G Roberts1, D Bellinger, M C McCormick. 1. Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC 3052, Melbourne, Australia. gehan.roberts@rch.org.au
Abstract
OBJECTIVES: Premature and low birth weight children have a high prevalence of academic difficulties. This study examines a model comprised of cumulative risk factors that allows early identification of these difficulties. METHODS: This is a secondary analysis of data from a large cohort of premature (<37 weeks gestation) and LBW (<2500 g) children. The study subjects were 8 years of age and 494 had data available for reading achievement and 469 for mathematics. Potential predictor variables were categorized into 4 domains: sociodemographic, neonatal, maternal mental health and early childhood (ages 3 and 5). Regression analysis was used to create a model to predict reading and mathematics scores. RESULTS: Variables from all domains were significant in the model, predicting low achievement scores in reading (R (2) of 0.49, model p-value < .0001) and mathematics (R (2) of 0.44, model p-value < .0001). Significant risk factors for lower reading scores, were: lower maternal education and income, and Black or Hispanic race (sociodemographic); lower birth weight and male gender (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). Lower mathematics scores were predicted by lower maternal education, income and age and Black or Hispanic race (sociodemographic); lower birth weight and higher head circumference (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). CONCLUSIONS: Sequential early childhood risk factors in premature and LBW children lead to a cumulative risk for academic difficulties and can be used for early identification.
OBJECTIVES: Premature and low birth weight children have a high prevalence of academic difficulties. This study examines a model comprised of cumulative risk factors that allows early identification of these difficulties. METHODS: This is a secondary analysis of data from a large cohort of premature (<37 weeks gestation) and LBW (<2500 g) children. The study subjects were 8 years of age and 494 had data available for reading achievement and 469 for mathematics. Potential predictor variables were categorized into 4 domains: sociodemographic, neonatal, maternal mental health and early childhood (ages 3 and 5). Regression analysis was used to create a model to predict reading and mathematics scores. RESULTS: Variables from all domains were significant in the model, predicting low achievement scores in reading (R (2) of 0.49, model p-value < .0001) and mathematics (R (2) of 0.44, model p-value < .0001). Significant risk factors for lower reading scores, were: lower maternal education and income, and Black or Hispanic race (sociodemographic); lower birth weight and male gender (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). Lower mathematics scores were predicted by lower maternal education, income and age and Black or Hispanic race (sociodemographic); lower birth weight and higher head circumference (neonatal); lower maternal responsivity (maternal mental health); lower intelligence, visual-motor skill and higher behavioral disturbance scores (early childhood). CONCLUSIONS: Sequential early childhood risk factors in premature and LBW children lead to a cumulative risk for academic difficulties and can be used for early identification.
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