|Title:||Reducing Time to Autism Diagnosis for Toddlers Enrolled in Early Intervention|
|Principal Investigator:||Roberts, Megan||Awardee:||Northeastern University|
|Program:||Research to Accelerate Pandemic Recovery in Special Education [Program Details]|
|Award Period:||4 years (09/01/2022 – 08/31/2026)||Award Amount:||$2,999,638|
|Type:||Development and Measurement||Award Number:||R324X220088|
Education Agency Partner: Illinois Bureau of Early Intervention
Co-Principal Investigator: Kaat, Aaron
Purpose: The purpose of this project is to test a virtual process for diagnosing autism spectrum disorder (ASD) to support earlier access to autism-specific intervention services in Illinois (IL). This new approach is intended to make the diagnostic process more efficient and reduce waitlists. At the start of the of the COVID-19 pandemic, face-to-face medical diagnostic evaluations provided through the IL Bureau of Early Intervention (EI) were temporarily discontinued, including ASD diagnoses that are required to access early intensive and specialized intervention through health insurance. This placed even greater demands than usual on the traditional process, resulting in long waitlists and an unprecedented need for feasible, acceptable, and valid telepractice methods for ASD diagnosis. Early access to specialized and intensive intervention during a critical window of neuroplasticity (before 36 months of age) is essential for optimizing developmental, academic, and quality of life outcomes. To address this increased demand, this project will provide immediate ASD diagnostic evaluations for over a thousand toddlers enrolled in the IL EI program while developing and evaluating a new EI ASD diagnostic pathway that is designed to be more efficient, equitable, and feasible.
Project Activities: In this project, the research team will evaluate a novel EI ASD diagnostic pathway comprised of existing, open-access ASD tools used within a telepractice model. They will examine the agreement between two different diagnostic teams (new EI team and traditional expert team), determine an optimal scoring algorithm for this new diagnostic pathway, and determine the costs associated with this diagnostic process.
Products: This project will result in a diagnostic protocol designed to optimize diagnostic accuracy of the novel EI ASD diagnostic pathway. The project will also result in peer-reviewed publications, dissemination to the IL EI system and parents, and presentations to a wide audience of stakeholders.
Setting: This project will take place in family homes across IL, connected virtually through a telehealth platform, representing a diverse range of rural, suburban, and urban settings.
Sample: The sample will consist of approximately 1,200 toddlers across the IL EI system. All toddlers will (a) be between 12 and 36 months of age, (b) be currently enrolled in the IL EI system, and (c) have an EI provider or caregiver with concerns about ASD and no concerns about additional medical conditions. The study will over-sample for families historically underrepresented in ASD diagnostic studies.
Intervention: For this novel ASD diagnostic pathway, EI diagnostic teamsócomprised of an EI-credential speech-language pathologist (SLP) and developmental therapist (DT)ówill be trained to use existing, open-access ASD tools to evaluate each child with data from multiple providers, measures, informants, contexts, and time points. Once trained in this new process, this team will meet virtually through a telehealth platform with children and their families to determine diagnosis for ASD.
Research Design and Methods: To evaluate the novel EI ASD diagnostic pathway, the diagnosis of the EI team will be compared to a more traditional expert diagnostic teamócomprised of a licensed clinical psychologist, SLP, and DTóafter the expert team observes a video recording of the EI diagnostic team conducting their assessments. Upon completion of conducting (or observing) the assessments, each diagnostic team member, the EI diagnostic team, and the traditional expert diagnostic team will independently determine their diagnoses. These diagnoses will be compared to one another and a final joint diagnosis will be conferred. The reliability, fidelity, diagnostic agreement, and judgements of certainty, feasibility, and acceptability of diagnosis will be evaluated. Data will be gathered on caregiver satisfaction of the process both before and after the evaluation. Child-, caregiver-, and provider-level factors that may influence outcomes of the EI ASD diagnostic pathway will also be evaluated. Finally, a data-driven approach will be used to investigate an optimal scoring method that most closely approximates the expert diagnosis.
Control Condition: Due to the nature of this study, there is no control condition. Instead, the diagnoses conferred by the traditional expert diagnostic team will serve as the comparison for the EI diagnostic team diagnoses.
Key Measures: The measurement tools to be used by the EI diagnostic team include the following: Early Screening for ASD and Communication Disorders (ESAC), Systematic Observation of Red Flags for ASD (SORF), Toddler ASD Symptom Inventory (TASI), and Telemedicine-based ASD Evaluation Tool for Toddlers and Young Children (TELE-ASD-PEDS). Two researcher-created caregiver surveys will also be administered to measure their satisfaction with the novel pathway.
Data Analytic Strategy: The research team will calculate descriptive statistics of the following outcomes: reliability, fidelity, and EI diagnostic team member agreement. They will then use mixed effects models to examine the association of child, caregiver, and provider factors related to these outcomes. Descriptive statistics will also be calculated for the diagnostic agreement between the EI team and the traditional expert team as well as the certainty the EI team has in their diagnoses. Mixed effects models will again be used to examine child, caregiver, and provider factors and interactions related to diagnostic agreement between EI and traditional expert teams. Regression will be used to evaluate the impact of the novel EI ASD diagnostic pathway on IL waitlists. Logistic regression will be used to design a more efficient diagnostic protocol for the EI ASD diagnostic pathway. The research team will also examine the feasibility, acceptability, and caregiver satisfaction related to the EI ASD diagnostic pathway with descriptive statistics.
Cost Analysis: The ingredients method will be used to determine the cost of the resources needed for the novel EI ASD diagnostic pathway as well as the cost per child, EI provider, and family. Finally, variation in costs for different subgroups of families (such as varying demographics, those who require the use of an interpreter, and those with less knowledge about ASD) will be explored.