Evidence for ABA interventions in autism: who, where, what, and when
Autism services have again come to the forefront of public attention with recent changes to the Ontario Autism Program (OAP). In light of this, we highlight the evolution of autism therapy and emerging directions to ensure that all children and youth with autism can access interventions that set them up for success.
Intervention based on the principles of applied behaviour analysis (ABA) has the most evidence for improving outcomes in autism. ABA examines why behaviours occur and teaches skills by systematically arranging the environment and providing consistent responses. ABA’s prominence as a treatment for autism began in the 1980’s with work by Ivar Lovaas who described a model where autistic children received 40 hours per week of one-on-one therapy for two years; although nearly half achieved good outcomes (as measured by mainstream school placement), this was not the right fit for everyone. Moreover, early applications of ABA sometimes over-focused on reducing outward appearances of autism like hand flapping, an experience that left some autistic people with enduring traumatic memories.
Thankfully, the field has evolved from its early days. The previous Ontario model showed that less intensive (25 hours/week) programs also yielded good outcomes for many children. Newer models of ABA focus on skill-building in natural environments and can be delivered more efficiently. Many models now focus on partnering with families to integrate ABA strategies into daily routines, either in addition to therapist-delivered care, or as the primary intervention. Ideally, techniques are woven into a parent’s natural interactions with their child. Such parent-mediated models are supported by emerging evidence and are likely cost-effective, but they may not be the right fit for all children and families. Decisions about the appropriate style and dose of intervention must be individualized based on need. Newer models speak to the benefits of providing ABA within a child’s natural environment, with potential applications in daycares and preschools. School is one of the most natural environments for any child; however, Ontario, and Canada more broadly, lag behind other jurisdictions in integrating qualified ABA professionals into schools. A major structural barrier to increasing the role of ABA therapists in classrooms is that the field is currently unregulated. Regulating ABA providers is a necessary first step to facilitating their integration into schools and is a potential quick win for governments looking to improve access to ABA for children and youth.
Evidence has repeatedly shown that more intensive applications of ABA (intensive behavioural intervention, or IBI, in Ontario) show the greatest impact for children under age five. A member of our group published a cost-effectiveness analysis showing that investing in timely access to IBI, which hinges on the ability to get children into evidence-based therapy before age five, reaps economic rewards in the long run. This early advantage notwithstanding, ABA techniques can be effective across the lifespan, and all individuals with autism should be able to access timely evidence-based therapies, as needed, to meet their individual needs.
Before focusing on the quality and fit of the intervention, many families will face challenges obtaining therapy in a complex system of unregulated fee-for-service providers. Particularly vulnerable are families who do not speak fluent English or those with intellectual/learning disabilities, who will struggle in a system that requires them to find, hire, and submit receipts for a behaviour therapist. The limited supply of behaviour therapists in rural and Northern communities is an urgent issue; innovations in distance learning and supervision by qualified providers may be one way to address this. We have a long way to go to ensure that Indigenous families can access ABA that is both effective and respectful of cultural identity. Finally, families and autistic self-advocates must have a seat at the table to contribute their invaluable experiences and perspectives on autism therapies.
ABA is an investment in building skills for a child’s future, and one that is likely to benefit individuals, the economy and society in the long run. As with many issues related to children, this investment takes decades to be realized and cannot easily be determined in the short-run. Autism policy should be future-oriented, transparent, evidence-informed, needs-based, access-focused, and have an evaluative component to ensure that it achieves the aim of fostering meaningful outcomes for children and families.
Dr. Melanie Penner is a developmental pediatrician and clinician investigator in the Autism Research Centre at Holland Bloorview Kids Rehabilitation Hospital and an assistant professor in the Department of Paediatrics at the University of Toronto. Her research focuses on optimal service delivery for children with autism.
Dr. Evdokia Anagnostou is a child neurologist, senior scientist, co-lead of the Autism Research Centre at Holland Bloorview Kids Rehabilitation Hospital, and an associate professor in the Department of Paediatrics at the University of Toronto. Her research focuses on translating basic understandings of the biology of autism into novel treatments.
Dr. Jessica Brian is a psychologist, clinician investigator, and co-lead of the Autism Research Centre at Holland Bloorview Kids Rehabilitation Hospital. Her research focuses on very early identification and the development and evaluation of novel behavioural intervention models. Dr. Brian is the co-developer of the Social ABCs parent-mediated intervention for toddlers with autism and related social communication challenges.
Dr. Azadeh Kushki is a scientist at the Autism Research Centre at Holland Bloorview Kids Rehabilitation Hospital and an assistant professor in the Institute of Biomaterial and Biomedical Engineering at the University of Toronto. Her research focuses on technology supports for children with autism, and improving our understanding of autism using machine learning.
