Friday, May 21, 2010
Franklin Hall B Level 4 (Philadelphia Marriott Downtown)
3:00 PM
Background: Predicting differential treatment outcomes in young children with Autism Spectrum Disorder (ASD) can lead to improved treatment planning. This is particularly important given the growing ASD population and limited access to treatment programs for many children. Previous research suggests that initial ability (IQ), language, and attention - but not the initial severity of repetitive behaviors - predict greater language gains and better outcomes of early intensive behavioral intervention (EIBI) (Howlin et. al., 2009; Bopp et. al. 2009). However, studies have not agreed as to the most important predictors, few studies have examined the incremental validity of multiple predictors, and no studies have predicted level of educational treatment support after EIBI – an important outcome for short-term treatment planning. Objectives: To determine which combination of baseline variables (autism symptoms, functional skills, and/or language) is effective in predicting level of educational support after EIBI.
Methods: Participants included 51 children (44 male; 7 female; mean age at baseline = 43.24 months) who graduated from an EIBI program. Youth received an average of two years (Median=22 months) of 1:1 instruction based on the principles of Applied Behavior Analysis (ABA) as well as at least one hour per week of speech and language therapy. Language was assessed using the Preschool Language Scale-4th Edition (PLS-4), Receptive One-Word Picture Vocabulary Test (ROWPVT) and Expressive One-Word Picture Vocabulary Test (EOWPVT). Adaptive functioning was determined using the Vineland Adaptive Behavior Scales-Second Edition (VABS-II) and the Childhood Autism Rating Scale (CARS) was used to obtain a rating of autism severity. Level of support at program exit was coded as minimal or significant by a psychologist blinded to youths test scores.
Hierarchical logistic regression analyses were computed with Preschool Language Scales-4 Total scores, VABS-II scales, and CARS scores as predictors of level of support at exit.
Results: PLS-4 total standard score was entered as the initial predictor because this measure showed the largest bivariate correlation with level of support at exit (n (50), r = -.74, p=<.001). CARS scores and VABS-II Daily Living and Motor Skills measures were individually added in the next step of these regressions based upon their strong bivariate correlations. Results of these hierarchical regressions indicated that adding each of the three baseline predictors individually (CARS, VABS-II Daily Living and Motor Scales) significantly and substantially improved the prediction of level of support at exit beyond baseline PLS-4 Total scores (ΔR2= .082 - .173; p=.003 - .050). Adding all three predictors simultaneously in Step 2 resulted in a significant and substantial increase in predicted variance beyond baseline PLS-4 Total scores (ΔR2= .271; p=.017).
Conclusions: Initial language ability, autism severity, daily living skills, and motor skills provided the best prediction of level of support after exit of EIBI. This information can help treatment professionals and families in their decision making on maximizing EIBI efforts and future educational needs.
Methods: Participants included 51 children (44 male; 7 female; mean age at baseline = 43.24 months) who graduated from an EIBI program. Youth received an average of two years (Median=22 months) of 1:1 instruction based on the principles of Applied Behavior Analysis (ABA) as well as at least one hour per week of speech and language therapy. Language was assessed using the Preschool Language Scale-4th Edition (PLS-4), Receptive One-Word Picture Vocabulary Test (ROWPVT) and Expressive One-Word Picture Vocabulary Test (EOWPVT). Adaptive functioning was determined using the Vineland Adaptive Behavior Scales-Second Edition (VABS-II) and the Childhood Autism Rating Scale (CARS) was used to obtain a rating of autism severity. Level of support at program exit was coded as minimal or significant by a psychologist blinded to youths test scores.
Hierarchical logistic regression analyses were computed with Preschool Language Scales-4 Total scores, VABS-II scales, and CARS scores as predictors of level of support at exit.
Results: PLS-4 total standard score was entered as the initial predictor because this measure showed the largest bivariate correlation with level of support at exit (n (50), r = -.74, p=<.001). CARS scores and VABS-II Daily Living and Motor Skills measures were individually added in the next step of these regressions based upon their strong bivariate correlations. Results of these hierarchical regressions indicated that adding each of the three baseline predictors individually (CARS, VABS-II Daily Living and Motor Scales) significantly and substantially improved the prediction of level of support at exit beyond baseline PLS-4 Total scores (ΔR2= .082 - .173; p=.003 - .050). Adding all three predictors simultaneously in Step 2 resulted in a significant and substantial increase in predicted variance beyond baseline PLS-4 Total scores (ΔR2= .271; p=.017).
Conclusions: Initial language ability, autism severity, daily living skills, and motor skills provided the best prediction of level of support after exit of EIBI. This information can help treatment professionals and families in their decision making on maximizing EIBI efforts and future educational needs.