Lead Lab Active Studies

• Testing the Impact of Early Screening on the Long-term Outcomes of Children with ASD (Project EARLY Follow-up Study)
Autism spectrum disorder (ASD) impacts almost 2% of children born today, yet very little is known regarding how to positively alter the outcomes of affected children. There is currently debate on whether universal early screening of ASD is linked to positive outcomes for children. What is needed to inform the debate is to examine the outcomes of a large cohort of children detected very early via universal screening at well-baby checks, and to compare them to children who did not participate in an early detection program. This study examines changes in symptoms, IQ, and self-help skills between toddlers and school aged children with autism who did and did not participate in an early detection program using various child development assessments and measures.

Since state context (e.g., policies, guidelines) could also play a role in outcomes for children with autism, this project also examines key state level information to place the findings of the larger study in context. Research personnel conducted interviews with experts in ASD, services, and state policy and asked them open-ended questions to elicit information on barriers and facilitators to early ASD diagnosis, treatment, and outcomes. Some of these experts will be selected to participate in the Delphi panel to use iterative consensus methods to identify the policies that are the most impactful on access to ASD diagnosis outcomes. These findings will be used to create an Autism State Index which is a scale that will quantify a state’s ASD environment, allowing for quantitative analyses.

• Improving the Part C Early Intervention Service Delivery System for Children with ASD: A Randomized Clinical Trial (RISE Study)
Despite strong consensus that early, specialized intervention for children with ASD can have a dramatic impact on outcomes, the U.S. health system’s capacity to provide such services is severely challenged by the rapid rise in ASD prevalence. The long-term goal is to improve outcomes for children with early signs of ASD by increasing the capacity to provide appropriately specialized treatment within an existing infrastructure: the Part C Early Intervention (EI) service delivery system. The study objective is to improve services and outcomes for children with early signs of ASD by conducting a randomized controlled trial (RCT) testing the effectiveness of training EI providers to deliver Reciprocal Imitation Training (RIT). RIT is a naturalistic developmental behavioral intervention (NDBI) that is ideally suited for EI settings because it is low intensity, play-based, easy to learn and implement, and can be taught to families for their independent use, thus increasing intervention dosage.

At UMass Chan, research personnel will be involved in the Implementation Aim of the study. This will consist of conducting interviews and data analysis. Research personnel will interview providers and parents involved in the study to understand how the intervention was used. Data regarding provider-initiated modifications to the intervention and delivery will be analyzed to identify fidelity-consistent vs. fidelity-inconsistent changes, which will inform refinement of future RIT training and quality assurance procedures.

• Evaluating Implementation of a Patient Navigator Intervention to Improve Access to Diagnostic and Treatment Services for Children with ASD (K23-Family Navigation Implementation)
Family navigation is a lay-delivered case management strategy designed to reduce health disparities by helping vulnerable populations overcome psychological and logistical hurdles to care. Navigation has proven effectiveness in multiple disorders such as cancer and HIV, and early data support its use as a strategy to promote timely engagement with evidence-based services among children with ASD. However, despite navigation’s substantial effectiveness data, multiple studies demonstrate the attenuation of its impact, and variable success, when implemented in real-world practice. This “research-practice” gap – whereby effective clinical innovations fail to be adopted (or practiced with appropriate fidelity) within real world clinical practice – is ubiquitous in healthcare; but it is particularly problematic for low-income and minority populations and the institutions that serve them. Furthermore, despite guidance to the contrary, few clinical trials proactively collect data on the complex patient, provider, and organizational factors that impact subsequent implementation of the clinical innovations being tested. As a result of this missed opportunity, implementation strategies are often arbitrary and prone to failure. Therefore, this research is to evaluate key implementation processes of family navigation to understand how to improve access to care for low-income children with ASD. This study examines data to better understand the implementation of Family Navigation (FN) and the variability of organizational climate to improve access to diagnosis and treatment for ASD.

• Usability of mHealth Autism Screeners in the Medically Underserved (mHealth ASD Study)
The NIH and the private sector have invested millions of dollars into development of mobile technology-based (mHealth) autism screening tools, with the hope that advanced technologies will improve early identification of ASD. However, no research to date has assessed how mobile device based ASD screening will address the problem of racial, ethnic, language, and income disparities in ASD identification. In addition, no current efforts explicitly assess what key mHealth ASD screener adaptations are needed for mHealth autism screeners to be successfully used by minority and low-income families and the professionals who interact with them. This study examined existing mHealth ASD screeners for overall quality and adaptability to ASD health disparities populations, by comparing them to established mHealth standards. Research staff performed user testing of key tools with a sample of racial/ethnic minority and low-income families and health and educational providers. We conducted interviews with parents, providers, and mHealth ASD screener developers, to understand the challenges of creating and using mHealth ASD screeners in health disparities populations. We will then use the results of this research, and the guidance of a multi-disciplinary expert panel, to develop a set of guidelines for mHealth autism screener usability in ASD disparities populations. We will disseminate these guidelines throughout the autism research and informatics communities. This research will ensure that the next generation of ASD screening tools effectively reduces disparities in ASD identification.

