From Data to Action: Capturing Meaningful Outcomes in Autism Through Harmonized Data - ABSTRACT There are more autistic adults in the service system than ever before, and this number is estimated to increase by as much as 300% by 2030. Outcomes for autistic adults are often characterized by low rates of employment and community participation, and high rates of anxiety, depression, and suicidality. Our community partners, including autistic adults and caregivers, have identified the need for research across the adult lifespan that identifies intervention and service targets to promote a high quality of life characterized by good mental health and community participation. We believe that generating a better understanding of autistic adults, including those in mid- and later life, is a pressing public health problem where rapid advancements are needed. To date, however, the field of adult autism research has primarily relied on small, non-representative samples limited to young adulthood. This has hindered our ability to understand outcomes and identify service targets across the adult lifespan and the full autism spectrum (i.e., inclusive of those with and without intellectual disability and a variety of levels of support needs). In response to ROA-OTA-25-006, we leverage an unprecedented collaboration between senior researchers across 4 NIH Autism Center of Excellence (ACE) sites focused on autistic adults. We employ a data driven approach to characterize service needs and targets in cross-sectional and longitudinal samples of autistic adults representative of the full adult lifespan and the full autism spectrum. We will deploy two complementary datasets that are optimally poised to make rapid advancements in service access, NIMH Data Archive (NDA) and Simons Powering Autism Research (SPARK). The NDA data collected across ACE projects provides one of the largest samples of well-characterized autistic adults (n=1,400). The SPARK dataset represents one of the largest longitudinal samples (n=400) of autistic adults. This proposal has three tasks: (I) Dataset Aggregation from the NDA to harmonize the parallel assessment protocol developed by the study team and utilized across 4 ACE sites to identify modifiable adult outcomes. (II) Data Generation to 1) generate geocodes to examine the influence of environmental and neighborhood factors (e.g., socioeconomic factors, provider density, environmental exposures) on service use and needs and trajectories of change in adult outcomes and 2) collect a third time point of data in a longitudinal cohort of 400 autistic adults from SPARK to examine trajectories of change in these modifiable outcomes across adulthood. This additional data collection will complement our aggregated NDA data by advancing our ability to answer questions about drivers of service needs measured over time across the entirety of the dynamic adult years. (III) Data Analyses using both machine learning and (longitudinal) latent transition analyses. We will use machine learning to identify subgroups in the NDA dataset based on their differing profiles of service use and needs and predict subgroup membership based on service targets and sociodemographic indicators. This will allow us to examine specific hypotheses focused on identifying the greatest service needs in this population, the relation between service needs and service targets to improve adult outcomes (mental health, community participation, quality of life). We will use latent transition analysis in the SPARK dataset to identify drivers of service use and needs based on trajectories of change across time. This will allow us to identify longitudinal trajectories of adult outcomes and their relation to service needs. Our work will be conducted in partnership with a 20-member Community Advisory Board (CAB) that includes autistic adults, family members/caregivers, researchers, clinicians and service providers for autistic adults, and state services representatives. The CAB will identify community-driven priorities for analysis. It will
