Back, Elisa, Jonkman, Kim M. and Beeger, Sander (2022) Predictors of treatment use in autistic children. In: INSAR 2022 Annual Meeting; 11-14 May 2022, Austin, Texas, U.S.. (Unpublished)

Abstract

Background: Autism is a heterogenous condition with characteristics that present in different ways and is often accompanied by co-morbid psychiatric disorders. Many different treatments are available for autistic individuals, some aimed at reducing core autism symptoms while others target co-occurring problems. The diverse nature of autism and its treatment options justify the need to understand which individual characteristics are associated with the use of which treatment. Objectives: This study aimed to predict treatment prevalence, treatment types (interventions and medication), and treatment load (duration and intensity of treatments) from demographic factors (age, sex, parental education level, IQ, educational setting and co-occurring diagnosis) and autism-specific features (severity of symptoms, social skills, repetitive and/restrictive behaviours and interests, and sensory issues). Methods: This study used parent reported data on autistic children (N=1464, aged 1-17 years) from the Netherlands Autism Register. Based on (inter)national guidelines (including: CDC, 2019 (US), ESCAP; Fuentes et al., 2020 (European) and NICE, 2013 (UK)) three treatment categories were devised: Guideline treatments targeted the core characteristics of autism (social communication and repetitive/restricted behaviours and interests), Mainstream treatments included interventions or medication not specifically for autism, targeting associated symptoms (e.g., sleep difficulties) and Other treatments are interventions or medications that are not included in the treatment guidelines (e.g., alternative therapies) or are actively discouraged in the guidelines. Results: Most children (88%) had received interventions (77% had received mainstream, 77% guideline and 28% other interventions) and about half (52%) had received medication (51% had received mainstream medication and 5% other medication). The most commonly reported interventions included: physiotherapy, parent training and social skills training. Children in special education received more interventions and children with co-occurring diagnoses had a higher number of total hours of treatment. Older age, higher IQ and being female were related to more guideline (specifically for autism) or mainstream (associated symptoms) intervention use. Lower parental education was related to more mainstream medication use. Autism specific measures (severity of autism symptoms, social skills, repetitive and restrictive behaviours) did not relate to treatment use. However, more severe sensory issues increased the chance of medication use. Conclusions: Treatment use is mostly guided by demographic factors (age, IQ, gender, parental education) rather than autism-specific characteristics (e.g., level of social skills, or repetitive behaviours). Ideally all children should receive the treatment they need based on the severity and profile of characteristics, regardless of demographic factors such as gender, IQ or parental education. Therefore, autism-specific characteristics should be taken into consideration more when selecting interventions for children as described in international treatment guidelines

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