Predicting 7‐year‐olds mental health in the perinatal period: Development and internal validation of a multivariable model using the prospective ALSPAC cohort

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: = 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.