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Interventions based on early intensive applied behaviour analysis for autistic children: a systematic review and
Mark Rodgers1, David Marshall1, Mark Simmonds1
1Centre for Reviews and Dissemination, University of York, York, UK.
Early intensive applied behaviour analysis (ABA) interventions show potential for improving cognitive ability and adaptive behavior in autistic children. However, long-term effectiveness and cost-effectiveness remain uncertain due to limited data.
Area of Science:
- Autism Spectrum Disorder Interventions
- Behavioral Science
- Developmental Psychology
Background:
- Early intensive applied behaviour analysis-based interventions (ABA) are intensive, one-to-one therapies for autistic children, typically 20-50 hours/week.
- Evidence on the clinical and cost-effectiveness of these interventions is crucial for guiding clinical practice and policy.
Purpose of the Study:
- To systematically review and meta-analyze the clinical effectiveness of early intensive ABA interventions for autistic children.
- To evaluate the cost-effectiveness of these interventions based on existing evidence and new economic modeling.
Main Methods:
- A systematic review and individual participant data meta-analysis of 20 studies were conducted.
- Economic analysis involved reviewing existing models and developing a new cost-effectiveness model.
Main Results:
- Interventions favored adaptive behavior (MD 7.00) and cognitive ability/IQ (MD 9.16 at 1 year, 14.13 at 2 years).
- Cost-effectiveness analysis indicated interventions are unlikely to be cost-effective unless significant long-term benefits or educational placement changes are demonstrated.
- Limited data and heterogeneity across studies necessitate caution in interpreting results.
Conclusions:
- Limited evidence suggests early intensive ABA may improve cognitive ability and adaptive behavior in autistic children.
- The long-term impact and cost-effectiveness are currently unclear, requiring further investigation.
- Future research should focus on identifying effective components and utilizing robust comparators and outcome measures.
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