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      Identification of children at risk for mental health problems in primary care—Development of a prediction model with routine health care data

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          Despite being common and having long lasting effects, mental health problems in children are often under-recognised and under-treated. Improving early identification is important in order to provide adequate, timely treatment. We aimed to develop prediction models for the one-year risk of a first recorded mental health problem in children attending primary care.


          We carried out a population-based cohort study based on readily available routine healthcare data anonymously extracted from electronic medical records of 76 general practice centers in the Leiden area, the Netherlands. We included all patients aged 1–19 years on 31 December 2016 without prior mental health problems. Multilevel logistic regression analyses were used to predict the one-year risk of a first recorded mental health problem. Potential predictors were characteristics related to the child, family and healthcare use. Model performance was assessed by examining measures of discrimination and calibration.


          Data from 70,000 children were available. A mental health problem was recorded in 27•7% of patients during the period 2007–2017. Age independent predictors were somatic complaints, more than two GP visits in the previous year, one or more laboratory test and one or more referral/contact with other healthcare professional in the previous year. Other predictors and their effects differed between age groups. Model performance was moderate ( c-statistic 0.62–0.63), while model calibration was good.


          This study is a first promising step towards developing prediction models for identifying children at risk of a first mental health problem to support primary care practice by using routine healthcare data. Data enrichment from other available sources regarding e.g. school performance and family history could improve model performance. Further research is needed to externally validate our models and to establish whether we are able to improve under-recognition of mental health problems.

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          Most cited references 16

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          External validation is necessary in prediction research: a clinical example.

          Prediction models tend to perform better on data on which the model was constructed than on new data. This difference in performance is an indication of the optimism in the apparent performance in the derivation set. For internal model validation, bootstrapping methods are recommended to provide bias-corrected estimates of model performance. Results are often accepted without sufficient regard to the importance of external validation. This report illustrates the limitations of internal validation to determine generalizability of a diagnostic prediction model to future settings. A prediction model for the presence of serious bacterial infections in children with fever without source was derived and validated internally using bootstrap resampling techniques. Subsequently, the model was validated externally. In the derivation set (n=376), nine predictors were identified. The apparent area under the receiver operating characteristic curve (95% confidence interval) of the model was 0.83 (0.78-0.87) and 0.76 (0.67-0.85) after bootstrap correction. In the validation set (n=179) the performance was 0.57 (0.47-0.67). For relatively small data sets, internal validation of prediction models by bootstrap techniques may not be sufficient and indicative for the model's performance in future patients. External validation is essential before implementing prediction models in clinical practice.
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            Child and adolescent problems predict DSM-IV disorders in adulthood: a 14-year follow-up of a Dutch epidemiological sample.

            Few studies exist that examine continuities between child and adult psychopathology in unselected samples. This study prospectively examined the adult outcomes of psychopathology in an epidemiological sample of children and adolescents across a 14-year period. In 1983, parent ratings of behavioral and emotional problems were obtained for 1,578 children and adolescents aged 4 through 16 years from the Dutch general population. At follow-up, 14 years later, subjects were reassessed with a standardized DSM-IV interview. High levels of childhood problems predicted an approximate 2- to 6-fold increased risk for adulthood DSM-IV diagnoses. The associations between specific childhood problems and adulthood diagnoses were complex. Social Problems in girls predicted later DSM-IV disorder. Rule-breaking behavior in boys predicted both mood disorders and disruptive disorders in adulthood. High levels of childhood behavioral and emotional problems are related to DSM-IV diagnoses in adulthood. The strongest predictor of disorders in adulthood was childhood rule-breaking behavior. Attention Problems did not predict any of the DSM-IV categories when adjusted for the associations with other Child Behavior Checklist scales.
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              Mental health in Dutch adolescents: a TRAILS report on prevalence, severity, age of onset, continuity and co-morbidity of DSM disorders.

              With psychopathology rising during adolescence and evidence suggesting that adult mental health burden is often due to disorders beginning in youth, it is important to investigate the epidemiology of adolescent mental disorders.

                Author and article information

                17 October 2019
                October 2019
                17 October 2019
                : 15
                : 89-97
                [a ]Department of Public Health and Primary Care, Leiden University Medical Centre, PO Box 9600 Postzone V0-P/V6-68, 2300 RC Leiden, The Netherlands
                [b ]Department of Child and Adolescent Psychiatry, Leiden University Medical Centre, Curium-LUMC, The Netherlands
                [c ]VU University Medical Center, Amsterdam, The Netherlands
                Author notes
                [* ]Corresponding author. N.R.Koning@
                © 2019 Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (

                Research Paper

                children, identification, mental health, primary care


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