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Empirical testing of an algorithm for defining somatization in children
Howard D Eisman1, Joshua Fogel, Regina Lazarovich
1Department of Behavioral Health, Coney Island Hospital, Brooklyn, New York, USA. howardeisman@verizon.net
Insights
This study validates an algorithm for classifying childhood somatization using the Child Behavior Checklist (CBCL). Results show the algorithm effectively identifies somatizers and well children, aiding in diagnosing somatic symptom disorders.
Area of Science:
- Pediatric Psychology
- Child Psychiatry
- Behavioral Science
Background:
- A prior algorithm proposed classifying children's somatization into well, medically ill, and somatizer categories.
- Empirical validation was suggested to confirm the algorithm's utility in clinical settings.
Purpose of the Study:
- To empirically validate the Postilnik et al. (2006) somatization classification algorithm.
- To assess the Child Behavior Checklist's (CBCL) effectiveness in differentiating these categories.
Main Methods:
- Parents of 126 children in pediatric clinics completed the CBCL.
- Physicians provided additional data via questionnaires.
- Statistical analyses included ANOVA and discriminant function analysis on CBCL subscales.
Main Results:
- Significant differences were found across categories on multiple CBCL scales, including somatic complaints, social problems, and internalizing behaviors.
- Discriminant function analysis achieved high accuracy for somatizers (78%) and well children (66%).
- Classification accuracy for medically ill children was lower (35%).
Conclusions:
- The somatization classification algorithm demonstrates potential for classifying children and adolescents with somatic symptoms.
- The CBCL appears useful in supporting this classification, particularly for identifying somatizers.
Introduction:
A previous article proposed an algorithm for defining somatization in children by classifying them into three categories: well, medically ill, and somatizer; the authors suggested further empirical validation of the algorithm (Postilnik et al., 2006). We use the Child Behavior Checklist (CBCL) to provide this empirical validation.
Method:
Parents of children seen in pediatric clinics completed the CBCL (n=126). The physicians of these children completed specially-designed questionnaires. The sample comprised of 62 boys and 64 girls (age range 2 to 15 years). Classification categories included: well (n=53), medically ill (n=55), and somatizer (n=18). Analysis of variance (ANOVA) was used for statistical comparisons. Discriminant function analysis was conducted with the CBCL subscales.
Results:
There were significant differences between the classification categories for the somatic complaints (p=<0.001), social problems (p=0.004), thought problems (p=0.01), attention problems (0.006), and internalizing (p=0.003) subscales and also total (p=0.001), and total-t (p=0.001) scales of the CBCL. Discriminant function analysis showed that 78% of somatizers and 66% of well were accurately classified, while only 35% of medically ill were accurately classified.
Conclusion:
The somatization classification algorithm proposed by Postilnik et al. (2006) shows promise for classification of children and adolescents with somatic symptoms.
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