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Predicting 7-year-olds mental health in the perinatal period: Development and internal validation of a multivariable
Emma Butler1, Michelle Spirtos2, Linda M O'Keeffe3,4,5
1Department of Population Health Royal College of Surgeons Ireland Dublin Ireland.
Insights
Predicting childhood mental health risks at birth is possible with moderate accuracy. This model identifies at-risk children early, improving public health prevention strategies for better child mental health outcomes.
Area of Science:
- Child and Adolescent Psychiatry
- Public Health
- Developmental Psychology
Background:
- Childhood mental health difficulties are rising globally.
- Early identification and prevention are crucial public health priorities.
- Predicting at-risk children before symptom onset remains a challenge.
Purpose of the Study:
- To develop and validate a perinatal multivariable model for predicting mental health in 7-year-old children.
- To identify key perinatal predictors of childhood mental health.
- To assess the model's performance across different demographic groups.
Main Methods:
- Utilized the Avon Longitudinal Study of Parents and Children cohort (N=6021).
- Developed a logistic regression model using 15 perinatal parameters (maternal health, psychosocial factors, behaviors, demographics).
- Employed least absolute shrinkage and selection operator (LASSO) for variable selection and bootstrapping for optimism-adjusted estimates.
Main Results:
- A model combining eight variables predicted poor mental health with a C-statistic of 0.66 (95% CI: 0.64-0.68).
- The model accurately predicted 85.6% of children's mental health status in the perinatal period.
- Application of the model identified 30.9% of children who later developed poor mental health during the perinatal period.
Conclusions:
- Childhood mental health can be predicted at birth with moderate accuracy.
- The model demonstrated similar performance in an English cohort compared to a previous French cohort.
- The model is most effective for ruling out low-risk infants at a population level; further validation is needed for service implementation.
Background:
Mental health difficulties in childhood are increasing. Prevention is the only sustainable and ethical public health approach. However, predicting which children are most at-risk of mental health difficulties prior to symptoms emerging remains elusive.
Methods:
We developed and internally validated a perinatal multivariable model, predicting 7-year-olds mental health, using the Avon Longitudinal Study of Parents and Children (N = 6021, 51.2% male, 98.6% White). Perinatal predictors were reported by the mother prospectively in pregnancy and the Strengths and Difficulties Questionnaire (SDQ) was completed by the mother at 7-years-old. This was dichotomised at recommended clinical cut-off (total>16) Building on our previous model in a French cohort, 15 perinatal parameters spanning maternal pre-pregnancy health, biological and psychosocial pregnancy-specific-experiences, maternal health behaviours in pregnancy and sociodemographic factors were entered into a logistic regression using the least absolute selection and shrinkage operator. Optimism-adjusted estimates were achieved using bootstrapping. Model performance was stratified by sex, sociodemographic risk and admission to a special-care baby unit.
Results:
Combining eight variables predicted poor mental health, with a C-statistic of 0.66; 95% Confidence-Interval (0.64-0.68). It accurately predicted 85.6% of the participants mental health at 7-years in the perinatal period. Model performance was similar across groups of interest. Applying this model leads to a higher benefit than serving 'all' or 'no' children, that is, using the model, 30.9% of children who later had poor mental health would have been identified in the perinatal period.
Conclusion:
It is possible to predict childhood mental health at birth with moderate accuracy. Similar patterns of model performance were observed in this English cohort compared to a previous French cohort. At population-level, the model is most useful for ruling-out babies who are not predicted to be high-risk. In addition to improving its positive predictive value and external validation, future research should examine the model's performance at service-delivery level before implementation.
