Dissemination & Implementation Science
Sarah R. Edmunds, Ph.D.
Assistant Professor of Psychology
University of South Carolina
Columbia, South Carolina, United States
Kyle M. Frost, Ph.D.
Assistant Professor of Pediatrics
University of Massachusetts Chan Medical School
Worcester, Massachusetts, United States
Anthuanet Espinel, B.S.
Research Assistant
Michigan State University
East Lansing, Michigan, United States
Isabelle E. Saligumba, B.A.
post-baccalaureate
Michigan State University
Lansing, Michigan, United States
Wendy L. Stone, PhD, Ph.D.
Professor of Psychology
University of Washington, Seattle
Seattle, Washington, United States
Brooke Ingersoll, Ph.D.
Professor
Michigan State University
Lansing, Michigan, United States
Sarabeth BroderFingert, M.P.H., M.D.
Professor
UMass Chan Medical Center
Worcester, Massachusetts, United States
Allison Wainer, Ph.D.
Associate Professor
Rush University Medical Center
Chicago, Illinois, United States
Alice S. Carter, Ph.D., Ph.D.
Professor
University of Massachusetts Boston
Boston, Massachusetts, United States
Evidence-based social communication interventions are still not consistently available to young autistic children in community settings (Hume et al., 2021). The IDEA Part C early intervention (EI) system serves children with developmental delays. As many as 12% of children aged birth-to-three are eligible for Part C services, making this service system an important target for implementation efforts (Eisenhower et al., 2021). The majority of infants and toddlers in the US have minoritized identities or backgrounds. Yet, the degree to which Part C serves children equitably and a representative, diverse group of children are involved in research efforts to improve early intervention, is unknown. Efforts to study the effectiveness and implementation of early interventions should be conducted with children who are representative of Part C and the communities it serves, so that findings are ecologically valid and generalizable to a diverse population of children.
Objective: To examine whether the participants in a hybrid type 1 effectiveness-implementation trial (“RISE”) of an evidence-based social communication early intervention are representative of the Part C and overall state demographics for birth to three-year-olds.
Part C EI providers in Illinois, Massachusetts, Michigan, and Washington participated in a hybrid RCT of Caregiver-Implemented Reciprocal Imitation Training (CI-RIT). They were asked to recruit one or more families in their caseload to participate. The percentage of children identifying as “non-white” (i.e., Hispanic/Latine ethnicity and/or non-white race) were compared by state to: 1) the State of Babies 2023 state data (a Census-style dataset compiled by ZERO TO THREE); and 2) the Office of Special Education Programs (OSEP) state Part C enrollment data.
The RISE study sample comprises 168 child-caregiver dyads recruited from IL (n=43), MA (n=43), MI (n=54), and WA (n=23). Part C across these four states recruits fewer non-White families compared to the population (38.59% vs. 44.45%, respectively). While the average percentage of families from non-white backgrounds recruited for RISE (60.23%) was 21 percentage points greater than in Part C and 15 percentage points greater than the birth-to-three population, there was variation by state (Table 1). While the RISE sample in IL and MA has 20-33% more non-White families than the state population, the RISE samples in MI and WA are more similar (0-9%). The percentage of non-White families in RISE compared to within Part C is greater by 10 to 33 percentage points (range: WA=10.15 to MA=33.13).
The RISE study is recruiting a sample of participants who are more racially/ethnically diverse than those served by Part C and representative of the population, which may enable researchers to better generalize findings and support equitable access. Differences in how datasets define and assess race and ethnicity make accurate assessment of diversity difficult. Recognizing a need to consider other factors beyond race and ethnicity, as well as substantial regional variation in demographics by state (e.g., MA vs. Boston), we plan to supplement these data with comparisons for a) income and language spoken and b) region/county-level data in catchment areas from our participating agencies.