|Title:||High Impact Models of Early Intervention Support: Accelerating Child Outcomes and Systems Policies|
|Principal Investigator:||Tomchek, Scott||Awardee:||University of Louisville|
|Program:||Early Intervention and Early Learning [Program Details]|
|Award Period:||2 years (07/01/2023 – 06/30/2025)||Award Amount:||$798,851|
Co-Principal Investigators: Little, Lauren M.; Rous, Beth Shanks
Purpose: The purpose of this project is to analyze two large early intervention (EI) databases from the state of Kentucky (KY) to determine which EI service delivery models (in person, telehealth, or hybrid) most positively influence child developmental trajectories and for which children, especially among historically underrepresented families. EI services provide supports to children identified with or at risk for developmental delay under the age of 3 years. Traditionally, these services were provided in person. During the COVID-19 pandemic, telehealth was introduced as a service delivery model. Currently, children and families may receive EI services in a hybrid format with some services via telehealth and others in person. This research project will investigate (a) how the introduction of telehealth into the EI system influenced service access for marginalized groups of children, (b) how EI service type and intensity differentially influence the impact of service delivery mode on child outcomes, and (c) the comparative cost-effectiveness of the various models of care.
Project Activities: This project will integrate two large EI databases from the KY EI services, including one that captures EI service utilization and the other capturing child developmental outcomes, to explore how modes of delivery impact service access, how service type and intensity moderate the impact, and the cost-effectiveness of various models of service delivery.
Products: This project will result in preliminary evidence of the association between EI service delivery models and child developmental trajectories. The project will also result in documentation, annotation, and archiving of statistical materials that will be stored in a publicly available repository, as well as an R package that will be made publicly available upon study completion. Additionally, the project will result in peer-reviewed publications and presentations and additional dissemination products that reach education stakeholders such as practitioners and policymakers.
Setting: The research will take place in the Commonwealth of KY EI Services IDEA Part C network. In this system, services are provided by cross-disciplinary service providers (developmental interventionists, speech pathologists, occupational therapists, physical therapists) in environments natural to the child and family (home, childcare center, and community) via tele-intervention, face-to-face, or hybrid formats.
Population: Children ages 0-3 years who are enrolled in the KY EI network will be included in the study. To investigate how the introduction of telehealth into the EI system influenced service access for marginalized groups of children, all children that have entered and exited the KY EI system from 2015 to 2023 (30,351 children) will be included. To investigate how EI service type and intensity differentially influence child outcomes, as well as the comparative cost-effectiveness of the various models of care, children with at least two data timepoints (entry and exit of EI) on standardized developmental assessments (7,096 children) will be included.
Factors: Systems-level factors investigated in this project will include service intensity, delivery model (in person, telehealth, hybrid), and type of service/therapy provided. Family-level factors will include race, ethnicity, location, and Medicaid status. Child-level factors will include communication, cognition, social/emotional development, fine and gross motor skills, and risk status for developmental delay.
Research Design and Methods: The research team will conduct secondary data analysis on two large EI databases from the KY EI services: (1) the Technology-assisted Observation and Teaming Support system (TOTS) database, which captures EI service utilization, and (2) the KY Early Childhood Data System (KEDS), which captures child developmental outcomes. To create the combined dataset, the research team will engage in data checking and exploration to ensure the accuracy and validity of the data. Next, specific variables found in both the TOTS and KEDS datasets will be combined and processed to transform them into constructs of interest. To investigate the research questions, the research team will use various forms of growth mixture modeling.
Control Condition: Due to the nature of the research design, there is no control condition.
Key Measures: The research team will use key variables from the two datasets, TOTS and KEDS. These contain data from the Carolina Curriculum for Infants and Toddlers with Special Needs, a curriculum-based assessment used to establish EI eligibility or the presence of a developmental delay in young children, as well as for progress monitoring. Additional variables of interest in these datasets include the intensity of recommended EI services, the intensity of services actually received by the child, demographics, service type (i.e., occupational, speech, physical, developmental), insurance type (i.e., Medicaid or private insurance), and whether the child is at risk for a developmental delay.
Data Analytic Strategy: To evaluate how the introduction of telehealth into the EI system influenced service access for marginalized groups of children, the research team will use latent growth curve modeling. To evaluate how EI service type and intensity differentially influence the impact of service delivery mode on child outcomes, the research team will fit a series of multiple group parallel process latent growth curve models.
Cost Analysis: To calculate costs, the research team will use a standardized payment amount per type of service and intensity based on the Medicaid reimbursement rate and estimate the delivery cost based on the average hourly wage for each provider type. The research team will use generalized linear regression models to determine the cost-effectiveness of in-person EI service delivery compared to telehealth and hybrid delivery. Specifically, they will calculate the total cost of EI service delivery per child from the state's perspective and compare these costs to improvements in child development for each delivery model.