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Psychiatric disorders in extremely preterm children: longitudinal finding at age 11 years in the EPICure study
Samantha Johnson1, Chris Hollis, Puja Kochhar
1Institute for Women's Health, University College London and the School of Clinical Sciences, University of Nottingham, UK.
Insights
Children born extremely preterm face a significantly higher risk of psychiatric disorders, including ADHD, emotional, and autism spectrum disorders, by age 11. Early identification of cognitive and behavioral issues is crucial for at-risk children.
Area of Science:
- Pediatrics
- Child Psychiatry
- Developmental Psychology
Background:
- Extremely preterm birth (<26 weeks gestation) poses significant developmental challenges.
- Psychiatric disorders are a growing concern in this vulnerable population.
- Understanding prevalence and risk factors is vital for early intervention.
Purpose of the Study:
- To determine the prevalence of psychiatric disorders in extremely preterm children at 11 years of age.
- To identify risk factors associated with these disorders.
- To compare psychiatric outcomes with term-born controls.
Main Methods:
- Longitudinal EPICure Study cohort of 219 extremely preterm survivors and 153 term-born controls.
- Structured psychiatric interviews with parents and questionnaires for teachers.
- DSM-IV diagnoses assigned based on comprehensive assessments.
- IQ testing and physical evaluations were also conducted.
Main Results:
- Extremely preterm children had over three times higher odds of psychiatric disorders (23% vs. 9%).
- Significantly increased risks observed for ADHD, emotional disorders (including anxiety), and autism spectrum disorders.
- Cognitive impairment and early parent-reported behavioral problems were independent predictors.
Conclusions:
- Extremely preterm children exhibit a substantially elevated risk for ADHD, emotional disorders, and autism spectrum disorders.
- Cognitive impairment and early adverse experiences likely contribute to psychiatric outcomes.
- Routine screening for cognitive and behavioral problems is recommended for early risk identification.
Objective:
To investigate the prevalence and risk factors for psychiatric disorders in extremely preterm children.
Method:
All babies born <26 weeks gestation in the United Kingdom and Ireland from March through December 1995 were recruited to the EPICure Study. Of 307 survivors at 11 years of age, 219 (71%) were assessed alongside 153 term-born classmates. Parents completed a structured psychiatric interview about their child, and teachers completed a corresponding questionnaire from which DSM-IV diagnoses were assigned for 219 (100%) extremely preterm children and 152 (99%) classmates. An IQ test and a physical evaluation were also administered. Longitudinal data were available for extremely preterm children.
Results:
Extremely preterm children were more than three times more likely to have a psychiatric disorder than classmates (23% vs. 9%; odds ratio [OR] = 3.2; 95% confidence interval [CI] = 1.7, 6.2). Risk was significantly increased for: attention-deficit/hyperactivity disorder (ADHD; 11.5% vs. 2.9%; OR = 4.3; CI = 1.5 to 13.0), with increased risk for ADHD inattentive subtype (OR = 10.5; CI = 1.4 to 81.1) but not ADHD combined subtype (OR = 2.1; CI = 0.5 to 7.9); emotional disorders (9.0% vs. 2.1%; OR = 4.6; CI = 1.3 to 15.9), with increased risk for anxiety disorders (OR = 3.5; CI = 1.0 to 12.4); and autism spectrum disorders (8.0% vs. 0%; p = .000). Psychiatric disorders were significantly associated with cognitive impairment (OR = 3.5; CI = 1.8 to 6.4). Parent-reported behavioral problems at 2.5 and 6 years were independent predictors of psychiatric disorders at 11 years.
Conclusions:
Extremely preterm children are at increased risk for ADHD, emotional disorders, and autism spectrum disorders at 11 years of age. The mechanism of association with psychiatric disorder may include both cognitive impairment and early traumatic experiences that have an impact on both child and parent. Early screening for cognitive and behavioral problems may identify those at greatest risk.
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