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Patterns of Service Utilization Among Children with Autism Spectrum Disorder: A Cluster-Analysis of the 2011 Pathways to Diagnosis and Services Survey
Despite extensive examination of demographic factors such as race-ethnicity and socio-economic status and how they are related to ASD identification, severity, and access to services, research has not been conducted at a nationally-representative level. In addition, cluster analysis as a method has only rarely been applied to this topic. When cluster analysis has been used, it has examined symptoms and phenotypes.
Objectives:
The objective of this project is to determine whether there are groups of children with ASD that have similar patterns of service utilization, access, or treatment type. Subsequent analysis will determine whether these clusters differ with regards to functional status, race, ethnicity, sex, and/or insurance coverage.
Methods:
Data from the 2011 Survey of Pathways to Diagnosis and Services (Pathways) were used in this analysis. Developed as a follow-up to the 2009/10 National Survey of Children with Special Health Care Needs, Pathways measures parental report of the emergence of symptoms, diagnosis, and use of services among children with intellectual disability, developmental disability, and ASD. The sample for this analysis was limited to include only children with complete data on variables of interest in the principal components analysis (n=324). Principal component analysis (PCA) was performed on 39 items from the Pathways Survey related to types of services and medication utilized and services not covered by health insurance. Once factors were determined, summated scores were calculated using the items that comprised each factor. These scores were used to develop a cluster analysis identifying groups of individuals whose service utilization and treatment represent similar, meaningful patterns. Post-hoc analyses were performed to name and describe distinct clusters. Analyses will be performed to examine potential differences between clusters with regards to functional status, race, ethnicity, sex, and/or insurance coverage.
Results:
The PCA revealed four factors related to utilization of medication, school-based, and non-school-based services, as well as report of services not covered by insurance. Four clusters emerged based on these factors. The largest cluster included 36.4% of the sample and was defined by high use of both school-based and non-school-based services with low medication use. One quarter (25.3%) of the sample was characterized by high utilization of both medications and services. The smallest cluster (15.7%) was characterized by high medication usage and low service utilization. The remaining cluster (22.5% of the sample) is characterized by higher levels of “not covered” services according to parents, though there do not appear to be remarkable patterns of lower utilization.
Conclusions:
Results show that there are distinct patterns of how children in the Pathways survey access services. Although the majority of the population in this analysis was defined as having high utilization of services and low medication usage, there were other segments of the population that varied in the utilization of service and medication usage. This suggests that the access issues may be related to demographics, access to care, and severity. Subsequent analyses studies will explore differences between clusters and potential disparities.