This competing renewal application proposes to continue a longitudinal study of 62 infants and toddlers with craniofacial anomalies (CFA) and a matched control group of 70 nonimpaired, healthy children into their school-age years (ages 5, 6 and 7). The CFA sample contains 3 types of disorders: Cleft lip and palate (CLP), isolated cleft palate (CP) and sagittal synostosis (SAG). This sample is now being assessed at ages 3, 12 and 24 months (NICHHD R01 HD25987). The current and proposed projects were designed to develop a multifactorial risk profile that would allow for the earliest possible detection of children with CFA at risk for later psychological maladjustment and/or school failure. This research is organized in relation to a developmental model with selected predictor variables (e.g., attachment, medical factors, family ecology) and child outcome variables (e.g., behavioral adjustment, peer status, school achievement). This research has 3 specific aims: l) To compare school-aged children with and without CFA on selected predictor 5, 6 and 7; 2) To compare these groups on selected outcome variables during the same age period; and 3) To determine the relative contribution of selected predictor variables to school-age outcomes for children with CFA. Among the hypotheses to be tested, we predict that group differences will be found in language skills, parent-child interactions, and parental emotional status and social support (all favoring the control group). By age 7, we anticipate that children with CLP/CP will have lower school achievement and poorer psychological outcomes. However, we expect attachment security to """"""""moderate"""""""" the adverse effects of these disorders on later development. Finally, some aspects of mother-child interaction are expected to predict school achievement, even after variation in child neuropsychological ability is considered. Hypotheses will be tested with data gathered from observations, questionnaires and tests, and subjected to multivariate analyses of variance, regression models, and growth curve analyses.
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