Cost-Effectiveness of MRI-Based Identification of Presymptomatic Autism in a High-Risk Population

Abstract

Biological siblings of children with autism spectrum disorder (ASD) have increased risk of receiving an ASD diagnosis. In the U.S., most children with ASD are diagnosed after the optimal age to initiate early intervention which can reduce symptom severity and improve outcomes. Recent evidence suggests magnetic resonance imaging (MRI) in the first year of life can predict later diagnostic status in high-risk siblings. We investigated whether MRIbased screening is a cost-effective method for assigning early intervention. A hybrid decision tree/Markov model was used to evaluate two MRI-based screening strategies at 6 and 12 months of age. Primary outcomes were costs in U.S. dollars and quality-adjusted life years (QALYs). Results were reported as incremental cost-effectiveness ratios (ICERs). Costs were estimated from societal, health care, and educational perspectives. One-way and probabilistic sensitivity analyses were performed. From a societal perspective, the ICER for

The role of caregiver speech in supporting language development in infants and toddlers with autism spectrum disorder

“…A criticism of this approach, using MRI-based screening of high-risk infants to determine who should receive treatment, is the cost associated with MRI brain scans. A recent report counters this claim, with analyses showing that MRI-based screening is a cost-effective strategy for determining which high-risk infants should receive intensive early intervention (Williamson, Elison, Wolff, & Runge, 2020). Early intensive behavioral intervention is a costly endeavor, but these upfront costs are offset by long-term societal and economic savings (Jacobson, Mulick, & Green, 1998; Penner et al, 2015).…”

Diagnostic shifts in autism spectrum disorder can be linked to the fuzzy nature of the diagnostic boundary: a data‐driven approach

“…Nevertheless, our findings underline the need for more vigilant surveillance and faster initiation of intervention rather than waiting for a categorical diagnosis to begin treatment. Such prudent practices become especially important for children with high familial risk (Ozonoff et al, 2014) to maximize access to intervention and improve the quality of life for those individuals and their families (Wiggins et al, 2020;Williamson, Elison, Wolff, & Runge, 2020). Identifying predictive behavioral or neurobiological patterns of children who may change diagnosis could provide improvements to current clinical practices (Lord, 2018;Rynkiewicz et al, 2016).…”