Predicting Autism in Infancy

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social communication and interaction deficits and restricted, repetitive patterns of interests and behavior that are evident in early childhood. Its prevalence has grown substantially over the past several decades, with current estimates ranging from 1.7% to 2.5% in the United States.^1,2 This represents more than 1.5 million children with ASD, the vast majority of whom receive or will receive specialized services.² Each year, approximately 100,000 (and growing) individuals with ASD reach adulthood, and many face myriad challenges related to employment, housing, mental health, and overburdened or insufficient support services.^3-5 A host of significant costs can be associated with ASD, from direct costs related to the provision of special education programs, housing, and medical care to indirect costs, such as loss of productivity affecting both individuals with ASD and their families.⁶ Currently, overall lifetime cost of care per person with ASD can exceed $3 million, totaling more than $265 billion annually in the United States and rising to an estimated $1 trillion by 2025.^7,8.

Figure 1.

Hypothetical model for infant prediction and intervention. Note: Gray trajectory represents typical development. The red dotted trajectory represents a child with ASD who recieves “business as usual” care. The blue dashed line represents an infant with ASD who is identified presymptomatically by way of biomarker (e.g. EEG, eyetracking, MRI) at age 6 months, followed by individualized intervention focused on key developmental milestones, followed by montitoring and evidence-based intervention as needed.