marital status were included. Median survival age till diagnosis of children born to married mothers was 53.4 months compared to 57.8 months and 63.7 months of children born to single and divorced or widowed mothers respectively. Median survival age till diagnosis for children of African American mothers was 53.8 months compared to 57.2 months for children of Caucasian mothers. No statistically significant difference of timing of ASD diagnosis was found for children of varying gestational age. Children born to older or married mothers and mothers of minority races were more likely to have an earlier ASD diagnosis. No statistically significant differences in timing of ASD diagnosis were found for children born at varying gestational ages. Identification of these factors has the potential to inform public health outreach aimed at promoting timely ASD diagnosis. This work could enhance clinical practice for timelier diagnoses of ASD by supporting parents and clinicians around the world in identifying risk factors beyond gender and SES and developing strategies to recognize earlier signs of ASD and contribute to improved development outcomes in children with ASD.