• Optimizing a Paraprofessional, Family Partner Navigation Model for Children (MOST Study)
Family Navigation (FN) is an evidence-based care management strategy that is a promising intervention to help low-income, diverse families access timely mental health services. Despite significant evidence supporting the effectiveness of FN, concerns exist about the ability to disseminate FN to a broad population due to inefficiency and cost. This study employs an innovative research methodology, the Multiphase Optimization Strategy (MOST), a framework for developing highly efficacious, efficient, scalable, and cost-effective interventions. We are conducting a randomized experiment to assess the individual components of FN and identify which components and component levels have the greatest effect on access to, and engagement in, diagnostic and treatment services for children with mental health disorders. This information will then guide assembly of an optimized FN model that achieves the primary outcomes with least resource consumption and participant burden.

• Validating Measures and Unpacking Differences in Service Use for Diverse Children with Autism (LEP R01 Study)
Families of children with limited English proficiency (LEP) have been systematically excluded from autism spectrum disorder (ASD) research, yet they likely experience greater barriers to care. As a result, little is known about what factors are associated with these disparities, and how they differ across populations and settings. Because of this lack of data, developing data-driven strategies to address disparities for children with ASD and LEP can be a challenge. Thus, we propose to address this critical gap by conducting a sequential mixed methods study to understand what patient, provider, and structural factors are associated with disparities in care for families with LEP. First, in collaboration with a team of experts in LEP survey research, we propose the largest ever diverse, multi-lingual national survey (n=2730) of families of underserved children with ASD to both validate measures (e.g., parent stress, stigma, discrimination) in five languages (Spanish, Haitian-Creole, Vietnamese, Mandarin, English) and use those measures to understand disparities in care between and across populations. The survey will be embedded within the Autism Cares Network (ACN), a national network of 20 large, geographically diverse hospital systems focused on improving care for ASD. We will then use qualitative interviews to better understand survey findings. Finally, based on best practices from the field of implementation science, we will use rigorous consensus methods to place findings in context and make recommendations for data and measure use, as well as policy decisions. Our results will impact the field by validating measures to be used in future studies of ASD interventions – from treatment trials to policy initiatives – as well as by producing data to be used as future intervention targets in disparities reduction efforts. Our aims align directly with the NIMH’s Strategic Plan to reduce disparities in treatment of ASD.

• Promoting Early intervention Timing and Attention to Language (PETAL Study)
Infants with Increased Likelihood for Autism (ILA) are likely to experience delays in language with ~ 40% at risk for language delay and/or later diagnosis of ASD. However, interventions for ILA infants remain rare and none of the current interventions explicitly focus on language or include many infants from lower socioeconomic circumstances who may be at even greater risk for language delays. Although enthusiasm is high for very early interventions due to known brain plasticity in the first years of life, we do not know WHEN or with WHAT MEASURES to determine if an intervention is appropriate for ILA infants who are not yet showing signs of autism or delay. The overarching goal of the proposed study is to determine the optimal timing of very early intervention for ILA infants (starting at 9, 12 or 15 months and who are at risk for autism by virtue of having an older sibling with autism) that explicitly targets communication and language. We will use a battery of brain- and behavioral-based markers to identify the combination of change in language, behaviors and brain measures that predict expressive language outcomes at 24 months. 140 infants beginning at 6 months of age will participate at two sites, Los Angeles, and Boston areas with many from traditionally marginalized and minoritized families. All parents will receive infant developmental monitoring beginning at 6 months, and then using a four-phase, sequential multiple assignment randomized trial design, parents will receive augmentation with a specialized language coaching intervention at 9, 12 or 15 months; all infant parent dyads receive coaching by 15 months. A diverse sample of dyads will be recruited; assessments will occur at home at 6-,9-,12-,15-, 18- and 24-months using brain-based EEG measures, social communication, and language measures. Intervention support will be provided by clinicians remotely. The study addresses key questions of whether earlier intervention is better on primary language and secondary outcomes of social communication behaviors that support language development (e.g., joint engagement, vocalizations, words, object play) and which measures will inform the ideal transition to intervention on language outcomes at 24 months. Moreover, using LENA to capture the home language environment, adult word count and conversational turns will be examined for mediation on language outcome. Results of this study have potential for determining when (9 vs 12 vs 15), and based on which measures (brain, language, or their combination), to augment parental support with a specialized parent-mediated coaching intervention.