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A cumulative risk factor model for early identification of academic difficulties in premature and low birth weight
G Roberts1, D Bellinger, M C McCormick
1Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC 3052, Melbourne, Australia. gehan.roberts@rch.org.au
Insights
Cumulative risk factors in premature and low birth weight (LBW) children predict academic difficulties by age 8. Early identification of these risks can help children succeed in reading and math.
Area of Science:
- Developmental Pediatrics
- Educational Psychology
- Public Health
Background:
- Premature and low birth weight (LBW) infants exhibit a higher incidence of academic challenges.
- Early identification of at-risk children is crucial for timely intervention.
Purpose of the Study:
- To examine a cumulative risk factor model for early identification of academic difficulties in premature and LBW children.
- To predict reading and mathematics achievement at age 8.
Main Methods:
- Secondary analysis of a large cohort of premature (<37 weeks gestation) and LBW (<2500 g) children.
- Regression analysis to develop a predictive model using variables from sociodemographic, neonatal, maternal mental health, and early childhood domains.
- Reading and mathematics scores at age 8 were analyzed.
Main Results:
- The cumulative risk model significantly predicted low reading (R²=0.49) and mathematics (R²=0.44) scores.
- Key risk factors included sociodemographic (maternal education, income, race), neonatal (birth weight, gender/head circumference), maternal mental health (responsivity), and early childhood factors (intelligence, visual-motor skills, behavioral disturbance).
Conclusions:
- Sequential risk factors in early childhood contribute to cumulative academic difficulties in premature and LBW children.
- The developed model enables early identification of children at risk for academic challenges.
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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