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Using the Child Behavior Checklist (CBCL) for Identification of Toddlers with Autism Spectrum Disorders

Thursday, 2 May 2013: 09:00-13:00
Banquet Hall (Kursaal Centre)
12:00
A. Narzisi1,2, S. Calderoni3, E. Mottes4, S. Maestro4 and F. Muratori4, (1)University of Pisa - Stella Maris Scientific Institute, Pisa, Italy, (2)Division of Child Neurology and Psychiatry, University of Pisa - Stella Maris Scientific Institute, Pisa, Italy, (3)Magnetic Resonance Laboratory, Division of Child Neurology and Psychiatry University of Pisa; Stella Maris Scientific Institute, Pisa, Italy, (4)University of Pisa – Stella Maris Scientific Institute, Pisa, Italy
Background:  

The diagnosis of ASD can be reliably made by the second year of age, the American Academy of Pediatrics recommends routine screening for autism risk at their 18- and 24-month well-baby visits. In fact, screening can offers the opportunity to alert primary care providers for further clinical evaluation and eventually early intervention. Instruments appropriate that evaluate the presence of autistic symptoms or the absence of socio-communicative skilss in children who are at least 18 months are available. However, none of them has yet been found to be appropriate to detect ASD because of the high number of false positive or false negative cases.

Objectives:  

To evaluate the sensitivity and specificity of the CBCL 1½-5 in the identification of children with Autism Spectrum Disorders (ASD), aged between 18 and 36 months. 

Methods:  

The CBCLs of 47 children with ASDwere compared to the CBCLs of 47 toddlers with Other Psychiatric Disorders (OPDs) and theCBCLs of 47 toddlers with Typical Development (TD) in a case control study. One-way analysis of variance (ANOVA) and logistic regression with odds ratio (OR) analysis were performed. ROC analysis were performed in order to establish the optimal threshold that discriminate children with ASD from children with OPDs and TD. 

Results:  

One-way ANOVA revealed significant differences between the three groups. Logistic regression analysis showed that the Withdrawn and the Pervasive Developmental problems (PDPs) subscales can differentiate children with ASD from both children with TD (P<.001) and OPDs (P<.001). ROC analysis showed very high sensitivity and specificity for the PDP (0.98 and 0.91) and Withdrawn (0.92 and 0.97) subscales when ASD was compared to TD. Sensitivity and Specificity of Withdrawn (0.90 and 0.83) and PDP (0.85 and 0.83) remained high when comparing ASD vs OPDs.

Conclusions:  

The CBCL 1½-5 seem to be able to differente chilren already diagnosed with ASD from children with TD and OPDs. The high sensitivity and specificity suggest to test this broadband tool in a screening survey.

See more of: Screening and Diagnosis
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